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Chemical engineering education

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Title:
Chemical engineering education
Alternate Title:
CEE
Abbreviated Title:
Chem. eng. educ.
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American Society for Engineering Education -- Chemical Engineering Division
Publisher:
Chemical Engineering Division, American Society for Engineering Education
Publication Date:
Frequency:
Quarterly[1962-]
Annual[ FORMER 1960-1961]
Language:
English
Physical Description:
v. : ill. ; 22-28 cm.

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Chemical engineering -- Study and teaching -- Periodicals ( lcsh )

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Chemical abstracts
Additional Physical Form:
Also issued online.
Dates or Sequential Designation:
1960-June 1964 ; v. 1, no. 1 (Oct. 1965)-
Numbering Peculiarities:
Publication suspended briefly: issue designated v. 1, no. 4 (June 1966) published Nov. 1967.
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Title from cover.
General Note:
Place of publication varies: Rochester, N.Y., 1965-1967; Gainesville, Fla., 1968-

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University of Florida
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70013732 ( LCCN )
0009-2479 ( ISSN )
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660/.2/071 ( ddc )

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Chemical engineering education




VOLUME 36 NUMBER 4 FALL 2002





t GRADUATE EDUCATION...

A Novel Approach for
Describing Micromixing Effects in Homogeneous Reactors (pg. 250)
V'emuri Balakotatah, Saikat Chakrabortv

SIntroducing Moleculr Biology to Environmental Engineers
t Through Development of a New Course Ipg. 258)
o Daniel B. Oerther


Articles of General Interest...

SChem-E-Car Downunder (pg. 288)
Rhodes
On Improving "Thought with Hands," ipg.292)
S. Sureshkunuar, Khilar
( h Making Phase Equilibrium More User-Friendly (pg. 284)
"So Misovich
W Random Thoughts: Speaking of Education-llI (pg. 282)
|Felder
E No\el Concepts for Teaching Particle Technology Ipg. 272.)
Peukeri, Schmid
Portfolio Assessment in Introductory ChE Courses (pg. 310)
A Baliai
l New Approach to Teaching Turbulent Thermal Convection tpg. 2641
SChurchill
,, Determining the Flow Characteristics of a Power Law Liquid (pg. 304)
S a Hillier, Ting. Kopplin, Koch, Gupta
The Earth's Carbon Cycle: Chemical Engineering Course Material (pg. 296)
5 Schmitz
Aspects of Engineering Practice: Examining Value and Behaviors in Organizations (pg.316)
Espino
Gas Station Pricing Game: A Lesson in Engineering Economics and Business Strategies (pg. 278)
Sin, Cenier
















I N DE*X

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EDITORIAL AND BUSINESS ADDRESS:
Chemical Engineering Education
Department of Chemical Engineering
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e-mail: cee@che.ufl.edu

EDITOR
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MEMBERS
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Chemical Engineering Education


Volume 36


Number 4


Fall 2002


GRADUATE EDUCATION
250 A Novel Approach for Describing Micromixing Effects in Homoge-
neous Reactors,
Vemuri Balakotaiah, Saikat Chakraborty
258 Introducing Moleculr Biology to Environmental Engineers Through
Development of a New Course,
Daniel B. Oerther

> CLASSROOM
264 A New Approach to Teaching Turbulent Thermal Convection,
Stuart W Churchill
278 Gas Station Pricing Game: A Lesson in Engineering Economics and
Business Strategies,
Aaron Sin, Alfred M. Center
284 Making Phase Equilibrium More User-Friendly,
Michael J. Misovich

> CURRICULUM
272 Novel Concepts for Teaching Particle Technology,
Wolfgang Peukert, Hans-Joachim Schmid
296 The Earth's Carbon Cycle: Chemical Engineering Course Material,
Roger A. Schmitz
316 Aspects of Engineering Practice: Examining Value and Behaviors in
Organizations,
Ramon L. Espino

RANDOM THOUGHTS
282 Speaking of Education-III,
Richard M. Felder

> LABORATORY
288 Chem-E-Car Downunder,
Martin Rhodes
292 On Improving "Thought with Hands,"
G.K. Sureshkumar K.C. Khilar
304 Determining the Flow Characteristics of a Power Law Liquid,
James R. Hillier, Dale Ting, Lisa L. Kopplin, Margaret Koch,
Santosh K. Gupta

> ASSESSMENT
310 Portfolio Assessment in Introductory ChE Courses, Surita R. Bhatia

257, 263, 270 Letter to the Editor
281 Announcements
320 Index for Graduate Education Advertisements

CHEMICAL ENGINEERING EDUCATION (ISSN 0009-2479) is published quarterly by the Chemical Engineering
Division, American Society for Engineering Education, and is edited at the University of Florida. Correspondence
regarding editorial matter, circulation, and changes of address should be sent to CEE, Chemical Engineering Department,
University of Florida, Gainesville, FL 32611-6005. Copyright 2002 by the Chemical Engineering Division, American
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necessarily those of the ChE Division, ASEE, which body assumes no responsibility for them. Defective copies replaced if
notified within 120 days of publication. Write for information on subscription costs andfor back copy costs and availability.
POSTMASTER: Send address changes to Chemical Engineering Education, Chemical Engineering Department., University
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Fall 2002










SGraduate Education


A Novel Approach for Describing

MICROMIXING EFFECTS IN

HOMOGENEOUS REACTORS



VEMURI BALAKOTAIAH, SAIKAT CHAKRABORTY
University of Houston Houston, TX 77204-4004


Reacting flow systems are hierarchical in nature, i.e.,
they are characterized by multiple length (or time)
scales. Scale separation exists in most reactors, how-
ever, and these disparate scales are typically characterized
by three representative ones, namely, micro (molecular), meso
(catalyst particle or tube diameter), and macro (reactor or pro-
cess) scales. In most cases of practical interest, a strong non-
linear coupling exists between reaction and transport at micro
and meso scales, and the reactor performance at the macro scale.
As a result, transport limitations at the smaller scales signifi-
cantly influence the reactor and hence the process performance.
Such effects could be quantified by numerically solving
the convection-diffusion-reaction (CDR) equation from the
macro down to the micro scale. But the solution of the CDR
equation from the reactor (macro) scale down to the local
diffusional (micro) scale, using computational fluid dynam-
ics (CFD), is prohibitive in terms of numerical effort and im-
practical for the purpose of reactor control and optimization.
Moreover, even with today's computational power, it is im-
practical to explore the different types of bifurcation features
and spatio-temporal behaviors that exist in the multidimen-
sional parameter space, using CFD codes. In such cases, low
dimensional models are a natural alternative.
Historically, chemical engineers have derived low dimen-
sional models for reactors using a top-down approach, which
is based on a priori assumptions on the length and time scales
of convection, diffusion, and reaction. The classical ideal re-
actor (CSTR and PFR) models are examples of such low-
dimensional models obtained on the basis of simplified (or
oversimplified) assumptions. These assumptions are usually
not justified since justification requires comparison of the
solution obtained from the simplified models with that ob-
tained from the CDR model.
In order to account for experimental observations that could
not be explained by these ideal reactor models, the latter have
been modified by introducing the concepts of dispersion co-


efficientsE"5] and residence time distribution3,6'71 to account
for macro- and micro-mixing effects. Several other reactive
mixing models followed in the next forty years: the two- and
three-environment model,[89' the coalescence-redispersion
model,"101 interaction by exchange with mean model,["" en-
gulfment-deformation-diffusion model,'12' and CFD models
using probability density functions (PDF) and direct numeri-
cal simulation (DNS).
This article presents an alternative (bottom-up) approach
and an elementary treatment of mixing effects on reactor per-
formance. We will present a brief historical review of homo-
geneous reactor models before discussing this new approach.

BRIEF HISTORY OF
HOMOGENEOUS REACTOR MODELS
The most widely used homogeneous reactor models are
the three classical ideal reactor models: the plug-flow reactor
(PFR) model, the continuous stirred tank reactor (CSTR)
model, and the batch reactor (BR) model. While the BR model
and the PFR model (which are identical for constant density
systems with time replaced by space time or dimensionless
distance along the tube) have existed since the late eighteenth
century. A conceptual leap came in the form of the CSTR
model through the work of Bodenstein and Wohlgast in
1908.[131 Unlike the PFR model, which assumes no gradients
in the radial direction and no mixing in the axial direction,

Vemuri Balakotaiah is Professor of Chemical Engineering at the Uni-
versity of Houston. He received his BTech from the Indian Institute of
Technology (Madras) in 1978 and his PhD from the University of Hous-
ton in 1982, both in chemical engineering. His teaching and research
interests are in the areas of chemical reaction engineering, multiphase
flows, and applied mathematics.
Saikat Chakraborty is a PhD candidate in the Department of Chemical
Engineering at the University of Houston. He received his BTech from
Jadavpur University in 1997 and his MS from the Indian Institute of Sci-
ence (Bangalore) in 1999, both in chemical engineering. His research
interests are in the areas of chemical reaction engineering and granular
materials.
Copyright ChE Division of ASEE 2002


Chemical Engineering Education










Graduate Education ]


the CSTR model assumes complete mixing at all scales. For
constant density systems, the three classical reactor models
are described by
PFR

(u)C) -R((C)) with (C) = Ci @ x = 0 (1)
dx
BR

d(C)
d = -R((C)) with (C)= Cin @ t = 0 (2)
dt
CSTR

(C) -R((C)) (3)
Tc
where (C) is the spatially (or cross-sectional) averaged reac-
tant concentration, Cln is the mean inlet concentration of the
reactant, R((C)) is the sink term due to the presence of ho-
mogeneous reaction, x is the coordinate along the length of
the PFR, (u) is the mean fluid velocity in the reactor, t is the
time, and Tc is the total residence time in the reactor.
Irving Langmuirt" first replaced the assumption of no axial
mixing of the PFR model with finite axial mixing and the
accompanying Dirichlet boundary condition ((C) = Cn @ x
= 0) by a flux-type boundary condition

Dm d( =(u)(C)-Cin] @ x=0 (4)
dx
where Dm is the molecular diffusivity of the species. The above
boundary condition was rediscovered several times in the
years that followed: first by Forster and Geibl61, which was
quoted and applied by Damk6hler,[12 and then, later, by
Danckwerts.13] Since then it has been known as the
"Danckwerts" boundary condition. In his paper, Langmuir
dealt with both the limiting cases of "mixing nearly com-
plete" and "only slight mixing."
Thirty years later, Gerhard Damk6hler in his historic pa-
per, summarized various reactor models and formulated the
two-dimensional CDR model for tubular reactors in complete
generality, allowing for finite mixing both in the radial and
the axial directions. In his paper, Damk6hler used the flux-
type boundary condition at the inlet and also replaced the
assumption of plug flow with parabolic velocity profile, which
is typical of laminar flow in tubes.
Forster and Geib first introduced the concept of residence
time distribution (RTD) to study the case of longitudinal dis-
persion in tubes. Twenty years later, Danckwerts, in his much
celebrated paper,131 devised a generalized treatment of RTD
and introduced the concepts of holdbackk" and "segregation."
Following this, it was Zweitering,[7' who quantified the de-
grees of mixing with the ideas of "complete segregation" and


"maximum mixedness" and brought forth the concept of
micromixing, or mixing at the molecular scale in homoge-
neous reactions.
In the last forty years, a wide range of micromixing mod-
els for homogeneous reactors have been formulated. While
most of these low-dimensional mixing models are phenom-
enological in nature, the rigorously derived CFD models are
high-dimensional and therefore numerically very expensive,
especially for the case of multiple reactions with fast/non-
isothermal kinetics. As a result, in spite of the simplifying
assumptions present, the century-old ideal classical reactor
models (Eqs. 1-3) are still the most popular choices among
chemical engineering practitioners (and teachers). The clas-
sical ideal reactor models, which are easy-to-solve ordinary
differential or algebraic equations with no adjustable param-
eter, are particularly preferred over the full CDR models
(which are partial differential equations in more than one di-
mension) in case of multiple reactions with complex kinetics.

SPATIAL AVERAGING OF
CONVECTION-DIFFUSION-REACTION
EQUATION
The main goal of this article is to illustrate a new approach
for deriving low-dimensional homogeneous reactor models,
capable of predicting mixing effects. These models are de-
rived through rigorous spatial averaging of the three-dimen-
sional CDR equations over local length scales by using the
Liapunov-Schmidt (L-S) technique of classical bifurcation
theory. We illustrate this spatial averaging technique using
the simple case of laminar flow in a tube with homogeneous
reaction. The scalar concentration C(r, 0, x,t') in a tubular
reactor is assumed to obey the CDR equation

oC /C
C + u(r)- C=
I t DxC I +-BC- D1
SD D r -+ l D D + D -R(C) (5)
r Lr ar r 2- ae d9 ) ax ( ax)

with accompanying initial and boundary conditions, given by

aC
C(r,6,x,t'=0)=Co =0 @ r=a
Dr
C(r,0, x,t')= C(r,0 + 27,x,t')
Dc
Dx-C =u(r)[C(r,9,x,t')-Cin @ x=0
ax


-=0 @ x=L
ax


where D and Dx are the transverse and axial diffusivities,
respectively; r,O,x are the radial, azimuthal, and axial coor-


Fall 2002










Graduate Education


dinates, respectively; and u(r) is the fluid velocity profile.
We take a (radius of the pipe) and L (length of the pipe) to be
the characteristic lengths in the radial and axial directions,
respectively; (u) is the cross-sectional average velocity; and
CR is a reference concentration. Then, we obtain four time-
scales in the system associated with convection ( C), radial
diffusion (tD), axial diffusion (t), and reaction (tR)


a2 L2
tD tx =
Dl Dx


SL CR
tC=- tR-(CR)
(u)' R(CR)


(7)


and the ratios of these time scales give rise to the dimension-
less parameters: p (transverse Peclet number), Pe (axial Peclet
number), Da (Damk6hler number), and 02 (local Damkohler
number), given by


a2(u) tD
LD TC


P(u)L tx
Pe= c
Dx TC


Da LR(CR) tC
(u)CR tR


a2R(CR) tDpDa
DxCR tR
In dimensionless form, Eq. (5) for the case of constant spe-
cies diffusivities, can be rearranged as

V2c aC c 1 (a2c)


p 2 +u()C-+Da^(c) pg(c) (8)
Pt Pe az2 az =
1 -^ ^( w-r\ ~+

^-^^-(^ "M^*'


sion time is small but finite compared to convection, reac-
tion, and axial diffusion time, local (transverse) gradients re-
main small and we can write
c( e,0,z,t)= (c)(z,t)+ c'(,O,z,t) (11)
where (c) is the transverse averaged concentration and c' is
the fluctuation about this average, and c' ---> 0 as p -> 0.
(Also, by definition (c')= 0.) Multiplying Eq. (11) by the
local velocity profile, u(4) = (u) + u', and averaging over the
cross-section gives
c = (c) + (u'c') (12)
where cm is the mixing-cup (velocity weighted) concentra-
tion. Similarly, transverse averaging of Eq. (8) over the cross-
section gives
/=1 \=27t
a(c) 1 a2(c) ac 8
(C) 1 +Da 1 (c)dJd4=0 (13)
at Pe az2 z
5=0 9=0
For the case of a tubular reactor, the spatial (transverse) aver-
age and mixing-cup concentrations are defined by


=1 0=2n
f I c(,6,x,t)d0d
S() =0 =0
S =1 0=2n
S0f d6d=
=0 0=0


with initial and boundary conditions being

c(,0,z,t=0)=co =0 @ i=1
ac(,zt)=c(
c(4,0,z,t)= c(,0 + 2t, z,t)


1 ac
Sa u(4)[c Cin] @z=0
Pe az


0@ z=l
az


where

t' r x u
t=- t = z=- u=
TC a L (u)
C R(C)
c= C (c) = (10)
CR R(CR)
The form of the CDR equation (Eq. 8) clearly illustrates that
a scale separation exists in the system, with p being the ratio
of the local to the global scale (when Pe and Da are of order
unity), and spatial averaging over the local scales is possible.
It can be seen from Eqs. (8) and (9) that in the limit of
p-> 0, V2c 0 and transverse (or small scale) concentra-
tion gradients vanish, in which case the equations simplify to
the classical one-mode axial dispersion model. If local diffu-


t=1 0=27n
f f i u(i)c(4,0,x,t)d6d(
cm- =1 0=2 (15)
f f u(i)dOd4
=0 0=0
It may be noted that in all flow reactors, cm is the experimen-
tally measured variable. We refer to (c) and cm as the two
modes of the system and our spatially averaged reactor mod-
els as Two-Mode Models (TMMs). Equation 13 is called the
global equation, while Eq. (12) is called the local equation.
The local equation shows that the difference between cm and
(c) depends on the local velocity gradients (u') and the local
concentration gradients (c') caused by molecular diffusion
and reaction at the local scales. Micromixing is captured by
the local equation as an exchange between the two modes
(scales), cm and (c).
In order to determine c' (and hence the term (u'c') or the
difference between cm and (c)), we substitute Eq. (11) in Eq
(8) to obtain
Vic'=pg((c)+c') (16)


Chemical Engineering Education










Graduate Education


The L-S technique solves Eq. (16) for c' by expanding it in
the parameter p as

c,'= pici (17)
i=1
and by using the Fredholm Alternative (i.e., the fact c' lies
in the function space orthogonal to which (c) resides). Such
an expansion (Eq. 17) is possible, since for p = 0, the trans-
verse diffusion operator in Eq. (8) has a zero eigenvalue with
a constant eigenfunction. Thus, (u'c') could be determined
to any order in p, i.e., closure of the local equation could be
accomplished to any desired accuracy. In practice, the lead-
ing term (that is of order p) is sufficient to retain all the quali-
tative features of the full CDR equation. For example, for the
case of azimuthally symmetric feeding, we have

eC -p -(c)L + +02) (18)
ac z L12 4 8 ]Op (18)
Substituting Eq. (18) into Eqs. (12) and (13) gives the two-
mode model to O(p) as

a(c) +acm a2(c) O
a + a) + Da r((c)) + O(2) 0 (19)
at az Pe az2

(c)- Cm = P + 0(p2)

= ,pip m + o(p2) (20)
az
with boundary and initial conditions given by

1 atc)
= cm Cmin @ z= 0 (21)
Pe az m
am 0 @ z=1 (22)
az
(c)= (c) @ t=0 (23)

where 1 / pi is called the exchange coefficient, which depends
on the local shear rates. For the case of fully developed lami-
nar flows, DL = Dx = Dm (molecular diffusivity of the spe-
cies), and P3 =1/48. We refer to this model as the two-mode
axial dispersion model. (Further details of the spatial averag-
ing procedure using the L-S technique can be found in
Chakraborty and Balakotaiah.l'4.'5')
It should be noted that the spatially averaged CDR equa-
tion (Eqs. 19 and 20) retains all the parameters (p, Pe, Da) of
the three-dimensional CDR equation (Eq. 8) and hence all
the qualitative features of the latter. It should also be men-
tioned that this model is capable of capturing macromixing
effects through the axial Peclet number Pe in the global equa-
tion (Eq. 19), as well as micromixing effects through the ex-
change coefficient Pi1 and transverse Peclet number p in the


local equation (Eq. 20). In fact, the L-S technique guarantees
that the solution of the averaged model (Eqs. 19-23) agrees
with the exact solution of the three-dimensional CDR equation
to O(p). [Three decimal accuracy is obtained for a second-or-
der reaction for the case of Pe -o if 02 < 1 (see ref. 14).]
Using the spatial averaging technique illustrated above,
accurate low-dimensional models could be obtained for dif-
ferent types of reactors and flow profiles. For example, the
two-mode model for a tubular reactor with fully developed
turbulent flow is the same as Eqs (19) through (23), where
D_ is the effective turbulent diffusivity and PI is a function
of Reynolds number (Re) and friction factor f. This model is
obtained by starting with the time-smoothed (Reynolds aver-
aged) CDR equation, where the reaction rate R(C) in Eq. (5)
is replaced by the Reynolds averaged reaction rate (after clo-
sure) R,(C). Spatial averaging by the L-S technique is then
performed on the time-averaged CDR equation (i.e., spatial
averaging follows time averaging) to obtain the two-mode model
(see ref. 15 for details). In the next section, we will present the
two-mode models for other types of homogeneous reactors.

TWO-MODE MODELS
FOR HOMOGENEOUS REACTORS

Tubular Reactors

The steady-state two-mode model for a tubular reactor for
the case of Pe o (i.e., no macromixing present) may be
obtained from Eqs. (19) through (21). In dimensional form,
it is given by

(u) d -R((C)) with Cm(x = 0)= C,in (24)
dx

Cm (C) = -tix (u) dC = txR((C)) (25)

where the local mixing time tmix (in the local Eq. 25 describ-
ing micromixing effects) is given by
2
tmix = Pi a (26)
D
where a is the local diffusional length scale over which spa-
tial averaging is performed, DI is the local diffusion coeffi-
cient, and P1l is the exchange coefficient. In the limit of com-
plete micromixing (i.e., tmix -- 0), the two-mode convection
model reduces to the ideal one-mode zero-parameter PFR model.

Loop and Recycle Reactors
In a loop reactor of length L, a flow rate of q,, and with an
average velocity of (Uin), enters and leaves the reactor at
points x = 0 and x = /, respectively (where x is the length
coordinate along the loop). The total flow rate in the loop is
Q + q between points x = 0 and x = 1, and is Q between


Fall 2002










Gmdwate Edke~gagion


points x = 1 and x = L, due to a recycle rate of Q. The recycle
ratio A is the ratio of the volume of fluid returned to the
reactor entrance per unit time to the volume of fluid leaving
the system per unit time, and is given by A = Q/qin. The two-
mode model for such a loop reactor can be obtained as


x I- R((C))
dx R((C))


0
l~xL


Cm -(C) = tmiR((C)) 0 x
with boundary conditions

Cm(x = 0)= Cmin+ACm(=L)
I+A

(C)(x = = (C)(x=/+) (29)

For the special case when no reaction occurs between x = 1
and x = L, i.e., Cm(x=l) = Cm(x=L), the loop reactor reduces
to a recycle reactor of length 1, the two-mode model for which
is given by


(uin)dCm 1 R((C))
dx 1+A

with Cm(x0)=Cmin +A (x (30)
I+A

Cm -(C)= tmixR((C)) 0
The two-mode loop and recycle reactor models, like the
two-mode axial dispersion model, are two-parameter two-
mode models. Here, the two parameters are the recycle ratio
A, and the local mixing time tmix, which describe macro- and
micro-mixing effects in the system, respectively.
Tank Reactors (CSTRs)
It is well known that as the recycle ratio A of a recycle
reactor is increased, the behavior shifts from a PFR at A = 0
(no macromixing) to a CSTR as A = o (perfect
macromixing). We use this idea to obtain the two-mode model
for a perfectly macromixed CSTR, by integrating Eq. (30)
along the length of the reactor x and simplifying the resulting
equation for A > > 1. This gives the two-mode model for a
perfectly macromixed CSTR as

Cm (C) C min- C (32)
tmix "C
Cm (C)= tmixR((C)) (33)

where c(=V/qin) is the total residence time in the reactor,
and tmx is the characteristic local mixing time, which cap-


tures micromixing effects. In the limit of complete
micromixing (i.e., tmix 0), the TMM for a CSTR reduces
to the ideal one-mode zero-parameter CSTR model.
It should be pointed out that the local equation (eqs. 25,
28, 31, 33) is the same for all reactor types. This is an impor-
tant observation, which shows that scale separation exists in
all types of homogeneous reactors.

PHYSICAL INTERPRETATION OF
TWO-MODE MODELS
Using the example of a tank reactor, we present a physical
interpretation of the two-mode models. The physical system
equivalent to the two-mode model of a CSTR is a tank reac-
tor consisting of two zones, each of size V, namely, a non-
reacting convection zone (A), represented by Cm, and a reac-
tion zone (B), represented by (C). Thus, Cm is representative
of the convection scale of the system and (C) is representa-
tive of the reaction scale of the system. The interaction be-
tween the two scales (or the two zones A and B) is quantified
by an exchange of materials at a rate of qE. This exchange
occurs only through local diffusion, and tmi(=V/qE), which is
the characteristic time scale for this exchange, therefore de-
pends on the local shear rate and diffusion coefficient. Equa-
tions (32) and (33) represent the steady-state material bal-
ances for zone B and zone A, respectively.
In general, any infinitesimal volume dV inside the tank
could be so imagined to consist of two zones/scales, and a
corresponding two-mode model could be written (Eqs. 32-
33) for the volume dV. If macromixing in the tank is com-
plete, the two-mode model for any control volume dV could
be integrated over the entire volume of the tank to generate a
single two-mode model (Eqs. 32-33) for the whole tank.
Macromixing effects are often not negligible in real tanks,
however, and are influenced by several factors including the
type and speed of impellers (turbines) and the manner of feed
distribution. Several macromixing models are available in
the literature, e.g., the two-compartment model, recycle
model, tanks-in-series model, exchange-with-stagnant-zone
model, any of which could be suitably coupled with the TMM
to describe both macro- and micro-mixing in tanks. How-
ever, if micromixing effects are dominant compared to
macromixing ones (as in well-stirred tanks), it could be shown
by using L-S reduction in finite dimensions, that these mod-
els (i.e., the two-mode n-compartment model, etc.) could be
reduced to Eqs. (32) and (33), where the local mixing time
tmix is replaced by an effective mixing time tM, which cap-
tures both macro- and micro-mixing effects. This effective
mixing time tM now not only depends on the local diffusion
time and local shear rates, but also intricately on the tank
geometry, type and number of impellers, baffle positions, and
power dissipation in the system.


Chemical Engineering Education











Graduate Education ]


SIMILARITY BETWEEN TWO-MODE MODELS
OF HOMOGENEOUS REACTORS AND
TWO-PHASE MODELS OF
CATALYTIC REACTORS

A striking structural similarity between the two-mode mod-
els for homogeneous reactors and two-phase models for het-
erogeneous catalytic reactors exists. This could be seen more
clearly when Eqs. (24) and (25) are rewritten as


(ux) dCm Cm -(C) -R((C))
dx tmix

with Cm =Cm.in @x=0 (34)

The two-phase model for a heterogeneous wall-catalyzed re-
action in a tubular reactor is given by

uxdCm Cm Cs -R(Cs)
dx tTp

with Cm = C,in @x = 0 (35)

It may be noticed that the spatially averaged concentration
(C) of the TMM (in Eq. 34) is replaced by the surface (wall)
concentration Cs in the two-phase model (Eq. 35), while the
local mixing time tmix of the TMM is replaced in the two-
phase model by a characteristic mass transfer time between
the two phases t,,, which is given by
1 2
tTP = PTptD -- (36)
Sh-,T Dm

where tD is the transverse diffusion time scale and Sh (=l/
PTP) is the two-phase dimensionlesss mass) transfer coeffi-
cient (asymptotic Sherwood number) that depends on the
velocity profile and tube geometry. For the case of fully de-
veloped laminar flow in a circular tube, ShT = 48/11 = 4.36,
while its analogue in the TMM (comparing Eqs. 26 and 36)
is Sh,E = 1/ P = 48 (the dimensionless mass exchange coef-
ficient in the TMMs).
As illustrated in the next section, just as the two-phase
models can capture the mass-transfer limited asymptote in
heterogeneous reactions (which is missed by the pseudo-
homogeneous models), so can the two-mode models capture
the mixing-limited asymptote in homogeneous reactions,
which is rendered inaccessible by the traditional one-mode
models. Thus, there exists the following one-to-one corre-
spondence between two-phase models of catalytic reactors
and two-mode models of homogeneous reactors: two-phase
transfer time (tr) -> local mixing time (tmix), two-phase trans-
fer coefficient (Sh_,) -> two-mode exchange coefficient
(ShE), surface (wall) concentration Cs -> spatially averaged
concentration (C), and mass-transfer limited reaction -> mix-
ing-limited reaction.

Fall 2002


APPLICATIONS OF TWO-MODE MODELS

Bimolecular Second-Order Reactions

Second-order reactions provide the simplest example of
nonlinear kinetics, where micromixing limitations have sig-
nificant effects on reactant conversion. We use the TMM to
determine micromixing effects on conversion of a typical
bimolecular second-order reaction of the type

A + B P with rate = kCACB
occurring in a CSTR, where k is the reaction rate constant.
For the case of stoichiometric feeding (i.e., CAi=C=B,in=Cin)
the conversion (X) obtained by using the TMM is given by

1 4 Da(l+1)+ 1 (37)
1+11 2 Da(l + T)2

where 1 (=t.i / c) is the dimensionless local mixing time,
and Da(=kC,, Tc) is the Damkohler number. Figure 1 shows
the variation of conversion X with Da for different values of
the dimensionless local mixing time 1. The case of rl = 0
corresponds to the ideal CSTR. For 11 > 0 and Da -> -, the
local concentrations (Ci)(i=A,B) approach zero, while the
mixing-cup concentrations approach a mixing limited asymp-
tote, given by


(CA)=(CB)=O CAm =CB,m-
1+11


1
X-
l+r|


As mentioned in the previous section, this mixing-limited


Figure 1. Variation of exit conversion with Damk6hler
number, Da, for a second order reaction in a CSTR, for
different values of dimensionless local mixing time, 1l.


100-









40



20
0 -
6 0 -
o








e.el


1 10 100
Da










Graduate Education

asymptote for homogeneous reactions is analogous to the
mass-transfer limited asymptotefor wall-catalyzed reactions.
Just as the wall (surface) concentrations approach zero for
the case of infinitely fast surface reactions (while the bulk/
mixing-cup concentrations remain finite), so do the local con-
centrations (Ci) for infinitely fast homogeneous reactions
(i=A,B). Unlike in catalytic reactions, where exchange be-
tween the phases occurs at the solid-fluid boundary, the ex-
change between modes (scales) in homogeneous reactors
occurs over the entire domain.


Competitive-Consecutive Reactions
Competitive-consecutive reactions of the type

A+B "C and B+C---D
are prototype of many multistep reactions such as nitration
of benzene and toluene, diazo coupling, bromination reac-
tions, etc. Experimental observations'61 show that if the first
reaction is infinitely fast as compared to the second one (i.e.,
k,/k -> oo), under perfectly mixed conditions B is completely
consumed by the first reaction and the yield of D is zero (if A
and B are fed in stoichiometric amounts). But it was observed
that if the mixing of A and B is not attained down to the mo-
lecular scale, the first reaction is not complete and there re-
mains a local excess of B, which can then react with C to
produce D. The yield of D increases monotonically as the
rate of the second reaction increases, finally attaining a mix-
ing-limited asymptote. We use the TMM for a CSTR to verify
this observation. Figure 2 shows the increase in the yield of
D, YD with Damk6hler number of the second reaction, Da2,
where YD = 2CDm/(Cm+2CDm), and Da2 = kCin Tc. The figure
corresponds to the case when the first reaction is infinitely
fast (i.e., k,/k, --> o), and A and B are fed in stoichiometric
amounts (i.e., CBin = CAi=Ci, and Cc,in = Cin= 0). While no
D is formed for the case of Tr = 0 (ideal CSTR), a significant
increase in yield of D is obtained if finite micromixing limi-
tations are present in the system. The maximum yield of D,
obtained when the mixing limited asymptote is attained also
for the second reaction, is

S2T1 for 1 < 1
YD1,mx = (39)
2 for n >1
1+21

Thus, in this case, an optimal yield of D is obtained for rl = 1.

CONCLUSIONS

In the hierarchy of homogeneous reactor models, the clas-
sical ideal reactor models stand at one end as the simplest,
while the generalized convective-diffusion-reaction (CDR)


model stands at the other end as the most detailed one. While
the former cannot capture the mixing effects due to local ve-
locity gradients, molecular diffusion and reaction, the latter
requires extensive computations, especially for large Schmidt
and/or Damkihler numbers, and for multiple reactions with
large number of species. The Two-Mode Models (TMMs)
proposed here bridge the gap between the two extreme cases
of reactor models and provide a practical approach for de-
scribing mixing effects on reactor performance. They retain
all the parameters present in the full CDR model and there-
fore all the qualitative features of the latter, and yet their so-
lution requires a numerical effort comparable to that of the
classical ideal reactor models.
The analogy between the two-mode models of homoge-
neous reactors and two-phase models of catalytic reactors
could be carried further by noting that for all cases of well-
defined flow-fields, where two-phase mass-transfer coeffi-
cients (Sh ) can be estimated theoretically, the exchange co-
efficient (ShE) or the local mixing time (tmix) of the TMMs
could also be estimated. For more complex flow-fields (e.g.,
packed beds), the local mixing time, like the mass-transfer
coefficient, could be correlated to Re, Sc, and the geometri-
cal characteristics of the system. Thus, the two-mode models
of homogeneous reactors are as general as the two-phase
models of catalytic reactors and have a similar range of ap-
plicability. (In fact, the classical two-phase models are also
two-mode models, the modes being the cup-mixing and the
surface (or solid-phase) concentrations. Thus, the two-mode/


Figure 2. Variation of the yield ofD with Damk6hler num-
ber for a competitive-consecutive reaction scheme
A+B- C, B+C- D, when the first reaction is infinitely
fast, for different values of the dimensionless local mixing
time, 1.


Chemical Engineering Education


10 100 1000
Da











Graduate Education ]


two-scale approach may be used to present a unified theory
of homogeneous and heterogeneous reactors!)
To summarize, the two-mode models are the minimal mod-
els that provide a low-dimensional description of mixing, by
coupling the interaction between chemical reaction, diffusion,
and velocity gradients at the local scales to the macro-scale
reactor variables. Due to their simplicity and generality, it is
hoped that they will find applications in the preliminary de-
sign and optimization of homogeneous chemical reactors, as
well as provide an alternative method for teaching
micromixing effects in homogeneous reactors.

ACKNOWLEDGMENTS
This work was supported by grants from the Robert A.
Welch Foundation, the Texas Advanced Technology Program,
and the Dow Chemical Company. We thank David West of
Dow Chemical, Dr. Grigorios Kolios of the University of
Stuttgart and Prof. Dan Luss of the University of Houston
for their help in locating and translation of the articles by
Bodenstein and Wolgast and Forster and Geib.

REFERENCES
1. Langmuir, I., "The Velocity of Reactions in Gases Moving Through
Heated Vessels and the Effect of Convection and Diffusion." J. Am.
Ceram. Soc., 30, 656 (1908)
2. Damk6hler, G., "Einflusse der StrOmung, Diffusion und
Wdrmeiiberganges auf die Leistung von Reaktions6fen. II Die
Isotherme, Raumbestindige, Homogene Reaktion Ester Ordnung," Z.
Elektrochem., 43, 1 (1937)
3. Danckwerts, P.V., "Continuous Flow Systems: Distribution of Resi-
dence Times," Chem. Eng. Sci., 2, 1 (1953)
4. Taylor, G.I., "Dispersion of Soluble Matter in Solvent Flowing Slowly
Through a Tube," Proc. Roy. Soc. Lond. A, 219, 186 (1953)
5. Aris, R., "On the Dispersion of a Solute in a Fluid Flowing Through a
Tube," Proc. Roy. Soc. Lond. A, 235, 67 (1956)
6. Forster, V.T., and K.H. Geib, "Die Theorietische Behandlung
Chemischer Reaktionen in Strbmenden Systemen," Annalen. der
Physik, 5, 250 (1934)
7. Zwietering, T.N., "The Degree of Mixing in Continuous Flow Sys-
tems," Chem. Eng. Sci., 11, 1 (1959)
8. Ng, D.Y.C., and D.W. T. Rippin, "The Effect of Incomplete Mixing on
Conversion in Homogeneous Reactions," Chem. Eng. Sci., 22, 65
(1965)
9. Miyawaki, O., H. Tsujikawa, and Y. Uraguchi, "Chemical Reactions
Under Incomplete Mixing," J. Chem. Eng. Japan, 8, 63 (1975)
10. Harada, M., "Micromixing in a Continuous Flow Reactor (Coales-
cence and Redispersion Models)," The Memoirs of the Faculty of En-
gineering, Kyoto Univ., 24, 431 (1962)
11. Villermaux, J., and J.C. Devillon, "Reprdsentation de la Coalescence
et de la Redispersion des Domaines de S6grdgation dans un Fluide per
Moddle d'Interaction Ph6nomdnologique," Proc. 2ndlnd. Symp. Chem.
React. Eng., Amsterdam, BI (1972)
12. Baldyga, J., and J.R. Bourne, "Mixing and Fast Chemical Reaction-
VIII. Initial Deformation of Material Elements in Isotropic Homoge-
neous Turbulence," Chem. Eng. Sci., 39, 329 (1984)
13. Bodenstein, M., and K. Wolgast, "Reaktionsgeschwindigkeit in
Str6menden Gasen," Ztschr Phys. Chem., 61, 422 (1908)
14. Chakraborty, S., and V. Balakotaiah, "Low Dimensional Models for
Describing Mixing Effects in Laminar Flow Tubular Reactors," Chem.


Eng. Sci., 57, 2545 (2002)
15. Chakrabory, S., and V Balakotaiah, "Two-Mode Models for Describ-
ing Mixing Effects in Homogeneous Reactors," AIChE J., in review
(2002)
16. Li, K.T., and H.L. Toor, "Turbulent Reactive Mixing with a Series-
Parallel Reaction-Effect of Mixing on Yield," AIChEJ., 32, 1312 (1986)


Ms letter to the editor


Dear Editor:

I recently used the illustration below to explain the ben-
efits of countercurrent flow to students in a separation pro-
cesses subject that I teach. I've never heard this illustration
used before and it seems to be a good one, so I thought it
would be good to put it in the public domain for the benefit
of other lecturers. However, it is very short and does not war-
rant being a "peer-reviewed" paper.

Explaining Why Counter-Current is
More Efficient than Co-Current

While washing the dishes one night, I realized that this ac-
tivity provides a useful everyday illustration of why counter-
current mass and heat transfer processes are more efficient
than co-current ones.
I asked the students in my class what would be the best
way to clean a pile of dirty dishes if they had at their disposal
one basin of dirty wash water and one basin of clean wash
water. The class quickly reached the consensus that it would
be best to first use the dirty water to clean off as much of the
dirt as possible and then use the clean water to perform a
second-stage clean. The dirty water would remove the bulk
of the dirt, minimizing the contamination of the clean water
and leaving it in better condition to clean off any remaining
stubborn dirt. Putting the dirty dishes straight into the clean
water would quickly dilute and waste its cleaning ability.
This is equivalent to having the countercurrent flow of
streams in a liquid-liquid extraction or gas-liquid absorption
column. The clean solvent is best used to perform the final
stage of cleaning, while the used solvent is still able to perform
some cleaning of the raw feed stream as it enters the column.
Students seemed to intuitively understand this illustration,
and it provides a non-graphical complement to the usual
method of explaining the benefits of countercurrent flow,
which involves showing how the average concentration (or
temperature) difference driving force differs between co- and
countercurrent flows.
Simon Iveson
University ofNewcastle
Callaghan NSW 2308, Australia
cgsmi @ cc. newcastle. edu. au


Fall 2002










Graduate Education


INTRODUCING MOLECULAR BIOLOGY

TO ENVIRONMENTAL ENGINEERS

Through Development of a New Course


DANIEL B. OERTHER
University of Cincinnati Cincinnati, OH 45221-0071
Historically, applications of biology in chemical and
environmental engineering have been approached
from different perspectives with different goals. For
example, chemical engineering optimizes biochemical reac-
tions of pure cultures of microorganisms in highly controlled
bioreactors used for manufacturing (e.g., fermentation),
whereas environmental engineering employs mixed micro-
bial communities with minimum controls as least-cost pro-
cesses for meeting regulatory requirements (e.g., sewage treat-
ment). Although chemical and environmental engineering
education often incorporates formal training in biology, the
motivation for course selection can be very different. Incre-
mental advances in biological knowledge that can be used to
increase manufacturing capability or improve efficiency are
useful in chemical engineering practice, and their integration
into chemical engineering education is justified.
The same principle does not hold for environmental engi-
neering, however. Once minimum regulatory requirements
are met, incremental advances in biological knowledge do
not offer the significant cost savings for environmental bio-
logical unit operations that are needed to encourage the adop-
tion and integration of the new knowledge into environmen-
tal engineering education.
Recently, development of 16S ribosomal ribonucleic acid
(16S rRNA)-targeted technology provided researchers in en-
vironmental engineering with new tools to identify
microorganisms and to study microorganisms in bioreactor
environments. As compared to classical techniques for iden-
tification and enumeration, 16S rRNA-targeted technology
allows in situ examination of the structure (i.e., who is
present?) and function (i.e., what are they doing?) of micro-
bial communities without a prerequisite for isolating pure cul-
tures.1E' For researchers in environmental engineering, 16S
rRNA-targeted technology has been extensively tested, and
current research activities have moved beyond the "proof-
of-concept" state to widespread applications.12'3 In contrast,
integration of 16S rRNA-targeted technology within the en-
vironmental engineering curriculum remains to be fully de-


veloped. At the University of Cincinnati, the author has de-
veloped and pilot tested a "proof-of-concept" course titled
"Molecular Methods in Environmental Engineering."
The course was designed to teach limited fundamentals of
molecular biology in the context of quantitative engineering
design and practice. During its first offering, fifteen graduate
students in environmental engineering were exposed to "state-
of-the-art" technology, including hands-on laboratory exer-
cises following the "full-cycle 16S rRNA approach."111 Stu-
dents learned the importance of detailed understanding of
microbial communities and microbial-mediated biochemical
networks in biological unit operations, natural biological sys-
tems, and the global biosphere. The format of the course in-
cluded a weekly lecture as well as a semester-long series of
hands-on laboratory exercises designed to teach students to
develop scientific questions, learn appropriate methodology,
conduct careful experimentation, analyze data, and draw con-
clusions worthy of presentation to peers. Thus the final out-
come of the course included preparation of peer-review quality
manuscripts by each team of students as well as one-on-one
interviews with the instructor.

FULL-CYCLE 16S rRNA APPROACH
Traditionally, the identification of microorganisms in en-
vironmental samples has relied upon semi-selective cultur-
ing or direct microscopic examination. These techniques have
led to a rudimentary understanding of the role of microor-
ganisms in the global biosphere as well as the importance of
microorganisms in public health and biocatalysis. Recently,
the techniques for determinative microbiology have been dra-
matically expanded to include cultivation-and-morphologic-
independent identification and enumeration of microorgan-
Daniel B. Oerther joined the Department of Civil and Environmental Engi-
neering at the University of Cincinnati in 2000. For ten years, he has been
adapting methods from molecular biology to identify, enumerate, and mea-
sure the physiology of microorganisms in biotechnology processes includ-
ing wastewater treatment and bioremediation. His research links the re-
sults of novel molecular biology assays with mechanistic modeling of
bioreactor performance.
Copyright ChE Division of ASEE 2002


Chemical Engineering Education


258










Graduate Education


Collect Sample Extract Genomc DNA











Polymerase Chain Reaction
Denature, Anneal, Extend for Exponential Growth









Cloning
Ligation, Transformation, Isolate Recombinants










FISH and Microscopic Examination









Figure 1. Schematic of the principal steps in the "full-cycle
16S rRNA approach." Genetic material is isolated directly
from an environmental sample and the 16S rDNA genes
are amplified in a PCR. The product of the PCR is cloned,
and recombinants are isolated for extraction of plasmid
DNA. Automated sequencing is used to provide the primary
nucleotide structure of the clones, and probe design is ac-
complished using semi-automated procedures and readily
available software. Finally, individual microbial cells are
visualized through fluorescence in situ hybridization (FISH)
with fluorescently labeled 16S rRNA-targeted oligonucle-
otide probes.


isms in environmental samples. Arguably, one of the most
widespread families of new techniques for determinative
microbiology targets rRNA. Comparative studies of rRNA
nucleotide sequences collected from a variety of microorgan-
isms led to the development of a universal phylogenetic frame-
work for understanding the evolutionary history of microor-
ganisms."- Subsequently, these comparative approaches were
coupled with oligonucleotide probe hybridizations to study
microorganisms in situ without prerequisite culturing." 6'
The "full-cycle 16S rRNA approach" refers to the process
of obtaining genomic information directly from an environ-
mental sample and then employing molecular methods to
assay the abundance of nucleotide sequences directly within
an environmental sample. The steps of the cycle, as applied
in my course, are briefly described and outlined in Figure 1.
Genomic deoxyribonucleic acid (DNA) is extracted from an
environmental sample using chemical and physical disrup-
tion of the microorganisms. Subsequently, a polymerase chain
reaction (PCR) is used to selectively "grow-up" target genes
from the heterogeneous pool of genetic material. In our case,
the target genes are 16S rRNA. The target genes, amplified
in the PCR, are cloned into bacterial vectors and transformed
into competent cells of Escherichia coli. The recombinant
clones are cultured and plasmid DNA is extracted. The re-
sults from commercial dideoxy terminal sequencing are used
to design an oligonucleotide hybridization probe purchased
from a commercial vendor. The fluorescently labeled probe
is hybridized to a "fixed" sample, and individual microbial
cells are identified using an epifluorescence microscope.
For my class, commercially available kits were used to the
extent possible to minimize the time spent by students and
the teaching assistant in preparing reagents. Genomic DNA
was extracted using an UltraClean Soil DNA Isolation Kit.r7'
PCR was conducted using a model 2400 thermal cycler'8' and
the Takara Ex Taq kit.'11 Cloning of the PCR products was
accomplished with the TOPO TA Cloning kit version K2,1"01
and plasmid DNA was prepared using PerfectPrep Plasmid
Mini preps."" Throughout the exercises a variety of equip-
ment was used including an ultra low temperature freezer,"2'
a Mini Beadbeater-8,"I a system for agarose gel electrophore-
sis,1"4 a Genesys 10uv,l'" a constant-temperature rotary
shaker,"6' and an epifluorescence microscope.["7

FORMAT FOR LABORATORY EXERCISES
Step 1 Students arranged themselves into teams of three.
The selection of teammates was based both on a common
interest in one environmental sample and on an effort to spread
previous experience and expertise in molecular biology
among the groups.
Ste 2 Teams identified, evaluated, and proposed an ap-
propriate environmental system for study. Each system se-


Fall 2002










_Graduate-Education


... we plan to expand the enrollment [in this course] to include undergraduate envi-
ronmental engineering students as well as graduate and undergraduate students from
related disciplines, including chemical engineering and biomedical engineering.


elected for the course was novel for the field of environmental
engineering and possessed the capacity to stimulate a more
extensive research question (e.g., supplemented a research
question in an existing/developing MS or PhD degree, or pro-
moted a novel research direction generally underexplored.)
A sample was obtained from the selected system. In all cases,
preference was placed on samples that were a part of a devel-
oping/ongoing research project with significant supplemen-
tary information generated from advanced process engineer-
ing and chemical/physical analyses (e.g., samples) from a
novel bioreactor configuration or a bioreactor treating a novel
waste stream).

Ste 3 Each team generated 16S rDNA sequence infor-
mation from their sampless. Genomic DNA was extracted
using an UltraClean Soil DNA Isolation Kit"' according to
the manufacturer's instructions. Mechanical lysis of the
samples was performed for one minute at the maximum set-
ting of a Mini Beadbeater-8.[13] Genomic DNA was quanti-
fied using a Genesys 10uv'115 spectrophotometer assuming
that an absorbance reading of 1.0 at a wavelength of 260 nm
corresponded to a concentration of 50 mg DNA/1.
The 16S rDNA genes of bacteria present in the sample were
amplified by PCR using primer set S-D-Bact-0011-a-S-17
(5' to 3' sequence = gTT TgA TCC Tgg CTC Ag) and S-D-
Bact-1492-a-A-21 (5' to 3' sequence = ACg gYTACC TTg
TTA CgA CTT).[8' The conditions for PCR included: 5 min.
at 94C; 30 cycles of 0.5 min. at 940C, 0.5 min. at 550C, and
0.5 min. at 720C; 7 min at 720C; and hold at 40C. Each reac-
tion tube contained: 1.25 U Takara Ex Taq polymerase,E9' lx
Takara Ex Taq reaction buffer, 200 M of each deoxy ribo-
nucleotide triphosphate (dNTP), 0.2 gM of each primer, and
500 ng of genomic DNA. PCR was conducted using a model
2400 thermal cycler.J81
Agarose gel electrophoresis was used to check the quality
of the PCR product. A 1% (wt./vol.) agarose gel was pre-
pared in 1 x tris buffered EDTA (1 x TBE is 90mM tris borate
and 2 mM ethylenediamine-tetraacetic acid [EDTA]) accord-
ing to the manufacturer's instructions.E191 Electrophoresis was
conducted for two hours using a setting of 100 V for the power
supply. DNA fragments were visualized with a hand-held UV
lamp after staining the agarose gel for ten minutes at room
temperature with 50 mg/1 of ethidium bromide.
The PCR products were cloned into component cells of E.
coli using the TOPO TA cloning kit, version K2110 according


to the manufacturer's instructions. The blue/white screen with
x-gal was used to detect the presence of insert in each plas-
mid, and the antibiotic ampicillin was used to screen for the
presence of plasmids in colony-forming units of competent
cells. Ten clones were selected for each team of students, and
plasmid DNA was prepared using Perfectprep Plasmid Mini
prepsi"' according to the manufacturer's instructions. Puri-
fied plasmid DNA was subjected to endonuclease restriction
analysis using EcoRI.120' Digested plasmid DNA was electro-
phoresed on 2% (wt./vol.) agarose gels and visualized using
ethidium bromide staining and a hand-held UV lamp as de-
scribed above.
Two clones from each team were selected for commercial,
automated dideoxy terminal sequencing by the DNA Core
Facility at the University of Cincinnati. Sequencing primers
included M13(-20) forward and M13 reversel1 as well as S-
*-Bact-0343-a-A-15 (5' TAC ggg Agg CAg CAg 3'), S-*-
0519-a-S-18 (5'gTATTACCg Cgg CTg CTg 3'), S-*-Bact-
0907-a-A-20 (5' AAA CTC AAA TgA ATT gAC gg 3'), and
S-*-Bact-a-S-16 (5' Agg gTT gCg CTC gTT g 3').Y'8

Step 4 An initial phylogenetic analysis was conducted,
and the results were used to design oligonucleotide hybrid-
ization probes for fluorescence in situ hybridization (FISH).
Assembled sequences were compared to the Ribosome Da-
tabase Project (RDP) (available at rdp.cme.msu.edu) using
Chimera Check and Probe Match. Preliminary phylogenetic
affiliation was confirmed using a BLAST (Basic Local Align-
ment Search Tool) search of GenBank (available at
www.ncbi.nlm.nih.gov, follow the links to BLAST). The
fluorescently labeled oligonucleotide probes were ordered
from a commercial vendor.
Ste 5 Each team conducted fluorescence in situ hybrid-
ization (FISH) analysis of their original samples. Aliquots of
the original sample were chemically "fixed" for one hour at
room temperature with 4% (wt./vol.) paraformaldehyde pre-
pared in 1 x phosphate buffered saline (1 x PBS is 130 mM
NaCl and 10 mM sodium phosphate buffer). The samples
were subsequently stored at -200C in a 50% (vol./vol.) mix-
ture of ethanol and 1 x PBS. The fixed samples were applied
in a sample well on a Heavy Teflon Coated microscope slide1[2
and air-dried. FISH was performed as previously described.1221
Briefly, each microscope slide was dehydrated in an increas-
ing ethanol series (50, 80, and 95% [vol./vol.] ethanol, one
minute each), each sample well was covered with 9 pl of


Chemical Engineering Education













hybridization buffer (20% [vol./vol.] formamide, 0.9 M NaCI, >
100 mM Tris HCI [pH 7.0], 0.1% SDS), and fluorescently
labeled oligonucleotide probe, 1 [il (50 ng), was added to
each sample well. Hybridizations were conducted in a mois-
ture chamber for two hours, in the dark, at 460C. The slides
were washed for 30 minutes at 480C with 50 ml of prewarmed >
wash solution (215 mM NaCI, 20 mM Tris HCI [pH 7.0], >
0.1% SDS, and 5 mM EDTA). Fixed, hybridized cells were
mounted with Cargille immersion oil2-31 and a cover slip. >
Probe-conferred fluorescence was visualized with a model
E600 upright epifluorescence microscope,124] and digital im-
ages were captured using
a Spot-2 charge coupled
device (CCD) cam- Sex N=13 Age N=13
era.[25] The results of the Male 5 <23 0
Female 8 23-26 4
FISH analysis included 27-30 5
30+ 4
determining the abun-
dance and spatial orga- Current Degree N=13 Current Degree Field N=13
nization of phylo- B.A. 1 EnvEng 5
B.S. 4 Env. Scil 1
genetically defined mi- M.S 7 Engineering 2
Ph.D. 1 Other 5
crobial populations Highest Degree N=13 Highest Degree Field N=13
identified by unique M.S. 4 Env. Env. 8
oligonucleotide hybrid- Ph.D. 9 Env. Si.
ization probes.


figure z. uemograpnlc o
The students learned
he students learned course as determined by
the procedures for the
laboratory exercises through
a video series produced specifically for this course. They were
given a laboratory manual at the start of the class, and videos
of the laboratory exercises were distributed biweekly in VHS
format. The manual outlined all of the procedures for the labo-
ratory and provided step-by-step instructions to complete each
exercise. The videos gave the students an opportunity to
view the instructor completing all of the steps of each
exercise. The laboratory exercises were completed inde-
pendently by the three-student teams according to a sched-
ule arranged at the start of the class. Approximately the
first fifteen minutes of the weekly lectures were dedicated
to reviewing the progress of each team toward meeting
the schedule for completion of the laboratory exercises.

TOPICS FOR THE LECTURES
Each week, approximately two hours were spent in a lec-
ture discussion format with the entire class. The nine topics
that were covered in the pilot course included:
> Overview of methods including the value of differ-
ent methods and an answer to the question, "Why do
Environmental Engineers need to learn molecular bi-
ology?"
> Measuring microbial community structure
> Measuring microbial community function


Graduate Education

Quantitative molecular biology for Environmental
Engineering versus qualitative molecular biology for
Environmental Science
Troubleshooting the laboratory exercises to improve
the course for the subsequent year
What is this "phylogeny stuff' anyway?
Historical development of molecular tools in Envi-
ronmental Science and Engineering
Success stories for molecular tools in Environmen-
tal Science and Engineering
Principles of microscopic examination


STUDENT
FEEDBACK
Figure 2 summarizes
the results of students'
responses to a demo-
graphic survey. Thirteen
of the fifteen students
enrolled in the course re-
sponded to the survey.
The class was divided
almost equally between
male and female stu-
dents with a median age
of 27-30 years old. Five of


the students had received significant formal training in biol-
ogy, previously participating in more than ten biology courses.
The majority of the students had already completed their MS
degree (eight out of thirteen), but more than 50% of the stu-
dents had received their degree outside of environmental en-
gineering or environmental science. Most students spent less
than six hours per week on the course, but some students
spent significantly more time. Overall, the students enrolled
in the pilot test of "Molecular Methods in Environmental
Engineering" could be categorized as mature students (i.e.,
in their late twenties working toward their doctoral degrees).
Furthermore, the class contained a significant number of stu-
dents with extensive previous experience in biology. Thus,
the students enrolled in the pilot course were well prepared
in maturity and previous biology experience to actively par-
ticipate in this novel course. As the course continues to be
offered, I plan to track the success of the course in relation-
ship to the demographics of the enrolled students.
In addition to collecting demographic information, at the
end of the class the students were asked to respond to three
open-ended questions. In response to the question, "In your
opinion, were the objectives of the course met?" students re-
sponded:
The course met some of the objectives, but some students


Fall 2002


Number of Previous N=13
Biolovy Courses
<2 2
<5 4
<10 1
<15 1
15+ 4
Hours per week on N=13
course
<4 2
<6 7
<8 1
<10 2
<12
<15 1


II 1 .I *1


f sruaenrs enrolled in the pilot
an anonymous, in-class survey.











[ Graduate Education


are not convinced why we use molecular biology to
identify microorganisms in systems that have been proved
or have been operating successfully.
SYes. I am equipped with knowledge about this approach,
and I can interpret research results and publications
from this developing field.
In response to the question, "What was the best aspect of this
course?" students responded:
Most of the procedures are basic/universal operations in
molecular biology which means that we understand how to
study biology and biotechnology at the molecular level.
Experimental work-because it is through applications
that a student gets a tight grip on ideas and concepts. In
addition, the challenging experiments and the value of
the final result make the work more interesting.
The lectures were interesting and informative. I learned
a great deal, and my ideas about environmental
engineering and science have been positively affected by
the knowledge I have gained.
Your perspective. We will never see "cutting edge"
developments in a book.
The whole structure of the course is similar to a research
project.
The best aspect was carrying the concepts from the
classroom to the lab in a manner relevant to our field.
Also, having a class that is new gives afresh perspective
into the future of environmental engineering.
In response to the question, "What part of the course would
you suggest improving?" students responded:
More theoretical basis, especially for the background of
molecular biology methods.
From their responses to the open-ended questions, it is ap-
parent that the students felt the pilot course was a success. It
is interesting to note that the students appreciated that the
pilot course represented an effort to integrate research into
the classroom. One of the greatest difficulties for developing
a role for molecular biology in an engineering curriculum is
discovering a mechanism for moving these "state-of-the-art"
research skills into a classroom setting. In the future, we plan
to expand the enrollment for "Molecular Methods in Envi-
ronmental Engineering" to include undergraduate environ-
mental engineering students as well as graduate and under-
graduate students from related disciplines, including chemi-
cal engineering and biomedical engineering.

CONCLUSIONS
To address the growing national need for integrating
genomics and molecular biology into the engineering cur-
riculum, the author developed and pilot tested a new course,
"Molecular Methods in Environmental Engineering." Fifteen
graduate students were successfully introduced to molecular
biology through lectures and hands-on laboratory exercises
following the "full-cycle 16S rRNA approach." Although the


Chemical Engineering Education


pilot course can be considered a success, future offerings of
this course must be modified to reduce the difficulty of com-
prehending molecular biology by inexperienced engineering
students. One of the most daunting challenges for this type
of "state-of-the-art" course is providing a supportive, yet in-
dependent learning environment. For highly motivated gradu-
ate students, the author demonstrated that the format for this
course is successful. To offer this course to undergraduate
students or poorly prepared graduate students represents a
future challenge. In upcoming course offerings, the author
plans to open enrollment for "Molecular Methods in Envi-
ronmental Engineering" to undergraduate students in envi-
ronmental engineering as well as students in chemical engi-
neering and biomedical engineering. As genomics and mo-
lecular biology become as common to an engineering cur-
riculum as chemistry and physics, engineering faculty need
to take the lead in developing courses that introduce these
topics from an engineering perspective with a focus upon
quantitative approaches and the application of science to find
cost-effective solutions to society's problems.

ACKNOWLEDGMENTS
This laboratory course would not have been possible with-
out the commitment of significant resources from the De-
partment of Civil and Environmental Engineering of the Uni-
versity of Cincinnati. For the success of the pilot test, the
author is grateful to the Department.

REFERENCES
1. Amann, R., W. Ludwig, and K.H. Schleifer, "Phylogentic Identifica-
tion and In Situ Detection of Individual Microbial Cells without Cul-
tivation," Microbiol. Rev., (59), p. 143, (1995)
2. Rittman, B. "Editorial: Molecular Understanding," Water Environ. Res.,
(70), p. 1107, (1998)
3. Stensel, H.D., 2001, "Editorial: Probing the Black Boxes, Water
Environ Res., (73), p. 259, (2001)
4. Woese, C.R., "Bacterial Evolution," Microbiol. Rev., (51), p. 221,
(1987)
5. Woese, C.R., "There Must be a Prokaryote Somewhere: Microbiology's
Search for Itself," Microbiol. Rev., (58), p. 1, (1994)
6. Hugenholtz, P., B.M. Goebel, and N.R. Pace, "Impact of Culture-In-
dependent Studies on the Emerging Phylogenetic View of Bacterial
Diversity," J. Bact., (180), p. 4765, (1998)
7. Catalog # 12800-100, MoBio, Solano Beach, CA
8. Applied Biosystems, Foster City, CA
9. PanVera Corp., Madison, WI
10. Invitrogen Corp., Carlsbad, CA
11. Eppendorf Scientific, Westbury, NY
12. Model Ultima II, Revco, Inc., Asheville, NC
13. Biospec Products, Bartlesville, OK
14. Catalog # CSSU1214 and EC105, E-C Apparatus Corp., Holbrook,
NY
15. Spectronic Unicam, Rochester, NY
16. Model C24, New Brunswick Scientific, Edison, NJ
17. Model E600, Nikon, Inc. Melville, NY










18. de los Reyes, M.F, FL. de los Reyes, M. Hernandez, and L. Raskin,
"Quantification of Gordona amarae Strains in Foaming Activated
Sludge and Anaerobic Digester Systems with Oligonucleotide Hybrid-
ization Probes," Appl. Environ. Microbiol., (64), p. 2503, (1998)
19. E-C Apparatus Corp., Holbrook, NY
20. Promega, Inc., Madison, WI
21. Cel-Line Associates, New Field, NJ


22. Oerther, D.B., J. Pernthaler, A. Schramm, R. Amann, and L. Raskin,
"Monitoring Precursor 16S rRNA of Acinetobacter spp. in Activated
Sludge Wastewater Treatment Systems," Appl. Environ. Microbiol.,
(66), p. 2154 (2000)
23. Type FF, Cedar Grove, NJ
24. Nikon Instruments, Inc., Melville, NY
25. Diagnostic Instruments, Inc. Sterling Heights, MI J


M letter to the editor


To The Editor:
This letter is motivated by the paper "An Undergraduate
Course in Applied Probability and Statistics" that appeared
in the Spring 2002 issue of Chemical Engineering Educa-
tion.1l Probability and statistics are difficult subjects to teach
to engineering students, and Professor Fahidy is to be con-
gratulated on his efforts in this area.
In this letter we would like to refer to the discussion and
examples related to regression analysis. Professor Fahidy dis-
cusses in detail the use of numeric information (such as error
variance, confidence intervals, correlation coefficient, etc.)
for regression analysis, but does not mention graphic infor-
mation (residual plots) and physical insight for regression
analysis. Using the examples presented by Professor Fahidy,tl
we would like to demonstrate the importance of including
graphical information and physical arguments in the regres-
sion analysis.
Let us refer first to Example 4 in the paper. In this example,
the integral method of rate data analysis is used for a (sup-
posedly) first-order reaction. Nonlinear regression can be used

TABLE 1
Regression Results for Example 4 in Reference 1
Reaction Order P'Order "' Order 0'h Order 2"d Order
Model logY=-k*t Y=exp(-k*t) Y=Y,+k*t l/Y=1/Y,+k*t
k (value) 0.0039888 0.0038126 -0.0042162 0.0059893
95% Conf. Interval +0.0011009 0.0010816 0.0015209 +0.0059893
Y, (or 1/Y., value) -- 1.0329275 0.9365288
95% Conf. interval 0.586582 +0.1012594
R2 0.7620164 0.7770319 00.8362884 0.7757433
Variance (based on Y) 0.0023055 0.002271 0.0018759 0.0021994













Figure 1. Residual plot for Example 4 in Fahidy paper.'1

Fall 2002


for finding the reaction rate coefficient (k) using concentra-
tion (Y) versus time (t) data, on the regression model Y =
exp(-kt). Alternatively, this equation can be linearized to yield
lnY=-kt, where linear regression can be applied. The results
of the linear and nonlinear regression that were obtained us-
ing POLYMATH 5.1 are shown in the first two columns of
Table 1. Note that these results are different from what is
presented in [1], but they are correct and were confirmed by
the author of the original article.121 Looking at the numerical
information presented in Table 1 (parameter values, confi-
dence intervals, correlation coefficients, and variances) leads
to the conclusion that there is no significant difference be-
tween linear and nonlinear regression for determining k (the
variances are almost the same, contrary to what is argued in
[1]). The same information may also lead to the conclusion
that the model fits the data reasonably well. This conclusion,
however, is contradicted by the residual plot shown in Figure
1. The residuals are not randomly distributed around a zero
value. This may indicate either lack of fit of the model, or
that the underlying assumption of a random error distribu-
tion for the dependent variables is incorrect.
Physical insight can suggest alternative regression models,
but more information regarding the reaction involved is
needed. Since no such information is available, we will as-
sume a homogeneous reaction, just for the sake of the dem-
onstration. Assuming 0'h order reaction or 2nd order reaction
yields the models shown in the third and fourth columns of
Table 1, respectively. The numeric information presented in
the Table points on the 0'h order reaction as the most appro-
priate one (smallest variance value-note that in order to be
on a unique scale, all the variance calculations must be based
on Y). The residual plot for the 0'h order reaction is not sig-
nificantly different, however, from that shown in Figure 1;
thus, this model is not supported by the residual plot either.
The conclusion from proper analysis of this example is that
the data available are insufficient (in quality, quantity, or both)
to determine in any certainty the order of the reaction it rep-
resents. To obtain a more definite result, additional measure-
ments must be made.
In Example 5, a linear model Y=a+bx is fitted to data of
mean fuel consumption rate (Y) versus vehicle mass (x).
The numerical results that were obtained for this example,
using POLYMATH, are: parameter values (including
95% confidence intervals) a=-0.86959752.0733031;
Continued on page 277










Classroom


A New Approach to Teaching

TURBULENT

THERMAL CONVECTION




STUART W. CHURCHILL
University of Pennsylvania Philadelphia, PA 19104-6393


At AIChE's annual meeting in 2000, I gave an oral
presentation of an early version of a pair of new ex-
pressions, completely free of explicit empiricism, for
the prediction of fully developed turbulent thermal convec-
tion in all channels and for all thermal boundary conditions.
At the same venue, In 2001 I also presented a greatly im-
proved version, although at the expense of a smidgen of em-
piricism. Both presentations prompted the same question from
participants: "Is this approach being taught to current stu-
dents, and if not, why not?" I explained in both instances that
this material is very new and is not in any textbooks, and
furthermore, that it may not appear in textbooks for some
time to come since the authors of transport textbooks must
first become aware of the concept and its results, and then be
convinced of its educational (as well as predictive superior-
ity) over the method they are currently teaching. Also, as
Andersontl1 has noted, textbooks in chemical engineering
seem to have a unique longevity, and the more successful of
them are replaced or revised only after long intervals of time.
Undoubtedly with these textbook characteristics in mind,
my mentor and departmental chairman, Donald L. Katz, long
ago made the suggestion (which to a young assistant profes-
sor was virtually an order) that every year I replace at least
20% of the graduate transport course content by embracing
new developments in the literature. Throughout my career,
that suggestion led to my use of notes incorporating these
new segments, together with using a book or books as a
supplement rather than the other way around. I conclude, a
full half-century later, that this process of annual supplemen-
tation and revision has, by virtue of the associated forced
self-study and self-learning in the fields of my teaching, more
than compensated me (and perhaps my students) for the ef-
forts, and that it is a worthy complement of the new materials
most of us introduce periodically from our own research and


consulting. I am here taking advantage of the platform pro-
vided by Chemical Engineering Education to encourage and
assist the process of supplementation for transport teachers
with respect to a new approach for the description and pre-
diction of turbulent thermal convection.
In a previous CEE article,11 I presented a new approach to
the description and teaching of turbulent flow with the same
objective. For that simpler and more restricted topic, it was
possible to include in the presentation a virtually complete
set of supplementary notes for direct use by any interested
faculty member. For the much more complex process of tur-
bulent thermal convection and the much more complex pro-
cess of development of the new model, however, the presen-
tation of a work ng set of supplemental notes in this format is
simply not feasible. Rather, this article has the more limited
objective of outlining the new approach with the hope that
faculty members who teach transport will be inspired to study
the more complete documentation in the key references and
make the effort to formulate their own supplemental notes.
Perhaps I will eventually find the time and motivation to pre-
pare a monograph on this topic, but I do not recommend that
anyone procrastinate with that as the excuse.
When an analogue of the approach that was so simple,


Stuart W. Churchill is the Carl V.S.
Patterson Professor Emeritus at the Univer-
sity of Pennsylvania, where he has been
since 1967. His BSE degrees (in ChE and
Math), MSE, and PhD were all obtained at
the University of Michigan, where he also
taught from 1950 to 1967. Since his formal
retirement in 1990, he has continued to teach
and carry out research on heat transfer and
combustion. He is also currently completing
books on turbulent flow and correlation.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education










straightforward, and successful for turbulent flow was first
attempted for the closely related topic of turbulent thermal
convection, I anticipated that the path of development would
closely parallel the previous one. While convection is inher-
ently more complex than flow in several respects, it is also
simpler in the sense that it merely consists of the superposi-
tion of a scalar quantity, the temperature, on the flow. The
path of development that emerged after considerable trial and
error proved to reflect the greater complexity that had been
anticipated, and the final results proved to reflect the antici-
pated greater simplicity.
The predictive equations for turbulent thermal convection
that are described in this paper are, by a significant margin,
more accurate, fundamentally sound, and general than any
prior ones. They also provide better insight into the relation-
ship between flow and convection and a better conception of
thermal convection itself that more than compensates for the
greater detail. This new material should therefore, as sug-
gested by audience members at the AIChE presentations, be
given serious consideration for inclusion in the final portfo-
lios of both our undergraduate and graduate students.
Apart from the merit of the predictive equations for turbu-
lent thermal convection that emerged, the path of their devel-
opment appears to have merit itself in an educational sense.
On the one hand, it provides insight into a creative process of
correlation that is within the capabilities of our students. On
the other hand, it provides a perspective within which the
strengths and weaknesses of all forms of correlation can be
evaluated, not only in flow and convection but also in every
aspect of chemical engineering. Our students should be
made to realize that whatever career they follow after
graduation, they will spend considerable time using and/
or formulating correlations.
I have a predilection for presentations in narrative and his-
torical contexts under the presumption that the personal char-
acteristics, as well as the triumphs and failures, of our prede-
cessors not only stimulate interest but also provide a mne-
monic for students. In this instance, a description of the ser-
endipitous and irregular path of development of a completely
new formulation in a relatively mature field may serve a simi-
lar role. Teachers who prefer a more orderly and skeletal ap-
proach are welcome to eliminate such diversionary material.
Many details concerning origins, proofs, uncertainties, and
limitations are deferred to the references, and in particular to
Churchill and Zajic.13' It is, however, essential that the teacher
present these details, or perhaps in the instance of graduate
students, assign key references as required collateral read-
ing. In either event, students should be encouraged to ques-
tion the validity of the many assertions and simplifications in
this article rather than accept them "on faith." Undergradu-
ate students may require more guidance than do graduate stu-
dents with respect to the new approach, but they have the


counterbalancing advantage of less to unlearn.


THE NEW APPROACH
FOR TURBULENT FLOW
A thorough understanding by students and faculty alike of
the new approach for the description and teaching of turbu-
lent flow, as previously described[2], is an essential prereq-
uisite for the complementary new approach presented here
for turbulent thermal convection. Because of space limita-
tions, however, only those results that are directly applied or
adapted for thermal convection will be reproduced here.
The time-averaged, once-integrated differential equation
of conservation for momentum in the radial (negative-y) di-
rection in steady-on-the-mean, full developed flow of a fluid
of invariant density and viscosity through a round tube can
be represented by

c(1 =g du -pu'V (1)
a) dy

Here, Tw is the shear stress on the wall, y is the distance
from the wall, a is the radius of the pipe, u is the time-aver-
aged velocity, and u' and v' are the fluctuating components
of the velocity in the x and y directions, respectively. The
superbar designates the time-average of their product, while
p and p are the dynamic viscosity and specific density of
the fluid. (Aside to teachers: The origin of this expression
and the physical meaning of the several variables and terms,
including the signs of the latter, should be described or reviewed
as appropriate. Any uneasiness of the students in this regard
can be expected to persist in what follows. Of course, this warn-
ing applies to some extent to subsequent details as well.)
Equation (1) can be rewritten in terms of the dimension-
less "wall" variables of Prandtl, namely

u = u(p / )/2

y+ = /y(,p)2 /

a+ = a(wp)1l2 / 2

and one new variable, namely the fraction of the transport of
momentum (or the total shear stress) due to the turbulent fluc-
tuations (u'v') =-pu'v' / as

1 yxF -7++1] du
1-- + = (2)
a dy+

Equation (1), with y+/a+ replaced by 1-R, can be integrated
formally to obtain the following expression for the radial dis-
tribution of the time-averaged velocity:

u+ = u )++dR2 (3)
2
R2


Fall 2002









The velocity distribution can in turn be integrated over the
cross-section to obtain, after utilizing integration by parts,
the following integral expression for the mixed-mean veloc-
ity and thereby the Fanning friction factor:
= u+dR2 1 u'v') dR4 (4)

[tf Um 4)
0 0
Equations (1) through (4) are exact insofar as the restrictions
mentioned above with respect to Eq. (1) are fulfilled. In or-
der to implement Eqs. (3) and (4), an expression is required
for (u'v') in terms of y+ and a+. For this purpose, Churchill14'
proposed the following semi-empirical expression:

I / \3i -i-8/7
++ 8/7 = 0.7 ( 8/7


+ exp -1 1 +6.9 y 8/(5)
0.436y+ 0.436a+ (

It is essential for the students to be aware of the origins and
uncertainties of Eq. (5) since this expression has a critical
role, both numerically and functionally, in all of the develop-
ments that follow for both flow and convection. The third-
power dependence on y+ for small values of y+ was originally
postulated on the basis of asymptotic analyses, but has since
been confirmed by direct numerical simulations, which have
also produced a theoretical value of approximately 7 x 10-4
for the numerical coefficient. The exponential term for mod-
erate values of y+, as well as the deductive term for y a+
were both derived by speculative analysis, but the coefficients
of 0.436 and 6.95 were determined from recent, improved
experimental data for the time-averaged velocity distribution.
The power-mean form of Eq. (5) is arbitrary and the combin-
ing exponent of -8/7 is based on experimental data for u'v.
(See Churchill and Zajict3] for further details, including com-
plete references.)
Numerical integration of Eqs. (3) and (4) using (u'v')+ from
Eq. (5) results in almost exact values of u+ and u+ owing to
the smoothing associated with integration. Such values of u+
may be represented with a high degree of accuracy for a+ >
300 by the following expression that invokes no additional
empiricism beyond that of Eq. (5):

(2 1/2 3.2 227 502 + a+ (6)
m) u a 3 + a+ 0.4366

Equations (1) through (6) are the only ones for flow that will
be referred to directly in the developments that follow for
convection.
It may occur to teachers and graduate students at this point
that the relevant consideration of turbulent flow has been
completed without any mention of the eddy viscosity or the


mixing length. One merit of the new approach, which carries
over to thermal convection, is that the need to introduce such
heuristic quantities is avoided completely by the more direct
and simple development in terms of(u'v')



AN ASIDE ON A
GENERIC CORRELATING EQUATION

Equation (5) is a particular application of the generic cor-
relating equation proposed by Churchill and Usagi[51 for two
regions, namely

yb Y= + y (7)
Here, y = y{x}, y,, = {x-0}, y_ = y{x->o}, and b is an
arbitrary exponent. Either yo or y_ or both are necessarily
functions of x rather than fixed values. For three regions, Eq.
(7) can be extended either directly as


ybq=(yb+yb) +ybq

or in staggered form as


(yb yb y qy b -y) (9)
Here, y, is an intermediate asymptote and q is a second arbi-
trary exponent. The reverse order of combination of y,, y,
and y_ leads to equally valid and, in general, fundamentally
different representations. Equations (7) through (9) have been
introduced here to avoid interrupting the continuity of the
development in which they are used.


DEVELOPMENT OF A NEW FORMULATION
FOR TURBULENT CONVECTION
The analogue of Eq. (1), with the additional idealization of
negligible viscous dissipation, is
kT
j = -k + pcT'v' (10)
ay
and that of Eq. (2) is

SI (T'v)++ T (11)
Jw ay+
Here, j is the heat flux density in the y-direction, T is the
temperature of the fluid, j and Tw are their values at the wall,
T+ = k(wp)/2(Tw T) / jw, T'v' is the time-averaged prod-
uct of these fluctuating quantities, (T'v') = pcT'v' / j is the
fraction of the radial heat flux density due to the turbulent
fluctuations, and k is the thermal conductivity of the fluid.
The terms j/j, and (T'v')+ in Eq. (11) depend on two param-
eters, namely the Prandtl number Pr = cp/k and the mode of
heating at the wall, as well as on y+ and a.


Chemical Engineering Education










From an energy balance over an inner cylindrical segment
of the fluid stream, it follows that


R I
j 1wRI u aT/ / ax
j, R 0 um Tm / ax


Here, Tm is the mixed-mean temperature of the fluid stream.
As contrasted with / Iw, which may be inferred from Eq.
(1) to vary linearly with R, j/j varies non-linearly because of
its dependence on the velocity distribution and in some in-
stances on the temperature distribution as well. Also, as can
be inferred from Eq. (12), T varies with x as well as with y,
even in fully developed thermal convection, whereas u var-
ies only with y in fully developed flow. Fully developed ther-
mal convection is ordinarily defined by two criteria, namely


a T -T
ax T, T,


Then T+, weighted by u+ / u can be integrated over the
cross section to obtain


Nu- ya
Nu 2a+= 4
Tm+= R2
R/


U dR2
Um )


and 0
ax


where h = j /(T Tm) is the local heat transfer coefficient.
Equation (11) can be put in a more tractable form for both
formal and numerical solution by introducing new variables
y and Prt defined as follows, in place ofj/jw and (T'v')

j j u1 R aT /dX
1+= I dR2 (13)
jw T jR R2 T /ax
0


For uniform heating at the wall, it follows from the criteria
for fully developed thermal convection that
aT / ax = aTm /I x. It then follows from the correspondingly
reduced form of Eq. (13), together with Eqs. (3) and (4), that
y is a function only of y+ and a+. Equation (17) can then by
virtue of the same considerations, be integrated by parts to
obtain


Pr, 1 v' (Tu'vuv
Pr 1 J(v lT +


The result is


(1+ y)R dT+ (15)
1+Pr (u + d(15)
++
Pr ( dy+



The use of y, the perturbation of the heat flux density distri-
bution from that of the shear stress distribution, was suggested
by Reichardt.'61 The variable Pr, was originally introduced in
connection with modeling in terms of the eddy viscosity and
eddy conductivity, and accordingly, by analogy with the cor-
responding ratio of molecular quantities, was called the tur-
bulent Prandtl number. Although the redefinition of Pr in
terms of (u'v') and (T'v') avoids these heuristic vari-
ables, the traditional name and symbol for this quantity are
retained herein out of respect for its historical origin. It should
be noted that Pr is not necessarily proportional to Pr since
(Tv')) is, in general, a function of Pr.
Equation -=R can be integrated formally to obtain


Equation (18) can be reduced for three special cases. For Pr
= 0, it can be expressed as

Nuo -Nu{Pr= 0}= 8 (l+)2dR =8/( l+y)2mR (19)

while for Pr = Pr, it can be reduced by virtue of Eq. (4) to


NuI =Nu{Pr = Prt}


8

f(l+y)2[1- (- )++ dR4
o


2a+
Um(1 + 1 mR


Fall 2002


+ 1 (1
2 f
R' Pr
1+-
Prt









Here, as can be inferred, ( +y))R4 designates the integrated-
mean value over R4, and (I+y)2mr 4 the integrated-mean
value weighted by 1 (u'v') Both quantities may readily
be evaluated numerically, using Eqs. (3), (4), and (5), and the
reduced form of Eq. (13). For Pr M-, the temperature field
develops almost completely very near the wall where (u'v')
can be approximated by 0.7 (y+/10)3 and y can be neglected.
Equation (16) can then be integrated in closed form to obtain

Nu- = Nu{Pr -* }= 33/2 (0.0007)/3a+(Pr/ Pr )1/3 / K =

0.07343Re(f / 2)1/2(Pr/Prt)/3 (21)

For uniform wall temperature, the criteria for fully devel-
oped convection require that

(aT / ax) / (aT, / ax) T+ / Tg+
Integration of Eq. (17) by parts is no longer possible, but
from the limiting form of Eq. (16) for R = 0, it follows that

Nuo = 4(T /T /(l + Y)mR2 (22)


Su+ (T+) Re(f2)
Nul = (1+ )wmR
u|=4 T| f+ Y)wmR2


(23)


Here, Tc is the temperature at the axis of the pipe. Equation
(21) remains applicable as is. The determination of numeri-
cal values of Y, T+, and Tn+ from Eqs. (13), (16), and (17)
now requires iteration, but the functional forms of Eqs. (22)
and (23) are adequate for the development herein.
On the basis of the previous experiences with various as-
pects of turbulent flow, I anticipated that Eqs. (19) through
(23) could be combined in appropriate pairings in the form
of Eq. (7) to construct satisfactory correlating equations for
Pr 2 Pr and for Pr < Pr, or alternatively, in appropriate trip-
lets in the form of Eq. (8). All such attempts failed, however.
I then found (somewhat serendipitously) that a successful cor-
relating equation for turbulent thermal convection could be
devised by using a particular analogy between momentum
and energy transfer in which the exact solutions for three par-
ticular values of Pr occur in the form of Eq. (9). Accordingly,
a brief and very selective review of such analogies is appro-
priate at this point.


SELECTIVE ANALOGIES
Reynolds171 postulated that the transport of both momen-
tum and energy between a turbulent stream and its confining
surface occurred wholly by means of a mass flux of eddies
and thereby derived the equivalent of
Nu = PrRe(f /2) (24)


Prandtl'81 improved upon the Reynolds analogy by postulat-
ing an added resistance due to linear molecular diffusion of
momentum and energy across a viscous boundary layer of
thickness 8 in series with transport by the eddies of Reynolds
in the turbulent core, thereby obtaining the equivalent of

N Pr Re(f / 2)
1+ 8+(Pr- 1)(f / 2)1/2
Equation (25), just as Eq. (24), is inapplicable for Pr < 1,
owing to neglect of thermal conduction in the turbulent core,
and also for Pr >> 1, owing to neglect of eddy transport within
the viscous boundary layer. Even so, it represents a great ad-
vance in that it correctly predicts a coupled, non-power de-
pendence on both Pr and Re, in the latter case by virtue of the
dependence of f on Re. Of the many analogies that have been
proposed to eliminate the deficiencies of the Prandtl analogy
for large and small values of Pr (see, for example, Churchill['),
only two need to be examined here.
Reichardt161 eliminated dy+ between the equivalents of Eqs.
(2) and (15) and made several ingenious approximations that
allowed him to integrate the resulting combined equation in
closed form. Churchill[19 assembled the fragments of this so-
lution into a single expression for Nu and corrected the erro-
neous expression used by Reichardt for the shear stress near
the wall, thereby obtaining


(1 + Y)mu T+ (Yu+ )Pr
1 -P
Nu Re(f/2) T + )u --Pr

13.62 T1 Prt Pr 1/3
Re(f / 2) 2T, Pr Pr


Equation (26) is limited in applicability to Pr 2 Prt by virtue
of one of the simplifications made by Reichardt in order to
be able to integrate analytically.
Churchill10 (also Churchill and Zajic'31) followed a com-
pletely different path to derive an expression, which for Pr 2
Prr, is exactly equivalent to Eq. (26) except for replacement
of the term 1 Pr/Pr by 1 (Pr/Pr)2/3. In retrospect, the differ-
ence in these expressions is a consequence of the approxima-
tion of Reichardt of du+ by dy+ in the differential term lead-
ing to the right-most term of Eq. (26).


FINAL FORMS
The final predictive expressions for turbulent thermal con-
vection emerged from the various expressions above by means
of the following lengthy series of insights, postulates, and
inferences, all of which were essential.

O Churchill, et al.,l 'I recognized that Eq. (26) was equiva-
lent, with Tm / Tc evaluated at the limiting conditions, to


Chemical Engineering Education










( Pr N 1 Pr )
tPr) Nu+ 1 Pr )Nu


(27)


O They further recognized that when Eq. (17) was rear-
ranged as

Nu Nu1 Nu Pr1
Nu=-Nui 1 [+ Nu (28)
Nu- Nul NuI Pr- Prt

it had the form of Eq. (9), with

b=-q=l
Yo =Nul
y = Nu-

yi = Nu (Nu Nu) Pr -I
Nu- Prt

The staggered independent variable, Pr/Pr 1, has the essen-
tial role of converting Nu, from a particular value to an as-
ymptote. According to Eq. (28), Nu goes through a sigmoi-
dal transition from Nu, to N-, a nuance of behavior that had
previously been overlooked. In retrospect, correlation in terms
of Eq. (7), that is, direct interpolation between Nu, and Nu-,
was doomed to fail. The relationship provided by the
Reichardt analogy was essential to the derivation of Eq. (27).


O The identification of Eq. (28) with Eq. (9) suggested
that the analogue of Eq. (28) in terms of Nu0 and Nu, might
be applicable for Pr < Pr1. That concept led to an expression
with a discrete step in the derivative of Nu with respect to Pr/
Prt at Pr = Pr1, but elimination of this discontinuity by means
of an arbitrary but ultimately vanishing coefficient resulted
in

Nu Nuo -/L Nui Nu Nu, (Pr-P
Nu1 -Nuo + Nu Nu Nuo Pr (29)

where Nu = Nu {Pr = Prt }= 0.07343 Re(f / 2)/2.


O4The absence of any allusion to geometry or to the ther-
mal boundary condition suggested that Eqs. (28) and (29)
might be applicable for all geometries and all thermal bound-
ary conditions. Plots of numerically computed values of Nu
versus Pr/Pr for round tubes with uniform heating and uni-
form wall temperature, and for parallel-plate channels with
equal uniform heating and with unequal uniform tempera-
tures, confirmed the validity of this speculation.


0 These plots in logarithmic coordinates appeared to pro-
vide an excellent overall representation for all values of Pr/


Pr, for all values of a+ or b* (where b is the half-spacing of
the parallel plates) greater than 145, which is the lower limit
for the existence of fully turbulent flow, for all geometries,
and for all thermal boundary conditions. The more critical
test provided by arithmetic plots, however, reveal errors in
Nu of up to 20% for both Pr/Prt = 0 {10} and Pr/Pr = 0 {0.01 }.
After many attempted correctives, substitution of the anal-
ogy of Churchill for that of Reichardt to obtain

1 Pr 1 Pr2/3 (30)
Nu Pr Nu Pr Nu(

was found to result in an almost perfect representation for
the dependence of Nu on Pr/Pr.
@ The analogue of Eq. (30) for Pr < Pr, corrected as was
Eq. (29) to remove the singularity in the derivative, and with
the arbitrary inclusion of the empirical factor (Pr/Pr)"/, is

Prt 1 Nu 2-Nu Nug
Nu Nuo = 1P r 3 (31)
NuI -Nuo (Prt/Pr) /8(Nu, -Nuo)NuL


This expression results in almost exact representations for
Pr < Pr, for all of the previously mentioned conditions-
thereby it is a complement in every respect to Eq. (30).


IMPLEMENTATION
The numerical calculation of values of Nu for specified
values of Re and Pr and for particular geometries and
boundary conditions requires numerical values or expres-
sions for f, Nu,,, Nu,, and Pr,. For a round tube, values of
f of sufficient accuracy can be determined from Eq. (6)
by noting that Re = 2 au+,. Values of Nu0 and Nu, can be
calculated from Eqs. (19) and (20), but an array of such val-
ues has already been calculated for representative values of
a+, and correlating equations have been devised for interpo-
lation. The slight inaccuracy associated with Eq. (5) is totally
negligible when it is used in conjunction with Eqs. (19) and
(20). Equivalent expressions for f, and values and expres-
sions for Nu0 and Nu, are also available or can readily be
derived and calculated for other geometries and thermal
boundary conditions. Equation (21) is directly applicable as
an asymptote for large values of Pr for all geometries and
conditions. Current correlative and predictive equations for
Pr, are quite uncertain (see, for example, Kays'121 or
Churchill131). However, Nu as predicted by Eqs. (30) and (31)
is fortuitously insensitive to the expression used for Prt, and
the following purely empirical equation
0.015
Pr, = 0.85 + 05 (32)
Pr
appears to be adequate for that purpose. The dividing value


Fall 2002











of Pr with respect to the use of Eq. (30) or (31), that is, the
value of Pr for which Pr = Pr, is 0.867 according to Eq. (32).
Other correlating equations for Pr, give only slightly differ-
ent numerical values for this pivotal value of Pr. Either Eq.
(30) or Eq. (31) can be used without serious error for 0.45 <
Pr < 1.7, which suggests that Eq. (30) is a sufficient expres-
sion for all fluids other than liquid metals.

SUMMARY
Equations (30) and (31), together with Eq. (32), predict
values of Nu within 1% or 2% of numerically calculated val-
ues for all geometries and conditions in the fully turbulent
regime. This is to be compared with deviations of 10% to
40% on the mean for all expressions in current use, many of
which are greatly restricted with respect to range and condi-
tions (see Churchill and Zajic131). The remarkable improve-
ment in accuracy for Pr 2 Prt, as provided by Eq. (27), is a
consequence of using the Reichardt analogy, which is free of
any explicit empiricism. This expression fails in exactness
only due to some minor mathematical simplifications made
in its derivation. This slight inaccuracy is in turn virtually
eliminated by use of the analogy of Churchill. On the other
hand, the greatly improved accuracy of Eq. (31) for Pr < Pr,
is a consequence of the identification of the structure of the
analogy of Reichardt with that of the generic correlating equa-
tion of Churchill and Usagi for three regimes in staggered
form, together with a minor empiricism. This same identifi-
cation revealed a virtual regime and a point of inflection for
Pr < Pr,, and another such pair that had never before been
recognized for Pr > Pr. The existence of these virtual regimes
explains the numerical and functional failures of most prior
correlating equations.
The generality of the new expressions for all geometries
and thermal boundary conditions is a consequence of the rec-
ognition that the analogy of Reichardt could be expressed in
terms of Nu0, Nu,, Nu_, and Pr/Pr. The supplementary ex-
pressions for Nu0, Nu,, and Nu, which are exact insofar as
Pr, is independent of y+, follow directly from formulation of
the equations of conservation in terms of the fraction of the
transport due to the turbulent fluctuations. They could have been
derived using eddy diffusional models, but not so simply.
Implementation of the new expressions for specified val-
ues of Re and Pr, and for particular geometries and thermal
boundary conditions, is not onerous since the entire calcula-
tion can be preprogrammed.
The path of development leading to Eqs. (30) and (31) could
now be streamlined, but the description of the irregular path
that was actually followed has educational value in that all
students and practicing engineers should be concerned with
the evaluation if not the construction of correlating equations.
Although the process of derivation of the new relationships
for thermal convection is much more complicated, and the
relationships themselves are slightly more complicated to


employ, these deficiencies appear to be a small price to pay
for their greater accuracy, sounder rationale, and broader ap-
plicability.
Students should be prompted to question any of the as-
sertions and non-obvious steps that were made in the ab-
breviated development herein and not expanded upon by
the teacher. Justifications may generally be found in the
references.

REFERENCES
1. Anderson, T.J., "Chemical Processing of Electrons and Holes," Chem.
Eng. Ed., 24(1), 26 (1990)
2. Churchill, S.W., "A New Approach to Teaching Turbulent Flow," Chem.
Eng. Ed., 32(2), 142 (1999)
3. Churchill, S.W., and S.C. Zajic, "The Prediction of Turbulent Con-
vection with Minimal Explicit Empiricism," AIChE J., 48, 927 (2002)
4. Churchill, S.W., "New Simplified Models and Formulations for Tur-
bulent Flow and Convection," AIChE J., 42, 1125 (1997)
5. Churchill, S.W., and R. Usagi, "A General Expression for the Correla-
tion of Rates of Transfer and Other Phenomena," AIChE J., 18, 1121
(1972)
6. Reichardt, H., "Die Grundlagen des Turbulenten Warmeii-
bertraganges,"Archivges. Wiirmetechn., 2, 129 (1951): English trans-
lation, "The Principles of Turbulent Transfer," Nat. Advisory Comm.
Aeronaut., TM 1408, Washington, DC (1957)
7. Reynolds, O., "On the Extent and Action of the Heating Surface of
Steam Boilers," Proc. Lit. Soc., Manchester 14, 7 (1874)
8. Prandtl, L., "Ein Beziehung zwischen Wirmeaustaush und
Strimungswiderstand der Fliissigkeiten," Phys. Z., 11, 1072 (1910)
9. Churchill, S.W., "Critique of the Classical Algebraic Analogies be-
tween Heat, Mass, and Momentum Transfer," Ind. Eng. Chem. Res.,
36, 3878 (1987)
10. Churchill, S.W., "New Wine in New Bottles: Unexpected Findings in
Heat Transfer. Part III. The Prediction of Turbulent Convection with
Minimal Explicit Empiricism," Thermal Sci. Eng., 5(3), 13 (1997)
11. Churchill, S.W., M. Shinoda, and N. Arai, "A New Concept of Corre-
lation for Turbulent Convection," Thermal Sci. Eng., 8(4), 49 (2000)
12. Kays, W.M., "Turbulent Prandtl Number: Where are We?" J. Heat
Transfer Trans ASME, 116, 234 (1994)
13. Churchill, S.W., "A Reinterpretation of the Turbulent Prandtl Num-
ber," Ind. Eng. Chem. Res., in press O



letter to the editor


Dear Editor:
Late last year, you published our Letter to the Editor re-
garding a survey we were carrying out on the use of Inher-
ently Safer Design (ISD), meant to make the process indus-
try a lot safer. Several of your readers downloaded our ques-
tionnaire and sent their responses to us. We got responses
from eleven countries world wide.
The findings of the survey have just been published under
the title "Inherently Safer Design: Present and Future" in the
Transactions of the Institution of Chemical Engineers, Pro-
cess Safety and Environmental Progress, 80, Part B, May
2002.
We are pleased to enclose a copy of the publication for


Chemical Engineering Education










your reference. Further, the following is a brief summary of
the survey paper. It's appearance would be a fitting finale to
the effort that started with the initial publication of our letter
in your journal.

Summary
A recent survey of the current use of Inherently Safer Design
(ISD) concepts attracted responses from 63 people in 11 coun-
tries. These included industrialists, consultants, regulators,
and academics. The salient results of the survey are noted
below in bullet form to focus attention, followed by recom-
mendations to expedite the adoption and spread of ISD.
Almost everyone responding knows of ISD. Their
knowledge stems from specialized lectures, short
courses, books, conferences, and training videos.
ISD has been practiced by some for decades, whereas
others started only recently.
ISD is used in almost all stages of chemical process
development, design, and operation.
ISD is used during the manufacture of a whole range of
products.
Almost all hazards have been targeted, both on-shore
and off-shore.
The above attests to the universality of ISD applica-
tions.
There is a favorable impact on balance sheets.
It is important to use "Management of Change" when
implementing ISD to avoid introducing any new
hazards.
There is very little additional cost if implemented early.
Payback is fast.
Some applications/practitioners have won awards.
ISD is included in lectures at several institutions. More
will do so now.
Many are not familiar with the current Inherent Safety
(IS) indices. Those familiar with them have used them
sparingly. A simple, realistic index is needed that also
shows economic benefits. Detailed examples of use at
different stages of process development are necessary.
ISD concepts can influence R&D in various areas of
chemical engineering and chemistry.
ISD should encompass inherent safety, health, and
environment (ISHE).
ISD concepts, suitably modified, can be used for other
branches of engineering such as mining, construction,
transport, etc.
Current regulations do not force the use of ISD.

Recommendations
The sad truth is that ISD is applied when an ISD enthusiast is
on the team and not otherwise. Implementation of the recom-


mendations below might encourage the uptake of ISD.
Every chemist and chemical engineer should be trained
in ISD. Academics and professional bodies should lead
in this.
Other scientists and engineers should be given intro-
ductory lectures in ISD with examples from different
industries.
IChemE should make ISD a part of its approved degree
syllabus. Subsequently, it should persuade other
engineering and science accrediting societies to do
likewise.
There is a need to teach IS to management and finan-
cial people also since their role is crucial in encourag-
ing applications of ISD.
Dedicated funding by government and industry for
research and teaching in ISD will encourage many
academics to take it up.
Incentives by the government to cost share demonstra-
tion plants and provide tax breaks for ISD.
Expand ISD to encompass ISHE since the environment
and occupational health are day-to-day concerns. It
may eventually be extended to ISHEQ (Q for Quality)
since improvements in SHE will decisively impact
quality of product.
Companies should provide examples of ISD use in
various situations and the economic benefits reaped in
order to convince other industries, regulators, govern-
ment, the media, the public, academics, R&D funding
agencies, etc.
Involve the mainstream print and audiovisual media to
favorably impact public opinion.
Amend regulations to enforce the use of ISD.
Insistence by international agencies to include ISD in
projects that they fund in the same way that the World
Bank now insists on environmental impact assessment
studies in projects funded by it.
Some expected results
Tall columns of chemical plants will be reduced to one-
or two-story heights. This will improve the image of the
chemical industry.
Increased investment in process industry.
Less restrictive regulations.
Greater enrolments in UG and PG courses.
Significantly enhanced funding for R&D.
Adoption of ISD by other engineering disciplines,
especially the more accident-prone ones such as
construction, mining, transportation, etc.
J.P. Gupta
David W. Edwards
Loughborough University


Fall 2002


271











curriculum


NOVEL CONCEPTS FOR TEACHING


PARTICLE TECHNOLOGY



WOLFGANG PEUKERT, HANS-JOACHIM SCHMID
Munich University of Technology 85748 Garching, Germany


Particle technology is an interdisciplinary subject deal-
ing with disperse systems, including all types of solid
particles (aerosols, suspensions), liquid particles (drop-
lets, emulsions), and gaseous particles (bubbles). The main
focus of our current research and curriculum, however, is on
solid particles.
The goal of particle technology is producing and handling
disperse materials under economical and ecological con-
straints. The materials are produced due to a surplus value of
the product properties. Typical examples for these properties
are the taste of chocolate, the color of pigments, the strength
of concrete, or the electrical properties of semiconductors.
Consequently, this is also a key point in our curriculum.
In order to prepare a young engineer for his possible tasks
in industry and research, we have organized the curriculum
to reflect the structure of the field (see Figure 1). The field
can be structured generally in four levels. The first and most
fundamental level covers the elementary processes, i.e., the
physical fundamentals. They include the statistical founda-
tions of particle technology, multiphase flow, bulk mechan-
ics and powder flow, interfacial phenomena, and the interac-
tions of dispersed matter with electromagnetic radiation. On
the second level, we apply the fundamentals to machines and
unit operations. In our curriculum, we concentrate on separa-
tion processes, further strengthening students' capabilities in
multiphase flow phenomena. The third level considers whole
processes. Here, we teach the concept of product engineering,
i.e., how to tailor product properties. Consequently, we have a
close link to the applications, which are actually very broad:
Materials science (e.g., all ceramics manufacturing is in
fact applied particle technology)
Life science (e.g., proteins may be treated as small
particles in some respects, drug delivery)
Information technology (e.g., quantum dots, clean room
technology, chemical mechanical polishing)
Environmental engineering (e.g., particle separation)


How can the new areas be included in the
curriculum without disregarding the conven-
tional ones? In our opinion, the only answer
is that teaching the fundamentals is even
more important, but the examples given
to the students should change.

Traditionally, chemical engineering has been taught in
Germany using the unit-operations concept. In most univer-
sities, teaching particle technology has followed the concept
of Hans Rumpf, who stressed the physical fundamentals in
the basic course, which is followed by courses in agglomera-
tion, solid-liquid separation, or particle characterization, to
name just a few. Unfortunately, in the USA particle technol-
ogy is taught extensively in only a few universities. Students
learn how to design machines and processes that either keep
the particle size constant (i.e., separation, mixing) or change
the particle size (i.e., size reduction and size enlargement). In
the past, only mechanical means to produce and handle par-

Wolfgang Peukert got his diploma degree in
Chemical Engineering (1984) and PhD (1990)
at Karlsruhe University In 1998 he became a
full professor at Munich University of Technol-
ogy. He is the chair of solids and interface pro-
cess technology. He also leads the particle
technology research group and teaches par-
ticle technology.



Hans-Joachim Schmid got his diploma de-
gree in chemical engineering (1993) and PhD
in mechanical process engineering (1998)
from the University of Karlsruhe. He is a re-
search assistant in the particle technology
group at MUT His main research interests
are multiphase flows and particle character-
ization.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education











tides were considered; therefore, particles larger than approxi-
mately Iim were mainly dealt with while the non-mechani-
cal methods of particle synthesis (e.g., crystallization, gas
phase processes) that lead to submicron particles were ne-
glected.
By introducing product properties, we address the overall
goal of a chemical process, i.e., the production of well-de-
fined product properties under economical and ecological
constraints. The concept of product engineering transcends
educational traditions and recognizes the end value of deal-

Main topics
Statistical foundations
-Fundamentals Physical-Chemical Aspects Multiphase flow
.Bulk mechanics
0 Interfaces
S(Interaction w. radiation)
-Unit operations Design Skills Particle separation
a CFD

L I Property and process function
L-Processes Particle formation
Particle consolidation
Application and
Characterization Information- Environmental -
Materials Life -
Sciences
Figure 1. Structure of particle technology curriculum and
courses offered at Munich University of Technology.


100 0,14


80- 0,10

property
S60-
handling 0,06
o property \ ,,
8 40- -
\------ ------------ -- 0,0o
0

0 0,1 0,2 0,35
particle size x / pm

Figure 2. Property functions of a typical pigment.

Showing the whole picture







molecule process
elementary unit operations









SIneraces and separation balance '


Figure 3. Teaching concept and new topics (gray).

Fall 2002


ing with process technology, i.e., the product property. Al-
though this point of view is not new, it is largely neglected in
the curriculum. Rumpf" coined the expression "property
function" for the end-product qualities as well as handling
characteristics. The property function is defined as
Product property = F(disperse properties and microstruc-
ture, chemical composition)
Disperse properties are particle-size distribution, particle
shape, particle morphology, and particle-surface characteris-
tics. As an example, Figure 2 shows the product quality of a
pigment (in this case the color strength per unit mass of pig-
ments) that improves with decreasing particle diameter. The
yield stress of the powder, as an important handling property,
also increases with smaller particles, indicating prohibitive
high resistance against powder flow. Obviously, there exists
an optimum where both product and handling quality are ac-
ceptable. One solution to this problem may be to optimize
powder formulation allowing both high product quality and
acceptable handling properties. Of course, there are many
other end-product qualities, such as taste (e.g., of chocolate),
strength (e.g., of concrete), activity (e.g., of a catalyst or a
drug), or the band gap (e.g., of a nanocrystalline semicon-
ductor). Typical handling characteristics are flowability, dust
development, filtration resistance, risk of explosion, and
abrasiveness, to name only a few. Polke and Krekel'2' intro-
duced the term "process function" to relate the disperse prop-
erties of the product to the production process and the educts
Disperse properties = F(process parameters, educts)
Process parameters include the types of machines and unit
operations as well as their interconnection, the operational
parameters. The art of chemical engineering in this context
involves designing the best process for producing the correct
dispersed properties, leading to the desired product quality
with a minimum of costs, including environmental costs.
This way, the product would achieve the highest profit
since it is the most competitive. Our point of view in-
cludes both the economical aspects and a global perspec-
tive of environmental responsibility.

EDUCATION IN PARTICLE TECHNOLOGY
AT TU MUNICH

Teaching Concept and New Topics

The particle technology courses are a part of the chemical
engineering and process engineering ("Verfahrenstechnik" in
German) curricula at the Munich University of Technology.
On one hand, the traditional education of chemical engineers
prepares students for well-known applications such as the
design of cyclones or heat exchangers, but many of the tradi-
tional applications have reached the point where their eco-
nomic success is decreasing. On the other hand, new oppor-
tunities are evolving in areas that are less familiar to engi-
neers, e.g., information technology or various aspects of ma-










trials science. The question is: How can the new areas
be included in the curriculum without disregarding the
conventional ones? In our opinion, the only answer is
that teaching the fundamentals is even more important,
but the examples given to the students should change.P3,4
In Figure 3, our approach is shown schematically. We
explain the whole picture to the students by showing
them the progression from molecular precursors to the
whole process, which actually covers many orders of
magnitude in both geometrical dimensions and time
scale. In other words, we pave the way from feed mate-
rials to end-product properties-this is the horizontal line.
In the vertical, depth is gained by explaining certain as-
pects in a detailed way. By reflecting the first three lev-
els of Figure 1, we stress particulate interfaces (funda-
mental level) since we believe that this aspect has not
been sufficiently covered in the past. Moreover, with the
advent of nanotechnology, interfacial aspects have be-
come increasingly important. The second level, compris-
ing unit operations, is handled in a more-or-less tradi-
tional way, although new aspects such as CFD model-
ing are included. On the process level, disperse systems
have to be treated mathematically by means of population
balance equations, which have so far not been covered in
traditional particle technology curricula.

Courses
The courses are organized into three levels. The first
and most fundamental level comprises a two-semester
course in "Fundamentals of Particle Technology" (see
Figure 4). In this course, the important foundations (rang-
ing from statistics, motion of particles in fluids, fracture
mechanics, to dimensional analysis) and their implica-
tion in mechanical process engineering are covered. In
addition, new elements such as population balances
(which are increasingly used in industry) and interfacial
phenomena are introduced. The latter comprise the fun-
damentals of interactions between molecules and par-
ticles, characterization of particulate interfaces and as-
pects of nanoparticle technology (e.g., coagulation and
stabilization of colloidal suspensions).
The second level stresses unit operations. Here, we
concentrate on "Particle Separation" (see Figure 5). This
course is principally organized in the traditional way,
focusing on separation of particles from gases as well as
solid-liquid separation. Different unit operations in gas-
solid separation are introduced systematically by focus-
ing on common principles, i.e., on transport mechanisms
of particles to the collecting surfaces of the respective
separators. In this way, various unit operations are treated
very efficiently, which allows for introduction of new,
modern methods such as CFD and its use for optimizing
such apparatuses. We also offer a complementary course


Figure 4. Fundamentals of Particle Technology course
(particle characterization included in separate course).


Gas solid separation
(dilute systems)
Fundamentals:
CFD and particle tracking


Solid liquid separation
(dense systems)
* suspension rheology
* sedimentation
* filtration
* flocculation


Figure 5. Particle Separation course.


) Particle production

process design property function
top down particle size and shape color
grinding
classification crystallinity taste
bottom up particle surface strength
gas phase synthesis
-crystallization particulate systems
agglomeratess, thin films...)
consolidation


S Pardti.
Populati


Figure 6. Product Engineering course.


Chemical Engineering Education












* relevant physics,
constituing equations,
assumptions
* realization:
arrangement,
sensor & signal
* how to calculate property
distribution? (-> inversion)
* derive capability and limits
of method
* practical aspects


Figure 7. Particle Characterization course.


Figure 8. Methodological approach.


Figure 9. Integrated approach of university education.


Aim:




promoting
understanding of
principles and
system
intercorrelations


physical-chemical
foundation


Technical Skills


Soft Skills





Soft Skills I


Fall 2002


dealing with "Downstream Processing of Biotechnologi-
cal Products" that focuses primarily on different unit op-
erations for separation, disintegration, and purification of
bioproducts as well as their interactions in the whole pro-
duction process. In several aspects, bioproducts such as
proteins can be regarded as nanoparticles, although the lim-
its of this point of view should be kept in mind.
A completely new course is being offered in product en-
gineering (see Figure 6). The key question is how to pro-
duce the physical properties that define the product prop-
erty, from the point of view of both handling and applica-
tion. Examples for property functions are presented to-
gether with various methods for producing the particles
(e.g., comminution and classification, gas phase synthesis
of nanoparticles, crystallization, and precipitation). Han-
dling and formulation topics round out this course. The
students learn key concepts for formation of structured
solids, product design, and powder processing systems. In
this context, the systems engineering approach is impor-
tant. There is also a course in particle characterization that
teaches the main principles in characterizing particle prop-
erties, e.g., concentration, size, shape, surface, and zeta
potential (see Figure 7). The purpose of this course is to
enable the students to choose an appropriate setup for ar-
bitrary particle characterization tasks. This is accomplished
by emphasizing the basic aspects of a measuring technique
(e.g., physical principle, signal recording, conditioning, and
evaluation) as well as a complete measurement system (in-
cluding sampling, transport, and preconditioning). These
principles are explained in conjunction with a choice of
the most important measurement techniques.
Whereas Fundamentals of Particle Technology I and II
are mandatory for all chemical engineering students, Par-
ticle Separation is one of a group of three courses (together
with Process and Plant Design and Design of Thermal Pro-
cesses) from which the students must choose two. The re-
maining courses are elective.
Methodology and Didactics
The course in particle technology follows several guide-
lines:
The key item is the product property approach, i.e.,
particles have physical properties such as particle
size distribution, particle shape, or particle morphol-
ogy that are closely related to product properties.
Although it is difficult to describe complete process
chains, we enhance the student's awareness of the
complete process.
From a methodological point of view, we believe that teach-
ing should follow a double-tracked approach. On one hand,
the teacher should stress the important physical founda-
tions, since excellent skills in the fundamental principles
will be essential for the students throughout their studies
and their professional lives. This implies that a large num-










ber of facts have to be taught, thus assigning an important
role to the teacher. On the other hand, to promote the stu-
dents' understanding of the underlying principles as well as
to sharpen their view of the complete process, active learn-
ing appears to be a key issue.13.5'61 We try to support this ac-
tive learning in different ways (see Figure 8).
Lab and virtual experiments are conducted so that students
can apply and transfer their acquired knowledge and get in-
volved with more realistic problems. This is accomplished
by a mandatory lab course (one semester) as well as lab com-
ponents that are integrated into the courses described above.
The lab experiments include a wide field of exemplary tasks
that include, for example, dust separation in cyclones, filtra-
tion, mixing, and particle characterization by laser diffrac-
tion as well as the investigation of the stability of colloidal
suspensions by dynamic light scattering. Furthermore, a com-
pletely new virtual lab is currently being established in the
course Product Engineering, with computer simulations of
disperse systems (e.g., crystallization, comminution) based
on population balances using commercial software (e.g.,
LabView and Parsival).
We also encourage the students to take an active role
throughout the courses wherever it is appropriate, for example,
in the particle characterization course. After introducing the
basic principles and the important characteristics of a mea-
surement systems (e.g., assessed equivalent particle size, sig-
nal recording, conditioning and evaluation, necessary sample
preparation, etc.) as well as discussing their application to
the most important measurement techniques, the students are
arranged in small groups. Each group is then assigned the
task of analyzing one measurement technique that is so far
unknown to them. They also have to prepare a presentation
of their results that will relay the most important facts to their
fellow students. The groups are supposed to work autono-
mously, with the teacher playing a more passive role and only
giving guidelines or help when asked. In this way, several
goals can be achieved.

The students work and access information autonomously,
e.g., from literature in a foreign language.
The group work necessitates that students find their roles
in a group and work together productively. 71
Finally, the students are given the chance to prepare and
give a presentation. Even listening and assessing the
presentation of other groups increases their ability in this
respect. This is a capability that is not practiced
enough."81

By actively preparing a small part of the course, the stu-
dents not only acquire valuable technical knowledge, but they
also get a chance to increase their "soft skills." Personal de-
velopment is often neglected in a university education. Stu-
dents should concentrate on both their technical skills and
their personal growth (see Figure 9). This includes an ability
for self-organization and focusing on defined targets, intrin-


sic motivation to reach goals, and an ability to communicate
results. On a deeper level, internal self-reflection is indis-
pensable for accepting personal strengths and weaknesses as
well as those of others. This is a precondition for all social skills.

CONCLUSIONS
Particle technology is a much wider field than many people
realize since it also comprises biochemical, chemical, and
thermal processes dealing with particles. Hence, it is not only
of the utmost importance in the chemical industry, where about
60-70% of all products are fabricated in dispersed form, but
also for a number of other fields, such as materials science
and information technology. Product properties and the sub-
sequently developed product engineering approach is at the
center of our considerations. With a continuously growing
number of applications for dispersed systems, we feel a need
to stress the fundamental aspects even more. With the gen-
erally observed trend toward finer particle sizes, new topics
such as particle interactions and population dynamics have
been included in order to prepare our students for newly de-
veloping areas such as nanotechnology. The technical courses
are complemented by various activities to strengthen the soft
skills of the students.
Recently, suggestions have been made by Cussler, et al.,[91
on how to change chemical engineering curriculae. Consid-
ering the shift in industrial practice from large-scale processes
producing commodities toward more specialized product
design, we feel that particle technology and particle design
methods deserve a prominent place in the curriculum.

ACKNOWLEDGMENTS
The authors would like to thank Professor Helmar Schubert
from the University of Karlsruhe for very valuable discussions.

REFERENCES
1. Rumpf, H. Uber die Eigenschaft von Nutzstiuben, Stab-Reinhaltung
derLuft, 27(1), p. 3 (1967)
2. Polke, R. and J. Krekel, "QualitAtssicherung bei der
Verfahrensentwicklung," Chem. Ing. Tech., 64(6), p. 528 (1992)
3. J.L. Cano, Garces, A., and Saenz, M.J. "Oral Presentations of Stu-
dents in Product Engineering Lectures." Int. J. Engg. Ed., 13(3), p.
175 (1997)
4. Cussler, E.L. "Do Changes in the Chemical Industry Imply Changes
in the Curriculum?" Chem. Eng. Ed., 33(1), p. 12 (1999)
5. Davis, R.H. "Helpful Hints for Effective Teaching," Chem. Eng.
Ed., 32(1), p. 36 (1998)
6. Felder, R.M., D.R. Woods, J.E. Stice, and A. Rugarcia. "The Future
of Engineering Education Part 2: Teaching Methods that Work."
Chem. Eng. Ed., 34(1), p. 26 (2000)
7. Humphreys, P., V. Lo, F. Chan, and G. Duggan, "Developing Trans-
ferable Groupwork Skills for Engineering Students," Int. J. Engg.
Ed., 17(1), p. 59 (2001)
8. Brostow, W., "Instruction in Materials Science and Engineering:
Modem Technology and the New Role of the Teacher," Mat. Sci.
andEng., A302, p. 181 (2001)
9. Cussler, E.L., D.W. Savage, A.P.J. Middelberg, and M. Kind. "Re-
focusing Chemical Engineering," Chem. Eng. Progr, 98(1), p. 26S
(2002) O


Chemical Engineering Education











Letter to the Editor
Continued from page 262.
b=8.5164364+1.5315505; the error variance
s2=0.467503; and correlation coefficient R2=0.953603.
Professor Fahidy advises not to put too much faith in the
linear regression model, in spite of the relatively large
R2 value, because of the extremely wide confidence in-
tervals on the parameter a. The fairly random distribu-
tion of the residuals (see Figure 2) suggests, however,
that the linear model may be the correct one. Further-
more, both physical considerations (fuel consumption
should be zero for a zero mass vehicle) and the wide
confidence intervals on the free parameter a, indicate that
the model can be improved by setting the free parameter
at zero. Indeed, carrying out the regression while setting
a=0 yields: b=7.8929160.3599903; s'=0.4641509, and
R2=0.9481781. Thus, this model is now acceptable, even
with respect to the confidence interval values.
One of Professor Fahidy's objectives in presenting this
example was to warn against accepting relatively large
R2 values as proof of good linear relationship between
the dependent and independent variables. The limitations
of the R2 statistics in this respect can be most strikingly
demonstrated using residual plots. Shacham, et al.,'31 for
example, fitted vapor pressure data of 1-propanol with
the two-parameter Clapeyron equation. This regression
yields the values: R2=0.9998818 and s-=1.659E-05
(based on log P). Such a high value of R2 can be inter-

m











Figure 2. Residual plot for Example 5 in Fahidy
paper.'




p
am




-D -- ---- .. .------------------------------------ -----------

Figure 3. Residual plot for vapor pressure data from
Reference 3.
Regression model: log P = 7.6380342-1622.8666/T

Fall 2002


preted as a perfect fit. But the residual plot (seen in Figure 3) shows
that the vapor pressure data set exhibits a curvature, which is not
predicted by the Clapeyron equation. Indeed, using the four-param-
eter Riedel equation for representation of the same data yields: R2=1;
s2= 1.327E-09 and randomly distributed residuals.
The last example, given in the Appendix of the article deals with a
linear model for representing coded effectiveness indicators versus
catalysts containing various coded platinum mass units. Analysis of
this example shows that if the free parameter, a, is set at zero (as
suggested by the wide confidence intervals on a and physical con-
siderations) the linear model is appropriate to represent the data with
8=1.64376590.0845917, R'=0.8860414, and s2=0.8508906.
We can conclude that teaching statistical analysis of data and re-
gression models is very important, but interpretation of numeric sta-
tistical indicators must be complemented by graphical analysis and
consideration of the physical nature of the model in order to arrive
at the correct conclusions.
Mordechai Shacham
Ben-Gurion University of the Negev
Neima Brauner
Tel-Aviv University
References
1. T.Z., "An Undergraduate Course in Applied Probability and Sta-
tistics," Chem. Eng. Ed., 36(2), 170 (2002)
2. Fahidy, T.Z., Personal communication (2002)
3. Shacham, M., N. Brauner, and M.B. Cutlip, "Replacing the Graph
Paper with Interactive Software in Modeling and Analysis of Ex-
perimental Data," Comp. Appl. Eng. Ed., 4(1), 241 (1996) 0


Author's Response
I am delighted at Professor Shacham's interest in my paper. I also
fully concur with the argument that the residual plots are an impor-
tant and integral part of regression analysis. This is now standard
textbook material, and I do routinely discuss this subject in my course.
Although my intention was to keep the article from being too long,
in retrospect I should have spent a paragraph or two on residual
analysis, and I regret the omission.
In Example 4 it was stated that the reaction mechanism was first-
order irreversible, but perhaps not strongly enough to imply an a
priori knowledge of non-statistical origin, so that 0'h and 2nd order
models are beyond consideration. With limited data and given a
physically correct model, the method that provides regression pa-
rameters relating data to model with the smallest error variance may
be acceptable in lack of something better, even if the residual plot
does not show randomness of a desired degree. The quest for addi-
tional measurements is almost universal in the case of limited-size data.
My views about R2 versus confidence intervals for true regression
parameters do not fully coincide with the respondents', but may I point
out the redundancy of seven-digit values, computer printouts notwith-
standing. An R2=0.8860414 is not more meaningful than R2=0.89
Thomas Z. Fahidy











, -classroom


GAS STATION PRICING GAME


A Lesson in Engineering Economics

and Business Strategies


AARON SIN, ALFRED M. CENTER
Cornell University Ithaca, NY 14850


he School of Chemical Engineering at Cornell Uni-
versity recently undertook an evaluation of its Mas-
ters of Engineering program to assess the curriculum
and the amount of value added to the student's education by
their participation in the program. One conclusion that we
reached was that students in a professional masters program
were most likely to go on, at least initially, to some kind of a
position in a corporate environment. To increase the likeli-
hood of their success in those early years on the job, we felt
that some level of knowledge of how a business unit works
and how an engineer fits into such a unit would be of signifi-
cant importance to their careers.
With this in mind we added a requirement that all M. Eng.
Candidates take a course that would give them some insight
into these areas. While there are a number of different courses
at Cornell that deal with related topics, there was no one course
that covered all of the areas that we thought were relevant.
This led to the development of a new course, primarily for
Masters of Engineering students, titled "Managing New Busi-
ness Development."
The course is an attempt to explain the business develop-
ment process as it is likely to be carried out in a major corpo-
ration. It deals with concept development, feasibility assess-
ment, front-end analysis to select the best implementation
strategy, tactics to take the concept forward, implementa-
tion of the selected strategy, and ongoing improvement
of the process once it is implemented to either increase or
maintain profitability.
The students are exposed to a number of different concepts.
As the course advances, they are asked to demonstrate their
knowledge through several case studies. The first case study
involves producing plans for executing a feasibility study to


introduce a new line of cosmetics in a newly opened over-
seas market. The second involves maximizing value from a
feedstock that contains a number of different components.
One of the concepts we found particularly difficult to get
across to the students was pricing strategy. To provide a means
for hands-on experience with this concept, we developed what
we call the "gas station game." Unlike most games in busi-
ness schools that generally involve multiple inputs and fo-
cuses at sitewide or businesswide optimization in a qualita-
tive manner, this is a quantitative pricing game that aims at
illustrating market forces at work. Since most people in the
U.S. regularly deal with the fluctuation of gas prices, it is
easy for the students to relate to it. We play this game every
time the class meets.

THE GAS STATION GAME
In the game, students are divided into four groups, with
each of them managing a gas station. Operating under differ-
ent restrictions ("mom and pop" versus "big chain"), students
are asked to decide on their business goals and facility sizes,
which in turn lead to pricing structure and marketing tactics.
We found that it is generally effective to have students per-


Aaron Sin received his B. ChE. in 1998 from the University of Delaware,
where he was trained to become a practical engineer. At Cornell, he
used this knowledge to design microfluidic devices for pharmaceutical
testing with his research advisor. Aaron is completing his Ph.D. thesis
and considering a career in academia.
Alfred Center is a registered professional engineer with over thirty years
of experience in the petroleum industry. He is now a senior lecturer in
chemical engineering at Cornell, teaching classes in unit operations labo-
ratory, senior design, project management process control, and busi-
ness development strategies.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education











form cash flow analyses for different scenarios. (The project
assignment is shown in Appendix A.) The cost parameters
are approximated and tested to produce realistic profit fig-
ures in the end. Capital costs include the storage tank mate-
rial and installation, gas pumps, land requirement, engi-
neering costs, etc. The operating costs are estimated as
10% of the capital investment, assuming a ten-year project
lifetime.
When the students are ready for the actual price bidding, a
simulation is used to determine the demand in each station,
based on the four stated prices (see Figure 1). The simulation
is modified from the Monte Carlo Gillespie algorithm from
reaction kinetics. Simply, the probability of customers visit-
ing each gas station is inversely proportional to the price dif-
ference between that particular station and the minimum bid-
der. The simulation then uses a random number generator
to determine the exact demand for each station. An extra
station with a fixed price is added to model gas stations
from outside this town.
To account for different levels of service provided by each
station (e.g., method of payment that is accepted), the prices
are adjusted before the probabilities are calculated. These ad-


justment amounts are based on polls conducted among stu-
dents regarding their own consumer preferences. The simu-
lation also includes some proportion of cars that stop at the
first gas station in sight instead of comparing prices, which
again is determined using a Gillespie algorithm with a prede-
termined probability.
The profit of each company is calculated based on the num-
ber of gallons sold minus operating costs of the gas station.
As mentioned before, each group decides in advance what
the suitable underground storage capacity will be, which gives
rise to certain capital costs and operating costs. In the event
that the gas station sells more gas than its capacity al-
lows, it will have to obtain extra gas at 115% of the maxi-
mum price among the four gas stations. In this way, each
gas station is equally profitable if the right price relative
to each other is found.

RESULTS AND DISCUSSIONS
The results of the game are quite encouraging. We are try-
ing to teach the concepts of customer perception of product
value, convenience, and price differentiation based on those
perceptions. We are also trying to show that the strategy of


cM 6 l v:fc Ed4iet Y 'w nsr ofrrr locs Dali Wiidow eHt;p







iM S Peaned Period 2 P,,od3 PeIod 4 Penod S Penod 6
4 PnI -' Gs Sttion Maome Car o 51mulaMon
5 N Cars 51 430
f agals Gas Scanon Monte Curio a87
7 R*nvnue 89ro
S u:sIall Ei ow 0 r60r
11 Cars 4
12' #gulu *9 1 1 *whcb-*1 496
13 Revenue 160
14 Pro Staot 1 2aAl0 2 lh I s 2 an4 1542
1i S1tti4ll II .18813
IaPtreo i 3 t"O 1401 [ | 1t33
17 #Cars 150
S. gals PaeI La I i0l i ratIoin c8 ton
19 Rvenu 2360
218lrM m" I Ahfol I >hlll Hohfr F ^r 3S r t 2 r1 I:ri
21i .illkn ( IV .466 I6
22 Pnca 1rpi I E0i ot I (iiE1 I al Im t |
23'Cars 1 1 470
24 i.-ia Trot [~jji SaaiBJ El iiiEl 2W6
2S R2venu 25450
25 Profo PerloO | t Owi 7 412
-i --- .3.' 66
2s:Tatal #cad
297

1 r1


Figure 1. The gas station game simulation in action.


Fall 2002











maximizing an individual player's revenue did not necessar-
ily mean defeating the others. And, in fact, the most favor-
able revenue picture is one in which all participants were able
to share the market in some fashion.
We found that within approximately ten iterations, the stu-
dents were able to arrive at the conclusion that a shared mar-
ket created more revenue and that cutthroat competition was
unlikely to succeed. With this realization, the students go on
to develop pricing strategies that allow each of them to sell
close to their facility's capacity and to maximize their in-
dividual revenues.
Figure 2 shows a typical adjustment process based on root-
mean-squared deviations in prices and revenues, as compared
to values at the last iteration. At around the tenth iteration,
prices begin to converge to the range where a reasonable profit
is sustained among all stations. The revenues continue to fluc-
tuate, on the other hand, since students often react to price
changes of the other stations after their demands have
changed, instead of anticipating the behavior of the oth-
ers. These fluctuations are likely to stabilize if we carry
the game further.

CONCLUSION
We think this game provides an easy way to teach pricing
strategy in a fairly simplistic business model, and we are happy
to pass along this game for your interest and use.



APPENDIX A
Assignment Sheet
for the Gas Station Pricing Game


There are four gas stations on Rt. 13, coming into Ithaca.
They are about a block apart, as indicated in the figure be-
low.


Figure 1A: Map of the four gas stations

Preliminary market research indicates a demand of about
120 cars/hr in the day and 20 cars/hr at night, at 10 gals/car.
While some percentage of the drivers go to the first gas sta-
tion in sight, most make that decision based on things such as
price, convenience (credit card/speed pass), and brand name.
They also have the choice of getting gas from the next town
if they feel prices are too high.
Your first task is to decide on the amount of investment,


15.00%a
*-7500
-o-Price
S12.50% Deviation
P -Revenue
o.o Deviation 5000
10.00%


.00% 2500


> 0
n0 &

2.50%

0.00% -2500
1 2 3 4 5 6 7 8 9 1011121314151617
Iteration
Figure 2. The adjustment process: root mean squared de-
viation in prices relative to final average price (left axis)
and root mean squared deviation in revenues (right axis)
plotted against iteration number.


TABLE 1
Differences between Mom/Pop Operations
and Chain Companies

Investment Supply Cost Personnel Service
Mom/Pop $300,000 $1.45/gal I @ $5/hr 12 hr
Chain Unlimited $1.47/gal 2 @ $5/hr Speed pass



TABLE 2
Gas Station Configurations and Costs

Capacities 20,000gal 25,000 gal 30,000 gal 40,000 gal
Capital Cost $200,000 $300,000 $400,000 $500,000

Operating Cost $56/day $84/day $111/day $138/day


level of service, and pricing strategy for your gas station. Your
decision will depend on the nature of your company (mom/
pop vs. chain), as listed in Table 1. Table 2 lists the available
gas station configurations.
The supply trucks come every seven days to refill the
underground gas tanks. If you sell more gas than your
designed capacity, the extra gas will be available at 115%
x Max gas price in Ithaca.
The goal of this exercise is to achieve the highest return on
investment among all groups, with a minimum acceptable
ROI at 12% per year. You will be able to change your
prices (and only prices) every week, depending on the
market situation. O


Chemical Engineering Education


I II IV
- Rt. 13 to Ithaca -

III












Mfjannouncements


CONFERENCE


TEACHING
ENTREPRENEURIAL ENGINEERING

Monterey, California
January 13-16, 2003

Engineering educators have done a great job of teaching
students engineering science and engineering design. In ad-
dition, engineering schools are beginning to address the de-
velopment of "soft skills" such as communications, team-
work, and ethics. In the current environment, it is increas-
ingly important for the engineering education system to also
find ways of teaching entrepreneurship and motivating stu-
dents toward such activities. This conference will set the stage
for a continuing and fruitful dialog between engineering edu-
cators and the business community.
The conference will assemble entrepreneurs, engineering
educators, and business school faculty to discuss

What are the attributes of successful entrepreneurs?
What are models of successful programs teaching
entrepreneurship to engineers?
What is the culture at a university that fosters a spirit
of innovation and entrepreneurship?
*How can engineering faculty become role models of
innovation and entrepreneurship?

The outcomes of the conference will be a set of recom-
mendations to engineering faculty, curricular integration op-
tions, model programs available for replication, and contacts
between academic and business that will be published in the
journals of various professional societies.
The Chairs of the Conference are Eleanor Baum of The
Cooper Union and Carl McHargue of the University of Ten-
nessee.
Additional information about this Conference, and a regis-
tration form, can be found at the Conference's web site:

Engineering Conferences International offices are located
at
6 MetroTech Center, Brooklyn, NY 11201
Telephone at 212-591-8144 Fax at 212-591-8145
e-mail at bhconf@poly.edu
web at www.engconfintl.org.


CONFERENCE


ENHANCEMENT OF THE GLOBAL PERSPECTIVE
FOR ENGINEERING STUDENTS BY PROVIDING
AN INTERNATIONAL EXPERIENCE

Tomar, Portugal
April 6-11, 2003

This conference will provide a forum for exchange of ideas
on methods of enhancing the global perspective of engineer-
ing students, identify the key obstacles, and discuss progress
toward eliminating the obstacles. The conference is jointly
sponsored by Engineering Conferences International, Ordem
des Engenheiros, Portugal, and E4 (Enhancing Engineering
Education in Europe). Thematic Network is financed by the
European Commission under SOCRATES II and co-financed
by the University of Florence. Contact
for more information
or go to
.
The conference will focus on the recognition that exposure
to other cultures brings personal enrichment to individuals
and can be an important component of the educational expe-
rience. With the increased globalization of economies, the
need extends beyond personal enrichment and has become
an important asset to student mobility. Among the issues that
must be addressed are compatibility of degree systems, ac-
creditation of courses and/or degrees, quality assurance, an
accepted credit system, language of instruction, and legal and
social issues such as visas, taxation, and financial support.
The Chairs of the Conference are Carl McHargue of the
University of Tennessee and Eleanor Baum of The Cooper
Union (New York, NY). The Co-Chairs are Antonio Salgado
Baros of the Orem dos Engenheiros (Portugal), G. Augusti
of the University of Rome (LaSapienza, Italy), and C. Borri
of the University of Florence (Italy).
Additional information about this conference, and a regis-
tration form, can be found at the Conference's web site

Engineering Conferences International (ECI) is the suc-
cessor to the United Engineering Foundation Conferences.
ECI offices are located at 6 MetroTech Center, Brooklyn,
NY 11201
Telephone at 212-591-8144,-Fax at 212-591-8145
e-mail at bhconf@poly.edu- web at www.engconfintl.org.


Fall 2002














Random Thoughts...






SPEAKING OF EDUCATION III





RICHARD M. FIELDER
North Carolina State University Raleigh, NC 27695


here is a theory which states that if ever anyone
discovers exactly what the Universe is for and why it
is here, it will instantly disappear and be replaced by
something even more bizarre and inexplicable. There is
another theory which states that this has already happened.
(Douglas Adams)


A lecture is a process by which the notes of the professor
become the notes of the students without passing through the
minds of either.


If a professor can be replaced by a CD-ROM, he/she should
be.
(Jack Wilson)


I'm sure the reason such young nitwits are produced in our
schools is because they have no contact with anything of any
use in everyday life.

(Petronius, d. ~66 AD)


(R.K. Rathbun)


Times are bad. Children no longer obey their parents, and
everyone is writing a book.


A teacher who is attempting to teach without inspiring the
pupil with a desire to learn is hammering on a cold iron.


(Horace Mann)


What's on your mind, if you'll forgive the overstatement?


Teachers who cannot keep students involved and excited for
several hours in the classroom should not be there.


(John Roueche)


(Cicero)


(Fred A lien)


Everything should be made as simple as possible, but not
simpler.

(Albert Einstein)



In theory, there is no difference between theory and practice;
in practice, there is.

(Chuck Reid)


Copyright ChE Division of ASEE 2002


Chemical Engineering Education


Richard M. Felder is Hoechst Celanese Pro-
fessor Emeritus of Chemical Engineering at
North Carolina State University. He received
his BChE from City College of CUNY and his
PhD from Princeton. He is coauthor of the text
Elementary Principles of Chemical Processes
(Wiley, 2000) and codirector of the ASEE Na-
tional Effective Teaching Institute

















To state a theorem and then to show examples of it is literally
to teach backwards.
(E. Kim Nebeuts)


Setting an example is not the main means of influencing
another, it is the only means.
(Albert Einstein)


There is a legend that the difference between classes of
freshmen and post-graduates is that if you say "Good
Morning" to the first, they reply "Good Morning." But the
graduate students write it down.


(Donald Bligh)


I used to keep my college roommate from reading my
personal mail by hiding it in her textbooks.


Education is what happens to the other person, not what
comes out of the mouth of the educator.


(Joan Welsh)


(Miles Horton)


Education is the ability to listen to almost anything without
losing your temper or your self-confidence.

(Robert Frost)


Lack of education is an extraordinary handicap when one is
being offensive.


Predicting the future is easy. It's trying to figure out what's
going on now that's hard.
(Fritz Dressier)



If I knew what I was looking for, it wouldn't be research,
would it?

(Richard Feynmann)


(Josephine Tey)


Education is one of the few things a person is willing to pay
for and not get.
(William Lowe Bryan)


Education is what survives when what has been learned has
been forgotten.


If I accept you as you are, I will make you worse; however if
I treat you as though you are what you are capable of
becoming, I help you become that.


(Goethe)


Teaching is the greatest act of optimism.


(B.F Skinner)


(Colleen Wilcox)


A graduation ceremony is an event where the commencement
speaker tells thousands of students dressed in identical caps
and gowns that individuality is the key to success.


Try not to have a good time...this is supposed to be educa-
tional.


(Robert Orben)


(Charles Schulz)


Fall 2002


All of the Random Thoughts columns are now available on the World Wide Web at
http://www.ncsu.edu/effectiveteaching and at http://che.ufl.edu/-cee/











e,] classroom


MAKING PHASE EQUILIBRIUM MORE


USER-FRIENDLY



MICHAEL J. MISOVICH
Rose-Hulman Institute of Technology Terre Haute, IN 47803


I believe phase equilibrium thermodynamics is the most
conceptually difficult undergraduate chemical engineer
ing class. Even students who perform calculations sat-
isfactorily seem confused over the meaning of what they
have learned.
Phase equilibrium is the single undergraduate chemical
engineering class in which abstract concepts are presented to
the near exclusion of practical applications. Table 1 gives
examples of practical or physically intuitive subject matter
found in classes that students typically consider abstract, theo-
retical, or mathematical. These actually contain some bal-
ance of theory and practice, giving students a point of refer-
ence to physical processes and equipment. Calculations such
as bubble and dew points are needed for practical design, of
course, but most phase equilibrium courses do not connect
these to real processes or equipment. Practical applications
of the material are taught as part of unit operations, mass
transfer, or distillation courses.
Students frequently have more intuition about the physical
meaning of abstract quantities in classes other than phase equi-
librium. Heat transfer students could define the Prandtl num-
ber as Cp/! k, give a physical interpretation for all three
variables, and potentially recognize related facts. For example,


"The Prandtl number could be derived by applying
the Buckingham Pi theorem to a heat transfer prob-
lem," or "Larger Prandtl numbers result in larger con-
vective heat transfer coefficients." They know that
the Prandtl number for liquid water at 100 atm and
1500C is unlikely to be 100 or 0.01.
When phase equilibrium students define chemical
potential, it is typically in terms of other abstract con-
cepts-free energy, standard states, fugacity, and ac-
tivity. They are unlikely to know whether a certain
chemical potential is positive or negative, nor what
practical significance its sign would have. Without
doing a calculation, how many phase equilibrium stu-
dents know whether the fugacity of liquid water at


100 atm and 1500C is closest to 5 atm, 50 atm, or 500 atm?
Most are at a complete loss when asked to apply abstract
quantities such as activity coefficients to practical questions,
e.g., "Is ethanol more likely to form an azeotrope with n-
hexane or n-octane?" Lacking qualitative understanding,
their only approach for answering this question is detailed
quantitative calculation.

STRATEGIES FOR BUILDING INTUITION
Prausnitz, et al.,"11 describes the phase equilibrium prob-
lem as a three-step process. First, a real problem is translated
into an abstract mathematical problem. Second, the math-
ematical problem is solved. In the final step, the mathemati-
cal solution is translated back into physically meaningful


TABLE 1
Content of "Theoretical" ChE Classes


Class
Fluid Mechanics

Mass Transfer
Transport Phenomena


Phase Equilibrium


Theoretical Concepts
Shear stress tensor,
Dimensional Analysis
Fluxes of all sorts
Partial differential
equations, Dimensionless
Greek variables
Chemical potential
fugacity, activity


Practical Concepts
Pumps, Valves, Piping

Packed absorption towers
Viscometers, Heat transfer
with free convection,
Wetted wall columns
Bubble and Dew Points,
Flash, Solubilities


@ Copyright ChE Division of ASEE 2002


Chemical Engineering Education


Michael Misovich will be Associate Profes-
sorin the Physics and Engineering Department
of Hope College in August, 2002. His research
interests include thermodynamic property pre-
dictions from equations of state, physical chem-
istry of polymer solutions, chemical engineer-
ing education, and its assessment.












TABLE 2
Common Intuition about Chemical Engineering Data

* High molecular weight compounds have high boiling points
* A substance with a density order of magnitude less than water is
probably a gas
* A Reynolds number in the laminar range for flow of water in
typical process piping is not typical
* Convective heat transfer coefficients are very low for gases as
compared to liquids





TABLE 3
Uncommon Intuition about Phase Equilibrium Data

* The fugacity of a liquid is approximately its vapor pressure, as
long as the pressure is not extremely high
* The fugacity of a component in an ideal gas mixture is its partial
pressure
* Substances we consider noncondensible gases have fugacity
coefficients larger than one; liquids and condensible vapors have
fugacity coefficients smaller than one
* Substances with large differences in boiling points are unlikely to
form azeotropes; substances with very close boiling points are
almost certain to form them
* Activity coefficients larger than approximately seven indicate
that liquid-liquid phase separation is possible
* The dilute component in either of two nearly immiscible phases
obeys Henry's Law up to its solubility limit


terms. Typically, this step consists of transforming highly
abstract variables into physically significant ones.
Chemical Potential -) Fugacity Activity 4 Composition
Each transformation results in a less abstract variable than
the previous step. Students do not seem to recognize this,
perhaps because we do not teach it explicitly. Instead, they
see chemical potential, fugacity, and activity as equally nebu-
lous and abstract concepts upon which a rote series of math-
ematical operations will hopefully produce a physically mean-
ingful variable such as composition, pressure, or temperature.
One of my principal goals in teaching phase equilibrium
thermodynamics is to help students develop an intuitive un-
derstanding of the topic. I point out to them in the beginning
that this class deals with techniques for generating data to
use in other classes to the nearly total exclusion of applica-
tions. Since students will not be able to rely on processes or
equipment to provide intuition, I emphasize understanding the
data and its significance. This type of intuition about data, rather
than equipment, occurs in other classes as the Prandtl number
example above and as similar examples in Table 2 indicate.
To promote this, I emphasize calculation and use of data
having an obvious physical interpretation, e.g., temperature,
pressure, volume, vapor pressure, composition, and enthalpy.
When concepts such as free energy, chemical potential, fugac-
ity, and activity are presented, the focus is partly on their use
in solving for the more physical variables. Whenever pos-
sible, I encourage students to examine how the abstract vari-
ables affect the physical variables, and thus to develop some
intuition about the significance of the abstract variables. Ex-


TABLE 4
Comparison of Graphical Figure Use in ChE Textbooks


Non-graph
Graph Figures Figures


Introduction to Chemical
Engineering Thermodynamics'2'
(Chapters 10-15)
Chemical and Process
Thermodynamics'3'
(Chapters 9-13)
Transport Phenomenal'4
Elementary Principles of Chemical
Processes'5'
(Chapter 6)
Momentum, Heat, and Mass
Transfer"5'
(Chapters 35, 37-40)


Graphs per Percent Graph
Pes" 100 paes Figures


44 568
(11) (199)

60 541
(6) (253)
105 711

15 587
(0) (71)


773
(143)


71
(84)

66
(91)
40

53
(100)


21 60
(44) (77)


'Graph figures include all two- and three-dimensional coordinate plots and nomographs. Any figure that
included both graphical and nongraphical information was treated as a graph figure. Only numbered, captioned
figures in text and examples were counted; figures with problems and in appendices were excluded.
bPages include all text, examples, questions, and problems but exclude appendices.


amples are given in Table 3;
these are sometimes
present, but not frequently
emphasized, in phase equi-
librium texts.

More so than in many
chemical engineering
classes, phase equilibrium
data are most useful and un-
derstandable when pre-
sented graphically. This is
evident from observations
given in Table 4 of how fre-
quently graphical material
is presented in textbooks.

Thermodynamics and
unit operations texts contain
more graphs and a higher
proportion of figures that
are graphs, as opposed to
schematic diagrams and
other drawings. Within each
text, the chapters more


Textbook


Fall 2002











closely related to phase equilibrium have a higher proportion
of graphs than the text as a whole, as indicated by the num-
bers in parentheses in Table 4.
Furthermore, many students have a visual learning style.
These students may struggle with equations and textual in-
formation, especially in an abstract context, and it is crucial
that they see data presented graphically and also learn how to
prepare data in a format that is most comprehensible to them.
Hence, students need to make the connection between calcu-
lations and equations discussed in class and graphical pre-
sentation of phase equilibrium data. To assure they are ca-
pable of both understanding and generating graphical data, I
assign a significant number of computer problems requiring
this, as explained in further detail later in this article. Com-
puter spreadsheets have been previously suggested17,' for use
in solving phase equilibrium and equation-of-state calcula-
tions, and they are well suited both for the calcula-
tions and for subsequent graphical presentation. One
recent text[91 includes a number of example spread-
sheets that may be used for applications similar to those
described in this article, although I prefer to have stu-
dents write their own spreadsheets. 10


DETAILS OF
PHASE DIAGRAM
COMPUTER ASSIGNMENT
As an illustration of such assignments, consider the
construction of a binary Pxy diagram for an ideal so-
lution at some constant temperature. Figure 1 is an
example generated by repetitive dew point pressure
and bubble point pressure calculations. Taking liquid
mole fraction x, as the independent variable, and as-
suming component vapor pressures plsat and p2at are
known, Eqs. (1-3) allow calculation of all dependent
variables in the problem. To generate the diagram, al-
low x, to vary over the range 0.0 to 1.0. These calcu-
lations are easily done using computer spreadsheet
software.


X2 = 1 1

P = XlPsat + x2P2at
X Psat
Pxl P
Yl-
P


Figure 2 shows the general organization of this spread-
sheet. The upper rows contain headings and constants
such as the vapor pressures. The middle rows are used
for calculations. The leftmost column is initially filled
with values between 0 and 1 at intervals of 0.01, or a
suitable small increment. (This should be done using
spreadsheet commands or formulas; occasionally, a
student will attempt to enter the numbers manually
and become frustrated that using the computer appar-

286


ently makes solving the problem too time-consuming.) Fill
the remaining three columns in the middle rows of the spread-
sheet with formulas given by Eqs. (1-3). If these formulas
are entered correctly in the first of the middle rows, a single
copy/paste command generates the entire table through the
remaining middle rows.
There may be one complication in producing a graph from
these results. In a conventional Pxy diagram, pressure is taken
as the vertical coordinate twice. With liquid composition as
the horizontal coordinate, a bubble point curve is produced,
then with vapor composition as the horizontal coordinate, a
dew point curve is produced. To do this on the spreadsheet, a
single y-coordinate must be paired with two different x-co-
ordinates. At one time, few spreadsheet packages included
this capability, but many recent versions (including Microsoft
Excel) now allow it. If using an older package without this

P-x-y DiagramatT= 100deg C
Methyl isopmpy keone (1) ietyl ketone(2)









,-- ---- -- -






0 01 0.2 0.3 0.4 05 0.6 07 0.8 0.9 1
xl. yt

Figure 1. Pxy diagram prepared using spreadsheet.


Headings
and
Constants
(xl values)
0.00
0.01
0.02
x2 values P values yi values (Blank)

0.99
1.00



Copy of (Blank) Copy of
yi values P values





Figure 2. General structure of spreadsheet for Pxy diagram.


Chemical Engineering Education


1000




soO
800

700

600











capability, set up the lower rows of Figure 2 as shown, then
define the first column as the x-coordinate for graphing and
each of the two columns containing pressure values as sepa-
rate y-coordinates. The lower rows of Figure 2 can be omit-
ted when using current versions of Excel and other spread-
sheets that allow multiple xy pairs to be graphed.

ADDITIONAL COMPUTER ASSIGNMENTS
Table 5 lists other thermodynamic data graphs prepared
using computer spreadsheets. A very brief discussion of each
follows. Many were prepared by students as homework as-
signments using techniques similar to those outlined for the
Pxy diagram. Copies of these assignments are available upon



TABLE 5
Graphs Prepared Using Spreadsheets
for Phase Equilibrium Class

Binary phase diagrams for ideal solutions
Pxya
Txyb
xya

Fugacity versus pressure
Numerical integration of PV datab
Generalized viral coefficient
Redlich-Kwong equation of state'

Volumetric properties of binary nonideal solutions
Excess volume
Partial molar excess volumes"

Activity coefficients in binary solutions versus composition
Margulesa
Van Laarb
Wilson"

Infinite dilution activity versus temperature
Wilson"

Phase diagram for nonideal azeotrope forming binary mixture
Pxyb
Txyb
xya

Excess free energy of homogeneous azeotrope forming binary
mixture versus composition
Experimental data"
Margules equation (fit to azeotrope data)"
Margules equation (best fit to VLE data)"
Wilson equation (literature constants)b

Excess free energy of heterogeneous azeotrope forming binary
mixture versus composition
Experimental data"
Margules equation (best fit to VLE data)"
Margules equation (best fit to LLE solubility data)"


"Prepared by students as homework assignment
"Prepared by instructor for class discussion


request. Some graphs were not assigned but were generated
by the instructor and presented during class discussion.
The same spreadsheet data used to produce a Pxy diagram
as described above could be used to plot an xy diagram at
constant temperature. Pxy and Txy are the predominant rep-
resentations of VLE data in phase equilibrium classes, but
xy is probably the most frequently used format of the phase
equilibrium data in other classes, e.g., distillation, absorp-
tion, mass transfer.
Using the method described above, generating Pxy data
for an ideal binary system at constant temperature does not
require trial and error. Calculation of a single Txy datum for
an ideal binary system at constant pressure requires iteration
or trial and error since the vapor pressures are functions of
temperature. But generating a Txy diagram for such a system
-the locus of dew and bubble point temperatures for all pos-
sible compositions- does not require trial and error. Taking
temperature as the independent variable rather than liquid
composition, all other variables can be calculated directly by
Eqs. (1-3). Selecting a range of temperatures in increments be-
tween the pure-component boiling points generates the diagram.
Plotting y versus x instead of T versus y and T versus x pro-
duces an xy diagram at constant pressure from the same data.
For nonideal binary mixtures, activity coefficients are func-
tions of liquid composition and possibly temperature. Pxy
and xy diagrams at constant temperature are generated in a
straightforward fashion without iteration since temperature
is fixed and liquid composition is taken as the independent
variable for generating the table as described above.
Iteration cannot be avoided when generating Txy and xy
diagrams at constant pressure for nonideal binaries. To find
activity coefficients and vapor pressures, liquid composition
and temperature are needed. Only one can be assumed. Di-
rect calculation of liquid composition from vapor pressure,
as in the ideal case, is not possible. If temperature is used as
the independent variable, as suggested for the ideal case, a
unique composition may not result because azeotropes are
possible. I recommend using liquid mole fraction as the in-
dependent variable ranging from 0 to 1, as in the Pxy dia-
grams. Iteration can be performed by circular recalculation
on the spreadsheet. Unfortunately, spreadsheets vary signifi-
cantly in their implementation of circular recalculation, even
from version to version, and it is difficult to give a "recipe"
that works in all cases. Often, particular rearrangements of
equations or ordering of the columns is necessary. No matter
what package was being used, however, I have always been
able to find some method that eventually worked.
Thermodynamics textbooks commonly contain graphs of
excess and partial excess properties such as volume and en-
thalpy for binary solutions. In the volumetric properties as-
signment, students generate similar graphs for ethanol-water
using density data as a function of composition taken from
Continued on page 291.


Fall 2002











MM laboratory


CHEM-E-CAR

DOWNUNDER


Victoria 3800 Australia


he Chem-E-Car competition has been run for under-
graduates by the AIChE for the past three years with
finals at the AIChE annual meetings. The idea is for
teams of undergraduate students to design and build a small
car powered by a chemical reaction. The objective is for the
car to travel a certain distance and then stop. The distance to
be traveled and the weight to be carried by the car are not
announced until the day of the competition. The emphasis is
on control of a chemical reaction, with a keen eye on safety
and the environmental impact of the design. The winner is
the team whose car stops nearest to the required distance. In
addition to designing and building the car, each team must
make a poster that describes the car's operation and include a
safety and environmental assessment.
Having witnessed the enthusiasm of the participating stu-
dents and spectators at the AIChE Chem-E-Car Competition
finals held in Dallas and Los Angeles, I decided to organize a
Chem-E-Car competition here in Australia. Early in 2001, I
contacted all chemical engineering departments in Australia
and New Zealand, sent them copies of the rules (for the AIChE
competition), and invited them to join. Six departments re-
sponded enthusiastically, and within a couple of months teams
of students were working away. The original plan was to have
local competitions within each department, with these com-
petitions generating finalists for the grand Australasian final.
University work and the difficulty of the Chem-E-Car task
took its toll, however. Several teams fell by the wayside, in-
cluding the team from my department. As time went on, it


Martin Rhodes is Professor in the Depart-
ment of Chemical Engineering at Monash
University in Melbourne, Australia. He has a
keen interest in chemical engineering educa-
tion and specializes in particle technology, a
subject on which he has written an under-
graduate textbook. His research interests in-
clude fluidization, gas-particle flows, interpar-
ticle forces, and particle mixing.


Copyright ChE Division of ASEE 2002


became clear that the grand final would be a fight between
five teams-four from Australia and one from the National
University of Singapore, who, upon hearing about the com-
petition, asked if they could take part. The grand final was
held on day three of the World Congress of Chemical Engi-


Figure 1. The NUS car (a) with bodywork removed to
reveal the inner detail and (b) in motion.


Figure 2. The UNSW car drifting through its self-
generated mist.


Chemical Engineering Education


MARTIN RHODES
Monash University Melbourne,


--~- '










neering at the Melbourne Exhibition Centre in late September.

THE TEAMS AND THE CARS
National University of Singapore (NUS)
The NUS car (Figure 1) used the decomposition of 15%
hydrogen peroxide solution with dilute potassium perman-
ganate solution as a catalyst to generate oxygen, which was
stored in the stainless steel reactor. Opening the ball valve at
the rear of the reactor released the contents in short order,
propelling the car along. The car was stopped by friction.
The distance traveled was controlled by adjusting the quanti-
ties of reactant used and the time for reaction.
During the test runs prior to the competition, this car an-
nounced itself with a loud bang and blew away the plastic
sheeting that had been specially erected as a splashguard be-
hind the start line. Race helpers hurriedly modified and re-
erected the splashguard. The valve on the rear of the reactor
was equipped with a lengthened handle. Starting the car in-


figure 3. The Sydney University
three-wheeled, two-cell car.


Figure 4. The Newt
experiencing ter
probl


Figure 5. The Newcastle Two team's car a) running without
b) in full sparkling glory.


volved swinging an oversized pair of laboratory tongs, golf-
iron style, to hit the handle and swiftly open the valve. The
swipe with the tongs only happened at the precise time, dic-
tated by the reaction countdown.
On its first official competition run, the team member wield-
ing the tongs was either a little too enthusiastic or had poor
aim; the result was that the car turned onto its side within a
few meters of the start line.
University of New South Wales (UNSW)
The UNSW car, named "Cold Power," was powered by a
1.5-3V electric motor running from an electrochemical cell.
The cell used solutions zinc sulfate and copper sulfate with
zinc and copper electrodes. The electrodes were made from
Imm sheet, totaling around 200cm2 for each metal. The dis-
tance was controlled using a switch that involved measuring
the speed of sublimation of solid carbon. A quantity of solid
carbon dioxide was placed in a container on one side of a
pulley. On the other side were a number of counterweights
such that the solid carbon dioxide con-
tainer rested on a metal electrode,
which completed the circuit. As the
solid carbon dioxide vaporized, the
weight on that side of the pulley de-
creased until it was outweighed by the
counterweights. Once this occurred,
the solid carbon dioxide container
lifted off the electrode and cut the
power to the motor. The amount of
solid carbon dioxide initially placed
in the container (anywhere from 20g
to 50g) was determined by the dis-
castle One team car tance to be traveled. The UNSW car
rminal technical was interesting to observe as it glided
lems. along in a white cloud generated by
the subliming carbon di-
oxide (see Figure 2).
Sydney University
The Sydney University
car (see Figure 3) was de-
signed and built by a team
of first-year engineering
students (mechanical and
chemical). It was driven
by an electric motor pow-
ered by an electrochemi-
cal cell comprised of
1.8M sulfuric acid and
potassium dichromate so-
lution (lg/100ml) with
zinc electrodes. This car
had three wheels and a
its sparkler timing device and low center of gravity. It


Fall 2002











was able to travel well in a straight line. The inventory of
acid was only 5ml, and the cell was enclosed to minimize
spillage problems in the event of a crash. The first run of the
Sydney team was good, but unfortunately, it started without
the required weight.

Newcastle University Team One
The Newcastle Team One car was driven by a small 3.5V
1A motor and powered by a zinc/copper copper sulfate bat-
tery, using 1M copper sulfate solution and 1M sulfuric acid.
This car made a promising start, getting third closest to the
line on its first run. Technical problems (a broken electrical
connection to the motor), however, prevented it from leaving
the starting line on its second run (see Figure 4).

Newcastle University Team Two
The Newcastle Team Two car (see Figure 5) was driven by
a 3V electric motor via a six-speed gearbox. The motor was
powered by a battery of four cells each producing 1.45V-two
cells in series with another two cells in series. The cell used
was an alkaline battery, very similar in chemistry to com-
mercial batteries.
A children's sparkler was used as a timing fuse to stop the
car. When the sparkler burned to the end, it melted through a
section of solder wire incorporated into the cell wiring and
disconnected the power supply from the car motor. The length
of the sparkler determined the running time of the car and
was decided according to the results of previous trials. Spar-

I


Chem-E-Car


University of Newcastle

The Cell
Th Cl


The car is powered by an electrolytc reaction taking place In a dry cell. The cell used Is
an alkaline battery, very similar in chemistry to commercial batterle.
.1 1 1 ncshere
Ionic Reaction in Cell: a n EMCrphal P a. I
Zn n2 + 2e [ .
Mn4+ + e--s Mn3* ,


ml I


I Features I d l fts produce 1.45 V. nn the car


klers were found to be remarkably consistent and had a burn-
ing rate of around 0.28 cm/s. Extensive safety testing had
been carried out on sparklers used indoors to ensure mini-
mum smoking or sparking.
With the sparkler burning away as the car rolled along, it
was pleasing to the eye. In practice on home turf, it had man-
aged to consistently stop only a few centimeters from the
desired distance. On this day it was the most consistent car
and eventually achieved second place.


THE RESULT

Team Newcastle Two won the poster competition with a
concise, informative display (see Figure 6). The performance
competition winner was the team from the National Univer-
sity of Singapore; after a crash on its first run, their car stopped
only 135cm short of the 20m designated distance on its sec-
ond and final attempt. Team Newcastle Two took second place
when their car stopped 180cm after the line. The trophy, a
polished Plexiglas CSTR on wheels, was made by the work-
shop staff at Monash University and is now in the hands of
the NUS team.


Reports from faculty involved in supervising the local de-
partment competitions suggested that the students benefited
greatly from the experience. To get to the start line with a car
that was competitive and worked according to the rules, each
team had to solve the series of specific engineering prob-
lems. Several teams went beyond mere functionality and con-
sidered aesthetics.
The concentration
and enthusiasm of
the participants was
1 palpable, and I was
privileged to witness
it. It is not often that
SThe Stopping Mechanism our students engage
in something that is
Coanrnrdal sparklers are used as
..r A- SS r...a.. fun and also a great
th sparkler bu to the and. it
ma 0 Ito a end oo learning exercise.
we Inoopor-etd into the o *
sup"' from the "ar to'r Th The Chem-E-Car
length of the spkler deterlines
he ruling m of the M...nd Is Competition was this
diddWl accotdng sto the result of
s* 1, x" o" and more.


lour calls ars arrangdasa below to gm u
.maimn perormenr e:
/Car dlimension 225 *200mm (top vi29eV S e a
-Weight (tar) = o1028 g: _*__ a
-car = 320g ed ou pars inhdoors, to
-zinc=4x37g' min. i smoking or-
-. -I=4xos I Safety _
-EMD=4x35g ': .
3V moo MSS orade Safety procdurs developed for sparkler use:
-6sedgearox Su contldaineril ld to hold c l foilrtspar :
High purity chemicals mean negligible No fmmabe material within aparkl
go-o by-products radius of 450 mm
Minimal in nc required wth cell. Skle to be handled with cuton for
-Minimal interferece required with cell, three minutes after uset
acomponentsalstforseveNal runs
Chem-E-CarTeam jamndidklrn tmdUpre mbennn ja skemp johnmcchy lukMorgn tnlar m KyIrollihmon salonnlker Wonmth rtahlkron


Figure 6. The winning poster of the Newcastle Two team.


The Chem-E-Car
Competition will be
held again next year
with the grand final
in Christchurch,
New Zealand, at the
CHEMECA 2002,
the annual confer-
ence of chemical en-
gineers in Australia
and New Zealand. O


Chemical Engineering Education


mo
_/


pw

rio-


290











User-Friendly Phase Equilibrium
Continued from page 287.

handbooks.10 "' By doing this assignment, students can de-
velop a better intuitive understanding of the meaning of such
excess property data because they see where the data came
from. Additionally, the magnitude of the variation of ac-
tivity coefficient with pressure is related to the partial
molar excess volume. Using these results, students can
prove to themselves why activity coefficients are typi-
cally assumed pressure-independent.
Before using activity coefficients in VLE calculations, stu-
dents prepare a few plots of activity coefficient versus com-
position or of infinite dilution activity coefficient versus tem-
perature. When they produce graphs similar to those in the
textbook, students reinforce their concept of what "shape"
these functions should have. Also, by plotting results from
several different equations on one graph, students see that
it makes little difference which correlation is chosen in
most cases. For subsequent VLE and LLE calculations,
they typically use the Margules equation because it is the
most simple mathematically.
In conjunction with VLE phase diagrams, students produce
plots of excess free energy functions. These plots can be used
to determine constants in an activity coefficient correlation.
For example, a plot of GE/RTx x, versus xi can be used to
determine Margules equation parameters by a straight-line
fit. When constants determined by several methods are used
to plot an xy diagram, students learn the fit of the data is as
important as which equation is used.
Phase separation and LLE are analyzed with graphs of free
energy of mixing versus liquid composition. For LLE, it is
the shape of these curves-convex or concave-that is the
determining factor in phase stability. As with the VLE data,
students generate plots of these functions from experimental
data points and, by fitting activity coefficient correlations in
various ways, compare the results.
Phase equilibrium and chemical reaction equilibrium are
often taught in one course. I have also successfully used com-
puter spreadsheet assignments or demonstrations for class dis-
cussion in the reaction equilibrium portion of the course.
It is a fundamental belief of mine that students will choose
to use the computer and specific software in cases where it
makes a problem easier to solve. When I assigned these prob-
lems, I did not require the use of specific software. (In fact, I
did not require the use of a computer at all, but with the avail-
ability of computing resources and the students' general fa-
miliarity with computers, no hand-plotted solutions have been
submitted in about ten years!) I typically discussed how to
structure a spreadsheet for the assignment and frequently had
the students work through a hand calculation for a single data
point as an in-class exercise.


The majority of students "follow the path of least resis-
tance" and complete the assignment using the standard spread-
sheet package, currently Microsoft Excel. The specific choice
of spreadsheet has little effect. Students have solved the prob-
lems using Quattro Pro, Lotus 1-2-3, SuperCalc, and the Smart
Spreadsheet in past years. Moreover, it is unnecessary to use
a spreadsheet, as a few students have demonstrated by solv-
ing the problems using programming languages (FORTRAN,
C), graphics packages, and math solvers (Mathcad, Maple).
All students eventually gravitated to spreadsheets by the end
of the class, however. The only warning I give to students
who use nonstandard computer software is that I may not be
able to assist them with computer-related problems if they
are using a package with which I am unfamiliar.

CONCLUSIONS
In teaching phase equilibrium thermodynamics, I have at-
tempted to promote understanding and intuition of the course
material. Initial explanation that the goals of the class relate
mainly to data handling and generation, unlike other chemi-
cal engineering classes, prevents confusing expectations from
developing. Meaning and consequences of data are empha-
sized, particularly for abstract quantities such as activity co-
efficients for which interpretation is not necessarily explicit.
Widespread presentation and students' use of graphical data
is made convenient using computer spreadsheet software.

ACKNOWLEDGMENTS
These computer assignments were developed over a series
of courses taught at Michigan State University and Villanova
University.

REFERENCES
1. Prausnitz, J.M., R.N. Lichtenthaler, and E.G. de Azevedo, Molecular
Thermodynamics of Fluid-Phase Equilibria, 2nd ed., Prentice-Hall Inc.,
Englewood Cliffs, NJ, p. 4 (1986)
2. Smith, J.M., H.C. Van Ness, and M.M. Abbott, Introduction to Chemi-
cal Engineering Thermodynamics, 5th ed., McGraw-Hill, New York
(1996)
3. Kyle, B.G., Chemical and Process Thermodynamics, 2nd ed., Prentice-
Hall, Englewood Cliffs, NJ (1992)
4. Bird, R.B., W.E. Stewart, and E.N. Lightfoot, Transport Phenomena,
John Wiley & Sons, New York (1960)
5. Felder, R.M., and R.W. Rousseau, Elementary Principles of Chemical
Processes, John Wiley & Sons, New York (1986)
6. Bennett, C.O., and J.E. Myers, Momentum, Heat, and Mass Transfer,
McGraw-Hill, New York (1985)
7. Savage, Phillip E., "Spreadsheets for Thermodynamics Instruction,"
Chem. Eng. Ed., 29(4), p. 262 (1995)
8. Pratt, R.M., "Thermodynamic Properties Involving Derivatives: Us-
ing the Peng-Robinson Equation of State," Chem. Eng. Ed., 35(2), p.
112(2001)
9. Elliott, J.R., and C.T. Lira, Introductory Chemical Engineering Ther-
modynamics, Prentice Hall PTR (1999)
10. Green. D.W., and J.O. Maloney, eds, Perry's Chemical Engineers'
Handbook, 7th ed., McGraw-Hill, New York, NY (1997)
11. Weast, R.C., ed., CRC Handbook of Chemistry and Physics, 60th ed.,
CRC Press, Boca Raton, FL, D-227 (1979) 1


Fall 2002











: laboratory


ON IMPROVING "THOUGHT WITH HANDS"



G.K. SURESHKUMAR, K.C. KHILAR
Indian Institute of Technology, Bombay India 400 076


L laboratory exercises are essential"1,21 toward the devel-
opment of a good chemical engineering graduate with
desirable skills such as independent learning, inter-
dependent learning, problem solving, critical thinking, cre-
ative thinking, interpersonal skills, teamwork, leadership,
integration, communication, and change management.'31 The
standard laboratory exercise in chemical engineering, how-
ever, revolves around an apparatus that remains unchanged
for several years and can lead to unethical practices among
students',41 such as submission of data/reports from previous
years. Moreover, the application of thought, which is crucial
for laboratory work and developing the skills mentioned
above, is almost nonexistent in the standard laboratory exer-
cise. From an instructional-objectives viewpoint,151 most labo-
ratory exercises are designed to be at Bloom level 2 (com-
prehension) out of the possible six levels. This leads to se-
vere resentment toward laboratory work among students and
professors alike. Students consider lab courses as a formality
to be completed, while faculty treat them as poor cousins of
theory courses, relegating the entire responsibility for lab
courses to lab supervisors or teaching assistants.
We believe that if students are challenged to think criti-
cally on laboratory exercises and encouraged to be creative,
their interest in and respect for laboratory work would im-
prove, and in turn, the faculty would be further motivated to
offer better laboratory courses/projects. With this belief, a
laboratory course consisting of both dual-step laboratory ex-
ercises and a recommendation/innovation exercise was con-
ceived and assigned to third-year (junior) undergraduate stu-
dents taking the fluid mechanics laboratory at the Indian In-
stitute of Technology, Bombay.
Our laboratory guidelines state that the overall aim of this
laboratory course is to inspire students to appreciate the un-
derlying themes of the experimental aspects/approaches to
engineering/science with fluid-flow aspects as a model sub-
ject. The goal is to develop students' abilities to "think with
their hands." Another purpose of this course is to improve
understanding of fluid-flow principles, to develop a physical
feel for some fluid-flow situations, to develop a familiarity


with some commonly used fluid-flow equipment, to incul-
cate a concern for safety, to improve communication of ex-
perimental results, to improve the quality of analysis and in-
quiry, and to kindle the spirit of discovery in students. Fur-
ther, we expect the exercise to develop some of the above-
mentioned skills in a chemical engineering graduate.

THE LABORATORY EXERCISES
The activities for the laboratory consisted of dual-step labo-
ratory experiments (performed by student groups) and a
recommendations report (an individual activity).
The Dual-Step Laboratory Exercise
Each laboratory experiment was conducted over two lab
sessions. During the first session, student groups were ex-
pected to follow the procedures given in the manual to carry
out the experiment. Students were expected to become com-
fortable with the equipment and the experiment, and the first-
session experiments were designed accordingly.
After the first session, students were required (as home-
work) to analyze the data taken during the lab session based on
the theoretical principles in the lab manual/fluid mechanics text-
book/notes and examine whether the results obtained were as

G.K. Sureshkumar (G.K.) is currently Associ-
ate Professor in the Chemical Engineering De-
partment at Indian Institute of Technology,
Bombay. He received his BTech. in Chemical
SEngineering from Indian Institute of Technol-
ogy, Madras, and his PhD from Drexel Univer-
sity. His research interest is free radical-based
improvements in the productivity of bioreactors.
SHe can be reached at


Kartic C. Khilar is currently Professor in the
Chemical Engineering Department at Indian
Institute of Technology, Bombay He earned
his BTech degree in Chemical Engineering
from Indian Institute of Technology, Kharagpur,
and his PhD from University of Michigan. He
and his students work in nanoparticle produc-
tion and colloid-associated contaminants
transport in porous media.


Copyright ChE Division of ASEE 2002
Chemical Engineering Education










expected. The following ensued:
a) If the experimental results matched the expected results,
students were expected to think of additional experi-
ments, preferably new ones, that could be done with the
same (or slightly modified) setup. But the additional
experiments need to be done within the time frame of
the second lab session. We believe that working with
these practical constraints would help students acquire
"street smarts," which are useful in handling real-world
problems.
b) If the experimental results did not match the expected
results, students were required to form hypotheses based
on the results and design ways to experimentally (with
certain calculations) prove or disprove their various
hypotheses in the second lab session. The emphasis was
on the technical/scientific rigor in proofs. The students
were also warned that their theories could be proved
false by their experiments and that it was acceptable to
admit they did not understand the reasons for disagree-
ment within the time available to them and therefore,
additional study would be required.
After the second lab session, each student group was ex-
pected to submit a single report in the regular format, i.e., (a)
Aim and Objectives, (b) Methodology, (c) Results and Dis-
cussion (which was required to be significant), (d) Conclu-
sions, and (e) the original data sheets. The reports were graded
on the following bases:
If the actual results matched the expected results:
Ability to follow procedures 10%
Data analysis (1st session) 15%
Discussion (1st session) 15%
Creativity/originality aspects (2nd session) 20%
Data analysis (2nd session) 15%
Discussion (2nd session) 15%
Presentation (mainly communication) 10%
Reports that addressed novel aspects to study in their sec-
ond session were rewarded handsomely in grading the cre-
ativity/originality criterion (see the student examples pre-
sented later).
If the actual results did not match the expected results:
Ability to follow procedures 10%
Data analysis (1st session) 15%
Discussion 15%
Clarity in thought and situation/problem
analysis (2nd session) 20%
Rigor (2nd session) 15%
Discussion (2nd session) 15%
Presentation (mainly communication) 10%
Reports that were well developed on both the possible rea-
sons for the disagreement between actual and expected data
and the experiments to prove or disprove them were given
high marks for the clarity-in-thought criterion. The difficulty
level in problem analysis was also recognized in that crite-
rion-reports that fully analyzed a difficult situation received
higher marks than those that, as a matter of chance, analyzed


a simple, easy-to-identify situation. Also, reports that un-
equivocally proved or disproved their points received high
marks for the rigor criterion. Other criteria, such as data analy-
sis, discussion, and presentation, carry their usual weight.
The Recommendations Report
Over the duration of the course, each student was expected
to think about an experiment or a set of experiments that could
be done in the fluid mechanics lab. Students were encour-
aged to be as creative as possible. Near the end of the course
(a week before the last day of classes), each student was ex-
pected to submit a detailed report on this experiment (or set
of experiments) and the equipment and instruments needed.
The reports were evaluated on the following bases:
Creativity/originality aspects 30%
Clarity in thought 20%
Detail 30%
Doability 10%
Presentation (mainly communication) 10%

The dual-step exercises evaluated through the reports carried
a 70% weight, and the recommendation report carried a 30%
weight toward the final grade.

IMPLEMENTATION OF DETAILS /RATIONALE
In the beginning of the semester before the experiments
began, the instructor met the class and discussed the exer-
cises and recommended strategies. In addition to experimen-
tal details for the first session, the laboratory manual carried
information on safety procedures for the lab, error analysis,
technical writing, and the unacceptability of academic dis-
honesty, all of which were seriously discussed in the initial
meeting. The instructor also emphasized the need for safety
procedures whenever he observed lapses during the lab ses-
sions. Student groups were asked to select their own leaders
who would assign duties for the group members and be gener-
ally responsible for the group's activities. This ensured that an
avenue for the development of teamwork and leadership skills
existed. Also, on many occasions, the instructor communicated
to the groups through their leaders.
Before the start of the first session, the groups were ad-
vised to familiarize themselves with the details for each ex-
periment using the lab manual and the textbook. The first-
session experiments were designed as shorter versions of the
experiments given in the usual lab course, and students were
encouraged to spend the additional time becoming comfort-
able with the setup and the various equipment used. For ex-
ample, the instructor encouraged the students to raise ques-
tions regarding the equipment or the reasoning behind the
various experimental steps, which the students normally took
for granted. The students took the first session seriously be-
cause they knew they had to consider the setup, the experi-
mental methods, and the underlying theory in order to have
an interesting second session. During the experiment (both
sessions), groups were advised to record the data in duplicate


Fall 2002









using a carbon sheet, and the members were asked to sign each
data sheet. The duplicate copy was submitted to the instructor
at the end of each session, and nonsubmission would result in a
grade of zero for that session. The instructor has never had to
give a zero over the past two years for this reason.
After the data analysis for the first session, the groups were
required to meet the instructor to discuss their plans for the


second session. This meeting was not to guide
the students on what they could do in the sec-
ond session, but for the instructor to listen and
comment on the possibility of doing the experi-
ments. This meeting was normally scheduled a
few days before the second session, primarily
to address any special requirements for the ex-
periment that needed to be communicated to
the lab superintendent. Also, this meeting
helped the instructor ensure that the second-
session experiments were of proper scope (nei-
ther too large nor too small) and reasonably well
thought out, especially if the actual data
matched the expected data in the first session.
In addition, it was communicated to the stu-
dents at the beginning of the semester that no
complete dismantling of the set-ups would be
allowed, except in rare cases. This encouraged


SAMPLES FROM STUDENT EXERCISES
Samples from the Dual-Step Laboratory Exercises
Agreement Between Actual and Expected Data An ex-
periment for the lab involved studying the relationship be-
tween Power number and Reynolds number in an agitated
system. One of the groups found good agreement between


... the overall
aim is...
to improve
the quality of
analysis and
inquiry,
and to kindle
the spirit of
discovery in
students.


the students to think of "non-invasive" means for testing their
theories. Also, this precaution was necessary because some
piping networks in our lab had packing to prevent leaks that
would be difficult for an inexperienced person to reassemble.
The lab reports for the dual-step exercises were due before
the start of the next experiment; the instructor graded them
and offered constructive criticism and feedback within a week
of submission. Students appreciated the timely feedback.
The grading of the recommendations report was time con-
suming (three to four consecutive, full days). As long as
grades are important, some students may cheat to get the best
grade;'6,7] therefore, a significant amount of time was spent
establishing the originality of submitted reports. This was
achieved through one-on-one interviews with students who
had submitted "doubtful" reports. During an interview, it was
easy to ascertain whether cheating had taken place by ask-
ing relevant questions, most of which were on the experi-
ment submitted.
All experiments were run on existing equipment; therefore,
this dual-step exercise does not require additional funds for
equipment. It can be run anywhere, even in the face of fund
crunches. It also provides a greater probability for disagree-
ment between actual and expected data, and thus helps stu-
dents develop lateral-thinking abilities while forming hypoth-
eses for the disagreement. Therefore, the dual-step labora-
tory exercise provides a way to turn a seeming disadvantage
in running an existing laboratory course into an advantage of
improving thought in students.


actual and expected data and therefore had to
think of additional experiments to do on the
same setup. They decided to compare the rela-
tionship between Power and Reynolds numbers
for an aqueous system with and without a sur-
factant. They found that the Power number for
the corresponding Reynolds number was lower
for the system with surfactant than for plain
water. Therefore, they concluded that the power
requirements for an aqueous system with sur-
factant are lower than that for plain water. They
also provided qualitative explanations for the
observed results from a molecular viewpoint.
Another experiment involved studying two-
phase flow characteristics in a vertical transpar-
ent tube such as the relationships between slug
length and slug velocity and between pressure
drop and void fraction, etc. The group that ob-


trained results as expected decided to study the relationship
between the radius of curvature of the slug's leading edge
and its length. They developed a theory based on geometri-
cal considerations for the variation of the leading-edge cur-
vature with slug length; they also showed correspondence
between the theoretically expected results and measured data.
Disagreement Between Actual and Expected Data An-
other experiment involved a piping network with various types
of pipes, fittings, and valves. The objectives for the first ses-
sion included determination of the frictional losses across the
pipe fittings and valves. The experiment required recording
readings from manometers attached to the pressure taps
across relevant fittings or valves and determining the water
flow rate using the pressure difference measured across
the orifice meter.
The first group that worked on the experiment found that
the friction loss constants obtained for the various fittings on
the network were higher by almost an order of magnitude
than literature values. Therefore, the group first postulated
that scale formation led to higher loss constants. To test the
postulate, they arranged for the network to be cleaned thor-
oughly and repeated the experiment in the second session.
This did not yield significantly different loss constants,
thereby partly disproving the postulate that the scale forma-
tion alone resulted in the discrepancy. Students in one of the
other groups that worked on the experiment postulated that
the water-flow rate measurements using the calibration curve
for the orifice meter may not have been correct; they noticed


Chemical Engineering Education


294










a discrepancy between flow rates measured using a measur-
ing jar/stop watch arrangement and the orifice meter read-
ings. So, the students prepared a fresh calibration graph for
the orifice meter and found it to be different from the exist-
ing, erroneous calibration chart. They also proved that the fric-
tion loss constants obtained using the new calibration graph
were comparable to the values found in the literature.

Samples from the Recommendations Report

A student named Nikhil Agarwal suggested an inexpen-
sive, simple method for determining the viscosity of a solu-
tion by allowing it to flow over a smooth, inclined flat plate
from a reservoir and taking measurements. Using suitable
balances, Nikhil expressed the viscosity as a function of mea-
surable parameters (with origins from the thickness of the
liquid layerE81) as:
pg3 cosp
3Q
where p is the fluid density, g is the acceleration due to grav-
ity, 8 is the film thickness, 3 is the angle between the plate
and the vertical, and Q is the flow rate. He carefully consid-
ered the details and limitations of the experimental proce-
dure and suggested a method to study the variation of viscos-
ity with temperature using the same setup.
Another student, Sikander Siraj, using input from a friend
in electrical engineering, suggested a photoelectric diode-
based (PED) device for the measurement of slug lengths in
the two-phase flow experiment. The idea had its origins in
the burglar alarm principle. For the measurement, he used
the deviation caused by the refraction of the infrared beam
when it passes through media of different refractive indices.

STUDENT AND STAFF FEEDBACK

The students were asked to send their comments through
e-mail to their class representative, who removed details per-
taining to the authors of the comments, compiled without ed-
iting, and forwarded the comments as a single file to the in-
structor. For the improved version of the lab, comments from
82 out of 83 students were received, and all except nine ex-
plicitly stated that the lab was useful to them. They said that
their learning included fluid-mechanics principles, applica-
tion of thought to a lab, leadership qualities, thinking cre-
atively, and working in a group. Some positive comments
over the past two years include, "Due to this lab alone, I can
say that I know some 'chemical engineering,'" and "This is
the first time I feel what a lab course is all about." Also, many
students suggested minor changes in equipment, etc., to im-
prove the lab. Of the nine students who did not state their
liking for the lab, seven were neutral, and the other two said
that the lab was not useful to them.
The staff associated with the lab were enthusiastic about
fulfilling the requirements of the lab. They also said that they


enjoyed setting up the various experiments although it in-
volved additional time.

INITIAL CHALLENGES
The first time it was offered, almost all students expressed
that the lab demanded a lot of their time. We believe this was
because students compared it with previous editions of the
same course. In addition, the same experiments that were
given in previous editions were packaged into a two-session
(dual-step) format, significantly increasing the work. There-
fore, in the next edition of the course, the experiments were
consolidated into half the original number, with all other de-
tails unchanged. Afterwards, there were very few comments
(3 out of 83) that there was too much work.
The first time the course was offered, the groups were as-
signed according to student roll numbers, which the students
hated. The next time, the students were asked to form their
own groups with the average cumulative performance index
(CPI) of the group members being close to the class average
CPI; this incorporates cooperative learning elements. Com-
plaints about unsuitable groups were almost eliminated.
The remaining challenge is group size. Six students in a
group is nonideal and should be reduced. We intend to re-
duce the number by running the experiments more frequently
in the future. The logistics constraint needs to be addressed
first, however.
In short, a focus on developing the critical thought process
in students made the laboratory course interesting to both
students and instructors and also developed students' respect
for experimental work.

ACKNOWLEDGMENTS
We would like to thank the students of CL333 for their
enthusiastic participation in the exercise as well as O.S.
Sawarkar, V.B.V. Nair, V. Ramachandran, and A.D. Kadam
for their contributions.

REFERENCES
1. Middleberg, A.P.J., "Laboratory Projects: Should Students Do Them
or Design Them?" Chem. Eng. Ed., 29(1), p. 34, (1995)
2. Jones. W.E., "Basic Chemical Engineering Experiments," Chem. Eng.
Ed., 27(l), p. 188. (1993)
3. Rugarcia, A., R.M. Felder, D.R. Woods, and J.E. Stice, "The Future of
Engineering Education. I. A Vision for a New Century," Chem. Eng.
Ed., 34(1), p. 16, (2000)
4. Macias-Machin, A., G. Zhang, and O. Levenspiel, "The Unstructured
Student-Designed Research Type of Laboratory Experiment," Chem.
Eng. Ed., 24(2), p. 78, (1990)
5. Felder, R.M., D.R. Woods, J.E. Stice, and A. Rugarcia, "The Future of
Engineering Education. II. Teaching Methods that Work," Chem. Eng.
Ed., 34(1), p. 26, (2000)
6. Felder, R.M., "Cheating: An Ounce of Prevention...Or the Tragic Tale
of the Dying Grandmother," Chem. Eng. Ed., 19(1), p. 12 (2000)
7. Sureshkumar, G.K., "A Choose-Focus-Analyze Exercise in ChE Un-
dergraduate Courses," Chem. Eng. Ed., 35(1), p. 80, (2001)
8. McCabe, W.L., J.C. Smith, and P. Harriott, Unit Operations of Chemi-
cal Engineering, McGraw-Hill, Singapore, 6th ed., (2000) D


Fall 2002










ref m curriculum


THE EARTH'S

CARBON CYCLE

Chemical Engineering Course Material



ROGER A. SCHMITZ
University of Notre Dame Notre Dame, IN 46556


On three occasions in recent years, I have taught an
elective course at the University of Notre Dame for
chemical engineering seniors titled "Topics on Ecol-
ogy and the Environment." I developed the course because I
felt it was important for our students (and myself as well) to
have a greater appreciation-from a chemical engineer's per-
spective-for the workings of Earth's natural processes, both
biotic and abiotic, and a knowledge of how human and in-
dustrial activities are disturbing or might disturb them.
One of the significant components is a module on the car-
bon cycle-the subject of this article. In gathering and devel-
oping material for this module and others in the course, I was
struck by these observations:
Many of the Earth's processes, including the carbon
cycle, though fundamentally very complex in detail, can
be represented by simple models that are useful for study
purposes and even for quantitative estimates, at least as
a first approximation.
The development, analysis, and application of models
are well within the scope of an undergraduate chemical
engineering curriculum.
The subject matter or bits and pieces of it, can be
integrated advantageously, straightforwardly, and nearly
seamlessly into core chemical engineering courses.
My objectives in this article are to demonstrate all of this,
using the carbon cycle as the means, and to provide conve-
nient material for others who may be persuaded by my third
observation.
Of the biogeochemical cycles of the six major "life" ele-
ments, C, N, P, S, O, and H, the carbon cycle receives the
lion's share of the attention in the literature. That's no sur-
prise inasmuch as most of our energy needs are met by the
burning of carbon-based fuels and inasmuch as the conse-
quent increasing level of atmospheric carbon dioxide and its


potential effect on the Earth's climate is a frequent focus of
attention in technical and nontechnical publications. What's
more, chemical engineers will have opportunities to play a
prominent role in any steps taken to moderate that level,
whether those steps be toward alternate energy sources or
toward sequestering or otherwise preventing emissions di-
rectly into the atmosphere.

THE CONCEPTUAL MODEL
Carbon is found in all of Earth's compartments or reser-
voirs-in the biota and in the atmosphere, hydrosphere, and
lithosphere. Mathematical models describing the cycle ac-
count for the movement of carbon among and within those
reservoirs and for anthropogenic disturbances, which are prin-
cipally due to fossil fuel burning and deforestation (i.e., mainly
burning of removed trees) for land use changes.
Figure 1 presents a schematic diagram of a conceptual
model of the carbon cycle consisting of six reservoirs, num-
bered one through six. (A seventh reservoir for fossil fuels
enters dynamically into the model later only as a disturbance
to the six-reservoir natural cycle.) Other reservoirs, includ-
ing sediments, marine biota, and lakes, rivers, and streams,
are omitted for reasons given later. In one way or another, all
models are based on this starting picture, which is sometimes

Roger Schmitz is the Keating-Crawford Pro-
fessor of Chemical Engineering at the Univer-
sity of Notre Dame. He received his bachelor's
degree from the University of Illinois and his PhD
from the University of Minnesota, both in Chemi-
cal Engineering. His current interests are in the
modeling and analysis of environmental and
ecosystem dynamics.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education










modified to include one or more of the omitted reservoirs.
Models differ primarily in the extent of detail and correspond-
ingly in the objectives of the modeler. For example, highly
detailed climate studies employ general circulation models
based on fundamental transport equations to describe pro-
cesses in the atmosphere and/or ocean reservoirs and several
types of vegetation to describe the atmosphere-biota ex-
change."' At the other extreme, so-called "box" (or "com-
partment" or "lumped") models that are intended to give es-
timates of global averages of carbon in major reservoirs, are
based on spatially aggregated descriptions, often with no more


detail, sometimes even less,
than that shown in Figure
1.[27] Except to allude to the
structure of high-end models
and their purposes (and
sometimes to compare re-
sults), I choose to work with
simple box models in the
course. In short, as tools for
study, they have suited my
purposes. Further, if prop-
erly calibrated and tuned,
they have proven useful for
quantitative purposes so
long as the principal interest
is in global averages, par-
ticularly in atmospheric car-
bon dioxide levels.
The conceptual model rep-
resented in Figure 1 and the
mathematical description to
follow are amalgamations of
several box models that I
have studied and used in the
course. The version pre-
sented here is closely pat-
terned after, but not identi-
cal to, that described in a re-
cent publication by Lenton.1'
I usually have the students
go through the development


The numbers in parentheses beside the arrows in Figure 1
represent estimates, in petagrams of carbon per year (PgC/
y), of the transport (commonly termed "fluxes" in the rel-
evant literature) of carbon between reservoirs. Such fluxes
are estimates, adjusted so that each box is balanced at a steady
state, where it would remain unless disturbed. There is no
common agreement on the values of the reference pre-indus-
trial masses and fluxes, or even on the reference year (gener-
ally between 1800 and 1860), but the variation from one ref-
erence source to another is of little significance. The values
shown in Figure 1 are in line with those used in the refer-
ences cited above.


1 M1
ATMOSPHERE (612)


F61 F15 F51 F F F21 F3 F3
(50) (100 (50) (57) (58) (19) (18)


lerrestal arm ocean cool ocean
biola surface waers surface walers
N5 I FdiI NlN3
1580s 17301 1 140-


\
6 4
soils & deep
detritus ocean waters
M6 M4
(1500) 1 370(K)0
Ff
Ff- -



Figure 1. Schematic diagram of a six-box model of the car-
bon cycle. Values shown for reservoir masses (M, in PgC)
and fluxes (F., in PgC/y) are representative of the pre-in-
dustrial steady state (- 1850).


of other models as complementary outside work.

THE REFERENCE PRE-INDUSTRIAL STATE

The quantities shown in parentheses in the boxes in Figure
1 represent estimates of the "pre-industrial" distribution of
carbon (i.e., the mass of element C in all of its compounds) in
petagrams (PgC, 1 Pg = 10lg.) These are typical reference
values presumed to represent the balanced (steady-state)
conditions around the year 1850-early in the industrial
revolution when there was little or no observable change
from year to year.


M the mass of carbon
in the atmosphere reser-
voir can be taken to be en-
tirely in the form of CO2.
The 612 PgC in that reser-
voir corresponds to a CO2
concentration of 286 ppmv
(parts per million by vol-
ume) -the concentration
unit used in most illustra-
tions to follow. (The con-
version factor of 2.128
PgC/ppmv is based on a
total atmosphere mass of
5.14 x 106 with a molecu-
lar weight of 29.)
Notice the notation in
Figure 1. M, stands for the
mass of carbon in box i; F..
for the flux of carbon from
box i to box j. The anthro-
pogenic disturbance flux
F moves carbon from a
nonrenewable fossil fuel
reservoir to the atmo-
sphere.* The other anthro-
pogenic disturbances, Fd
and F, take carbon from
the renewable terrestrial


biota reservoir to the atmosphere (deforestation) and from
the atmosphere to the terrestrial biota (reforestation), respec-
tively. (There is increasing interest in sequestering part of Ff
by redirecting it to cavities in the lithosphere and/or to the
deep ocean.'8"91 Those slight but interesting variations to the
model will be mentioned in suggested exercises near the
end.) The following list gives a succinct description of
the other fluxes:

Actually, Ff accounts for all carbon emissions to the atmosphere except
those due to deforestation. It is commonly termed "emissions due to fossil
fuel burning"-a term that I shall use throughout. Other industrial sources,
such as cement manufacturing, account for only a few percent of the total.


Fall 2002










SF1,, F F and F31 are simply mass transfer rates for the
exchange of carbon (as carbon dioxide in this case since
nearly all atmospheric carbon is in that form) between
the atmosphere and the ocean waters. Basically, the rates
are described by the product of a mass transfer coeffi-
cient and a concentration driving force, but the nuances
involved in using that description warrant further
attention later.
SF23 represents the advective flow of carbon from the
warm to cool surface ocean reservoirs. This flow, which
accounts for most of the ocean mixing, results from the
downflow of cool surface water at high latitudes and the
corresponding upwelling to the warmer surfaces at low
latitudes. There is also an eddy-mixing component
contained in the fluxes between the surface and deep
ocean waters. The model could be further simplified
without affecting results noticeably by lumping boxes 2
and 3 into a single box.
D F5 is the rate of photosynthetic uptake of carbon from
the atmosphere by terrestrial vegetation. This flux,
assumed often in models of this type to be describable by
a single overall rate expression, gets special attention
later. M5 is the total carbon in terrestrial biota, but we
might think of it as being the mass of vegetation since
about 90% of it is in forests.
F56 is the flux of carbon in litter fall-mostly dead leaves
and the like, but generally including all dead and waste
products from the terrestrial biota.
SF 5 and F are the fluxes of carbon, mostly as carbon
dioxide with small amounts as methane and other
compounds, to the atmosphere by biotic respiration.

As mentioned above, a more complete box structure would
include additional elements for aquatic biota; sediments; and
rivers, streams, and lakes. Such additions are more suited for
discussions and assigned work than for incorporation into a
working model for the following reasons: The inventory of
carbon in aquatic biota and in rivers, streams, and lakes is
negligibly small; sediments, the largest of all reservoirs with
a total carbon mass of about 108 PgC, are the most sluggish
by far; the small fluxes (~0.3 PgC/y) into and out of the sedi-
ments lead to a first-order time constant of the order of sev-
eral hundred million years! For the reservoirs represented in
Figure 1, first-order time constants, calculated as the ratio of
the mass of carbon in a reservoir to the flux of carbon out of
it, range from 1.19 years for the cool surface waters in box 3
to 330 years for the deep ocean waters in box 4. For the at-
mosphere, box 1, it's 3.48 years. The illustrations in simula-
tions to come will cover time spans up to 250 years, over
which time the sediment reservoirs are virtually steady.


THE EQUATIONS
The mathematical description of the box model of Figure 1
consists of a set of carbon balance equations. For the atmo-


sphere, box 1, for example
dM1
dt
F21 -F12 + F31 F13 + F51 F15 + F61 +(Ff +Fd Fr) (1)

In general
dMi 6
d- = (Fi Fi + disturbances (2)
jdt

If a particular F.. does not appear in Figure 1, its value in Eq.
(2) is zero. The disturbances, as represented in Figure 1, ap-
pear only in the balances for boxes 1 and 5.
To keep account of the fossil fuel supply, a seventh box is
added, an out-of-cycle, nonrenewable reservoir of the car-
bon in fossil fuels. The following balance describes the deple-
tion of that reservoir:
dM = -Ff (3)
dt
All terms in these equations have units of petagrams of car-
bon per year (PgC/y).
The initial conditions are the reference pre-industrial res-
ervoir levels in 1850. I use 5300 PgC for the initial value of
M7, somewhat arbitrarily, but based on rather common state-
ments that while the total carbon stored in fossil fuels is
about 10,000 PgC, only about half of it can actually be
recovered for use.
Since most of the reservoirs undergo relatively small
changes over periods of interest, as later simulations will show,
the fluxes can be related to the reservoir masses by first-or-
der processes. That is
Fij = kijMi (4)

Such relationships are frequently employed in box models
of the biogeochemical cycles, including the carbon cycle, with
three exceptions: F5, F21, and F31. For the others, the numeri-
cal value of k can be obtained readily from the reference
data given in Figure 1.
If the carbon in the ocean were present simply as carbon
dioxide in aqueous solution, we would expect all four of the
F's connecting the ocean surface waters to the atmosphere to
be describable by Eq. (4)-under the safe assumption that
Henry's law applies to the dilute CO2 solution. The situation
is complicated, however, by the fact that CO2 in aqueous so-
lution enters into equilibrium chemical reactions involving
carbonate and bicarbonate forms. Therefore, while the fluxes
F21 and F3 can be related linearly to aqueous CO2, they are
not linearly related to the total C; that is, to M2 and M3. The
relationship to the total carbon in solution is complicated. It
is affected by all of the factors that affect acid-base equilib-
rium in ocean water-total alkalinity, salinity, temperature,
and dissolved salts of weak bases, such as boron. A rigorous
treatment requires linking a set of equations for ocean chem-


Chemical Engineering Education


29R










istry dynamics to the above set. Some studiesi3.5 have fol-
lowed that procedure, as have I in some instances. Others'2.471
have opted for a simpler empirical approach that uses the
following relationships:
F21 =k21M2 F31 k31M3 (5)
Values of the exponents p2 and [3, called buffer factors or
Revelle factors, can be obtained from charts of the type given
in the book by Butcher, et al.1 I0 They can also be obtained by
delving into the intricacies of ocean chemistry dynamics and
correlating results of calculations. I used the latter approach
to obtain the values shown later, but to save space and to stay
on track, I shall spare further detail.
My testing has shown that results of computations using
constant values of the 's hardly differ from those obtained
by appending detailed ocean dynamics to the model, so long
as changes in M, and M3 are relatively small, generally less
than 5%. The numerical values of P range between 9 and 15;
the nonlinearity is surprisingly strong. Notice that with val-
ues of P2 and 03 given, numerical values of the rate con-
stants k,, and k31 can be determined from the reference con-
ditions given in Figure 1.
The rate of photosynthetic uptake, F of carbon from the
atmosphere cannot be represented realistically as a linear func-
tion of M,. The basic reason is that the function should ac-
count for a saturation effect with regard to the nutrient CO,.
That is, the rate increases with increasing CO, but approaches
a limit. For small changes in M,, the function may be ap-
proximated by a linear relationship, but as a later illustration
will show, changes in M, are large over the periods of interest.
There seems to be no clear consensus as to what form to
use for F5 in models of this type. Whatever the specific form,
a common feature is a dependence on atmospheric carbon
that suggests an ultimate saturation. The particular one cho-
sen does not seem to be a critical matter so long as the con-
stants are calibrated or tuned to fit existing data. Neverthe-
less, this is a fertile item for classroom discussion, debate,
and outside work. Here I shall use the form employed by
Lenton'[3

k 5 M-y forM1 >
15M8M-- forM>Y
F,15 = M, +r (6)
0 forM1 where
y is the threshold value of M, (I used Lenton's value of
62 PgC.)
F is a saturation parameter (Lenton used it as a tuning
parameter and arrived at a value of 194 PgC. By methods
described later, I arrived at a value of 198 PgC.)
k,, is a rate coefficient to be calculated from the reference
state.
M is a function that depends on the disturbances Fr and
Fd as explained and described below. In short, it accounts


for changes in the Earth's capacity for terrestrial biota.

The role of the function M8 is important but not obvious at
first glance, and definitions and explanations do not come
easily. Let me first define it by way of the following equation
and then offer brief explanations.

M8(t)=+ +I (krFr -kdFd)dt ()
M.((t) = I + f (7)
1850 M5.ref
where
kd is the fraction of forested area or mass (or forest
capacity) that cannot be reforested (is not available for
regrowth) following deforestation activities-for
example, forest areas cleared for urban development.
k is that fraction of the reforested area or mass that
increases the Earth's capacity for terrestrial biota. (This is
sometimes termed "aforestation" as opposed to "reforesta-
tion" that directly renews deforested areas.
Ms.r is a normalizing factor inserted arbitrarily to make
M, dimensionless. I take it to be the initial value of M,.


Lenton used this form but did not include kr and Fr explic-
itly in his formulation. Reforestation can be accounted for
without those factors if Fd is allowed to have negative values.
I prefer to show F and Fd separately for clarity in simulations
later.
Simply stated, the integral in Eq. (7) accounts for perma-
nent effects of the disturbances Fd and Fr. Were that integral
not included, the model equations would lead to the follow-
ing illogical conclusion, among others: If F -= 0, and if Fd
and Fr eventually settle to zero, the ultimate steady state of
carbon in the reservoirs would be identical to the starting ref-
erence state; the effects of the temporary nonzero values of
the disturbances would die away, according to the model. But
obviously the effects of some land use changes must per-
sist-for example, if forest areas are cleared and urbanized
with no offsetting reforestation. With the integral included in
M, with kd # 0 and F = 0, such land use change would per-
manently affect the distribution of carbon, not its total amount.
Other illustrations can be given to justify the form of M8, but
perhaps further explanation, if needed, is better sought in stu-
dent exercises later.
An alternate form of the integral equation above is this dif-
ferential equation:

dM8 krFr kd d
dM8- krFr kdFd withinitialcondition Ms(1850)= 1 (8)
dt Ms5,ref

The numerical value of the coefficient k,5 in Eq. (6) can be
calculated from the reference values shown in Figure 1, given
values for F and y and taking M8 = 1 (its initial state).
With Eq. (8) added to the material balance equations, the
complete mathematical model consists of the following set
of eight ordinary differential equations:


Fall 2002














dM, =_ M Y +k2 MP
dM1=- (kl2 +k3)M1 1-k M +k21 2
dt Mi +F

+k31M3 +k51M5 +k61M6 +Ff(t)+Fd(t)-Fr(t)

dM kl2M -(k23 + k24)M2 k21M2 +k42M4
dt
dM3 MP3 +k
dM3= kM3 +k23M2-k34M3-k31M3 +k43M4
dt

dM4 = k24M2 +k34M3 (k42 +k43 )M4 (9)
dt
dM5 -k M8 M1 (k51+k56)M5-Fd(t)+Fr(t)
dt M, +F
dM6 =k56M5 k61M6
dt
dM7 -Ff(t)
dt
dM8 _-[kdFd(t)-krFr(t)]/
dt /M5,ref



Numerical values for the constants are given in Table 1.
Determining the values of the k's, as described earlier, cali-
brates the model to the data for the reference year 1850. The
value for y is taken from Lenton's model. The value for kd is
somewhat arbitrary and could be adjusted by tuning the model,
but I have taken it to be constant throughout at 0.23. (Lenton
used a value of 0.27.) I have arbitrarily chosen a value of
unity for k,. My method for determining the value for F, the
only tuning parameter, will be described in the next section.
The values for P2 and 33 were determined as described earlier.
Implicit in this development is the assumption that the car-
bon cycle is independent of all other state variables, or that
all others are constant, such as temperature, moisture, and
other nutrient levels. That assumption is frequently invoked,
but it may be an oversimplification if the model results are to
be applied to global climate dynamics, for example. In the
aforementioned work of Lenton[31 the carbon cycle is coupled
to the Earth's energy balance, and in that of Ver et al.1[7 to
other nutrient cycles.

TUNING AND TESTING
WITH HISTORICAL DATA

Extensive historical records are available for testing and
tuning the model. Figure 2 shows data on emissions due to
fossil fuel consumption, F, taken from Marland et al.,'"i and
deforestation, Fd, taken from Houghton and Hackler,1"2 as well
as the total of the two over the period 1850 through 1990. (I
used 1990 as the endpoint because the deforestation data given


by Houghton and Hackler are not tabulated beyond that year.
We can safely assume that reforestation, F, has been negligi-
bly small in the past.) The dramatic increase in fossil fuel emis-
sions since the middle of the twentieth century is evident.
The solid curves in Figure 2 show my empirical fit of the
reported data. In order to get a rather precise representa-
tion I used separate functions over four segments of Ff
and over six segments of Fd. This detailed fitting may seem
to be overkill. I simply wanted to eliminate an inaccurate


1850 1870 1890 1910 1930 1950 1970 1990
year
Figure 2. Historical record of carbon emissions to the at-
mosphere. Symbols represent reported data;'11'12 solid
curves are empirical fits.


Chemical Engineering Education


TABLE 1
Numerical Values and Unitsfor
Model Constants
symbol value units
k1, 0.0931 y-
k,, 0.0311 y-'
k,, 147 y-I
k,, 58(730 1' ) PgC(-2)y-
k3 0.0781 y-'
k 0.0164 yI
k,, 18(140- 3) PgC('- y3)y-
k3 0.714 y-
k42 0.00189 y-
k,, 0.00114 y1
k,, 0.0862 y-
k16 0.0862 y_
k6, 0.0333 y_
P2 9.4
P3 10.2
7 62.0 PgC
r 198 PgC
k, 0.230
k 1.0











representation of the disturbance record as an explana-
tion for any model failure.
With this representation of the historical disturbances and
the model constants in Table 1, the system of ordinary differ-
ential equations in Eq. (10) can be solved readily, by numeri-
cal routines available in a number of software packages, to
obtain a model-generated record of carbon in the reservoirs
from 1850 through 1990. (I used Mathcad for this particular
exercise and extensively throughout the course.) The solid
curve of Figure 3 shows the result for atmospheric CO,; the
data points are reported estimates or measurements from the
Worldwatch Institute database."31 The good agreement be-
tween model results and reported data was assured over a
portion of the curve, at least by my method of determining
the value of F. Its value of 198 PgC, as given in Table 1, was
determined by an iterative search aimed at minimizing the
total squared difference between model results and reported
data over the period 1980-1990. Admittedly, the good agree-
ment over the early years was also virtually assured because
model constants were calculated to give a perfect fit of the

TABLE 2
Model Computed
Quantitiesfor 1990
i M'
1 753
2 744
3 143
4 37071
5 577
6 1489
7 5086
8 0.952
*Units of M are PgC, except
Ms, which has no units.


1850 1875 1900 1925 1950 1975 2000
year


reference data of 1850. Over the other years, the maximum
disagreement, which occurs around 1925, is less than 1.3%.
All such things considered, this test of the model lends legiti-
macy to its use in predicting carbon distributions through some
years ahead.
Table 2 lists the calculated 1990 levels of carbon for all
reservoirs. Notice that changes in the five of the six reser-
voirs have been relatively small over the 140-year period,
according to the model. The terrestrial biota in box 5 increased
only from 577 to 580 PgC owing to the offsetting effects of
decreases by deforestation and increases by atmospheric CO,
fertilization. The atmospheric reservoir increased by 23% by
1990 and is obviously destined to go higher, but changes in
others have amounted to about 2% or less.
A total of 214 petagrams of new carbon was injected into
the cycle from the fossil fuel reservoir and distributed among
the other reservoirs over the period 1850 through 1990. Even-
tually most of that will reside in the deep oceans, box 4, but
by 1990 that reservoir has increased by only 71 petagrams.
Atmospheric carbon increased by 141 petagrams. Some of
that redistribution of carbon, but not any of the increase in
the total, is due to deforestation with a nonzero value of kd.
In the simulations to follow, the ending values of the M's
for 1990, given in Table 2, are used as the initial state.

SIMULATIONS
The simulations described in this section engage the stu-
dents in the use of the model and exhort them to learn about
current trends, issues, and possible future actions-and to
become informed about likely consequences regarding fu-
ture disturbances to the carbon cycle. The principal interest
is in the prediction of atmospheric carbon dioxide levels
through the 21" century. Such predictions, based on models
of varying degrees of complexity, have been reported in a
number of recent studies. '1..5,7.14]*

Disturbance Scenarios
Postulated scenarios for future carbon emissions over a
century of time when human activities, worldwide econo-
mies, and international politics are involved are naturally laden
with uncertainty, the effects of which, in fact, probably over-
shadow the effects of the assumptions and simplifications in
the model itself. Notwithstanding such, predictions through
simulations require inserting the disturbance functions F,, Fd,
and Fr into the model equations.
The most commonly employed scenarios for carbon emis-
sions are those in a set of five that were suggested in a 1992
report to the International Panel on Climate Change, IPCC.[1315

The list given in the References section is only a small sample. The inter-
ested reader will be led to a much larger assortment of models and
related subjects simply by entering the keyword "carbon" on a web
browser.


Fall 2002


Figure 3. Reported and model-calculated records of
atmospheric carbon dioxide since 1850.










Known by the names IS92a, IS92b,.. .IS92e, they are based
on likely or possible trends in population changes, economic
growth, energy supplies, etc. in developed and developing
countries. There is also a Kyoto protocol, which, if en-
acted according to Article 3 of the agreement, would call
for a worldwide decrease in emissions to 95% of the 1990
level by the year 2012.[161
Shown in Figure 4 are slightly modified versions of three
of the IS92 scenarios for total carbon emissions for 1990 on-
ward, including the most pessimistic (IS92e) and the most
optimistic (IS92c) cases, and what's usually referred to as
the "business-as-usual" scenario (IS92a).* The latter is the
most commonly used version, and as its description im-
plies, is based on the assumption that carbon emissions
can be predicted from current trends with no major
changes in policies and practices.
Also shown in Figure 4 is a representation of the scenario
for the Kyoto protocol, based on the assumption that emis-
sions would be held constant after 2012. (Ver, et al., used a
similar representation.t71) The IS92 scenarios break down the
anticipated emissions into fossil fuel use and deforestation.
All of them use the same deforestation pattern, which de-
clines to zero by 2100. A curve showing the modified defor-
estation scenario is also included in Figure 4. The differences
between that curve and the others in the figure are the fossil
fuel components. Reforestation is not included in the sce-
narios as a separate disturbance.

Some Results
I use two different approaches for simulations, each hav-
ing certain advantages over the other. One is a straightfor-
ward numerical solution of the differential equations using
Mathcad-basically similar to the method used to generate
the historical curve in Figure 3. It's the workhorse that I
incorporate into classroom presentations and the major tool
used by the students for assigned work. I constructed the other
using LabVIEW** to give a convenient user interface, a vir-
tual laboratory, for certain classroom demonstrations and stu-
dent experiments. It provides the user with hands-on control
of the disturbances during a simulation, showing effects of
manipulations "live" on virtual strip-chart recorders and digi-
tal displays. (Actually, I've used the LabVIEW simulation
for classroom demonstration at the very beginning of the

I modified the IS92 scenarios for both the fossil fuel and deforestation
components in order to bring the 1990 values of the scenarios in agree-
ment with the data actually reported for that year'"t121 This amounted to
adding 0.1 PgC to all of the IS92fossilfuel quantities and increasing all
of the deforestation values by about 50%. These modifications are more
for refinement and fastidiousness than for any significant effect on cal-
culations.
** LabVIEW developed by the National Instruments Corporation in Aus-
tin, Texas, is graphical programming software developed mainly for data
acquisition and instrument control. It also serves as a powerful tool for
constructing virtual laboratories.


module because it is illustrative and serves to introduce goals
and whet the appetite for learning about model development
and simulations.) Space limitations prohibit a full descrip-
tion of the LabVIEW simulator and its operation here, but
the gist of it is shown in the photo of the user's panel in Fig-
ure 5 and the brief description in the caption. Notice that those
features afford the user an option of sequestering carbon by
reforestation and by capturing a fraction of emissions, F,, in
the deep ocean and geologic reservoirs.
Figure 6 presents an example of the results of Mathcad
simulations using the four scenarios of Figure 4. (For those
simulations, I used linear interpolation between the data points
shown in Figure 4 for the period 1990-2100.) The results in
Figure 6 are based on the parameters listed in Table I ex-
cept that here the values used for P2 and P3 are 11.0 and
12.3, respectively. (As I mentioned above, those values
depend on the total carbon in the surface ocean reservoirs.
I used the 1990 values of M, and M, given in Table 2 as a
basis for the new p values for the period 1990-2100.) F,
is taken to be zero.
Notice that the model predicts atmospheric CO, would in-
crease to 702 ppmv by the year 2100 if the IS92a business-
as-usual scenario were followed. Based on that scenario, pre-
dictions by models used by others1,3,141 range between 697
and 724 ppmv. Over the entire 110-year period, the maxi-
mum difference in atmospheric CO, between any two of the
four models (the three cited above and the present one) is
about 4%, an observation that buttresses confidence in dis-
cussions of quantitative results from the model at hand. No-
tice the wide range of predicted CO, levels in 2100 resulting
from the different scenarios for carbon emissions. The high-
est is nearly twice the lowest; both are probably unrealis-
tic extremes. Business-as-usual would result in nearly
doubling the 1990 CO2 level by the year 2100, according
to the model prediction.


Figure 4. Carbon emissions to the atmosphere; historical
data and possible future scenarios.


Chemical Engineering Education













































Additional Work
Using Mathcad and LabVIEW simulations, students obvi-
ously can be involved in examining all sorts of questions,
model variations, and parameter effects. Here is a partial list
of exercises that I have used, some of which require consult-
ing outside references.
I Extend simulations beyond 2100 to address a number of
questions raised about the ultimate steady state. (Actu-
ally, I ask the students to use the steady-state forms of
the equations to address some of these.) What would that
ultimate state be if emissions were halted immediately?



950
o reported data to 2000
850 ------ for IS92e scenario from 1990 -
E IS92a
S750 -- --- IS92c
702
I650 -- Kyoto
650 /


472
450

350 ---
a a o o a o o o 0
250
1850 1900 1950 2000 2050 2100
year

Figure 6. Atmospheric carbon dioxide levels; reported
historical data and model predictions.


Fall 2002


What would it be if all carbon in the fossil fuel reservoir
were eventually used? How long will it take to approach
a steady state if carbon emissions to the atmosphere are
halted at a certain time?
> Carry out simulations to clarify, if necessary, the roles
and effects of kd, kr, and M,-or to test entirely different
forms of F,,, the rate of photosynthetic uptake of car-
bon.
> What is a realistic mathematical description for the dis-
turbance, F, if reforestation begins with new trees that
require a number of years for maturation?
> Examine the predicted changes in the strengths of the
terrestrial and oceanic sinks (or sources?) of atmospheric
carbon over the 21s" century.
It is sometimes suggested that the most realistic goal that
can be achieved regarding the control of atmospheric CO2
is to "stabilize" it at twice the pre-industrial level by the
year 2100. Try to achieve that goal by manipulating the
emissions (or by fabricating an emissions scenario) in
such a way that atmospheric CO2 lines out at about 1224
PgC (572 ppmv) by the year 2100. (This is an ideal exer-
cise-even an entertaining one-for the LabVIEW simu-
lator. In fact, the data shown on the digital displays and
charts in Figure 5 are the end states of this exercise.)
Notice that the difference between the emissions level
so achieved in 2100 and that dictated by the IS92a sce-
Continued on page 309.

303











,M1 laboratory


DETERMINING THE

FLOW CHARACTERISTICS OF A

POWER LAW LIQUID




JAMES R. HILLIER, DALE TING, LISA L. KOPPLIN, MARGARET KOCH, SANTOSH K. GUPTA
University of Wisconsin Madison, WI 53706


Non-Newtonian liquids present unique problems with
respect to their flow behavior. These problems are
seldom addressed in undergraduate courses in chemi-
cal/mechanical engineering and are possibly covered only
through a single experiment in one of the laboratory courses.
Tjahjadi and Guptal' extended the work of Walawender and
Chen'2' and developed an experimental scheme that illustrates
how the apparent viscosity, ir, of a pseudoplastic liquid (di-
lute aqueous solution of Na-CMC) decreases with increasing
shear rate, y. They also suggested performing additional
experiments after adding some sodium chloride to the
CMC solution, to observe a dramatic decrease in Tr and
relate it to the contraction of the polyelectrolyte molecules
in an ionic medium.
Although the results had considerable educational value,
the equations used were quite complex and cumbersome
to use, with the result that a student obtained little insight
into the method of analysis-this limits the value of their
experiment.
In the present work (developed as part of the "informal"
experiments'31 at the Summer 2000-I laboratory at the Uni-
versity of Wisconsin-Madison), a much simpler experiment
has been developed that uses the easily understood macro-
scopic energy balance (the engineering Bernoulli equation[4])
to obtain experimental results.
A 0.07% (by weight) solution of a sodium salt of carboxy-
methyl cellulose (Na-CMC; weight average molecular weight
= 7 x 105; DS = 0.9; Aldrich Chemicals, Milwaukee, WI) in
deionized water was used for our study. CMC was selected
because of its pseudoplastic nature over a range (1 105 s-')
of shear rates. In addition, CMC is an inexpensive, nontoxic,
biodegradable, water-soluble polymer, commonly used in
mining applications, food thickeners, adhesives, and textiles.


The results obtained could also be compared to existing val-
ues in the literature'' for consistency.

EXPERIMENTAL SET-UP
The experimental set-up is similar to that used for studying
the flow characteristics of Newtonian liquids, as described
by Crosby.'si Flush-mounted glass capillaries (in one case, a
copper tube) of different diameters and lengths are used with
a drain tank,"'1 as shown in Figure 1. Two different kinds of
experimental units were made so as to vary the shear rate
over a reasonable range. The detailed dimensions are pro-
vided in Table 1.

PROCEDURE
The CMC solution to be used in all the experimental runs
was prepared using laboratory-grade carboxymethyl cellu-
lose powder. A solution of 0.07 wt% CMC in deionized wa-

James R. Hillier received his BS degrees from the University of Wiscon-
sin-Madison in Chemical Engineering (2000), Biochemistry (2000), and
Molecular Biology (2000). He is currently the Plant Engineer for Equistar
Chemicals in Fairport Harbor, OH, while working on a master's degree in
polymer engineering and a diploma in disaster management.
Dale Ting received his BS in Chemical Engineering from the University of
Wisconsin-Madison in 2000. He is currently working in process develop-
ment at The Procter and Gamble Co. in Cincinnati, OH.
Lisa Kopplin received a BS in Chemical Engineering from the University
of Wisconsin-Madison (2000). She is currently serving as a Project Engi-
neer for General Mills, Inc., in their West Chicago manufacturing facility.
Margaret R. Koch graduated from the University of Wisconsin-Madison
with a BS in Chemical Engineering in 2000. She is currently working in
Process Development at S.C. Johnson & Son, Racine, WI.
Santosh K. Gupta received his BTech (1968) from I.I.T, Kanpur, and his
PhD (1972) from the University of Pennsylvania-Philadelphia. He has been
on the faculty of /.l.T, Kanpur, since 1973, and has also been a Visiting
Professor at the University of Notre Dame, National University of
Singapore, and the University of Wisconsin-Madison. His research inter-
ests include polymerization engineering and optimization using Al tech-
niques.
Copyright ChE Division of ASEE 2002


Chemical Engineering Education

























-ri-2ro -Zro 2ro I
(a) (b) (c)
Figure 1. Experimental set-ups for Phases 1 and 2.
a and b, 50 ml graduated tube (buret with lower end cut) con-
nected to aligned glass capillaries, flush-mounted to minimize
entrance losses.
c, 5 lit SS tank (diameter 0.158 m) with sight glass to measure
h, used. Glass or Cu capillaries/tubes used. Details provided in
Table 1.


Fall 2002


ter was prepared well in advance to guarantee the ho-
mogeneity of the solutions."' The solution was heated
to 30-500C for about 4 to 8 hours and stirred for over
24 hours. Homogeneity of the solution was con-
firmed by observing its clarity against a very bright
light source."1.6
In each experimental run, a specified amount of poly-
mer solution was added to the holding tank. The ini-
tial values, ho, of the level of solution in the tank (see
Figure 1) are given for the different experimental runs
(Table 1). Flow was started, and data on h was recorded
over time, t, starting at the calibration mark. This al-
lowed flow patterns to establish so that data would not
be altered by flow development. Experimental runs
were stopped prior to complete efflux of the liquid
from the tank, so as to reduce the significance of
end effects.

THEORY

Since CMC solutions behave like pseudoplastics,
their apparent viscosities, ir, decrease with increasing
shear rates, y. The general dependence of 11 on is
quite complex, but over small ranges of the shear rate,
y, the following power law model[4.6.71 is followed quite
well:

T = Ky" (1)
where t is the shear stress. In Eq. (1), the constant, K,
is referred to as the consistency index, and the expo-
nent, n, is the power law index. The apparent viscosity
is then given by


y
r -= K (2)

A macroscopic (mechanical energy balance for this
system. Eq 5201 leads to (see Appendix 1 for details)

(3n+l+n vn
pg(L + h) 2 KL n ) rn+l (3)

In Eq. (3), p is the density of the solution, ro and L are
the (inner) radius and length of the capillary (Figure
1), h is the height of the solution above the capillary
entrance at time, t, g is the acceleration due to grav-
ity, and v is the mass-average velocity inside the
capillary at time t.
The mass-average velocity of the solution inside the
capillary can be obtained using the continuity equa-
tion

TR )2(-dh (
v=J -s T (4)
where R is the inner ra dthe drain tank. A second
where R is the inner radius of the drain tank. A second











The primary advantage of the present study is that analysis of the raw data can be
performed using equations that are easily understood by juniors in chemical
engineering, and standard computer packages can be used...


or third degree polynomial can be fitted to data on h(t). This
gives excellent values of the coefficient-of-determination of
about 0.999 and higher. This polynomial is then used with
Eq. (4) to obtain v. Eqs. (3) and (4) can be combined and
integrated for Newtonian fluids (n = 1) to give the standard'4n
equation for the efflux time for a vertical tank-pipe assembly
under laminar-flow conditions. The students find these deri-
vations easier to comprehend (in fact, they can make the
derivations themselves) than the equations described by
Tjahjadi and Gupta.i '
The validity of the assumption of laminar flow should be
confirmed by calculating the Reynolds number for the
pseudoplastic liquid using[7: Eq 550]

Re= 23-n( n n Dnpv2-n
3,3n+l) K (5)

For pseudoplastic flows present in the laminar region, as in
this study, the sudden contraction/entrance losses are expected
to be negligible."'21 In the more general case where the en-
trance losses are important, the Bagley correction'8'9 can
be used. This could be a possible avenue of further study
for a student.
Equation (3) can be rewritten as

i 2KL (3n+l .
log [pg(L + h)] = log 2 KL (3 n + n log (v) (6)
r_ i- I n ) I

An appropriate log-log plot of Eq. (6) gives n (= slope). K
can then be obtained using n and the intercept, a, using


K = exp fn (7)
S2L 3n+l 1 (

Once values are obtained for both n and K, the shear rate (at
the wall of the tube, r = r ) can be evaluated using[4'7;App 1]


Spg(L + h)ro ]n (8)
S2LK J I

The apparent viscosity, r, can then be evaluated (at this wall
shear rate) using Eq. (2). Equation (8) assumes that the power
law dependence is valid, and so the value of y obtained is
inferred from the data-fitting procedure.
Unfortunately, use of the power law assumption, though
helpful in simplifying the experiment at the undergraduate
level, can give a false idea of the complexity of the method
of analysis routinely used by professional, non-Newtonian


rheologists (who commonly use the Rabinowitsch tech-
nique'6'91). An alternative procedure of data analysis that is
not as difficult and that can be attempted by an undergradu-
ate student, is the use of the Schummer approximation'101 (de-
scribed in Appendix 2). Such an analysis preserves, to some
extent, the physics of mechanical energy balance and closely
follows the steps that would be employed in the professional
theological evaluation of non-Newtonian viscosity. One set
of experimental data generated herein is analyzed later to
compare the results using the power law and the
Schummer approaches.

RESULTS AND DISCUSSION
Details of the several experimental set-ups and runs are
given in Table 1. These experiments were designed and per-
formed in two phases-Runs 1 and 11 through 16 in Table 1
comprising the first phase, followed by Runs 2-10. The re-
sults of the first phase were analyzed and used to help im-
prove the designs for Phase 2. Figure 2 shows data from Phase
1. It demonstrates the decrease of the apparent viscosity with
increasing shear rates. Although the viscosity vs. shear rate
diagram is incomplete, the shear-thinning effect characteris-
tic of pseudoplastic fluids is quite evident. The straight-line
segments on this log-log plot confirm the validity of the
power-law model over small ranges of shear rate. The data
overlap in some regions, which confirms the accuracy of the
results. The value of the power law index varies from about
0.3 to 1.0 (see Table 1). The range of shear rates covered
extends over almost two decades, and the data appears to fall






0 12






o Ra 1oo 00


Figure 2. Apparent viscosity vs. shear rate for a 0.07 wt%
Na-CMC aqueous solution, assuming power law behavior
of the liquid. Phase 1 results shown with Runs indicated.
Results from Ref. 11 also shown for comparison. Tempera-
ture = 23C.


Chemical Engineering Education










on a smooth curve over this range.
The data is also found to be consistent with some earlier
workE"' performed using the same solution, using a stainless
steel tank with a copper tube, similar to that used in Run No.
16. Our data is also consistent with the earlier data"' on a 0.07
wt% Na-CMC solution having a slightly larger weight-aver-
age molecular weight of 7.5 x 105 (the apparent viscosity at
1000 s- was about 7 cP earlier, and is about the same in Figure
2). The replicability of our results was found to be excellent.
It should be mentioned here that an interesting activity would
be to confirm the experimental results obtained here with those
using more sophisticated capillary-flow or Couette viscom-
eters available in research laboratories. Use of the former
would also illustrate the use of the more exact Rabinowitsch
technique of analysis.'"9
The experimental results shown in Figure 2 were then used
to design a few additional experiments (Phase 2) so as to ex-


Figure 3. Results for Phase 2, assuming power law
behavior of the liquid. Run Nos. 2,3, x; 4, ; 5, -;
6, --; 7, o; 8, +; 9, D; 10 0; Temperature = 23 C.


01
Schummer
0 Power Law

'3

0.0





0 001
100 1000 10000
Shear Rate (1/s)

Figure 4. Comparison of r1 vs y obtained assuming power
law behavior of the liquid with that using the Schummer
correction. Set 9 (Table 1) data used.


tend the range of shear rates. The corresponding plot for the
apparent viscosity vs. shear rate for these runs is given in
Figure 3, and the values of K and n in Table 1. It was found
that the data for the two sets of experimental runs, in the
range of shear rates of about 300 to 1000 s superposed
very well (these have not been shown since the data points
get too cluttered). It is interesting to observe that Runs 9
and 10 give data over a very large range of shear rate, and
one could as well use just one or both of these set-ups for a
routine laboratory experiment.
It should be emphasized that Eq. (3) is applicable only
over small ranges of shear rate (and so over a small range of
t, as the meniscus falls). A log-log plot of this equation does
not show straight lines for some cases, and one must exer-
cise some judgment to fit the points. Moreover, the viscos-
ity of CMC (a polyelectrolyte) solutions in deionized water
is very sensitive to the concentration of small amounts of
salts that may be present.'" The addition of small quantities
of NaCI to the solution could help improve the reproduc-
ibility of the results substantially, and would help if one were
to compare the results obtained by different groups of stu-
dents taken over several weeks.
Figure 4 shows one set of data (Run 9, Table 1) that has
been analyzed using both the power law assumption for the
solution as well as the more accurate Schummer technique.
The results superpose quite well, but a shift in the curves is
quite evident, as discussed in Ref. 10.

CONCLUSIONS
A simple experimental set-up was developed to study the
decrease of the apparent viscosity of a 0.07% (by weight)
aqueous solution of Na-CMC with increasing shear rate. Two
experimental units were found that covered a reasonably
large range of shear rates of 500 to 6000 s-. The primary
advantage of the present study is that analysis of the raw
data can be performed using equations that are easily un-
derstood by juniors in chemical engineering, and standard
computer packages (e.g., Excel, etc.) can be used for this
purpose.
Additional experimental data can easily be taken after
adding sodium choride to the CMC solution, to study the
effect of molecular contraction of the polyelectrolyte.I'1 The
results obtained using the power law assumption are com-
pared to more elaborate methods of analysis, and a few ad-
ditional experiments have been suggested for the more
enterprising student.



APPENDIX 1

Details of the Derivation of Eqs. (3) and (8)

The macroscopic mechanical energy balance[4] is applied


Fall 2002










between points 1 and 2 (Figure la) with the following as-
sumptions:
The column is vertical
The kinetic energies of the liquid at 1 and 2 are
negligible
Entrance or other losses are negligible, and the only
losses are due to viscous effects in the capillary
This leads to


g(L + h)= c -
SP capillary to


(Al.1)


where to is the shear stress at the capillary wall, r = ro, and
(AP)capiary is the pressure drop across the length, L, of the
capillary.
A force balance over a control volume of radius, r, and hav-
ing a differential length, dz, gives[4'
-P =21 (A1.2)
dz r


-dP( AP) 2T
dz L Jcapillary ro


(AI.3)


Equations (A1.2) and (Al.3) give


-^o


Kvn (3n+ln _ro (AP
o0 = ro n ) 2 I L )capillary


(A1.9)


Equation (Al.9) can be combined with Eq. (A1.1) to give
Eq. (3).
Equation (Al.6) can be simplified to give


( tor p=/n( g(L +h) rl
which leads to Eq. (8) (with r = r).
which leads to Eq. (8) (with r = rQ).


(Al.10)


APPENDIX 2

Details of the Schummer Approximationt[1


The apparent shear rate Yap, and the apparent viscosity,
lap, are defined'"' by


4v 4Q
Yap 3-

o_ rgrpg(L+h)
pYap 8vL


(a)


(b) (A2.1)


Schummer states that the "true" shear rate, j, corresponding
to 'ap (at which the viscosity is equal to Tlap) is given by


Using the following variation of Eq. (1)

:= K du (A1.5)

where u is the axial velocity at location, r, in Eq. (A1.4), we
obtain

-du It0o /n
(r) = I rl/n (A1.6)
S d r = roK

This can be integrated fromr=r0(T=t0)tor=r(t=t)to


/r /1I/n I/n+l I/n+1
t0 r0 -
u(r) = K ro 1+l
Kr01 i+ 1


(A1.7)


Equation (A1.7) can easily be integrated over 0 < r < r0 to
give the mass average velocity, v, as


v= ro I+
K 13+


(Al.8)


which can be rearranged (and Eq. A1.1 used) to give


03.32v
/=0.83ap -


(A2.2)


The experimental data can be used to give the average veloc-
ity, v, in the capillary, as a function of time. This can be used
with Eqs. (A2.lb) and (A2.2) to evaluate rlap and the "true"
(or the corresponding) shear rate, ,, to give a more accurate
plot of rl vs y.

REFERENCES
1. Tjahjadi, M., and S.K. Gupta, Chem. Eng. Ed., 20, 84 (1986)
2. Walawender, W.P., and T.Y. Chen, Chem. Eng. Ed., 9, 10 (1975)
3. Sather, G.A., and J. Coca, Chem. Eng. Ed., 22, 140 (1988)
4. Bird, R.B., W.E. Stewart, and E.N. Lightfoot, Transport Phenomena,
2nd ed., John Wiley and Sons, New York, NY (2001)
5. Crosby, E.J., Experiments in Transport Phenomena, Department of
Chemical Engineering, University of Wisconsin, Madison, WI (1961)
6. Kumar, A., and S.K. Gupta, Fundamentals of Polymer Science and
Engineering, Tata McGraw Hill, New Delhi, India (1978)
7. McCabe, W.L., J.C. Smith, and P. Harriot, Unit Operations of Chemi-
cal Engineering, 5th ed., McGraw Hill, New York, NY (1993)
8. Bagley, E.B., J. Appl. Phys., 28, 624 (1957)
9. McKelvey, J.M., Polymer Processing, John Wiley and Sons, New York,
NY (1962)
10. Dealy, J.M., and K.F. Wisbrun, Melt Rheology and Its Role in Plastics
Processing, van Nostrand Reinhold, New York, NY (1990)
11. Zhang, J., J. Jenkins, B. Linden, and A. Kristopeit, UW-Madison Trans-
port Lab Memo, Madison, WI (2000) J


Chemical Engineering Education


(A1.4)











The Earth's Carbon Cycle
Continued from page 303.

nario (i.e., the difference between the end points of curves
of the lower strip chart of Figure 5) is the amount of
carbon that would have to be replaced by an equivalent
energy source. Follow-up questions for consideration
and/or further simulations: What alternate sources of
energy might fill the gap? Could it be filled by seques-
tering carbon in the terrestrial biota (reforestation ac-
tivities)? ...in geologic storage? ...in the deep ocean
waters? Would those possibilities lead to a permanent
stabilization? What is the trend of the fabricated emis-
sions curve in 2100? What is its ultimate fate if atmo-
spheric CO2 is to stay level at 572 ppmv?
0 Start from the beginning with an alternative model that
presumably improves on this one (e.g., by adding layers
to the ocean or atmosphere, a spatial variation to the ter-
restrial reservoirs). Calibrate, tune, and test the model
against the results shown here.

CONCLUDING COMMENTS
Many of the Earth's biogeochemical processes can be stud-
ied and modeled within the context of the usual chemical
engineering curricular material. The carbon cycle, the focus
of this article, is a particularly apt example because, though
basically complex, it can be usefully described by a simple
mathematical model. Additionally, it is being disturbed and
altered by human activities, possibly to the extent of causing
global warming and other climate changes, and is therefore a
subject of current interest and concern.
Aside from students learning about this particular subject,
important and timely as it is, in my view another worthwhile
outcome is that they gain confidence in their ability to ana-
lyze physical situations that may not be on their usual bill-of-
fare and to apply their chemical engineering tools to the for-
mation of a mathematical description. Never mind that the
description is soaked with simplifications and assumptions-
such as perfectly mixed boxes for oceans, single-rate expres-
sions for all of the Earth's photosynthesis, and so on. A great
deal is learned by pondering, investigating, and debating the
bases for such simplifications and assumptions.
This article describes my coverage of the subject in a course
devoted to topics on ecology and the environment. The cov-
erage is scalable-downward to a brief treatment and selected
homework assignments integrated into some of the usual core
course offerings, or upward to the development of more so-
phisticated models and the application of more advanced de-
scriptions of the rate processes, mathematical analysis, and
computational methods. Whatever the scope, students ben-
efit from the broadening experience of applying their chemi-
cal engineering tools in a quantitative way to an important
subject outside the mainstream.


Readers who would like to have an electronic copy of this
module, which consists of a slide show with links to spread-
sheets, simulations, etc., including the LabVIEW simulator,
should contact me at .


ACKNOWLEDGMENT
Development of the material for this article was part of a
project supported by the CRCD program (Grant EEC97-
00537-CRCD) of the National Science Foundation.


REFERENCES

1. Cox, P.M., R.A. Betts, C.D. Jones, S.A. Spall, and I.J. Totterdell, "Ac-
celeration of Global Warming Due to Carbon-Cycle Feedbacks in a
Coupled Climate Model," Nature, 408, 184 (2000)
2. Chameides, W.L., and E.M. Perdue, Biogeochemical Cycles: A Com-
puter-Interactive Study of Earth System Science and Global Change,
Oxford University Press (1997)
3. Lenton, T.M., "Land and Ocean Carbon Cycle Feedback Effects on
Global Warming in a Simple Earth System Model," Tellus, 52B, 1159
(2000)
4. Rodhe, H., and A. Bjorkstrom, "Some Consequences of Non-Propor-
tionality Between Fluxes and Reservoir Contents in Natural Systems,"
Tellus, 31, 269 (1979)
5. Schnoor, J.L., Environmental Modeling: Fate and Transport of Pol-
lutants in Water, Air and Soil, John Wiley & Sons (1996)
6. Siegenthaler, U., and F. Joos, "Use of a Simple Model for Studying
Oceanic Tracer Distributions and the Global Carbon Cycle," Tellus,
44B, 186(1992)
7. Ver, L.M.B., FT. Mackenzie, and A. Lerman, "Biogeochemical Re-
sponses of the Carbon Cycle to Natural and Human Perturbations:
Past, Present, and Future," Ami. J. of Sci., 299, 762 (1999)
8. Herzog, H., B. Eliasson, and O. Kaarstad. "Capturing Greenhouse
Gases," Sci. Am., February 2000, 72 (2000)
9. Kane, R.L., and D.E. Klein, "Carbon Sequestration: An Option for
Mitigating Global Climate Change," Chem. Eng. Prog., June 2001,44
(2001)
10. Butcher, S.S., R.J. Charlson. G.H. Orians, and G.V. Wolfe (eds), Glo-
bal Biogeochemical Cycles, Academic Press (1992)
11. Marland, G., T.A. Boden, R.J. Andres, A.L. Brenkert, and C.A.
Johnston, Trends: A Compendium of Data on Global Change, Carbon
Dioxide Information Analysis Center, Oak Ridge National Labora-
tory, Oak Ridge. TN (1998)
12. Houghton, R.A., and J.L. Hackler, Trends: A Compendium of Data on
Global Change, Carbon Dioxide Information Analysis Center, Oak
Ridge National Laboratory, Oak Ridge, TN (1998)
13. Worldwatch CD-ROM, Worldwatch Institute, Washington, DC (2001)
(This CD-ROM and downloadable datasets are available at www.worldwatch.org/pubs/>
14. Houghton, J.T., L.G. Meira Filho, B.A. Callander, N. Harris, A.
Kattenberg, and K. Maskell (eds), Climate Change 1995: The Science
of Climate Change, Contribution of Working Group I to the Second
Assessment Report of the Intergovernmental Panel on Climate Change,
See Figure 5 of Technical Summary (Published for the Intergovern-
mental Panel on Climate Change, IPCC), Cambridge University Press
(1995) (This and other reports of the IPCC are available online at /www.ipcc.ch/pub/reports.htm>
15. Leggett, J., W.J. Pepper, and R.J. Swart, "Emissions Scenarios of the
IPCC: An Update," Climate Change 1992: The Supplementary Re-
port to the IPCC Scientific Assessment (J.T. Houghton, B.A. Callander,
and S.K. Varney, eds), p. 69-95, Cambridge University Press (1992)
16. United Nations Framework Convention on Climate Change, COP 3
Report, Document FCCC/CP/1997/7/Add. 1. (The full text of this re-
port is available at O


Fall 2002











[S assessment


PORTFOLIO

ASSESSMENT

In Introductory ChE Courses



SURITA R. BHATIA
University of Massachusetts Amherst, MA 01003-9303


As defined by Feuer and Fulton,111 performance-based
assessment refers to assessment techniques that re-
quire students to create a final product, such as a
written report, oral presentation, or portfolio of their work,
as opposed to the more conventional assessment techniques
of written quizzes or exams. Performance assessment can also
be defined as an assessment method that evaluates a student's
ability to perform a specific procedure or task;121 in this con-
text, the assessment must contain a performance task, a stu-
dent-response format, and a scoring system. Examples
would include judging a student's ability to manipulate
laboratory equipment or respond to an open-ended prob-
lem.121 Slater suggests designing a performance task that
is "somewhat undefined, complex, and has multiple entry
and exit points;" that is, a task that has more than one
correct solution path.[21
The advantages of performance-based assessment tech-
niques have been documented by several studies in the edu-
cational literature." '6 Many studies emphasize the "real-
world" nature of performance assessment;l"3 student work is
evaluated in a manner that is much closer to what will be
encountered in the work environment. Perhaps most impor-
tantly, research has shown that alternative assessment helps
in the evaluation of students with various learning styles and
educational backgrounds, promoting excellence among a
more diverse student population.14'
These "alternative assessment" techniques131 are not new
to engineering education. Traditional performance-based as-
sessment is often used (although not often acknowledged as
such) in junior- and senior-level courses in the form of labo-
ratory experiments, written lab reports, design projects, and
oral presentations; and the ABET EC 2000 guidelines have
brought increased attention to outcomes-based assessment.17'81
But alternative assessment is not widely used in the fresh-


man- and sophomore-level courses for a variety of reasons.
Educators may worry that freshmen and sophomores do not
have the depth and breadth of knowledge to complete a de-
sign project or written paper, or that there is simply not enough
class time to have students give oral presentations...after
all, there is barely enough class time to teach these stu-
dents mass and energy balances and thermodynamics.
There is another means of implementing performance-
based assessment in these courses, however-one that has
remained largely under-used in engineering education:
student portfolios.

WHAT IS A PORTFOLIO?
Portfolios are collections of student work, typically selected
according to guidelines set forth by the instructor.131 These
guidelines may have a one-to-one correspondence with the
course objectives, or an instructor may choose to highlight
particular course objectives. An example of required items
from the freshman chemical engineering course at UMass,
which I will discuss in more detail below, is given in Table 1.
Along with each item, students are asked to submit a state-
ment of why the item was chosen. This element of self-analy-
sis or self-reflection is crucial if portfolios are to be more
than just "student folders."['9 For comparison, the course ob-

Surita R. Bhatla is an assistant professor in the
ChE Department at the University of Massachu-
setts. She received her BChE from the Univer-
sity of Delaware, her PhD from Princeton Uni-
versity, and held a postdoctoral position at the
CNRS/Rhodia Complex Fluids Laboratory. Her
research interests are associative polymers, rhe-
ology, shear-induced structure, and structured
cell encapsulation materials. She has taught
mass balances and heat transfer at the under-
graduate level and coteaches a graduate course
on colloidal dispersions.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education













TABLE 1
Required Portfolio Entries for Freshman Course in
Chemical Engineering Fundamentals


1. A problem with a "nonroutine" solution, where students had to
employ new strategies or methods of solution

2. A homework problem that involved teamwork or group work

3. A problem that gave the student a good sense of real-world
applications

4. A problem involving data analysis or data fitting

5. A problem involving the use of MathCAD

6. A problem involving the use of Microsoft Excel

7. A self-analysis of the student's strengths and weaknesses with
regards to concepts learned in class

8. Reflections on chemical engineering, this class, and any thoughts
on career choices




TABLE 2
Course Objectives for Freshman Course in
Chemical Engineering Fundamentals

At the end of this course, students should

E[ Understand concepts of engineering calculations, including
significant figures and dimensional analysis, and be able to
perform unit conversions

E[ Understand process flowsheets, know how to draw and label a
flowsheet, and be able to clearly define subsystems within
processes to set up conservation equations

[ Understand conservation of mass and be able to solve material
balances on steady processes

E[ Understand thermodynamic quantities such as internal energy,
enthalpy, and heat capacity

[ Understand the concept behind distillation and be able to perform
simple vapor-liquid equilibria calculations using Raoult's Law
and Henry's Law

1 Understand conservation of energy and be able to set up simple
energy balances

[ Be able to use software packages (for instance, Microsoft Excel
or MathCAD) to set up and solve engineering calculations and
aid in data analysis

[I Be able to use the principles and tools learned in this course to
solve problems not covered in detail as part of the course and to
continue learning related material as needed in the future.


Fall 2002


Many studies emphasize the
"real-world" nature of performance
assessment; student work is evaluated in a
manner that is much closer to what will be
encountered in the work environment.


jectives are listed in Table 2.
A widely cited benefit of portfolio assessment is an im-
provement in communication skills and creative-thinking
skills, particularly in mathematics and science, two disciplines
where students often have difficulty communicating their
results.1"49' These assessment techniques also promote stu-
dent self-assessment and reflection. This allows students
to become better at selecting and presenting their best
work, which helps them gain confidence in their abili-
ties.141
Studies in college physics classest61 have shown that port-
folios may serve to help students organize work and internal-
ize concepts; however, preliminary studies of portfolio use
in undergraduate chemistry coursesi"01 indicate that there is a
disconnect between student performance on exams and in
portfolio entries with regard to specific course objectives.
Educators in chemical engineering may feel uncomfortable
with the concept of "student self-reflection"; after all, we are
here to teach students, not to ask them how they "feel" about
engineering, right? We prefer hard numbers and are more
accustomed to quantitative assessment methods. But the util-
ity of portfolios has been demonstrated in several science,
mathematics, and engineering courses.14"60 161 Many states
require use of portfolios in all subject areas for grades
four through twelve,14-5 and portfolios have been success-
fully used in undergraduate physics, chemistry, and geol-
ogy courses.1691
The chemical engineering program at the Colorado School
of Mines has relied heavily on portfolio assessment for over
a decade, and Olds and Miller"I4' give an excellent descrip-
tion of the use of portfolios in the ChE curriculum. Both
Alvero CollegeE"5 and Rose-Hulman Institute of Technol-
ogyt16' have implemented an electronic portfolio system for
all students. Preliminary results from the Rose-Hulman project
indicate that students find the electronic portfolio system easy
to use, and that use of a web-based system reduced some of
the disadvantages of conventional portfolios, including stor-
age, user access, and availability."6'
It is important to keep in mind the difficulties and limita-
tions associated with portfolio assessment. Portfolios are not
appropriate for assessing factual knowledge or recall abili-
ties; thus, they should be used in conjunction with conven-
tional, quantitative assessment techniques.'91 Portfolios can
be difficult to manage and time-consuming to grade, which

311












Perhaps most importantly, research has shown that alternative assessment
helps in the evaluation of students with various learning styles and
educational backgrounds, promoting excellence
among a more diverse student population.


makes them easiest to implement in courses with small to
medium enrollments. Slatert'9 and Winkl101 have reported tech-
niques to extend the use of portfolios to large lecture courses,
however.
Although there has been an emphasis on the use of portfo-
lios in upper-level "capstone" courses, such as senior design
and the unit operations laboratory,1'4 I focus on their use in
introductory chemical engineering courses. I believe portfo-
lio assessment has unique benefits to beginning engineering
students, as described further in the following paragraphs.

GRADING PORTFOLIOS
Implementing innovative assessment is all well and good,
but how are we going to evaluate and grade student portfo-
lios? Since the portfolio entries have presumably been graded
as part of a homework assignment or exam earlier in the se-
mester, it does not seem fair to me to place the students in
"double jeopardy" by basing the portfolio grade on whether
or not the problems are correct. I chose to grade portfolios by
giving equal weight to three criteria:

Completeness and organization
Quality and style of writing
Level of thought, analysis, and reflection in each entry

The first two criteria are easy to evaluate. The first refers to
whether students have all the required items, including a table
of contents and page numbers. The second criterion refers to
writing style and grammar, again fairly straightforward to
evaluate.
The third criterion is a little more subjective and requires
some planning on the part of the instructor. I evaluated the
level of thought and analysis by judging the extent to which
each entry addressed two to three "thought questions," which
are listed in Table 3. Students were given these questions at
the start of the semester to help guide them through the self-
analysis process.
Slater'91 recommends developing a "scoring rubric,"
whereby the portfolio grade is based on the extent to which
students demonstrate mastery of the required number of ob-
jectives. For example, you may require students to have at
least eight entries, each of which is related to a specific course
objective. A simple scoring rubric could then be an "A" grade
for demonstrating adequate mastery in seven or more objec-


tives (as evidenced by the portfolio entries), a "B" grade in
five or more objectives, and so on. More detailed examples,
developed for a unit operations course, are given by Olds
and Miller;"4' see also the examples given by Slater.[91

EXAMPLE
Portfolios in the Introductory ChE Course
In the spring of the freshman year, students at UMass take
a course titled Chemical Engineering Fundamentals. The
course content covers units and dimensions, mass balances,
simple reactive systems (i.e., CSTRs and PFRs), and forms
of energy. The typical enrollment is 40-50 students, most of
whom are engineering majors with an interest in chemical
engineering. After completing the freshman year require-
ments, students can apply for admission into the chemi-
cal engineering major. Thus, many students in the ChE
Fundamentals course are still unsure of their choice of
major.
I chose to implement portfolio assessment in this course as
an optional assignment. The portfolio assignment could be
used to replace a low grade on either of two midterm exams
or a low homework grade, but not the final exam. Many in-
structors give students the option of "dropping" one low grade,
so I did not feel that the use of portfolios would cause grade


TABLE 3
Questions for Student Self-Analysis
in Portfolio Entries

E[ What concept or topic was involved with this problem? What
skills did you use in solving it?
E How did this problem help you learn something new?
E[ Did you learn anything about yourself, your thought process, or
your strengths and weaknesses as a result of this activity?
E What strategies did you use? What were you thinking as you
worked the problem?
E[ Would you do anything differently if you had more time?
E[ Can you describe any connections between the activity and other
concepts, subject areas, or real-life situations?
El Does the problem represent a special achievement for you, a
sense of accomplishment at having learned a particular concept,
or a sense of improvement over time?


Chemical Engineering Education




































































Figure 1. Results from student surveys after complet-
ing course. Responses to questions are as follows:
1 Strongly agree; 2 Agree; 3 No strong opin-
ion; 4 Disagree; 5 Strongly disagree.
Columns and error bars represent the average and stan-
dard deviation for each question, from a sample size
of 13 surveys for questions 1-3 and 28 surveys for ques-
tions 4-7. Question numbers correspond to those given
in Table 4.


TABLE 4
Student Evaluation Survey

0. Did you complete the optional portfolio assignment for this
class?
1. (If"Yes" to the first question) I enjoyed completing the portfolio
assignment.
2. (If "Yes" to the first question) I felt that I learned more about
myself and my strengths and weaknesses in chemical engineer-
ing and problem solving as a result of completing the portfolio.
3. (If "Yes" to the first question) My written communication skills
have improved as a result of completing the portfolio assign-
ment.
4. I feel that the use of both qualitative (e.g., written reports, oral
reports, and portfolios) and quantitative (e.g., exams and
homework) methods of assessment were appropriate for this
class.
5. I dislike qualitative methods of assessment (e.g., written reports,
oral reports, and portfolios) because I feel that they are
subjective.
6. I feel that quantitative methods of assessment (e.g., exams and
homework) are most appropriate for engineering and science
classes.
7. I would like to see qualitative methods of assessment (e.g.,
written reports, oral reports, and portfolios) incorporated into
other science and engineering classes.


inflation.
On the first day of class, I gave students a handout de-
scribing the portfolio assignment, including the informa-
tion in Tables 1 through 3, and a summary of the grading
protocol for portfolios. I also held a short class discus-
sion on what portfolios are and why they were being used
for this course.
Students were required to have at least eight portfolio en-
tries, which are listed in Table 1. Six of these entries were
related to course objectives or outcomes, with a focus on
objectives that are difficult to assess using conventional exam
techniques (i.e., the use of Microsoft Excel, data-fitting tech-
niques, etc.). These entries were expected to be copies of prob-
lems, either from the homework or exams. Students were re-
quired to attach a copy of their solution to the problem and a
short (one paragraph to one page) explanation of why the
problem was chosen.
In addition, two one-page essays (the last two items in Table
1) were required. I also handed out a list of questions to keep
in mind as they wrote their portfolio entries (listed in Table
3). Finally, students were asked to organize their entries, num-
ber each page, and include a table of contents in the portfo-
lio. Periodically throughout the semester, I reminded students
to work on the portfolio assignment and to come see me if
they had questions on the assignment.



RESULTS
Student Feedback and Assessment Survey
The class enrollment was 41 students. Forty-one percent
of the students (17 students) completed the portfolio assign-
ment. Grades on the portfolios were roughly in the low "C"
to high "A" range. For most students, the portfolio grade was
used to replace a low homework grade, but the difference in
the final grade for the course with and without the portfolio
was never more than a letter grade.
I was somewhat distressed to find that several students
counted on the portfolio to bring up their low homework grade
and thus did not spend as much time on the homework as-
signments throughout the semester as I would have liked. I
have since altered the portfolio guidelines to allow students
to replace a low midterm exam grade, but not the final exam
or a low homework grade.
I found that grading of the portfolios was time consuming,
but I did not feel that it took longer than grading exams. The
time commitment is similar to that required for evaluating
written reports, and I made comments on all portfolios re-
garding grammar and writing style.
Students were asked to complete a survey upon comple-
tion of the course, and the survey questions and student re-
sponses are given in Table 4 and Figure 1, respectively.


5
5 *--

4

3

P 2



1


1


2 3 4 5 6 7
Question number


Fall 2002












Portfolios can be particularly useful for beginning chemical engineering students,
who often do not have class projects that require them to synthesize concepts
and present their results in a written format.


These are preliminary results; obviously, data need to be
taken on a larger sample size before conclusions can be
drawn. The results also may be biased due to wording of
the survey questions. This needs to be addressed before
definitive conclusions can be reached, and I am currently
updating and redesigning the survey questions for future
classes.
On the whole, the response from students was quite posi-
tive. The strongest and most uniform response was to Ques-
tions 2 and 4; 86% of students who completed a portfolio
strongly agreed or agreed that the portfolio helped them to
learn more about themselves and their strengths and weak-
nesses in chemical engineering and problem solving, and
89% of all students felt that the use of both quantitative
and qualitative assessment methods were appropriate in
the course. It remains unclear whether or not the portfo-
lio assignment helped students improve their written com-
munication skills.
Several of the written comments that accompanied port-
folio entries were quite encouraging, and I have listed some
of the more memorable comments in Table 5. There were
also comments both positive and negative, that were useful
to me as an educator. Students were very honest about com-
ponents of the class that they liked and disliked. Most of
these comments were made in response to Item 8, Table 1,
reflections on chemical engineering and the class. Examples
of these comments are also given in Table 5.

CONCLUSIONS AND RECOMMENDATIONS
Portfolios can be particularly useful for beginning chemi-
cal engineering students, who often do not have class projects
that require them to synthesize concepts and present their
results in a written format. Interestingly, students did not feel
as though the assignment improved their written communi-
cation skills, but the portfolio assignment did seem to give
these incoming students an opportunity to reflect on their
abilities and their choice of major. Portfolios can also be used
to assess course objectives that are difficult to evaluate using
traditional techniques.
Based on my experience, I have some guidelines and rec-
ommendations for implementation of portfolios:


Be prepared to read up on assessment tech-
niques. Several of the references listed contain


excellent examples of student entries and grading
schemes.'45,9 I" I found the National Institute of
Science Education Field-Tested Learning
Assessment Guide website particularly useful.
(Found at flag/default.asp>.)


- Be clear about expectations for portfolios at the
start of the semester. You may want to give
students sample entries.


N Remind students that they should be saving
homework sets and collecting problems for
entries in their portfolio. This is extremely
important for freshman-level students who are
still learning how to organize their coursework.


P If you allow students to use a portfolio grade as a
replacement, make sure their expectations are
realistic. One fabulous portfolio assignment will
not pull a final "D" grade up to an "A"-as I
mentioned above, the overall effect on the final
grades in the course was never more than a letter
grade.
It is worth noting that implementing portfolios as
a "replacement" for a poor exam could allow a
student to bring a failing grade up to a "D."
Instructors need to decide for themselves
whether this is permissible and to develop their
own guidelines accordingly.
For example, I specified that if students received
a zero grade on an exam or homework due to
academic dishonesty, this grade could not be
"replaced" under any circumstances. One could
imagine extending this rule to any failing grade
to prevent the above scenario. Finally, I found
that it was problematic to allow students to
replace a low homework average with the
portfolio grade.


> Create a grading scheme that places emphasis on
what you think is most important, whether this is
good writing, clear organization, self-reflection,


Chemical Engineering Education















TABLE 5
Sample Comments from Student Portfolios

New Strategies of Problem Solving (Item 1)
and Self-Analysis (Item 7)

"I now have more confidence knowing that if I can't solve a problem
using the accepted method of solution, I will be able to come up with
a new method, perhaps something nonroutine, in order to solve the
problem."

"This problem showed me that I should have more confidence in my
ability to find a solution when it doesn't simply present itself after a
series of steps."

"I could apply things I had learned in a completely different context
to other situations. This is actually quite comforting, as I've always
wondered if I'll be able to use the things I learn now later on in life
when I might actually need them."
"I've had trouble [with] time management, as I have usually been
able to understand the problems but have not left myself enough time
to gather it all in a presentable format."

"My weakness is that every time I hit a wall, I tend not to do anything
about it. I can only blame myself for not attempting, [but] I already
made my choice in staying in this major and it is all up to me in
keeping that choice."


Reflections on Chemical Engineering
and The Fundamentals Course (Item 8)

"All in all I enjoyed the class, I enjoy being a chemical engineering
student, and I look forward to the day when I am employed as a
fabulous chemical engineer."

"I dislike computers and I dreaded using them for this class. I
probably would have stuck with this major if it were not for
MathCAD and Excel. I do not think being taught [MathCAD] for one
class period is enough class time."

"Since the class is almost over, I feel a real sense of accomplishment.
I know that it is only a freshman level class, but I put a great deal of
effort and time into the class...It makes me proud to say that I'm a
chemical engineering major when people ask me."

"I feel like I've gotten a much better idea about what chemical
engineers do through the various assignments and from the oral
presentations of my peers."
"I feel that we did not [spend] much time on using the computer."

"Before taking this class I wasn't positive that chemical engineering
was the right major for me. I felt that perhaps I would not be able to
handle the workload or grasp all of the material that I needed to
know. However, I now feel that I am actually capable of becoming an
engineer."

"I love going to my chemical engineering classes, they are the only
ones that I don't purposely skip."

"As a result of this class I am much more confident about my choice
of major and the preparation it will give me to succeed in the career I
want to pursue."


or assessment of a specific course objective.
Make sure your grading scheme is clear to the
students at the start of the semester.


ACKNOWLEDGMENTS

I would like to acknowledge my Chemical Engineering
Fundamentals students for participating in this work. Pro-
fessor Donald Wink (Chemistry, University of Illinois at
Chicago) provided me with a copy of his recent ACS pre-
sentation on portfolio assessment and suggested several of
the works cited in this article, which was greatly appreci-
ated. The manuscript reviewers, particularly Reviewer #3,
made several useful and constructive comments. Mrs.
Kanak Bhatia (Ed.D. candidate, University of Delaware)
also suggested several helpful references and made com-
ments on the manuscript.


REFERENCES
1. Feuer, M.J., and K. Fulton, "The Many Faces of Performance As-
sessment," Phi Delta Kappan, 74, 473 (1993)
2. Slater, T.F., "Performance Assessment," in Field-Tested Learning
Assessment Guide, National Institute of Science Education (2000)

(accessed 6/6/02)
3. Herman, J.L., P.R. Ashbach, and L. Winters, A Practical Guide to
Alternative Assessment, Association for Supervision and Curricu-
lum Development, Alexandria, VA (1992)
4. Lambin, D.V., and V.L. Walker, "Planning for Classroom Portfolio
Assessment," Arithmetic Teacher, 41, 318 (1994)
5. Abruscato, J., "Early Results and Tentative Implications from the
Vermont Portfolio Project," Phi Delta Kappan, 74, 474 (1993)
6. Slater, T.F., "The Effectiveness of Portfolio Assessments in Science,"
J. Coll. Sci. Teach., 26, 315 (1997)
7. Shaeiwitz, J.A., "Outcomes Assessment: Its Time Has Come," Chem.
Eng. Ed., 33(2), 102 (1999)
8. DiBiasio, D.A., "Outcomes Assessment: An Unstable Process?"
Chem. Eng. Ed., 33(2), 116 (1999)
9. Slater, T.F, "Portfolios," in Field-Tested Learning Assessment Guide,
National Institute of Science Education (2000) www.wcer.wisc.edu/nise/cll/flag/cat/perfass/perfass.htm> (ac-
cessed 2/15/02)
10. Wink, D.J., "Portfolio Assessment in Large Lecture Class," Ab-
stracts of Papers of the ACS, 220, 49 (2000)
11. Johnson, J.M., "Portfolio Assessment in Mathematics: Lessons from
the Field," The Computing Teacher, 21, 22 (1994)
12. Adamchik, Jr., C.E, "The Design andAssessment of Chemistry Port-
folios," J. Chem. Ed., 73, 528 (1996)
13. Phelps, A.J., M.M. LaPorte, and A. Mahood, "Portfolio Assessment
in High School Chemistry: One Teacher's Guidelines," J. Chem.
Ed., 74, 528 (1997)
14. Olds, B.M., and R.L. Miller, "Using Portfolios to Assess a ChE
Program," Chem. Eng. Ed., 33(2), 110 (1999)
15. "Alverno's Diagnostic Digital Portfolio," academics/ddp.html> (accessed 6/6/02)
16. Rogers, G.M., and J. Williams, "Building a Better Portfolio," PRISM,
8, (1999) 0


Fall 2002










, curriculum


ASPECTS OF

ENGINEERING PRACTICE

Examining Value and Behaviors in Organizations



RAMON L. ESPINO
University of Virginia Charlottesville, VA 22904-4741


Since 1995, the School of Engineering and Applied Sci-
ences at the University of Virginia has offered an elec-
tive course that examines human values and practices
in engineering organizations. The course is available to all
fourth-year engineering students and is taken by 40 to 50 stu-
dents each year. It is taught by the Brenton S. Halsey Visiting
Professor of Chemical Engineering, who is selected annu-
ally from individuals with high-level experience in industry.
Support for the Chair comes from a generous endowment by
The James River Corporation in honor of its founding CEO,
Brenton Halsey. Previous Halsey Professors and their affilia-
tions are given in Table 1.
The details of the course content and execution are left to
the discretion of the Halsey Professor, but its core objective
is to provide engineering students with significant insight into
the professional and nontechnical aspects of engineering prac-
tice. The intention is to better prepare the University of Vir-
ginia engineering graduates to succeed in the business and
technical world that they will be entering after graduation.
This paper describes the course materials, assignments, and
assessments for the spring semester of 2001, which is repre-
sentative of recent offerings.

DEVELOPING THE COURSE
The teaching experiences of previous Halsey Professors
contributed significantly to the current course content. Al-
though the objectives have remained the same, there is now
more emphasis on the students reading and analyzing infor-
mation prior to class. This information is generally in the form
of Harvard Business School (HBS) Cases and Notes. The
result of this approach is more in-depth discussion in class.
I built the course syllabus around the HBS Cases and Notes.
Harvard Business School Publishingm' offers an Index of
Cases and Notes available for purchase. I suggest one HBS


The objective of the course was to
increase student awareness of the non-
technical competencies they should pos-
sess in order to succeed in
the work world.

Case and two HBS Notes per week, requiring about nine hours
of homework (reading and writing a summary) per week.
Lectures to reinforce and elaborate upon the major themes of
the course are strongly recommended. We have found that
many of these should be given by outside speakers from busi-
ness and government in order to emphasize the broad appli-
cability of the concepts being discussed. Finally, additional
reading material can be used to round out the course.

COURSE STRATEGY
AND TEACHING METHOD
I developed the syllabus to follow the chronological order
of the professional and business career of an engineering
graduate. Selecting the first employer is the starting point,
followed by early career assignments and culminating with
the complex organizational, personal, and business challenges
of a senior manager. HBS Cases provide a well-written plat-


Ramon L. Espino received his BS degree from
Louisiana State University in 1964 and his Doc-
tor of Science degree from the Massachusetts
Institute of Technology in 1968, both in chemi-
cal engineering. Hejoined the faculty at the Uni-
versity of Virginia in 1999 after twenty-six years
with Exxon Mobil. His research interests are in
fuel cell technology and methane conversion to
clean fuels and chemicals.


Copyright ChE Division of ASEE 2002


Chemical Engineering Education











form that describes specific situations with no direct answers
or outcomes.
The additional reading assignment consisted mainly of HBS
Notes, which provided a conceptual framework for the stu-
dents to analyze the cases with some knowledge of basic con-
cepts on business practices, interpersonal behavior, and hu-
man values. The students were all expected to read two books:
Getting to Yes12i and The Seven Habits of Highly Effective
People. 1'
The classes were designed to be highly interactive, with
the bulk of the time spent discussing the HBS Cases and Notes.
In addition, there were lectures on
Styles of communicating and interacting
Individual competencies


TABLE 2
HBS Cases


Title
Kevin Simpson
Elizabeth Fisher
Lisa Benton
Amelia Rodgers
Anne Livingston
Tech Transfer at...
Thurgood Marshall...
Conflict in a diverse...
David Fletcher
MOD IV Product...
PPG-Developing...
John Smithers at Sigtek
Jenssen Shoes
Coming Glass Works


Topic
Interviewing and selecting your employer
Dual career decisions
Conflicts in your first assignment
First group-leader assignment
Changing jobs and new leadership role
Conflict between development and production
Leader of middle-level managers
Harassment and social conflict
Hiring your ideal business team
Effective teamwork
Risks and rewards of empowerment
Leading a quality process initiative
Managing a diversity conflict
Leadership during a business downturn


Conflict management
Teams and team performance
Strategic planning
Developing a personal career plan
Six outside speakers led discussions on various aspects of
their business careers. These included
Managing family and business life
How to improve leadership skills
Conflict management and negotiation
Working with consulting companies
Attending business school
Reinforcing organizational values

A detailed outline of the course is presented in Table 3 (next
page). The two 75-minute class periods each week allowed
adequate time for discussion of the Case and the Notes, as
well as for the lectures given by the Halsey Professor or by
invited speakers.


LEARNING THROUGH THE HBS CASES

The "Case Method" is based on real-life situations that rep-
resent the kind of challenges that engineers and managers
are likely to face during their work life. The cases helped
students sharpen their analytical skills, their ability to com-
municate clearly and forcefully, and most importantly, helped
them to develop their problem-solving abilities. Table 2 indi-
cates the topic being discussed in each case.
The students were assigned the HBS Case a week in ad-
vance. They were required to write a 3-to-4-page summary
of their assessment of the situation and their proposed
solutionss. They were also asked to document the key learn-
ings they had derived from the case. It was gratifying to ob-
serve their increasing sophistication in analysis and problem
solving during the course of the semester.
There were a number of interesting observations that re-
sulted from discussion of the HBS Cases. The students paid a
lot of attention to the interpersonal style of the protagonists
and were quite sensitive to antisocial behavior. They were, to
my surprise, expecting to experience such behavior in the
workplace. This applied even to harassment situations. An-
other class-wide attitude was to view most conflicts as rooted
in poor communication, and it took a lot of discussion for
them to see poor communication simply as the external mani-
festation of a more profound conflict.

LEARNING KEY CONCEPTS
THROUGH THE HBS NOTES
The course provides an introduction to a number of critical
competencies engineers need in order to succeed in organi-
zations. These were provided mainly through reading and
discussion of HBS Notes. The Notes were also given to the
students a week in advance of the class discussion. There


TABLE 1
Halsey Professors
at the University of Virginia

Year Name Company/Position
1995 N.H. Prater Mobay/CEO
1996 J.M. Trice, Jr. Monsanto/Director-HR
1997 R.A. Moore, Jr. International Paper/VP
1998 D.L. Ashcraft Temple-Island/VP
1999 J.D. Stein BASF/CEO
2000 V.A. Russo Scott Paper/VP
2001 R.L. Espino Exxon/R&D Manager
2002 A.R. Hirsig ARCO Chemical/CEO


Fall 2002












was a close coupling between the teachings in the Notes and
the Case being discussed in parallel. This worked well, as
confirmed by the frequent references to concepts presented
in the Notes in the students' analyses of Cases. It is unrealis-
tic to expect the students to fully master all the concepts, but
it was clear that they became very aware of their importance.
The hope is that when they are confronted with similar situa-
tions, they will refer to these Notes for guidance.

We discussed the differences between management and
leadership and the many complex and ambiguous issues that


most managers face. We spent very productive time on the
influence of culture and history on subtle but important dif-
ferences in managers' behavior in the USA, Europe, Japan,
India, China, and Latin America. Having some students from
outside the USA gave immediacy to these discussions.

As expected, issues of business ethics grabbed the students'
attention and elicited strong and quite varied opinions. In fact,
I was surprised at the diversity of viewpoints, how strongly
they were held, and that there was no correlation with gen-
der, race, or economic background.


TABLE 3
Course Outline


Week 1
Homework/Class Discussion HBS Notes on "Learning by the case
method" and "How to choose a leadership pattern"
Lecture Individual and team competencies

Week 2
Homework/Class Discussion HBS Notes on "Understanding
context" and "Conflicting responsibilities"
HBS Case "Kevin Simpson"
Lecture Styles of communicating and interacting

Week 3
Homework/Class Discussion HBS Notes on "Managing your career"
HBS Case "Elizabeth Fisher"
Lecture Invited Speaker-Managing family and business life

Week 4
Homework/Class Discussion HBS Notes on "Power dynamics in
organizations"
HBS Case "Lisa Benton"
Lecture The seven habits of highly effective people

Week
Homework/Class Discussion HBS Notes on "Managing your boss"
and "Exercising influence"
HBS Case "Amelia Rodgers"
Lecture Invited Speaker-Improving your leadership skills

Week 6
Homework/Class Discussion HBS Notes on "Evaluating an action
plan" and "Understanding communications in one-to-one
relationships"
HBS Case "Ann Livingston and Power Max Systems"
Lecture The seven habits of highly effective people

Week 7
Homework/Class Discussion HBS Notes on "Beyond the myth of a
perfect mentor" and "Managing networks"
HBS Case "Technology transfer at a defense contractor"
Lecture Invited Speaker-Conflict management and negotiation

Week 8
Homework/Class Discussion HBS Notes on "Power dependence and
effective management" and "Influence tactics"
HBS case "Thurgood Marshall High School"
Lecture Conflict management styles


Week 9
Homework/Class Discussion HBS Notes on "Integrity management"
and "Managing a task-force"
HBS Case "Managing conflict in a diverse environment"
Lecture Invited Speaker-Working in a consulting company

Week 10
Homework/Class Discussion HBS Notes on "Barriers and gateways
to communications" and "On good communications"
HBS Case "David Fletcher"
Lecture Invited Speaker-Should you get an MBA?

Week 11
Homework/Class Discussion HBS Notes on "The power of talk" and
"The discipline of teams"
HBS case "Mod IV product development team"
Lecture Getting to Yes

Week 12
Homework/Class Discussion HBS Notes on "The challenge of
commitment" and "A note on high-commitment work systems"
HBS Case "PPG-Developing a self-directed workforce"
Lecture Strategic planning

Week 13
Homework/Class Discussion HBS Notes on "Organization
structure," "Organization effectiveness," and "The challenge of
change"
HBS Case "John Smithers at Sigtek"
Lecture Invited Speaker-Reinforcing organizational values

Week 14
Homework/Class Discussion HBS Notes on "Business ethics: the
view from the trenches," "Ethics without a sermon," and "Ways
of thinking about and across differences"
HBS Case "Jenssen Shoes"
Lecture Developing a personal career plan

Week 15
Final Homework:
A personal career plan
Analysis of the "Most admired company..."
Group report of HBS Case "Coming Glass Works"


Chemical Engineering Education










I was disappointed in the students' lack of interest in learn-
ing about team building, task-force management, and build-
ing commitment in the workplace. The students felt that they
knew about these topics and that they were already profi-
cient. I do not believe I ever convinced them there was a lot
for them to learn and that success in these areas requires skills
they actually did not possess.

OTHER FEATURES OF THE COURSE

The students were given a three-part final homework as-
signment. One element was a personal mission statement with
an associated five-year career development plan. The plan
could also include other facets of their life, such as family,
health, religion, community involvement, etc. For each of
the elements they were encour-
aged to follow a disciplined ap-
proach that included short-term
(6 months), midterm (2-3 TA
years), and long-term (5 years) Courst
plans. For each time period,
Not Useful 1 2
they were asked to state goals
and specific objectives and to February %
define strategies and action March -
steps. They were initially unen- April %
thusiastic about this task, but
the final product indicates that
they thought hard about it and
put together a realistic and credible plan.
The second element of the final homework was a team
project. Groups of four students were asked to analyze a fairly
complex HBS Case of a Coring Glass Works Division un-
dergoing a change in management during a business down-
turn. They were asked to devise strategies and specific action
plans for the division as well as a self-assessment of their
team performance. The reports indicated a wide range of team
performance, with the key problems being an inability to agree
on an action plan, finding time to work together, and uneven
participation by team members. This assignment came at the
very end of the semester, which was too late to refute their
earlier assertions that "teamwork was something they knew
how to handle."
The third element of the final homework was an analysis
of a company's performance during the last four years. Each
student selected a company from those reviewed by Fortune
Magazine in its annual publication of "America's Most Ad-
mired Companies.""-45 They were asked to analyze the per-
formance of the company they chose, to identify reasons for
any change in rankings during the four-year period, and to
forecast future trends.
The objective of this exercise was to allow the students to
apply to a specific company-wide situation what they had
learned about effective management, leadership, and manag-


ing change. The companies chosen reflected the students' wide
range of career interests and included, among others, enter-
tainment, communications, financial, computer technology,
oil and chemicals, consumer products. They were asked to
suggest the future direction the company needed to take to
improve performance. A majority suggested expanding glo-
bal reach and more technology investment, while only a few
focused on improving cost competitiveness.

STUDENT ASSESSMENT AND FEEDBACK
During the semester, the students were asked to provide
feedback on course content and to assess its effectiveness.
The data are summarized in Table 4 and show that the major-
ity of the class found the course very useful. They rated the
discussions of HBS Cases and Notes, my work experiences
and personal stories, and the
outside speakers the highest.
E 4 They were less enthusiastic
essment about the other reading mate-
rial, perhaps because they
5 6 7 8 Very Useful were not used to this amount
25 45 30 of reading in an engineering
3 29 50 18 course.
3 25 37 35 SUMMARY

The objective of the course
was to increase student aware-
ness of the nontechnical competencies they should possess
in order to succeed in the work world. It is unrealistic to ex-
pect that at the end of a semester they would have mastered
all these competencies, but it was evident that they were much
more sensitive to the importance of such skills and had grasped
the essentials. Also, they were left with an excellent collec-
tion of HBS Cases and Notes that could serve them well when
confronted with similar situations. As I frequently indicated
to them, I wished that I had such a learning experience in my
engineering schooling and early career.
The main reason for writing this article is to encourage other
colleges and universities to consider offering a course along
the general outline that I have described. I also encourage
experienced business practitioners to teach such a course. The
Halsey Professors are unanimous: it was an exciting and grati-
fying experience to share what you have learned with the
next generation of engineering and business leaders.

REFERENCES
1. Harvard Business School Publishing, 60 Harvard Way, Boston MA
02163
2. Fisher, R., W. Ury, and B. Patton, Getting to Yes, 2nd ed., Penguin
Books
3. Covey, S.R., The 7 Habits of Highly Effective People Simon and
Schuster
4. Fortune Magazine, March 6, 1997
5. Fortune Magazine, February 21, 2001 0


Fall 2002


BL
eAss

3 4
















I*NG D*EREEX



GRADUATE EDUCATION ADVERTISEMENTS


Akron, University of.................................. 321
Alabama, University of .............................. 322
Alabama, Huntsville; University of.............. 323
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Brigham Young University ........................... 427
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Bucknell University .................................... 428
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Worcester Polytechnic Institute .................. 425
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Chemical Engineering Education














Graduate Education in Chemical Engineering


Teaching and
research assistantships
as well as
industrially sponsored
fellowships
available
up to
$20,000.

In addition to
stipends,
tuition and fees
are waived.

PhD students
may get
some incentive
scholarships.

The deadline for
assistantship
applications
is
April 15th.


G. G. CHASE
Multiphase Processes,
Fluid Flow, Interfacial
Phenomena, Filtration.
Coalescence




H. M. CHEUNG
Nanocomposite Materials,
Sonochemical Processing,
Polymerization in
Nanostructured Fluids,
Supercritical Fluid
Processing


S. S. C. CHUANG
Catalysis, Reaction
Engineering, Environ-
mentally Benign
Synthesis




J. R. ELLIOTT
Molecular Simulation,
Phase Behavior, Physical
Properties, Process
Modeling


E. A. EVANS
Materials Processing and
CVD Modeling


L. K. JU
Biochemical Engineering,
Environmental






S. T. LOPINA
BioMaterial Engineering
and Polymer Engineering






B.Z. NEWBY
Surface Modification,
Polymer Thin film






H. C. QAMMAR
Nonlinear Control,
Chaotic Processes






P. WANG
Biocatalysis and
Biomaterials


For Additional Information, Write
Chairman, Graduate Committee
Department of Chemical Engineering The University of Akron Akron, OH 44325-3906
Phone (330) 972-7250 Fax (330) 972-5856 www.ecgf.uakron.edu/-chem


Fall 2002












THE UNIVERSITY OF

ALABAMA



Chemical

Engineering


A dedicated faculty with state of the art
facilities offer research programs leading to
Doctor of Philosophy and Master of Science
degrees.


Research Areas:
Biomaterials, Catalysis and Reactor Design,
Drug Delivery Materials and Systems,
Electrohydrodynamics, Electronic Materials,
Environmental Studies, Fuel Cells, Interfacial
Transport, Magnetic Materials, Membrance
Separations and Reactors, Microelectro-
Mechanical Systems, Nanoscale Modeling,
Polymer Processing and Rheology, Process
Dynamics, Self-Assembled Materials,
Suspension and Slurry Rheology, Transport
Process Modeling
For Information Contact:
Director of Graduate Studies
Department of Chemical Engineering
The University of Alabama
Box 870203
Tuscaloosa, AL 35487-0203
Phone: (205) 348-6450


Faculty:
G. C. April, Ph.D. (Louisiana State)
D. W. Arnold, Ph.D. (Purdue)
C. S. Brazel, Ph.D. (Purdue)
E. S. Carlson, Ph.D. (Wyoming)
P. E. Clark, Ph.D. (Oklahoma State)
W. C. Clements, Jr., Ph.D. (Vanderbilt)
R. A. Griffin, Ph.D. (Utah State)
D. T. Johnson, Ph.D. (Florida)
T. M. Klein, Ph.D. (NC State)
A. M. Lane, Ph.D. (Massachusetts)
M. D. McKinley, Ph.D. (Florida)
S. M. C. Ritchie, Ph.D. (Kentucky)
L. Y. Sadler III, Ph.D. (Alabama)
J. M. Wiest, Ph.D. (Wisconsin)
M. L. Weaver, Ph.D. (Florida)


An equal employment / equal
educational opportunity institution


Chemical Engineering Education


,4,1










Chemical &


Materials Engineering




_ FACULTY & RESEARCH AREAS


he Department of Chemical and Materi-
als Engineering at the University of Ala-
bama in Huntsville offers you the oppor-
tunity for a solid and rewarding graduate career
that will lead to further success at the forefront
of academia and industry.
We will provide graduate programs that educate
and train students in advanced areas of chemical
engineering, materials science and engineering,
and biotechnology. Options for an MS and PhD
degree in Engineering or Materials Science are
available.
Our faculty are dedicated to international lead-
ership in research. Projects are ongoing in Mass
Transfer, Fluid Mechanics, Combustion,
Biosparations, Biomaterials, Microgravity Mate-
rials Processing, and Adhesion. Collaborations
have been established with nearby NASA/
Marshall Space Flight Center as well as leading
edge biotechnology and engineering companies.
We are also dedicated to innovation in teaching.
Our classes incorporate advances in computational
methods and multi-media presentations.

Department of Chemical Engineering
The University of Alabama in Huntsville
130 Engineering Building
Huntsville, AL 35899


Ram6n L. Cero Ph.D. (UC-Davis)
Professor and Chair
Capillary hydrodynamics, multiphase flows, enhanced heat transfer
surfaces.
(256) 824-7313, rlc@che.uah.edu
Chien P. Chen Ph.D. (Michigan State)
Professor
Multiphase flows, spray combustion, turbulence modeling,
numerical methods in fluids and heat transfer.
(256) 824-6194, cchen@che.uah.edu
Krishnan K. Chittur Ph.D. (Rice)
Professor
Protein adsorption to biomaterials, FTR/ATR at solid-liquid
interfaces, biosensing.
(256) 824-6850, kchittur@che.uah.edu
Douglas G. Hayes Ph.D. (Michigan)
Associate Professor
Enzyme reactions in nonaqueous media, separations involving
biomolecules, lipids and surfactants, surfactant-based colloidal
aggregates.
(256) 824-6874, dhayes@che.uah.edu
James E. Smith Jr. Ph.D. (South Carolina)
Professor
Kinetics and catalysis, powdered materials processing, combustion
diagnostics and fluids visualization using optical methods.
(256) 824-6439, jesmith@che.uah.edu
Jeffrey J. Weimer Ph.D. (MIT)
Associate Professor, Joint Appointment in Chemistry
Adhesion, biomaterials surface properties, thin film growth, surface
spectroscopies, scanning prode microscopies.
(256) 824-6954, jjweimer@matsci.uah.edu






UAH
The University of Alabama in Huntsville
An Affirmative Action/Equal Opportunity Institution
Web page: http://chemeng.uah.edu
Ph: 256*824*6810 FAX: 256-824-6839


Fall 2002














































The University of Alberta is well known
for its commitment to excellence in teach-

ing and research. The Department of

Chemical and Materials Engineering has

37 professors and over 100 graduate

students. Degrees are offered at the M.Sc.

and Ph.D. levels in Chemical Engineer-

ing, Materials Engineering, and Process
Control. Allfull-time graduate students in

the research programs receive a stipend

to cover living expenses and tuition.



For further information, contact

Graduate Program Officer
Department of Chemical and Materials Engineering
University of Alberta
Edmonton, Alberta, Canada T6G 2G6

PHONE (780) 492-1823 FAX (780) 492-2881
e-mail: chemical. engineering@ ualberta. ca
web: www.ualberta.ca/chemeng


M. BHUSHAN, Ph.D. (I.I.T. Bombay)
Sensor Location Fault Diagnosis Process Safety
R.E. BURRELL, Ph.D. (University of Waterloo)
Nanostructured Biomaterials Drug Delivery Biofilms Tissue Integration with Materials
P. CHOI, Ph.D. (University of Waterloo)
Molecular Modeling of Polymers Thermodynamics of Polymer Solutions and Blends
K. T. CHUANG, Ph.D. (University of Alberta)
Fuel Cell Catalysis Separation Processes Pollution Control
I. G. DALLA LANA, Ph.D. (Univ. of Minnesota) EMERITUS
Chemical Reaction Engineering Heterogeneous Catalysis
J. A. W. ELLIOTT, Ph.D. (University of Toronto)
Thermodynamics Statistical Thermodynamics Interfacial Phenomena
D. G. FISHER, Ph.D. (University of Michigan) EMERITUS
Process Dynamics and Control Real-Time Computer Applications
J.F. FORBES, Ph.D. (McMaster University) CHAIR
Real-Time Optimization Scheduling and Planning
M. R. GRAY, Ph.D. (California Inst. of Tech.)
Bioreactors Chemical Kinetics Bitumen Processing
R. E. HAYES, Ph.D. (University of Bath)
Numerical Analysis Reactor Modeling Computational Fluid Dynamics
B. HUANG, Ph.D. (University of Alberta)
Controller Performance Assessment Multivariable Control Statistics
S. M. KRESTA, Ph.D. (McMaster University)
Turbulent & Transitional Flows Multiphase Flows CFD
S. LIU, Ph.D. (University of Alberta)
Fluid-Particle Dynamics Transport Phenomena Kinetics
D. T. LYNCH, Ph.D. (University of Alberta) DEAN OF ENGINEERING
Catalysis Kinetic Modeling Numerical Methods Polymerization
J. H. MASLIYAH, Ph.D. (University of British Columbia)
Transport Phenomena Colloids Particle-Fluid Dynamics Oil Sands
A. E. MATHER, Ph.D. (University of Michigan)
Phase Equilibria Fluid Properties at High Pressures Thermodynamics
E. S. MEADOWS, Ph.D. (University of Texas)
Process Control Fuel Cell Modeling and Control Optimization
W. C. MCCAFFREY, Ph.D. (McGill University)
Reaction Kinetics Heavy Oil Upgrading Polymer Recycling Biotechnology
K. NANDAKUMAR, Ph.D. (Princeton University)
Transport Phenomena Distillation Computational Fluid Dynamics
A.E. NELSON, Ph.D. (Michigan Technological University)
Heterogeneous Catalysis UHV Surface Science Chemical Kinetics
M. RAO, Ph.D. (Rutgers University)
AIl Intelligent Control Process Control
S. L. SHAH, Ph.D. (University of Alberta)
Computer Process Control System Identification Process and Performance Monitoring
J.M. SHAW, Ph.D. (University of British Columbia)
Petroleum Thermodynamics Multiphase Mixing Process Modeling
U. SUNDARARAJ, Ph.D. (University of Minnesota)
Polymer Processing Polymer Blends Interfacial Phenomena
H. ULUDAG, Ph.D. (University of Toronto)
Biomaterials Tissue Engineering Drug Delivery
S. E. WANKE, Ph.D. (University of California, Davis)
Heterogeneous Catalysis Kinetics Polymerization
M. C. WILLIAMS, Ph.D. (University of Wisconsin) EMERITUS
Rheology Polymer Characterization Polymer Processing
Z. XU, Ph.D. (Virginia Polytechnic Institute and State University)
Surface Science & Engineering Mineral Processing Waste Management
T. YEUNG, Ph.D. (University of British Columbia)
Emulsions Interfacial Phenomena Micromechanics


Chemical Engineering Education













ROBERT G. ARNOLD, Professor (CalTech)
Microbiological Hazardous Waste Treatment, Metals Speciation and Toxicil
PAUL BLOWERS, Assistant Professor (Illinois, Urbana-Champaign
Chemical Kinetics, Catalysis, Surface Phenomena
JAMES C. BAYGENTS, Associate Professor (Princeton)
Fluid Mechanics, Transport and Colloidal Phenomena, Bioseparations

WENDELL ELA, Assistant Professor (Stanford)
Particle-Particle Interactions, Environmental Chemistrn
JAMES FARRELL, Associate Professor (Stanford)
Sorption/desorption of Organics in Soils
JAMES A. FIELD, Associate Professor (Wagenigen Agricultural Un
Bioremediation, Microbiology, White Rot Fungi, Hazardous Waste
ROBERTO GUZMAN, Associate Professor (North Carolina State)
Affinity Protein Separations, Polymeric Surface Science
ANTHONY MUSCAT, Assistant Professor (Stanford)
Kinetics, Surface Chemistry, Surface Engineering, Semiconductor Processir
Microcontamination
KIMBERLY OGDEN, Associate Professor (Colorado)
Bioreactors, Bioremediation, Organics Removal from Soils
THOMAS W. PETERSON, Professor and Dean (CalTech)
Aerosols, Hazardous Waste Incineration, Microcontamination
ARA PHILIPOSSIAN, Associate Professor (Tufts)
Chemical/Mechanical Polishing, Semiconductor Processing
JERKER PORATH, Research Professor (Uppsala)
Separation Science
EDUARDO SAEZ, Associate Professor (UC, Davis)
Rheology, Polymer Flows, Multiphase Reactors
FARHANG SHADMAN, Professor (Berkeley)
Reaction Engineering, Kinetics, Catalysis, Reactive Membranes,
Microcontamination
JOST 0. L. WENDT, Professor and Head (Johns Hopkins)
Combustion-Generated Air Pollution, Incineration, Waste
Management


For further information, write to

http://ww.che.arizona.edu

or write

Chairman, Graduate Study Committee
Department of Chemical and
Environmental Engineering
P.O. BOX 210011
The University ofArizona
Tucson, AZ 85721

The University of Arizona is an equal
opportunity educational institution/equal opportunity employer.
Women and minorities are encouraged to apply.


(


CHEMICAL AND


ENVIRONMENTAL


ENGINEERING

at


THE

UNIVERSITY

OF -


ARIZONAN A

The Chemical and Environmental Engineering Department
at the University of Arizona offers a wide range of research
opportunities in all major areas of chemical engineering and
environmental engineering, and graduate courses are offered in
most of the research areas listed here. The department offers a fully
accredited undergraduate degree as well as MS and PhD graduate
degrees. Strong interdisciplinary programs exist in bioprocessing
and bioseparations, microcontamination in electronics manu-
facture, and environmental process modification.
Financial support is available through fellowships, government
and industrial grants and contracts, teaching and
research assistantships.
Tucson has an excellent climate and many
recreational opportunities. It is a growing modern city that
retains much of the old Southwestern atmosphere.


Fall 2002


I












S ARIZONA STATE


UNIVERSITY


Department of Chemical and Materials Engineering


A Distinguished and Diverse Faculty A multi-disciplinary research
environment with opportunities
Chemical Engineering in electronic materials
Jonathan Allen, Ph.D., MIT. Atmospheric aerosol chemistry, single-particle measurement processing biotechnology *
techniques, environmental fate of organic pollutants processing, characterization,
Stephen Beaudoin, Ph.D., North Carolina State. Semiconductor materials processing, environ- and simulation of materials *
mentally-benign semiconductor processing, particle and thin-film adhesion, chemical- ceramics air and water
mechanical polishing, polymer dielectrics
purification atmospheric
James Beckman, Ph.D., Arizona. Unit operations, applied mathematics, energy-efficient water
purification, fractionation, CMP reclamation chemistry process control
Veronica Burrows, Ph.D., Princeton. Surface science, environmental sensors, semiconductor
processing, interfacial chemical and physical processes in sensor processing
Ann Dillner, Ph.D., Illinois, Urbana-Champaign. Atmospheric particulate matter (aerosols)
chemistry and physics, ultra fine aerosols, light scattering, climate and health effects of
aerosols
Chan Beum Park, Ph.D., POSTTECH, South Korea. Bioprocess in extremis, novel cell-free
protein synthesis, biolab-on-a-chip technology
Gregory Raupp, Ph.D., Wisconsin. Gas-solid surface reactions mechanisms and kinetics,
interactions between surface reactions and simultaneous transport processes, semiconductor
materials processing, thermal and plasma-enhanced chemical vapor deposition (CVD)
Anneta Razatos, Ph.D., Texas at Austin. Bacterial adhesion, colloid interactions, AFM, biofilms,
genetic engineering
Daniel Rivera, Ph.D., Caltech. Control systems engineering, dynamic modeling via system
identification, robust control, computer-aided control system design
Michael Sierks, Ph.D., Iowa State. Protein engineering, biomedical engineering, enzyme
kinetics, antibody engineering


Materials Science and Engineering
James Adams, Ph.D., Atomistic stimulation of metallic surfaces, adhesion, wear, and automotive
catalysts, heavy metal toxicity
Terry Alford, Ph.D., Cornell. Electronic materials, physical metallurgy, electronic thin films
Nikhilesh Chawla, Ph.D., Michigan. Lead-free solders, composite materials, powder metallurgy
Sandwip Dey, Ph.D., Alfred. Electro-ceramics, MOCVD and ALCVD, dielectrics: leakage, loss
mechanisms and modeling
Stephen Krause, Ph.D., Michigan. Characterization of structural changes in processing of semiconductors
Subhash Mahajan (Chair), Ph.D., Berkeley. Semiconductor defects, high temperature semiconductors, structural materials deformation
James Mayer, Ph.D., Purdue. Thin film processing, ion beam modification of materials
Nate Newman, Ph.D., Stanford. Growth, characterization, and modeling of solid-state materials
S. Tom Picraux, Ph.D. Caltech. Nanostructured materials, epitaxy, and thin-film electronic materials
Karl Sieradzki, Ph.D. Syracuse. Fracture of solids, thin-film deposition and growth, corrosion
Mark van Schilfgaarde, Ph.D. Stanford. Methods and applications of electronic structure theory, dilute magnetic semiconductors, GW approximation

For details concerning graduate opportunities in Chemical and Materials Engineering atASU, please call Marlene Bolf
at (480) 965-3313, or write to Subhash Mahajan, Chair, Chemical and Materials Engineering, Arizona State University,
Tempe, Arizona 85287-6006 (smahajan@asu.edu).


Chemical Engineering Education













AUBURN UNIVERSITY'


Chemical Engineering


Robert P. Chambers University of California, Berkeley
Harry T. Cullinan Carnegie Mellon University
Christine W. Curtis Florida State University
Steve R. Duke University of Illinois
Said Elnashaie University of Edinburgh
James A. Guin University of Texas, Austin
Ram B. Gupta University of Texas. Austin
Gopal A. Krishnagopalan University of Maine
Y. Y. Lee Iowa State University
Glennon Maples Oklahoma State University
David R. Mills Washington State University
Ronald D. Neuman The Institute of Paper Chemistry
Stephen A. Perusich University of Illinois
Timothy D. Placek University of Kentucky
Christopher B. Roberts University of Notre Dame
A. R. Tarrer Purdue University
Bruce J. Tatarchuk University of Wisconsin


Research Areas
* Biochemical Engineering
* Pulp and Pper
* Process Systems Engineering
SIntegrated Process Design
* Environmental Chemical Engineering
* Catalysis and Reaction Engineering
* Materials lymers
* Surface and Interfacial Science
* Thermodynamics Supecritical Fluids
* Electrochemical Engineering
* Transport Phenomena
* Fuel CellTechnoloy'
* Microfibrous Materials
* Nanotechnology


-Inquiriesto:.
Director of Graduate Recruting '
Department of Chemical Engineering
Auburn University. AL 36849 F
Phone (334) 844-4827 '
Fax (334)844-2063 .
http:!lwwwrAg.aubiimied -
I-e i:t che ickal@enauburn.edu
Financial assistance is available to qualified applicants.


Fall 2002


I












DEPARTMENT OF CHEMICAL

AND PETROLEUM ENGINEERING


FACULTY
R. G. Moore, Head (Alberta)
J. Azaiez (Stanford)
H. Baheri (Saskatchewan)
L. A. Behie (Western Ontario)
C. Bellehumeur (McMaster)
P. R. Bishnoi (Alberta)
P. J. Farrell (Calgary)
R. A. Heidemann (Washington U.)
J.M. Hill (Wisconsin)
A. A. Jeje (MIT)
M. S. Kallos (Calgary)
A. Kantzas (Waterloo)
B. B. Maini (Univ. Washington)
A. K. Mehrotra (Calgary)
S. A. Mehta (Calgary)
B. J. Milne (Calgary)
M. Pooladi-Darvish (Alberta)
A. Settari (Calgary)
S. Srinivasan (Stanford)
W. Y. Svrcek (Alberta)
M. A. Trebble (Calgary)
H. W. Yarranton (Alberta)
B. Young (Canterbury, NZ)
L. Zanzotto (Slovak Tech. Univ., Czechoslovakia)


The Department offers graduate programs leading to the M.Sc. and Ph.D.
degrees in Chemical Engineering (full-time) and the M.Eng. degree in Chemical
Engineering, Petroleum Reservoir Engineering or Engineering for the
Environment (part-time) in the following areas:
Biochemical Engineering & Biotechnology
Biomedical Engineering
Catalysis and Fuel Cells
Environmental Engineering
Modeling, Simulation & Control
Petroleum Recovery & Reservoir Engineering
Polymer Processing & Rheology
Process Development
Reaction Engineering/Kinetics
Thermodynamics
Transport Phenomena
Fellowships and Research Assistantships are available to all qualified applicants.

SFor Additional Information Write *
Dr. W.Y. Svrcek Associate Head, Graduate Studies
Department of Chemical and Petroleum Engineering
University of Calgary Calgary, Alberta, Canada T2N 1N4
E-mail: gradstud@ucalgary.ca


The University is located in the City of Calgary, the Oil capital of Canada, the home of the world famous Calgary Stampede and the 1988
Winter Olympics. The City combines the traditions of the Old West with the sophistication of a modern urban center Beautiful Banff
National Park is 110 km west of the City and the ski resorts of Banff, Lake Louise,and Kananaskis areas are readily accessible. In the
above photo the University Campus is shown in the foreground. The Engineering complex is on the left of the picture, and the Olympic
Oval is on the right of the picture.


[ ~UNIVERSITY OF

S CALGARY


.0


Chemical Engineering Education














U i rtoC io i B r l


The Chemical Engineering Department at the
University of California, Berkeley, one of the pre-
eminent departments in the field, offers graduate pro-
grams leading to the Master of Science and Doctor
of Philosophy. Students also have the opportunity
to take part in the many cultural offerings of the San
Francisco Bay Area and the recreational activities
of California's northern coast and mountains.

FACULTY


Nitash P. Balsara
Harvey W. Blanch
Arup K. Chakraborty
David B. Graves
Alexander Katz
C. Judson King
Susan J. Muller
John M. Prausnitz
Jeffrey A. Reimer
Alexis T. Bell


Elton J. Cairns
Douglas S. Clark
Enrique Iglesia
Jay D. Keasling
Roya Maboudlan
John S. Newman
Clayton J. Radke
David V. Schaffer
Rachel A. Segalman


BIOENGINEERING
Blanch, Clark,
Keasling, Schaffer,
Chakraborty, Muller,
Prausnitz & Radke













I















POLYMERS &
SOFT MATERIALS

Balsara, Chakraborty,
Muller, Prausnitz, Radke,
Reimer & Segalman


Chairman: Arup K. Chakraborty I


FOR FURTHER INFORMATION, PLEASE VISIT OUR WEBSITE:

http://cheme.berkeley.edu/index.shtmi


Fall 2002


CATALYSIS &
REACTION ENG.

Bell, Chakraborty,
Iglesia, Katz & Reimer


ELECTROCHEMICAL
ENGINEERING

Cairns, Newman &
Reimer


ENVIRONMENTAL
ENGINEERING

Bell, Graves, Iglesia,
Keasling & King


MICROELECTRONICS
PROCESSING &
MEMS

Graves, Maboudian,
Reimer & Segalman













University of California, Davis


Department of Chemical Engineering & Materials Science
Offering M.S. and Ph.D. degree programs in both Chemical Engineering and Materials Science and Engineering

Faculty


David E. Block, Assistant Professor Ph.D., University of Minnesota, 1992 Industrialfermentation, biochemical processes in phannaceutical industry
Roger B. Boulton, Professor Ph.D., University of Melbourne, 1976 Fermentation and reaction kinetics, crstallization
Stephanie R. Dungan, Associate Professor Ph.D., Massachusetts Institute of Technology, 1992 Micelle transport, colloid and interfacial science in food
processing
Roland Faller, Assistant Professor Ph.D., Max-Planck Institute for Polymer Research, 2000 Molecular modeling of soft-condensed matter
Bruce C. Gates, Professor Ph.D., University of Washington. Seattle, 1966 Catalysis, solid superacid catalysis, zeolite catalysts, bimetallic catalysts,
catalysis by metal clusters
Jeffery C. Gibeling, Professor Ph.D., Stanford University, 1979 Deformation, fracture andfatigue of metals, layered composites and bone
Joanna R. Groza, Professor Ph.D., Polytechnic Institute, Bucharest, 1972 Plasma activated sintering and processing ofnanostructured materials
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Area-ti i -


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.r as ring/M etiBir hI


Chemical Engineering Education


I Jh" Ilullll J, ckd p.auJuJl luJt ".I '". L .' IIh',
Department of Chemical Engineering and Materials
Science allows students to choose research projects and
lih.- I .I I. In Il ..i I.j .u ll I lIh rr ,
,,', i,:.rT,jljII ll ..tL L ,,I h ,l, Lal.l ::.l. H I. .:. I a .d
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rd1 1.] I .. .ihri I l I.., [N.. M 1 -;.,J 4i h. I.-I'Iti
I' f,










UNIVERSITY OF



CALIFORNIA

Graduate Studies in
Chemical Engineering IR VINE
and Materials Science and Engineering
for Chemical Engineering, Engineering, and Materials Science Majors
Offering degrees at the M.S. and Ph.D. levels. Research in frontier areas
in chemical engineering, biochemical engineering, biomedical engineering, and materials
science and engineering. Strong physical and life science and engineering groups on campus.
FACULTY
Ying Chih Chang (Stanford University)
Nancy A. Da Silva (California Institute of Technology)
James C. Earthman (Stanford University)
Steven C. George (University of Washington)
Stanley B. Grant (California Institute of Technology)
Juan Hong (Purdue University)
Enrique J. Lavernia (Massachusetts Institute of Technology)
Henry C. Lim (Northwestern University)
Jia Grace Lu (Harvard University)
Martha L. Mecartney (Stanford University)
Farghalli A. Mohamed (University of California, Berkeley)
Frank G. Shi (California Institute of Technology)
Vasan Venugopalan (Massachusetts Institute of Technology)
Joint Appointments:
G. Wesley Hatfield (Purdue University)
Noo Li Jeon (University of Illinois)
Sunny Jiang (University of South Florida)
Roger H. Rangel (University of California, Berkeley)
William A. Sirignano (Princeton University)
Adjunct Professors
Russell Chou (Carnegie Mellon University)
Andrew Shapiro (University of Califoria, Irvine)
Victoria Tellkamp (University of Califoria, Irvine)

The 1,510-acre UC Irvine campus is in Orange County, five miles from the Pacific Ocean and 40 miles south of Los
Angeles. Irvine is one of the nation's fastest growing residential, industrial, and business areas. Nearby beaches,
mountain and desert area recreational activities, and local cultural activities make Irvine a pleasant city in which to
live and study.
For further information and application forms, please visit
http://www.eng.uci.edu/cbe/
or contact
Department of Chemical Engineering and Materials Science
School of Engineering University of California Irvine, CA 92697-2575


Fall 2002


* Biomedical

En
Engineering
* Bioreactor
Engineering
* Bioremediation
* Ceramics
* Combustion
* Composite
Materials
* Control and
Optimization
* Environmental
Engineering
* Interfacial
Engineering
* Materials
Processing
* Mechanical
Properties
* Metabolic
Engineering
* Microelectroi es
Processing and
Modeling
* Microsucture
of Materials
* Nanocrystalline
Materials
* Nucleation,
Chrystallization
and Glass
Transition-
Process ._


* Recomb- ant"---

og -- -
g*egeaia sL^


ing
.. -._; -- :;:- .:- ---* -----

SWater&- --tiotr
Control.


331









CHEMICAL ENGINEERING AT


RESEARCH
AREAS

* Aerosol Science and Technolog.
* Biochemical Engineering
* Combinatorial Catalysil
* Complex Systems Engineering
* Electrochemistry
* Membranes
* Molecular and Cellular
Bioengineering
* Pollution Prevention

* Polymer Engineering
* Process Design, Optimiiltion.
Dynamics, and Contriol
* Reaction Kinetics and
Combustion
* Semiconductor Manufacturing


FACULTY
J. P. Chang
P. D. Christofides
Y. Cohen
J. Davis
(Vice Chancellor for
Information Technology)
S. K. Friedlander
R. F. Hicks
E. L. Knuth (Prof Emeritus)
J. C. Liao
V. Manousiouthakis
H. G. Monbouquette
K. Nobe
L. B. Robinson (Prof Emeritus)
S. M. Senkan
Y. Tang
W. D. Van Vorst (Prof Emeritus)
V. L. Vilker (Prof Emeritus)
A.R. Wazzan


PROGRAMS


UCLA's Chemical Engineering Department offers a
program of teaching and research linking fundamental en-
gineering science and industrial practice. Our Department
has strong graduate research programs in Bioengineering,
Energy and Environment, Semiconductor Manufacturing,
Engineering of Materials, and Process and Control Sys-
tems Engineering.
Fellowships are available for outstanding applicants


interested in Ph.D. degree programs. A fellowship in-
cludes a waiver of tuition and fees plus a stipend.
Located five miles from the Pacific Coast,
UCLA's attractive 417-acre campus extends from Bel
Air to Westwood Village. Students have access to
the highly regarded science programs and to a vari-
ety of experiences in theatre, music, art, and sports
on campus.


CONTACT


5531 B -e Ha : *e. ll,, UCL-4, Lo Angeles, C 0-
Telehon at 310 825906 or isi us t ww~chmen+ucl~ed


Chemical Engineering Education


UCLAsm^^^^










University of California, Riverside
Department of Chemical and Environmental Engineering




The Graduate Program in Chemical and En- arlan and Rosemary Bourns College of
vironmental Engineering offers training lead-
ing to the degrees of Master of Science and engineering
Doctor of Philosophy. All applicants are re-
quired to submit scores from the general apti-
tude Graduate Record Examination (GRE).
For more information and application mate-
rials, write:
Graduate Advisor
Department of Chemical and
Environmental Engineering
University of California
Riverside CA 92521
Visit us at our website:
http://www.engr.ucr.edu/chemenv


Faculty
Wilfred Chen (Cal Tech) Environmental Biotechnology, Microbial Engineering, Biocatalysis
David R. Cocker (Caltech) Air Quality Systems Engineering
Marc Deshusses (ETH, Zurich) Environmental Biotechnology, Bioremediation, Modeling
Robert C. Haddon (Penn State) Carbon Nanotubes, Advanced Materials
Eric M.V. Hoek (Yale) Environmental Membrane Processes, Collodial and Interfacial Phenomena
Mark R. Matsumoto (UC Davis) Water and Wastewater Treatment, Hazardous Waste, Soil Remediation
Ashok Mulchandani (McGill) Bioengineering, Biomaterials, Biosensors, Environmental Biotechnology
Joseph M. Norbeck (Nebraska) Advanced Vehicle Technology, Air Pollutants, Renewable Fuels
Mihri Ozkan (UC Sn Diego) Biomedical Microdevices, Bio-MEMS and Bio-Photonics
Anders O. Wistrom (UC Davis) Particulate and Colloidal Systems
Jianzhong Wu (UC Berkeley) Molecular Simulation, Theory of Complex Fluids, Nanomaterials
Yushan Yan (CalTech) Zeolite Thin Films, Fuel Cells, Nanostructured Materials, Catalysis

The 1,200-acre Riverside campus of the University of California is located 50 miles east of Los Ange-
les within easy driving distance to most of the major cultural and recreational offerings in Southern
California. In addition, it is virtually equidistant from the desert, the mountains, and the ocean.


Fall 2002













UNIVERSITY OF CALIFORNIA


SANTA BARBARA


ERAY S. AYDIL Ph.D. (Houston) Microelectronics and Plasma Processing
SANJOY BANERJEE Ph.D. (Waterloo) Environmental Fluid Dynamics, Multiphase Flows, Turbulence, Computational Fluid Dynamics
BRADLEY F. CHMELKA Ph.D. (U.C. Berkeley) Molecular Materials Science, Inorganic-Organic Composites, Porous Solids, NMR, Polymers
PATRICK S. DAUGHERTY Ph.D. (Austin) Protein Engineering and Design, Library Technologies
MICHAEL F. DOHERTY Ph.D. (Cambridge) Design and Synthesis, Separations, Process Dynamics and Control
FRANCIS J. DOYLE III Ph.D. (Caltech) Process Control, Systems Biology, Nonlinear Dynamics
GLENN H. FREDRICKSON Ph.D. (Stanford) Statistical Mechanics, Glasses, Polymers, Composites, Alloys
G.M. HOMSY Ph.D. (Illinois) Fluid Mechanics, Instabilities, Porous Media, Interfacial Flows, Convective Heat Transfer
JACOB ISRAELACHVILI Ph.D. (Cambridge) Colloidal and Biomolecular Interactions, Adhesion and Friction
EDWARD J. KRAMER Ph.D. (Carnegie-Mellon) Fracture and Diffusion of Polymers, Polymer Surfaces and Interfaces
L. GARY LEAL Ph.D. (Stanford) Fluid Mechanics, Physics and Rheology of Complex Fluids, including Polymers, Suspensions, and Emulsions.
GLENN E. LUCAS Ph.D. (M.I.T) Mechanics of Materials, Structural Reliability.
DIMITRIOS MAROUDAS Ph.D. (M.I.T) Theoretical and Computational Materials Science, Electronic and Structural Materials
ERIC McFARLAND Ph.D. (M.I.T) M.D. (Harvard) Combinatorial Material Science, Environmental Catalysis, Surface Science
DUNCAN A. MELLICHAMP Ph.D. (Purdue) Computer Control, Process Dynamics, Real-Time Computing
SAMIR MITRAGOTRI Ph.D. (M.I.T) Drug Delivery and Biomaterials
DAVID J. PINE Ph.D. (Cornell) (Chair) Polymer, Surfactant, and Colloidal Physics, Multiple Light Scattering, Photonic Crystals
ORVILLE C. SANDALL Ph.D. (Berkeley) Transport Phenomena, Separation Processes
DALE E. SEBORG Ph.D. (Princeton) Process Control, Monitoring and Identification
MATTHEW V. TIRRELL Ph.D. (Massachusetts) Polymers, Surfaces, Adhesion Biomaterials
T. G. THEOFANOUS Ph.D. (Minnesota) Multiphase Flow, Risk Assessment and Management
JOSEPH A. ZASADZINSKI Ph.D. (Minnesota) Surface and Interfacial Phenomena, Biomaterials


PROGRAMS
AND FINANCIAL SUPPORT
The Department offers M.S. and
Ph.D. degree programs Finan-
cial aid, including fellowships,
teaching assistantships, and re-
search assistantships, is avail-
able.
THE UNIVERSITY
One of the world's few seashore
campuses, UCSB is located on
the Pacific Coast 100 miles
northwest of Los Angeles. The
student enrollment is over
18,000. The metropolitan Santa
Barbara area has over 150,000
residents and is famous for its
mild, even climate.

For additional information
and applications, write to
Chair Graduate Admissions Committee Department of Chemical Engineering University of California Santa Barbara, CA 93106


Chemical Engineering Education








Chemical Engineering at the


SCALIFORNIA


INSTITUTE


OF


TECHNOLOGY

"At the Leading Edge"


Frances H. Arnold
Anand R. Asthagiri
John F Brady
Mark E. Davis


~
CI
II
S
O


Richard C. Flagan
George R. Gavalas (Emeritus)
Konstantinos P. Giapis
Julia A. Korfield


John H. Seinfeld
David A. Tirrell
Nicholas W Tschoegl (Emeritus)
Zhen-Gang Wang


Aerosol Science
Applied Mathematics
Atmospheric Chemistry and Physics
Biocatalysis and Bioreactor Engineering
Biomaterials
Biomedical Engineering
Bioseparations
Catalysis
Chemical Vapor Deposition


Combustion
Colloid Physics
Fluid Mechanics
Materials Processing
Microelectronics Processing
Microstructured Fluids
Polymer Science
Protein Engineering
Statistical Mechanics


For further information, write
Director of Graduate Studies
Chemical Engineering 210-41 California Institute of Technology Pasadena, California 91125-4100
Also, visit us on the World Wide Web for an on-line brochure: http://www.che.caltech.edu


Fall 2002







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Case Western Reservei II UnivIerit
M .S .~~ an-h D r g a si C e i a n i e r n


Faculty

John Angus
Harihara Baskaran
Robert Edwards
Donald Feke
Jeffrey Glass
Uziel Landau
Chung-Chiun Liu
J. Adin Mann
Heidi Martin
Philip Morrison
Peter Pintauro
Syed Qutubuddin
Robert Savinell
Thomas Zawodzinski


Research Opportunities

Advanced Energy Systems
Fuel Cells and Batteries
Hydrogen Infrastructure
Membrane Transport
Sensors
Microfabrication
Biomedical Engineering
Transport in Biological Systems
Biomedical Sensors and Actuators
Wound Healing
Inflammation and Cancer Metastasis
Neural Prosthetic Devices
Advanced Materials and Devices
Diamond and Nitride Synthesis
Coatings, Thin Films, and Surfaces
In-Situ Diagnostics
Fine Particle Science and Processing
Polymer Nanocomposites
Electrochemical Microfabrication


For more information on
Graduate Research, Admission, and Financial Aid, contact:


Graduate Coordinator
Department of Chemical Engineering
E-mail: grad@cheme.cwru.edu
Web: http://www.cwru.edu/cse/eche


Case Western Reserve University
10900 Euclid Avenue
Cleveland, Ohio 44106-7217


Fall 2002









Opportunities for Graduate Study in Chemical Engineering at the







M.S. and Ph.D. Degrees in Chemical Engineering


Faculty

Carlos Co

Joel Fried

Rakesh Govind

Vadim Guliants

Daniel Hershey

Chia-chi Ho

Sun-Tak Hwang

Yuen-Koh Kao

Soon-Jai Khang

William Krantz

Jerry Y. S. Lin

Neville Pinto

Peter Smirniotis



The University of Cincinnati is
committed to a policy of
non-discrimination in
awarding financial aid.

For Admission Information
Director, Graduate Studies
Chemical Engineering
PO Box 210171
University of Cincinnati
Cincinnati, Ohio 45221-0171
E-mail:
mcarden@alpha.che.uc.edu
or
jlin@alpha.che.uc.edu


The faculty and students in the Department of Chemical Engineering are engaged in a diverse range
of exciting research topics. Assistantships and tuition scholarships are available to highly qualified
applicants to the MS and PhD degree programs.


E Advanced Materials
Inorganic membranes, nanostructured materials, microporous and mesoporous materials,
advanced materials processing, thin film technology, fuel cell and sensor materials, self-
assembly

E Biotechnology (Bioseparations)
Novel bioseparation techniques, affinity separation, biodegradation of toxic wastes, con-
trolled drug delivery, two-phase flow

O Catalysis and Chemical Reaction Engineering
Heterogeneous catalysis, environmental catalysis, zeolite catalysis, novel chemical reactors,
modeling and design of chemical reactors

O Environmental Research
Desulfurization and denitrication of flue gas, new technologies for coal combustion power
plant, wastewater treatment, removal of volatile organic vapors

O Membrane Technology
Membrane synthesis and characterization, membrane gas separation, membrane reactors,
sensors and probes, pervaporation, biomedical, food and environmental applications of
membranes, high-temperature membrane technology, natural gas processing by membranes

O Polymers
Thermodynamics, polymer blends and composites, high-temperature polymers, hydrogels,
polymer rheology, computational polymer science, polymerization technology

D Separation Technologies
Membrane separation, adsorption, chromatography, separation system synthesis, chemical
reaction-based separation processes


Chemical Engineering Education












Chemical


Engineering at


The City College of


New York CUNY

(The City University of New York)


A 154-year-old urban University, the oldest public
University in America, on a 35-acre Gothic and modern
campus in the greatest city in the world

FACULTY RESEARCH:


OAndreas Acrivos*oo<4 Rheology of
concentrated suspensions; Dielectro-
phoresis in flowing suspensions;
Dynamical systems theory and chaotic
particle motions

Alexander Couzis: Polymorph
selective templated crystallization;
Molecularly thin organic barrier layers;
Surfactant facilitated wetting of
hydrophobic surfaces; soft materials

"Morton Dennoo<: Polymer science
and rheology; non-Newtonian fluid
mechanics

Lane Gilchrist: Bioengineering with
cellular materials; Spectroscopy-guided
molecular engineering; Structural
studies of self-assembling proteins;
Bioprocessing
Robert Graff: Coal liquefaction;
Pollution prevention; Remediation
Leslie Isaacs: Preparation and charac-
terization of novel optical materials;
Recycling of pavement materials;
Application of thermo-analytic
techniques in materials research
Jae Lee: Theory of reactive distillation;
Process design and control; Separations;
Bioprocessing
OCharles Maldarelli: Interfacial fluid
mechanics and stability; Surface tension
driven flows and microfluidic applica-
tions; Surfactant adsorption, phase be-
havior and nanostructuring at interfaces
Irven Rinard: Process design
methodol-ogy; Dynamic process
simulation; Micro-reaction technology;
Process control; Bioprocessing

David Rumschitzki: Transport and
reaction aspects of arterial disease;

Fall 2002


Interfacial fluid mechanics and stability;
Catalyst deactivation and reaction
engineering
Reuel Shinnarm: Advanced process design
methods; Chemical reactor control;
Spinodal decomposition of binary solvent
mixtures; Process economics; Energy and
environment systems
Carol Steiner: Polymer solutions and
hydrogels; Soft biomaterials. Controlled
release technology
Gabriel Tardos: Powder technology;
Granulation; Fluid particle systems,
Electrostatic effects; Air pollution
Sheldon Weinbaum.m: Fluid mechanics,
Biotransport in living tissue; Modeling of
cellular mechanism of bone growth; bioheat
transfer: kidney function
Herbert Weinstein: Fluidization and
multiphase flows: multiphase chemical
reactor analysis and design, Multiphase
reactor analysis and design

ASSOCIATED FACULTY:
OJimmy Feng: (Mechanical Eng.) Liquid crystals
'Joel Koplik: (Physics) Fluid mechanics;
Molecular modeling; Transport in random media
Hernan Makse: (Physics) Granular mechanics
Mark Shattuck: (Physics) Experimental granular
rheology; Computational granular fluid dynamics:
Experimental spatio-temporal control of patterns
SLevich Institute
National Academy of Sciences
National Accadenn of Engineering


CONTACT INFORMATION:
Department of Chemical Engineering
City College of New York
Convent Avenue at 140th Street
New York. NY 10031
www-che.engr.ccny.cuny.edu
che.hr@aol.com













Cleveland State University


Grdut Stde in Chmia and Appie Bimdia Engneein


Engineering Degrees
M. Sc. Chemical Engineering
D. Eng. Applied Biomedical Engineering
D. Eng. Chemical Engineering

CSU Faculty
A. Annapragada (University of Michigan)
J.M. Belovich (University of Michigan)
G. Chatzimavroudis (Georgia Institute of Technology)
G.A. Coulman (Case Western Reserve University)
J.E. Gatica (State University of New York at Buffalo)
B. Ghorashi (Ohio State University)
E.S. Godleski (Cornell University)
R. Lustig (Institute of Thermo- and Fluiddynamics of the
Ruhr-University Bochum, Germany)
D.B. Shah (Michigan State University)
O. Taln (Arizona State University)
S.N. Tewari (Purdue University)
S. Ungarala (Michigan Technological University)
CCF Collaborating Faculty
J. Arendt (Ohio State University)
B. Davis (Pennsylvania State University)
K. Derwin (University of Michigan)
A. Fleischman (Case Western Reserve University)
M. Grabiner (University of Illinois)
S. Halliburton (Vanderbilt University)
G. Lockwood (University of Toronto, Canada)
C. McDevitt (University of London, U.K.)
S. Roy (Case Western Reserve University)
R. Shekhar (Ohio State University)
W. Smith (Cleveland State University)
A. van den Bogert (University of Utrecht, The Netherlands)
I. Vesely (University of Western Ontario, Canada)
G. Yue (University of Iowa)


For more information, write to:
Graduate Program Coordinator Department of Chemical Engineering
Cleveland State University Cleveland, OH 44115
Telephone: 216-687-2569 E-mail: ChE@csvax.egr.csuohio.edu
http://www.csuohio.edu/chemical_engineering/


Fenn College has more than 75 years of experience in provid-
ing outstanding engineering education.

Graduate Studies in Chemical and Applied Biomedical Engineering
at Cleveland State University's (CSU's) Fenn College of Engineering
offers a wealth of opportunity in a stimulating environment.
Research opportunities are available in collaboration with the Bio-
medical Engineering Department of the renowned Cleveland Clinic
Foundation (CCF), Cleveland's Ad-
vanced Manufacturing Center, local and
national industry, and Federal agencies,
to name a few. Assistantships and Tuition
Fee Waivers are available on a competi-
tive basis for qualified students.
Cleveland State University has 16,000
students enrolled in its academic pro-
grams. It is located in the center of the
city of Cleveland, with many outstand-
ing cultural and recreational opportuni-
ties nearby.


RESEARCH AREAS
Adsorption Processes
Agile Manufacturing
Artificial Heart Valves
Biomechanics
Bioreactor Design
Bioseparations
Blood Flow
Combustion
Computational Fluid Dynamics
Drug Delivery Systems
Environmental Pollution Control
Materials Synthesis and Processing
Medical Imaging
MEMS Technology
Orthopedic Devices
Process Modeling and Control
Reaction Engineering
Statistical Mechanics
Surface Phenomena and Mass Transfer
Thermodynamics and Fluid Phase Equilibrium
Tissue Engineering
Tribology
Ventricular Assist Devices
Zeolites: Synthesis, Adsorption, and Diffusion


Assistantships and Tuition/Fee Waivers are available on a competitive basis for qualified students.

340 Chemical Engineering Education













University of Colorado at Boulder


The Boulder campus has a controlled enrollment of about 22,000 undergraduates and 5,000 graduate students. The beautiful
campus has 200 buildings of rough-cut sandstone with red-tile roofs. The excellent educational opportunities and beautiful
location attract outstanding students from every part of the United States and 85 countries.
The University of Colorado has its main campus located in Boulder, an attractive community of 90,000 people located at the
base of the Rocky Mountains. Boulder has over 300 days of sunshine per year, with relatively mild and dry seasons. The city is
an active and innovative town that provides a rich array of recreational and cultural activities.

-- Department of Chemical Engineering Faculty and Research Interests -


Kristi S. Anseth
Polymers, Biomaterials, Tissue Engineering
Christopher N. Bowman
Polymers, Membrane Materials
David E. Clough
Process Control, Applied Statistics
Robert H. Davis
Fluid Mechanics, Biotechnology, Membranes
John L. Falconer
Catalysis, Zeolite Membranes
R. Igor Gamow
Biophysics, High Altitude Physiology, Human Performance,
Diving Physiology
Steven M. George
Surface Chemistry, Thin Films, Nanoengineering
Doug Gin
Polymers
Ryan Gill
Biotechnology


Christine M. Hrenya
Fluidization, Granular Systems, Fluid Mechanics
Dhinakar S. Kompala
Biotechnology, Animal Cell Cultures, Metabolic
Engineering
J. Will Medlin
Heterogeneous Catalysis, Solid-State Sensors,
Computational Chemistry
Richard D. Noble
Membranes, Separations
W. Fred Ramirez
Process Control, Biotechnology
Theodore W. Randolph
Biotechnology, Supercritical Fluids
Robert L. Sani
Transport Phenomena, Applied Mathematics
Daniel K. Schwartz
Interfacial and Colloid Science
Alan W. Weimer
Ceramics, Energy, Reaction Engineering


Graduate students may participate in the interdisciplinary Biotechnology Training Program and the
interdisciplinary NSF Industry/University Cooperative Research Center for Membrane Applied Science and Technology
and the Center for Fundamentals and Applications of Photopolymerizations.

For information and application
Graduate Admissions Committee Department of Chemical Engineering
University of Colorado Boulder, CO 80309-0424
Phone (303) 492-7471 Fax (303) 492-4341 E-mail chemeng@spot.colorado.edu
http://www.Colorado.EDU/che/


Fall 2002





















Evolving from its origins as a school of
mining founded in 1873, CSM is a unique,
highly-focused University dedicated to
scholarship and research in materials,
energy, and the environment.

The Chemical Engineering Department at
CSM maintains a high quality, active, and
well-funded graduate research program.
According to the NSF annual survey of
research expenditures, our department has
placed in the top 25 nationally each of the
last 5 years. Research areas within the
department include:
Materials Science and Engineering
Org aic and inorganic membranes (Way, Baldwin)
Polymeric materials (Dorgan, McCabe, Wu)
Colloids and complex fluids (Marr, Wu)
Electronic materials (Wolden)
Fuel cell membranes (Way)

Theoretical and Applied Thermodynamics
Natural gas hydrates (Sloan)
Molecular simulation and
modelling (Ely, McCabe)

Transport Properties and Processes
Dermal absorption (Bunge)
Microfluidics (Marr)

Space and Microgravity Research
Membranes on Mars (Way, Baldwin)
Water mist flame suppression (McKinnon)

Reacting Flows Finally, located at the
Flame kinetics (McKinnon, Dean) foot of the Rocky
Reaction mechanisms (Dean, McKinnon) Mountains and only 15
High-T fuel cell kinetics (Dean) miles from downtown
Denver, Golden enjoys
over 300 days of
sunshine per year.
These factors combine
to provide year-round
cultural, recreational,
and entertainment
opportunities virtually
unmatched anywhere
in the United States.


Chemical Engineering Education












tate University

CSU is located in Fort Collins, a pleasant commu-
nity of 100,000 people with the spirit of the West the
vitality of a growing metropolitan area, and the
friendliness of a small town. Fort Collins is located
about 65 miles north of Denver and is adjacent to
the foothills of the Rock, Mountains. The climate is
excellent, with 300 sunny days per year mild tem-
peratures, and low humidity. Opportunities for hik-
ing, biking, camping, boating, fishing, and skiing
"- -abound in the immediate and nearby areas. The cam-
S pus is within easy walking or biking distance of the
S .- __ town's shopping areas and its Center for the Per-
forming Arts.

I.S. and Ph.D. programs in

chemical engineering FACULTY


rESEARCH IN ... Brian C. Batt, Ph.D.
Advanced Process Control University of Colorado
I Biochemical Engineering Laurence A. Belfiore, Ph.D.
I Biomedical Engineering University of Wisconsin
Chemical Thermodynamics
David S. Dandy, Ph.D.
> Chemical Vapor Deposition California Institute of Technology
Computational Fluid Dynamics
Environmental Biotechnology M. Nazmul Karim, Ph.D.
Environmental Engineering University ofManchester
Magnetic Resonance Imaging James C. Linden, Ph.D.
I Membrane Separations Iowa State University
Metabolic Engineering
SPolymertic iMaterials Vincent G. Murphy, Ph.D.
University of Massachusetts
0 Porous Media Phenomena
Thin Films Kenneth F. Reardon, Ph.D.
Tissue Engineering California Institute of Technology

Kristina D. Rinker, Ph.D.
FINANCIAL AID AVAILABLE North Carolina State University
Teaching and research assistantships paying a
monthly stipend plus tuition reimbursement. A. Ted Watson, Ph.D.


For applications and further information, write
Graduate Advisor, Department of Chemical Engineering
Colorado State University
Fort Collins, CO 80523-1370


A


F


Fall 2002


California Institute of Technology

Ranil Wickramasinghe, Ph.D.
University of Minnesota


y
a,












University of Connecticut

Chemical Engineering Department
Graduate Study in Chemical Engineering

[1 Biochemical Engineering and Biotechnology
James D. Bryers, Ph.D., Rice University (Joint Appointment)
Biochemical Engineering, Biofilm Processes, Biomaterials
Robert W. Coughlin, Ph.D., Cornell University
Biotechnology, Biochemical and Environmental Engineering Catalysis, Kinetics, Separations,
Surface Science
Ranjan Srivastava, Ph.D., University of Maryland
Experimental and Computational Biology, Biomolecular Network Analysis, Stochastic Biological
Phenomena, Evolutionary Kinetics
Thomas K. Wood, Ph.D., North Carolina State University
Microbiological Engineering, Bioremediation with Genetically-Engineered Bacteria,
Enzymatic Green Chemistry, Biochemical Engineering, Biocorrosion

El Polymer Science
Patrick T Mather Ph.D., University of California, Santa Barbara

Richard Parnas, Ph.D., University of California, Los Angeles
Composites, Biomaterials
Montgomery T Shaw, Ph.D., Princeton University
Polymer Rheology and Processing, Polymer-Solution Thermodynamics
Robert A. Weiss, Ph.D., University ofMassachusetts
Polymer Structure-Property Relationships, Ion-Containing and Liquid Crystal Polymers,
Polymer Blends
Lei Zhu, Ph.D., University ofAkron
Polymer Phase Transitions, Structures of Morphologies of Block Copolymers, Polymeric
Nanocomposites, Biodegrabable Block Copolymers for Drug Delivery

El Computer Aided Modeling
Luke E.K. Achenie, Ph.D., Carnegie Mellon University
Modeling and Optimization, Molecular Design, Artificial Intelligence, Flexibility Analysis
Thomas F Anderson, Ph.D., Univesity of California at Berkeley
Modeling of Separation Processes, Fluid-Phase Equilibria
Douglas J. Cooper, Ph.D., University of Colorado
S-~_ Process Modeling, Monitoring and Control
Michael B. Cutlip, Ph.D., University of Colorado
Kinetics and Catalysis, Electrochemical Reaction Engineering, Numerical Methods
Suzanne Schadel Fenton, Ph.D., University of Illinois, Urbana-Champaign
Computational Fluid Dynamics, Turbulence, Two-Phase Flow

[1 Environmental and Energy Engineering
Can Erkey, Ph.D., Texas A&M University
Supercritical Fluids, Catalysis, Nanotechnology
James M. Fenton, Ph.D., University of Illinois, Urbana-Champaign
Electrochemical and Environmental Engineering, Mass Transfer Processes, Electronic
191 Auditorium Road, Unit 3222 Materials, Energy Systems, Fuel Cells
Storrs, CT 06269-3222
Joseph J. Helble, Ph.D., Massachusetts Institute of Technology
Tel: (860) 486-4020 Fax: (860) 486-2959 Air Pollution, Aerosol Science, Nanoscale Materials Sythesis and Characterization, Combustion
www.engr.uconn.edulcheg
www.engr.uconn.edu/cheg Emeritus Professors
cheginfo@engr.uconn.edu C.O. Bennett, J.P. Bell, A.T. DiBenedetto, G.M. Howard, H.E. Klei, D.W. Sundstrom


Chemical Engineering Education














CORN0LL


At Cornell University, graduate students in chemical engineering have the flexibility to
design research programs that take full advantage of Cornell's unique interdisciplinary
environment and enable them to pursue individualized plans of study.
Cornell graduate programs may draw upon the resources of many excellent departments
and research centers such as the Biotechnology Center, the Cornell Center for Materials
Research, the Cornell Nanofabrication Facility, the Comell Supercomputing Facility, and
the Nanobiotechnology Center.
Degrees granted include Master of Engineering, Master of Science, and Doctor of
Philosophy. All Ph.D. students are fully funded with tuition coverage and attractive
stipends.


A. Brad Anton
Lynden A. Archer
Paulette Clancy
Claude Cohen
Lance Collins
T. Michael Duncan
James R. Engstrom
Fernando A. Escobedo
Emmanuel P. Giannelis
Peter Harriott
Yong Lak Joo
Donald L. Koch
Kelvin H. Lee
Leonard W. Lion
Christopher K. Ober
William L. Olbricht
David Putnam
Ferdinand Rodriguez
Michael L. Shuler`,'
Paul H. Steen
Larry Walker
Ulrich Wiesner

member, National Academy of Engineering
member American Academy of
Arts & Science


Research Areas
* Advanced Materials Processing
* Biochemical and Biomedical Engineering
* Fluid Dynamics, Stability, and Rheology
* Molecular Thermodynamics and
Computer Simulation
* Polymer Science and Engineering
* Reaction Engineering: Surface Science,
Kinetics, and Reactor Design
Situated in the scenic Finger Lakes region of
New York State, the Cornell campus is one of
the most beautiful in the country. Students
enjoy sailing, skiing, fishing, hiking, bicycling,
boating, wine-tasting, and many other
activities.
For further information, write:
Director of Graduate Studies, School of Chemical Engineering, Cornell University, 120 Olin Hall, Ithaca, NY 14853-5201,
e-mail: DGS @CHEME.CORNELL.EDU, or "visit" our World Wide Web server at: http://www.cheme.comell.edu


Fall 2002










Graduate Study & Research in Chemical Engineering
at


Dartmouth's Thayer School of Engineering


Dartmouth and its affiliated professional schools offer PhD degrees in the full range of science disciplines as well as
MD and MBA degrees. The Thayer School of Engineering at Dartmouth College offers an ABET-accredited BE degree, as
well as MS, Masters of Engineering Management, and PhD degrees. The Chemical and Biochemical Engineering Pro-
gram features courses in foundational topics in chemical engineering as well as courses serving our areas of research
specialization:
Biotechnology and biocommodity engineering
Environmental science and engineering
Fluid mechanics
Materials science and engineering
Process design and evaluation
These important research areas are representative of those found in chemical engineering departments around the world.
A distinctive feature of the Thayer School is that the professors, students, and visiting scholars active in these areas have
backgrounds in a variety of engineering and scientific subdisciplines. This intellectual diversity reflects the reality that
boundaries between engineering and scientific subdisciplines are at best fuzzy and overlapping. It also provides opportu-
nities for students interested in chemical and biochemical engineering to draw from several intellectual traditions in
coursework and research. Fifteen full-time faculty are active in research involving chemical engineering fundamentals.


Faculty & Research Areas


Ian Baker (Oxford) 1- Structure/property relationships of materials, electron microscopy
John Collier (Dartmouth) > Orthopaedic prostheses, implant/host interfaces
Alvin Converse (Delaware) Kinetics & reactor design, enzymatic hydrolysis of cellulose
Benoit Cushman-Roisin (Florida State) > Numerical modeling of environmental fluid dynamics
Harold Frost (Harvard) N Microstructural evolution, deformation, and fracture of materials
Tillman Gerngross (Technical University of Vienna) > Engineering of glycoproteins, fermentation technology
Ursula Gibson (Cornell) 0 Thin film deposition, optical materials
Francis Kennedy (RPI) 0 Tribology, surface mechanics
Daniel R. Lynch (Princeton) 0 Computational methods, oceanography, and water resources
Lee Lynd (Dartmouth) Biomass processing, pathway engineering, reactor & process design
Victor Petrenko (USSR Academy of Science) 0 Physical chemistry of ice
Horst Richter (Stuttgart) > Thermodynamics, multiphase flow, energy conversion, process design
Erland Schulson (British Columbia) N Physical metallurgy of metals and alloys
Charles E. Wyman (Princeton) Biomass pretreatment & hydrolysis, cellulase synthesis & kinetics, process design



For further information, please contact:

Chemical Engineering Graduate Advisor Thayer School of Engineering Dartmouth College Hanover, NH 03755
http://thayer.dartmouth.edu/thayer/research/chem-biochem
5 Chemical Engineering Education




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A .... I:: C ... .... C:! l.l ::: ,:s 0,0 I:: ... r... <:II <:II I:: ... 0,0 I:: r... ,:> <:II ... l.l C VJ I:: C:! l.l ... r... "' <:II r... E: <:II <:II I:: ... I::~ 0,0 C I:: ... "' ... ;:,. C:! ... l.l Cl ... 0,0 E: I:: <:II ... .::: r... CJ <:II <:II 'c> I:: ... <:II 0,0 .... I:: ::: .... ... .... "' C:! I:: l.l ..... ... E: I:: <:II C:! .::: l.l CJ ... r... <:II E: chemical engineering education VOLUME 36 NUMBER4 FALL2002 G RA D UATE E D UCAT IO N ... ___________ A Novel Approach for Describing Micromixing Effects in Homogeneous Reactors (pg. 250) Vemuri Balakotaiah, Saikat Chakraborty Introducing Moleculr Biology to Environmental Engineers Through Development of a New Course (pg. 258) Daniel B. Oerther Articles of Gene ra l I nte r est .. Chem-E-Car Downunder (pg. 288) Rhodes On Improving Thought with Hands," (pg.292) Sureshkumar Khilar Making Phase Equilibrium More User-Friendly (pg. 284) Misovich Random Thoughts: Speaking of Education-III (pg 282) Felder Novel Concepts for Teaching Particle Technology (pg. 272) Peukert Schmid Portfolio Assessment in Introductory ChE Courses (pg. 310) Bhatia New Approach to Teaching Turbulent Thermal Convection (pg. 264) Churchill Determining the Flow Characteristics of a Power Law Liquid (pg. 304) Hillier, Ting, Kopplin, Koch, Gupta The Earth's Carbon Cycle: Chemical Engineering Course Material (pg. 296) Schmit z Aspects of Engineering Practice: Examining Value and Behaviors in Organizations (pg.316) Espino Gas Station Pricing Game: A Lesson in Engineering Economics and Business Strategies (pg 278) Sin Center

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I N D E X GRADUATE EDUCATION ADVERTISEMENTS A k ro n U ni ve r s it y of.............................. 32 1 A l a b a m a, Un i versi t y of.... ............ .. ..... 322 A l a b a m a, Hunt sv ill e; U ni versi t y of .............. 323 A l be rt a, U ni ve r si t y of... .............. 324 Ar i zo n a, U ni ve r s it y of. ................. .... 325 Ar i zo n a S t a t e U ni ve r s it y .... .... 326 A uburn U ni ve r s it y .. .. ... .. .. .. ..... Bri g h a m Y o un g U ni ve r s it y ......... Briti s h Co lum b i a, U ni ve r s it y o f B row n U ni ve r s it y .. .. Bu c kn e ll Uni ve r s it y .. Ca l ga r y, U ni ve r s it y of Ca li fo rni a, B e r ke l ey; Un i vers it y of 327 ..... 427 .... 427 .... 44 1 ......... 428 ...... 328 329 Ca li fo rni a, D avis; U ni ve r s i ty o f ...... 330 Ca li fo rni a, I rv in e; U ni ve r s i ty of .... ......... ... 331 Ca li fo rni a, L os An ge l es; U ni vers it y of .. 332 Ca li fo rni a, R ive r s id e, U ni ve r s it y of .. .. .... 333 Ca li fo rni a, S ant a B ar b ara; U ni ve r s it y of .. 334 Ca lif o rni a In s titut e o f T ec hn o l ogy... .. 335 Car n eg i eM e ll o n U ni vers i ty. .. .. ... .. 336 Case W es t e rn R ese r ve U ni ve r s it y ..... 337 C in c inn a ti U ni ve r s it y of Ci t y Co ll ege of New Y o r k C l eve l a nd St a t e U ni ve r s it y. Co l orado, B o uld e r ; U ni ve r s it y of Co l o rad o S c h oo l o f Min es .. Co l o rad o St a t e U ni ve r s it y .. Co lumbi a U ni vers it y Co nn ec ti c ut U n ive r s it y of .... 338 ..... 339 340 .. ......... 34 1 ............... 342 ..... 343 ... 428 ....... 344 Co rn e ll U ni ve r s it y D a rtm o uth Co ll ege .............. ... 345 ... 346 D e l awme, U ni ve r si t y of.. .. .. .. .. ..... 347 D rexe l U ni vers it y .......... .. 348 Eco l e P o l y t ec hniqu e M o nt rea l ...... .. ... .. 349 E n g in eer in g R esea r c h Ce nt e r ... ..... -1 29 F lorid a, U ni ve r s it y o f ... ................. 350 Flo ri da A&M/F l o rid a S t a t e U ni versity .. .. ... 351 Fl o ri d a In s titut e o f Tec hn o l ogy .......... 352 Geo r gia In s titut e of Tec hn o l ogy. .... 353 H o u s t o n U ni ve r si t y of ............. ........... ...... 354 H owa rd U ni ve r s it y .. .... ... .. .. .................. 355 Id aho, U ni vers it y of..................... .... 429 Illin o i s, C hi cago; U ni ve r s it y of. ... 356 Illin o i s, U rb a n a-C h a m pa i g n ; U ni vers it y of .. 357 Illin o i s In s titut e o f T ec hn o l ogy ... 358 Iowa, U ni ve r s it y o f ..................... ... 359 I owa S t a t e U ni vers it y ... .. Joh n s H opkins U ni ve r si t y K a n sas, U ni ve r si t y of Ka n sas S t a t e U n ivers it y Ken tu cky, U ni versity of L a m a r Un i ve r s it y L ava l Un i vers it e .... L e hi g h U ni vers it y ... 360 .. 361 .. 362 363 .... 36-1 ............. 430 L o ui s i a n a, L afaye tt e: U ni versi t y o f .... Lo ui sia n a S t a t e U n ive r s i ty 365 366 ..... 367 .. 368 ...... 430 431 369 .......... 370 L o ui s i a n a T ec h Unive r s i ty Lo ui svi ll e, U ni vers it y of. Ma nh a tt a n Co ll ege Ma r y l and U ni ve r si t y of Ma r y l and, Ba lt i m ore Co un ty: Un i vers it y of 371 M assac hu se tt s, L owe ll ; Un i ve r s it y of .......... 44 1 Massac hu se tt s, Am h e r s t : U n ive r s it y of 372 M assac hu se tt s In s titut e of Tec hn o l ogy .. .. 373 M cG ill U ni ve r s it y... 43 1 M cMaster U ni vers i ty Mi c h iga n U ni ve r s it y o f Mi c h igan S t a t e University ................ Mi c h igan Tec hn o l ogica l Un i ve r s it y .. . ....... 37-1 375 376 -132 Minn eso t a, U ni ve r s it y o f ........... ... 377 Mi ss i ss i pp i S t a t e U ni vers it y ....... .. 378 Mi sso uri Co lumbi a; U n ivers it y of ......... .... 379 Mi sso uri R o ll a: U ni ve r si t y of ............ ..... 380 M o n as h U ni ve r si t y .. 432 M o nt ana S t a t e U n ive r s it y .. -133 Nebraska, U ni ve r sity of.. ................ 381 Neva d a, R e n o; Un i ve r s it y of .. 4 33 New J ersey In sti tut e of Tec hn o l ogy 382 New Mex i co, Un i ve r s it y of .................. 383 New M ex i co St a t e U ni ve r s it y .. 384 New So uth Wa l es, U n ive r s i ty of ............ 434 No rth Caro lin a St a t e U ni ve r s it y .... No rth Dako t a, Unive r s i ty of .... Nor th easte rn U ni versity .. .. Nort h western U n iversi t y No tr e Dame. Un i versity of O hi o S t a t e U ni ve r s it y O h io U n ivers it y .. Ok lah o m a, U ni ve r s it y of .. ......... 385 ... -134 .. 386 ..... 387 388 ...... 389 390 39 1 Ok lah o m a S t a t e U ni ve r s it y 392 O r ego n Sta t e U ni ve r s i ty .. ................. ...... 393 P e nn sy l va ni a, U ni ve r s it y of .... ....... 394 Pe n sy l va ni a Sta t e U ni ve r si t y Pittsburgh. University of Poly t echnic University .. Princeton University ... Purdue Universit) Re n sse l aer Poly t ec hni c In sti tut e ........ ... 395 396 ...... 397 .. 398 .. 399 ... 400 435 Rhode I sland. Universi t y of R ice Un i versi t y .... ........... 40 1 Roc h ester, Universi t y of. ..... 402 Rose H u l man I nsti t ute of Tec h no l ogy .. .... 435 Rowan Univers it y.................... .... 403 Rutgers Uni,ersity .................. Saska t chewan. University of Singapore, Nationa l University of .. .... 404 .... 436 405 .. 406 South Carolina, University of ....... So uth F l or i da, Un i ve r s it y of ........................ -1 37 Sout h ern California, U n ivers i ty of ..... 4 36 Sta t e Un i vers it y of New Yo r k 407 Stevens I nstitu t e 4 08 Sydney University of Syracuse, Unive r s it y of Tennessee. University of Texas, Un i versity of ...... 437 438 .. -109 4 1 0 Texas A&M Un i vers i ty... 4 11 Texas A&M Un i versi t y, Ki n gsvi ll e .. .... .. -138 To l edo. U ni ve r s it y of .... 4 1 2 Tufts U n iversity Tulane Universi t y .. Tulsa, Uni,ersit) of .... Utah. Universi t y of .. Va n derbi l t University Villanova University Virgi ni a, Un i ve r sity of .... Virgi n ia Tec h .. Wash in gton. U n iversi t y of Wash in gton Sta t e U ni ve r s it y Washington University .. Waterloo, University of .. Wayne State Univer,ity .... West Virginia University Wisco n s i n, U n iversi t y of Worcester Poly t ec h nic I nstitute Wyo mi ng, Un i vers i ty of ..... Ya l e U n iversity .. ..... 4 1 3 .. 4 1 4 415 ....... .. .. 439 ......... 4 1 6 439 4 1 7 ................ 4 1 8 .... 4 1 9 ......... 420 .. 421 4-10 422 .... -123 .. -1 24 .. -125 .... 440 .. 426

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EDITORIAL AND B USINESS ADDRESS: Chemical Engineering Education Depart m ent of C h e m ical Engineering University of Florida Gainesville, FL 32611 PHO NE and FAX: 352-392-0861 e -mail : cee @c h e .ufl. e du E DI TOR Tim An d erson ASSOCIATE EDITOR Phillip C. Wankat MANAGING EDITOR Carole Yocum PR O B LEM EDITO R James 0. Wilkes, U. Michigan LEARNING IN INDUSTRY EDITOR William J. Koros Georgia In stitute of T ec hnolo gy P UBLI C ATION S B O ARD CHAI R MAN Fall 2002 E Dendy Sloan Jr. Colorado S c ho ol of M i nes MEMBERS Pablo Debenedetti Prin ceto n University Dia1111e Dorland R owa n Un i vers i ty Thomas F. Edgar Unive r sity of T exas a t Aust in Richard M Felder Nort h Caroli n a Stat e Univ e r s i ty Bruce A. Finlayson Univ e rsity of Wa sh in g t o n H Scott Fogler University of Mi c hi ga n William J. Koro s Georgia In s titut e of T ec hnolo gy David F. Ollis Nor th Carolina Stat e University Rona l d W. Rousseau G eo r g i a In stitute of T ec hn o l ogy Stanley I Sand l er Unive r s i ty of D e l aware Richard C. Seagrave Iowa State U ni versity C. Stewart Slater R owan Uni ve rsity James E. Stice University of T exas at Aus tin Donald R. Woods M c Mast e r University Chemical Engineering Education Volume 36 Number 4 Fall 2002 GRADUATE EDUCATION 250 A Novel Approach for D esc ribin g Micromixing Effects in Hom oge n eous R eactors, Vemuri Balakotaiah, Saikat Chakraborty 258 Int roducing Moleculr Biology to Environ m e nt a l E n gi n eers Through D eve lopment of a New Course Dani e l B Oerther CLASSROOM 264 A New Approach to T eac hin g Turbulent Thermal Convection, Stuart W Churc hill 278 Gas Station Pricing Game : A Le sso n in Engineering Economics a nd Bu s ines s Strategie s, Aaron Sin Alfred M. Center 284 Makin g Pha se Equilibri um More User-Friend l y, Michael J Misovich CURRICULUM 272 Nov e l Co n cepts for Teaching Particle Technology W olfgang P e ukert, H ansJ oachim Schmid 296 Th e Eart h s Ca rb o n Cycle : Chemical Engin ee rin g Course Mat er ial R oge r A S c hmit z 316 A s p ec t s of E n gi n eeri n g Practice : Exami nin g Value a nd Beha v i ors in Or ga ni za tion s, Ram on L. E s pino RANDOM THOUGHTS 282 Speaking of Ed u cation-ill, Ri c hard M. F e lder LABORATORY 288 Chem-E-Car D ow nund e r Martin Rh odes 292 On Impro v in g T h o u g ht w ith H a nd s," G.K. Sureshkumar, K.C Khilar 304 D etermini n g the Flow Characteri s tic s of a Power L aw Liquid J am es R H illi e r, D ale Ting, Lisa L. Kopplin Mar ga ret Ko c h, Santosh K. Gupta ASSESSMENT 310 P ortfo li o Assess m e nt in Int ro duct o r y ChE Courses Surita R Bh atia 257, 263 270 Letter t o the Editor 281 A nn o un ce m en t s 320 Index for Graduate Education Advertisement s C H EM I CA L ENG I NEE RI NG E DUC A TIO N ( I SSN 0009-24 79) is publi s h e d quarterl y by t h e C h e m ica l E n gi n ee rin g Di visio n America11 Society for E 11gineering Edu c ation and is e dit e d at th e U n ive r sity of Florida. Co rr es p on d e n ce r eg arding e dit orial matt e r c ir c ulati o 11 and c ha11 ges of addres s s h o uld b e sent to CEE C h e mical E 11 g i11 eeri 11 g Departm e nt Universi t y of Fl o rida Gainesv ill e, FL 326 JJ-600 5. Copyright 2002 b y tir e C h e mi cal E n gi n ee rin g Di v i sio n A m eric an Society for E n gifleerillg E du c atio11 T h e s tat e m e n ts and opinions expressed i11 this periodical ar e th ose of th e writers and 11 0 1 nece ssari l y tho se of t h e C h Di v i s i o n ASE, w h ic h bod y as s um es 110 re s pon sib ility for th e m Defective co pi es r e pla ced if notifi e d with in 1 20 da ys of publi c ati o 11. Writ e for illformatio11 011 s ub scriptio 11 cos t s and for back copy cost s and availability. POSTM ASTE R : Se 11d address c han ges to C h e mi ca l E 11 g ill eerillg E du ca ti o n C h e mi c al E 11gi11 ee rill g Departm e nt ., U ni ve r s it y of Florida Gai11e sv ill e, FL 32611 P e ri o di c al s Postage Paid at Ga in esv ill e, Fl o rida a11d additio 11 a/ p os t offices. 249

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[ Graduate Education ) A Novel Approach for Describing MICROMIXING EFFECTS IN HOMOGENEOUS REACTORS VEMURI BALAKOTAIAH SAIKAT CHAKRABORTY University of Houston Hou ston, TX 77204-4004 R eacting flow systems are hierarchical in nature i.e., they are characterized by multiple length (or time) sca les Scale separatio n exists in most reactors how eve r and these di spara te sca le s are typically characterized by three representative one s, namel y, micro (mo lecular ), me so (ca taly st particle or tube di a meter ), a nd m acro (reac tor or pro cess) sca le s. In most cases of practical interest, a strong non linear coupling exists between reaction and transport at micro and me so sca le s, and the rea c tor performance at the macro scale. As a re s ult transport limitations at the smaller scales s ignifi can tl y influ e nce the reactor and h ence the process performance. Such effects could be quantified b y numerically so lving the convection-diffusion-reaction (C DR ) equation from the macro down to th e micro sca l e. But th e so lution of the CDR eq uation from the reactor ( macro ) scale down to the local diffusional (micro) sca le u s in g computational fluid dynam ic s ( CFD ), i s prohibiti ve in terms of numeri ca l effort and im practical for the purpose of reactor co ntrol a nd optimization Moreover even with today 's com put atio nal power it i s im practical to explore the differ e nt type s of bifurcation features and s patio-temporal behavior s that ex i s t in the multidimen s ional parameter s pace u si ng CFD co de s. In s uch cases, low dimensional model s are a natural alternative. Hi s torically chemical engineers h ave derived low dimen s ional model s for reactor s u s in g a top-down approach which is ba se d on a priori assumptions on th e length and time scales of convection, diffu s ion and reaction. The classical ideal re actor ( CSTR a nd PFR ) model s a r e exa mple s of such low dimen s ional models obtained on the ba s i s of si mplified (or oversimplified) assumptions. The se assumptions are u s ually not justified si nce justification requir es comparison of the so lution obtained from the s implified model s with that ob tained from the CDR mod e l. In order to account for experimental observations that could not be explained by thes e ideal reactor model s, the latter ha ve been modified b y introducing the co ncept s of di s per s ion co250 efficientsl 1 5 l and residence time distribution l 3 6 71 to account for macroand micro-mixin g effects. Several other reactive mixing model s followed in the next forty years: the twoand three-environment modeJ ,f 8 91 the coalescence-redispersion model 1 1 01 interaction by exchange with mean model ,11 11 en gulfment-deformation-diffusion modeJ 112 1 and CFD mod e l s u s ing probability den sity functions ( PDF ) and direct numeri cal si mulation ( DNS ). Thi s article pre se nt s an alternative (bottomup ) a pproa c h and an elementary treatment of mixing effects on reactor per formance. We will pre se nt a brief hi storica l review of homo geneous reactor model s before discussing this new approach BRIEF HISTORY OF HOMOGENEOUS REACTOR MODELS The mo st widely u se d homo ge neou s reactor model s are the three cla ss ical ideal reactor mod e l s: the plug-flow reactor (PFR) model the continuous st irred tank reactor (C STR ) model and the batch reactor ( BR ) model. While the BR model and the PFR model (which are identical for constant den sity sys tem s with time replaced by s pace time or dimen s ionle ss di s tan ce along the tube ) have ex i ste d since the late eighteenth century. A conceptual leap came in the form of the CSTR model through the work of Boden s tein a nd Wohl gas t in 1908 1 1 3 1 Unlike the PFR model which ass ume s no gra dient s in the radial direction and no mixing in the axial direction V e muri Balako tai ah is Professor of Chemical Engineering at the Uni versity of Houston. He received his BTech from the Indian Institute of Technology (Madras) in 1978 and his PhD from the University of Hous ton in 1982 both in chemical engineering His teaching and research i nterests are in the areas of chemical reaction engineering, multiphase flows and applied mathematics S a i k a t Ch a k r abo rt y is a PhD candidate in the Department of Chemical Engineering at the University of Houston He received his BTech from Jadavpur Universit y in 1997 and his MS from the Indian Institute of Sci ence ( Bangalore ) in 1999 both in chemical engineering. His research interests are in the areas of c hemical reaction engineering and granular materials. Copyri g ht ChE Di v ision o f ASEE 2002 C h e mi ca l Engineering Education

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the CSTR model assumes complete mixing at all scales. For constant density systems, the three classical reactor models are described by PFR ( u ) d(C) = -R( (c)) with ( C) = C;n @ X = 0 (I) dx BR d (C) = -R((C)) with (C) = C;n @ t = 0 (2) dt CSTR ( C)-C;n =-R((C)) tc (3) where (C) is the spatially (or cross-sectional) averaged reac tant concentration, C is the mean inlet concentration of the reactant R( (c)) is th 1 ; sink term due to the presence of ho mogeneous reaction, x is the coordinate along the length of the PFR ( u ) is the mean fluid velocity in the reactor tis the time, and tc is the total residence time in the reactor. Irving Langmuir 111 first replaced the assumption of no axial mixing of the PFR model with firute axial mixing and the accompanying Dirichlet boundary condition ( ( c ) = C in @ x = 0) by a flux-type boundary condition Dm d (C) =(u)[ ( C ) -C;n] @ x=O dx (4) where D m is the molecular diffu sivity of the species. The above boundary condition was rediscovered several time s in the years that followed: first by Forster and Geib 161 which was quoted and applied by Damkohler ,1 2 1 and then later by Danckwerts .l3 1 Since then it ha s been known as the Danckwerts boundary condition In his paper Langmuir dealt with both the limiting cases of mixing nearly com plete and "o nly slight mixing. Thirty years later Gerhard Darnkohler in his hi storic pa per summarized various reactor models and formulated the two dimensional CDR model for tubular reactors in complete generality, allowing for finite mixing both in the radial and the axial directions In his paper Darnkohler used the flux type boundary condition at the inlet and also replaced the assumption of plug flow with parabolic velocity profile which is typical of larrunar flow in tubes. Forster and Geib first introduced the concept of residence time distribution (RTD) to study the case of longitudinal dis persion in tubes. Twenty years later Danckwerts in his much celebrated paper 1 3 1 devised a generalized treatment of RTD and introduced the concepts of"holdback" and "segregation." Following this it was Zweitering, 171 who quantified the de grees of mixing with the idea s of "complete segregation" and Fall 2002 Graduate Education ] maximum mixedness and brought forth the concept of micromixing, or mixing at the molecular scale in homoge neous reactions In the la s t forty years, a wide range of micromixing mod els for homogeneou s reactors have been formulated. While most of these low-dimensional mixing model s are phenom enological in nature the rigorously derived CFD models are high-dimensional and therefore numerically very expensive, especially for the case of multiple reactions with fast/non isothermal kinetics As a result in spite of the simplifying assumptions present the century-old ideal classical reactor models (Eqs. 1-3 ) are still the most popular choices among chemical engineering practitioners (and teachers ) The clas sica l ideal reactor models which are easy-to-solve ordinary differential or algebraic equations with no adjustable param eter are particularly preferred over the full CDR models (w hich are partial differential equations in more than one di mension ) in case of multiple reactions with complex kinetics. SPATIAL AVERAGING OF CONVECTION-DIFFUSION-REACTION EQUATION The main goal of this article is to illustrate a new approach for deriving low-dimen s ional homogeneous reactor models capable of predicting mixing effects. These models are de rived through rigorou s spa tial averaging of the three-dimen sio nal CDR equations over local length scales by using the Liapunov-Schmidt (L-S) technique of classical bifurcation theory We illu s trate this s patial averaging technique using the simple case of larrunar flow in a tube with homogeneous reaction. The sca lar concentration C( r 8 x, t ') in a tubular reactor is assumed to obey the CDR equation ac ac -+u(r)-= a t ax _!_~ ( D _j_ rac J +...!._l._ ( D _l acJ+l._ ( D x acJ-R(C) (5) r ar ar r 2 as as ax ax with accompanying initial and boundary conditions, given by C(r 8 x t' =O)=Co ac -=0@ r=a ar C( r 8 x t') = C( r 8 + 2rc x, t ') D X ac =u(r)[c(r 8 x t')-C;n] @ x=O ax ac -=0 @ x=L ax (6) where D _j_ and D x are the transverse and axial diffusivities respectively; r 0,x are the radial, azimuthal and axial coor251

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[ Graduate Ed ucation dinate s, respectively ; and u(r) is the fluid velocity profile. We take a (radius of the pipe) and L (length of the pipe) to be the characteristic lengths in the radial and axial directions, respectively; (u) is the cross-sectional average velocity; and CR i s a reference concentration. Then, we obtain four time sca les in the sys tem associated with convection ( tc) radial diffusion (t 0 ), axial diffusion (t.), and reaction (~) (7) and the ratios of these time scales give rise to the dimension less parameters: p (transverse Peclet number), Pe (axial Peclet number), Da ( Damkohler number), and <1> 2 (local Damkohler number) given by a 2 (u) t 0 p---LD _1_ tc Da = _L R_(_cR_) (u)CR a 2 R(CR) t cp2 =-~~=_!2_=pDa DxCR tR In dimensionless form, Eq. ( 5) for the case of constant spe cies diffusivities, can be rearranged as with initial and boundary conditions being c(s 8,z t=O)=co ck t -=0@ .,,=l as c(s,8,z, t )= c(s,8 + 21t,z, t) :e :: =u(s)[c-cin]@ z=O ac =0 @z=l az where t t='tc a X U z=L u= ( u ) r(c)= R(C) R(CR) (9) (10) The form of the CDR equation (Eq. 8) clearly illustrates that a sca le se paration exists in the sys tem with p being the ratio of the local to the global scale (w hen Pe and Da are of order unity) a nd s patial averaging over the local scales is possible. It can be seen from Eqs. (8) and (9) that in the limit of p 0 Vic O and transverse ( or small scale) concentra tion gradients vanish, in which case the equations simplify to the clas s ical one-mode axial disper s ion model. If local diffu252 sion time is small but finite compared to convection, reac tion, and axial diffusion time, local (transverse) gradients re main small and we can write c(s 8 Z, t) = (c)(z, t) + c'(S, 8, Z, t) (11) where (c) is the transverse averaged concentration and c' is the fluctuation about this average, and c' 0 as p 0. (Also, by definition (c') = 0 .) Multiplying Eq (11) by the local velocity profile u(s) = (u) + u', and averaging over the cross-section gives Cm = (c) + ( u'c' ) (I 2) where cm is the mixing-cup (velocity weighted) concentra tion. Similarly, transverse averaging ofEq. (8) over the cross section gives 2 s=I 8=2n: a(c) _!_a (c)+acm+Da J f sr(c)d0d~=O (13) at P e az 2 az s=0 8 =0 For the case of a tubular reactor the spatial (transverse) aver age and mixing-cup concentrations are defined by s=I 8=2n: f f sc(s 8 x, t )d8 ds ( c)= s=O 8 = 0 s=I 8 =2 n: (14) J J sct0cts s=0 8 =0 and s=I 8=2n: f f su(s)c(s 8 x,t)d8ds C = s=0 8=0 m s=I 8 =2 n: (15) f f su(s)d8ds s=0 8 =0 It may be noted that in all flow reactors, c m is the experimen tally measured variable. We refer to (c) and c as the two m modes of the system and our s patially averaged reactor models as Two-Mode Models (TMMs). Equation 13 is called the global equation, while Eq. (12) is called the local equation. The local equation shows that the difference between c and (c) depends on the local velocity gradients ( u') and the local concentration gradients (c') caused by molecular diffusion and reaction at the local sca le s. Micrornixing i s captured by the local equation as an exchange between the two mode s (sca les) cm and (c). In order to determine c' (and hence the term (u'c') or the difference between cm and (c) ), we substitute Eq. (11) in Eq (8) to obtain V ic'= pg( (c) + c') (16) Chemical Engin ee ring Education

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The L-S technique solves Eq. (16) for c' by expanding it in the parameter p as c'= L, P ;c; (17) i=I and by using the Fredholm Alternative (i.e., the fact c' lies in the function space orthogonal to which (c) resides) Such an expansion (Eq. 17) is possible since for p = 0, the trans verse diffusion operator in Eq (8) has a zero eigenvalue with a constant eigenfunction. Thus, (u'c') could be determined to any order in p, i e., closure of the local equation could be accomplished to any desired accuracy. In practice, the lead ing term (that is of order p) is sufficient to retain all the quali tative features of the fu ll CDR equation. For example, for the case of azimuthally symmetric feeding, we have (18) Substituting Eq. (18) into Eqs. (12) and (13) gives the two mode model to O(p) as a(c) ac I a 2 (c) ( 2) -+_Bl.----+Da r((c))+O p =0 at az Pe az 2 (c)-cm = ~IP a~:) +o(p 2 ) = ~,Pa;; + o(p2) with boundary and initial conditions given by 1 a(c) @ z=0 --=c -c Pe az m m m acm = 0 az @ z=l (c)=(c 0 ) @ t = 0 (19) (20) (21) (22) (23) where I/~, is called the exchange coefficient, which depends on the local shear rates. For the case of fully developed lami nar flows, D 1. = D x = Dm (molecular diffusivity of the spe cies), and ~, =1/48. We refer to this model as the two-mode axial dispersion model. (Further details of the spatial averag ing procedure using the L-S technique can be found in Chakraborty and Balakotaiah. 14 1 5 1) It should be noted that the spatially averaged CDR equa tion (Eqs. 19 and 20) retains all the parameters (p, Pe Da) of the three-dimensional CDR equation (Eq. 8) and hence all the qualitative features of the latter It should also be men tioned that this model is capable of capturing macromixing effects through the axial Peclet number Pe in the global equa tion (Eq. 19), as well as micromixing effects through the ex change coeff i cie n t ~ 1 1 and transverse Peclet number pin the Fall 2002 Graduate Education ] local equation ( Eq. 20). In fact, the L-S technique guarantees that the solution of the averaged model (Eqs. 19-23) agrees with the exact solution of the three-dimensional CDR equation to O(p ) [Three decimal accuracy is obtained for a second-or der reaction for the case of Pe oo if qi2 < 1 (see ref. 14) ] Using the spatial averaging technique illustrated above, accurate low-dimensional models could be obtained for dif ferent types of reactor s and flow profile s. For example, the two-mode model for a tubular reactor with fully developed turbulent flow is the same as Eqs (19) through (23), where D 1. i s the effective turbulent diffusivity and ~, is a function of Reynolds number (Re) and friction factor f. This model is obtained by starting with the time-smoothed (Reynolds aver aged) CDR equation where the reaction rate R(C) in Eq. (5) is replaced by the Reynold s averaged reaction rate (after clo sure) R .v (C) Spatial averaging by the L-S technique is then performed on the time-averaged CDR equation (i.e., spatial averaging follows time averaging) to obtain the two-mode model (see ref 15 for details). In the next section, we will present the two-mode models for other types of homogeneous reactors TWO-MODE MODELS FOR HOMOGENEOUS REACTORS Tubular R eactors The s teady-state two-mode model for a tubular reactor for the case of Pe oo (i.e., no macromixing present) may be obtained from Eq s (19) through (2 1 ). In dimensional form, it is given by ( u) dCm = -R((c)) with Cm(x = 0) = Cm in dx (24) (25) where the local mixing time t mix (in the local Eq. 25 describ ing micromixing effects) is given by a 2 tmi x = ~I D 1. (26) where a is the local diffusional length scale over which spa tial averaging is performed D 1. is the local diffusion coeffi cient, and ~ 1 1 is the exchange coefficient. In the limit of com plete micromixing (i.e., tmix 0), the two-mode convection model reduces to the ideal one-mode zero-parameter PFR model. Loo p an d R ecycle R eact or s In a loop reactor of length L a flow rate of q in' and with an average velocity of (u;n), enters and leaves the reactor at points x = 0 and x = l, respectively (where x is the length coordinate along the loop) The total flow rate in the loop is Q + qin between points x = 0 and x = l, and is Q between 253

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( Graduate Education points x = land x = L due to a recycle rate of Q. The recycle ratio A is the ratio of the volume of fluid returned to the reactor entrance per unit time to the volume of fluid leaving the sys tem per unit time and is given b y A= Q/q in' The two mode model for s uch a loop reactor can be obtained a s ( u;n ) dCm =1-l:J\ R((c )) dx _.!_ R( ( c)) J\ Cm (C) = tm ix R( (C)) with boundary conditions 0 x < l l~x~L O~x > 1. Thi s gives the two-mode model for a perfectly macromixed CSTR as Cm (C) Cm in Cm tm ix 'tc (32) (33) where 'tc ( = V /q;) is the total residence time in the reactor and tmi x i s the characteristic local mixing time, which cap254 ) tures mi crom1xrng effect s. In th e limit of comp let e micromixing (i.e., tmi x 0), the TMM for a CSTR reduces to the ideal one-mode zero-parameter CSTR model. It s hould be pointed out that the lo ca l e quation ( e qs. 25, 28 31 33) i s th e same for all r eac tor types. Thi s i s a n impor tant observation, which s how s that scale s eparation exis ts in all types of homogeneous reactor s PHYSICAL INTERPRETATION OF TWO-MODE MODELS Using th e exam ple of a tank reactor we pre s ent a physical interpretation of the two-mode models. The phy s ic al syste m equivalent to the two-mode model of a CSTR i s a tank reac tor consisting of two zones each of size V namely a non reacting convection zone (A), represented by C m, and a reac tion zone (B) repre se nted by (C) Thus C "' is representative of the convection sca le of the sys tem and (C) i s representa tive of the reaction scale of the sys tem The interaction be tween the two scales (o r the two zones A and B) is quantified by an exchange of material s at a rate of q E Thi s exchange occurs only through local diffu s ion and t mi ( =V/q E), which i s the characteristic time s cale for this exchange, therefore d pends on the local s hear rate and diffusion coefficient. Equa tions (32) and (33) represent the steady-state material b a l ances for zone B and zone A respectively. In general any infinitesimal volume dV in s ide the tank could be so imagined to consist of two zones/scales and a corresponding two-mode model could be written (E qs 3233) for the volume dV. If macromixing in the tank is com plete the two-mode model for any control volume dV could be integrated over the entire vo lume of the tank to generate a si ngle two mode model (Eqs. 32-33) for th e whole tank. Macrornixing effects are often not negligible in real tank s however and are influenced by severa l factors including the type and speed of impellers (turbines) and the manner of feed distribution Several macromixing model s are available in the literature, e.g., the two-compartment model, recycle model tanks-in-series model exchange-with-stagnant-zone model any of which could be s uitably coupled with the TMM to describe both macroand micro-mixing in tank s. How ever if micromixing effect s are dominant compared to macromixing ones (as in well-stirred tank s), it could be shown by using L-S reduction in finite dimension s, that the se mod els (i.e., the two-mode n-compartment model etc ) could be reduced to Eqs. (32) and (33) where the local mix i ng time t mix is rep l aced by an effective mixing time t M, which cap tures both macroand micro-mixing effects. This effective mixing time now not only depend s on the local diffusion time and local shear rates but also intricately on the tank geometry type and number of impellers baffle po si tions and power di ss ipation in the system. Chemi ca l En g in ee rin g Education

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SIMILARITY BETWEEN TWO-MODE MODELS OF HOMOGENEOUS REACTORS AND TWO PHASE MODELS OF CATALYTIC REACTORS A striking str u ctural similarity between the two-mode mod els for homogeneous reactors and two-phase models for het erogeneous catalytic reactors exists. This could be seen more clearly when Eqs. (24) and (25) are rewritten as with cm= cm in @x = 0 (34) The two-phase model for a heterogeneous wall-catalyzed re actio n in a tubular reactor is given by (u x )dCm =-Cm-Cs =-R(Cs) dx tTP with cm = cm in @ x = 0 (35) It may be noticed that the spatially averaged concentration (C) of the TMM (in Eq. 34) is replaced by the surface (wall) concentration C s in the two-phase model (Eq 35) while the local mixing time tm ix of the TMM is replaced in the two phase model by a characteristic mass transfer time between the two phases S.r, which is given by (36) where t 0 is the transverse diffusion time scale and Sh ~ /=11 ~TP ) i s the two-phase (dimension l ess mass) transfer coeffi cient (asymptotic Sherwood number) that depends on the velocity profile and tube geometry. For the case of fully de veloped laminar flow in a circular tube Sh ~. T = 48/11 = 4.36 while its analogue in the TMM (comparing Eqs. 26 and 36) is Sh ~. E = 1/ ~1 = 48 (t he dimensionless mass exchange coef ficient in the TMMs). As illustrated in the next section, just as the two-phase models can capture the mass-transfer limited asymptote in heterogeneous reactions (w hich is missed by the pseudo homo ge neou s models), so can the two-mode models capture the mixing-limited asymptote in homo geneous reactions, which is rendered inaccessible by the traditional one-mode model s. Thus, there exists the following one-to-one corre s pondence between two-phase models of catalytic reactors and two-mode models of homogeneous reactors: two-phase transfer time (S.r) local mixing time (tm;), two-pha se trans fer coefficient (Sh ~, T) two-mode exchange coefficient (S h ~ E) s urface (wa ll ) concentration C s spatially averaged concentration (C), and mass-transfer limited reaction mix ing-limited reaction Fall 2002 Graduate Education ] APPLICATIONS OF TWO-MODE MODELS Bimolecular Second-Order Reaction s Second-order reactions provide the simplest example of nonlinear kinetics where micromixing limitations have sig nificant effects on reactant conversion We use the TMM to determine micromixing effects on conversion of a typical bimolecular second-order reaction of the type A+ B P with rate= kC A C 8 occurring in a CSTR, where k is the reaction rate constant. For the case of stoichiometric feeding (i.e., c A.i n=CB .i n=Cin), the conversion (X) obtained by using the TMM is given by I .J4 Da(l + TJ) + 1 -1 X = -~-----=--1 +r] 2 Da(l+TJ)2 (37) where YI (=tmj 'tc) is the dimensionless local mixing time, and Da(=kC ; 't c ) is the Damkohler number. Figure I shows the variation of conversion X with Da for different va lue s of the dimensionless local mixing time YI The case of YI = 0 corresponds to the ideal CSTR. For YI > 0 and Da 00 the local concentrations (Ci) (i=A,B) approach zero, while the mixing-cup concentrations approach a mixing limited asymp tote given by C -C _2]_ A m B m l + TJ X=-11 +T] (38) As mentioned in the previous section, this mixing-limited 80 60 = .s "' .. ., .. 40 u 20 k A + B -,. P C =C A.In B in D a=kC t A.in C n = I / t mu:. C 0.1 ~=10 .0 IO l00 Da Figure 1. Variation of exit conversion with Damkohler number Da for a second order reaction in a CSTR, for different values of dimensionless local mixing time, ri. 255

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Graduate Education asymptote for homogeneous reactions is analogous to the mass-transfer limited asymptote for wall-cataly ze d reactions. Just as the wall (surface) concentrations approach zero for the case of infinitely fast surface reactions (while the bulk/ mixing-cup concentrations remain finite), so do the local con centrations (C;) for infinitely fast homogeneous reactions (i=A,B) Unlike in catalytic reactions, where exchange be tween the phases occurs at the solid-fluid boundary the ex change between mode s (scales) in homogeneous reactor s occurs over the entire domain. Competitive-Consecutive Reactions Competitive-consecutive reactions of the type A+B~C and B+C ~ D are prototype of many multistep reactions s uch as nitration of benzene and toluene, diazo coupling, bromination reac tions, etc Experimental observationsf 1 6 1 s how that if the first reaction is infinitely fast as compared to the seco nd one (i.e. k/ls 00 ), under perfectly mixed conditions Bi s completely co n s umed b y the first reaction and the yield of D is zero ( if A and Bare fed in stoichiometric amounts). But it was observed that if the mixing of A and Bi s not attained down to the mo lecular scale, the first reaction is not complete and there re mains a local excess of B which can then react with C to produce D The yield of D increa ses monotonically as the rate of the seco nd reaction increases, finally attaining a mix ing-limited asymptote. We use the TMM for a CSTR to verify this observation. Figure 2 shows the increase in the yield of D Y 0 with Darnkohler number of the seco nd reaction Da 2 where y D = 2CDm /(C Cm +2C Dm), and Da 2 = k 2 C in 'tc The figure corresponds to the case when the first reaction is infinitely fast (i.e., k 1 ~ 00 ), and A and B are fed in stoichiometric amounts (i.e., C 8 =C A =C. and C c = C 0 = 0) While no Di s formed for th~ case ~f ~ = 0 (ide~ CSTR), a significant increase in yield of D is obtained if finite micromixing limi tations are present in the system. The maximum yield of D obtained when the mixing limited asymptote i s attained also for the seco nd reaction is l 2T) l + 2T) Yo max_2_ l + 2rt for T) 1 for rt> l (39) Thus, in thi s case an optimal yield of D is obtained for 11 = l. CONCLUSIONS In the hierarchy of homogeneous reactor models the clas sical ideal reactor models stand at one end as the simplest, while the generalized convective-diffusion-reaction (CDR) 256 ) model stands at the other end as the most detailed one. While the former cannot capture the mixing effects due to local ve locity gra dient s, molecular diffusion and reaction the latter require s extensive co mputations, especially for lar ge Schmidt and/or Darnkohler number s, and for multiple reactions with large number of species. The Two-Mode Models (TMMs) propo se d here bridge the gap between the two extreme cases of reactor models and provide a practical approach for de scribing mixing effects on reactor performance. They retain all the parameters present in the full CDR model and there fore all the qualitative features of the latt er, and yet their so lution requires a numerical effort comparable to that of the classical ideal reactor model s. The analogy between the two-mode models of homoge neou s reactors and two-phase models of catalytic reactors could be carried further by noting that for all cases of well defined flow-fields, where two-phase mas s -tran sfer coeffi cients (ShT) can be estimated theoretically the exchange co efficient (Sh E ) or the local mixing time ( t m;) of the TMMs could also be estimated For more complex flow-fields (e.g. packed beds), the local mixing time like the mass-transfer coefficient, could be correlated to Re Sc, and the geometri cal characteristics of the sys tem. Thus, the two-mod e models of homogeneous reactors are as general as the two-phase models of catalytic reactors and have a similar range of ap plicability. (In fact, the classical two-phase model s are also two-mode models, the modes being the cup-mixing and the surface (or so lid-phase ) concentrations. Thus, the two-mode/ so C > Q 30 ... 0 "0 al ;;:: 20 10 0 1 A+B~C k B+C-'>D C =C A.In 8,ln Da k t C 2 2 C B.1 1 11 i t mb C 11 = 0 5 11 = 0,2 11 =0,1 11 = 0.05 10 100 1000 Da 2 Figure 2. Variation of the yield of D with Damkohler num ber for a competitive-consecutive reaction scheme A+B C B+C D, when the first reaction is infinitely fast for different values of the dimensionless lo ca l mixing time 11 Chemical Engineering Education

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( twosca le approach may be u se d to present a unified theory of homogeneous and hetero ge neou s re ac tor s!) To s ummarize the two-mode model s are the minim al mod e ls that provide a low-dim e n sio nal de sc ription of mixing, b y coupling the interaction between chemical re ac tion diffusion and velocity gradients at the local sca le s to the macro -sca le reactor variables. Due to their s implicity and generality, it i s hoped that they will find applications in the preliminary de s ign and optimization of homogeneou s c h e mical reactor s, as well as provide an a lt ernat i ve method for tea c hin g micromixing effects in homo geneous re ac tor s ACKNOWLEDGMENTS Thi s work was supported b y grants from the Rob ert A Welch Foundation, the T exas Advanced Technology Program and the Dow Chemical Company. We thank David West of Dow Chemical, Dr. Grigorio s Kolia s of the Univer s ity of Stuttgart and Prof. Dan Lu ss of the University of Hou s ton for their help in locatin g and translation of the articles b y Boden s tein and Wolga s t and Forster a nd Geib. REFERENCES I. Langmuir I. Th e V e l oc it y of R eac ti ons in Gases Moving Through H ea t ed Ve ss e l s and the Effect of Convection and Diffusion J Am C e ram So c 30 656 (I 908 ) 2 Damkiihler G. "Ei nflii ss e der Stromung Diffu s ion und Wiirmeiiberganges a u f d i e Lei s tung van R eaktionsiifen II Die I sot h e rm e, Raumb es tiindi ge H o m oge n e Reaktion E s t er Ordnung, Z. El e ktrochem 43 I (I 937) 3 D anckwert s, P.Y. Co nt inuou s Flow Sy s tem s: Di s tribution of Re s dence Times ," Ch e m En g S c i. 2 I ( 1953 ) 4. Ta y l or G.I. Dispersion of Solub l e Matt e r in So l vent Flowin g Sl o wl y Through a Tube ," Pro c. R oy. S oc. Lond A 219 186 ( 1953 ) 5 Ari s R. On the Di sper ion of a Solute in a Fluid Flowing Through a Tub e ," Pr o c. R oy. So c Lond. A 235 67 ( 1 956) 6 Forster Y.T. and K H Geib Di e Theorietische B e h a ndlun g Chemischer R eak ti o n e n in Striimenden Sy s t e men," Annal e n d e r Ph ys ik 5 250 (1934) 7 Zwietering T.N ., The Degre e of Mixing in Continuo u s F l ow Sy s tem s Ch e m Eng S c i ., 11 I (1959 ) 8. Ng D. Y.C. a nd D .W. T. Rippin The Effect of in co mpl e t e Mixing on Conversion in Hom oge n eo u s R eac ti ons ," Ch e m En g S c i ., 22 65 ( 1 965) 9. Miy a wak.i 0. H. Tsujikaw a, a nd Y. Uraguchi Chemical Reaction s Unde r incomplete Mixing ," J Ch e m. En g. Japan 8 63 ( 1975 ) 10. H arada M. Micromixing in a Continuo u s Flow Reactor ( Coale s ce n ce a nd R e di spe r s i o n Models) ," Th e Mem o ir s of th e Fa c ulty of En gineer in g K yoto U ni v., 24 431 ( 1 962) 11. Yill erma u x J ., and J.C D ev illon Repr ese nt a tion d e la Coalescence e t de l a R ed i s p e r s ion d es D o main es d e Segregation dans un Fluide per Modeled lnt eractio n Phenomenologique Pro c 2nd Ind S y mp Ch e m. R e a c t. En g., Amsterdam BI ( 1972 ) 1 2. B a ld yga J ., and J.R Bourne, Mixing and F as t Chemical Rea c tion YUL Initi a l D efo rm a ti o n of Material Elements in I sotrop i c Homoge n eo u s Turbulence, Chem En g. Sc i 39 329 (1984) 1 3. B ode n s tein M. and K Wolgast, R eak ti on s geschwi ndi gk e it in Striimenden Gasen Zt sc h,: Ph y s. Ch e m ., 61 4 2 2 ( 1908 ) 1 4 Chakraborty S. and V. Balakotaiab Low Dimensional Model s for D esc ribin g Mixing Effect s in L a minar Flow Tubular R eactor s Ch e m Fall 2002 Graduate Education ) En g. S c i 57 2545 ( 200 2) 15 Chakrabory S ., and V. Balakotaiah Two-Mode Models fo r D esc rib in g Mixing Effect s in H o m oge n eo u s R eac t o r s, A ChE J ., in review ( 2002 ) 16 Li K T. and H L. T o or Turbule nt R eac ti ve Mixing w ith a Series Parallel Reaction-Effect of Mixing on Yield ," A / Ch J. 32 I 3 1 2 ( 1 986 ) .tA.-5-3.._:_,e_tt_e_r_t_o_t_h_e_e_d_it_o_r ____ ) D ear Editor: I r ece ntl y u se d the illu stra tion below to explain the ben efits of co untercurr e nt flow to s tudent s in a separation pro cesses s ubject that I t eac h. I've never heard this i llu stration u sed before and it see m s to be a goo d one, so I thought it wo uld b e goo d to put it in the public domain for the benefit of other l ec turer s How eve r it is very s hort and doe s not war rant being a peer-reviewed paper Explaining Why Count er-Cu rrent is More Efficient than Co-Current While was hin g the di s he s one ni g ht I realized that thi s ac ti v ity provide s a u se ful everyday il l u s tration of why counter current mas s and heat transfer proce sses are more efficient than co -current ones. I aske d th e s tudent s in my cla ss what would be the best way to clean a pile of dirty dishe s if the y had at their disposal one ba s in of dirty wash water and one basin of clean wash water. The cla ss quickl y reached the consensus that it would b e b est to first u se th e dirt y water to cle a n off as much of the dirt as po ss ible and then u se the c lean water to perform a seco nd -stage clean The dirty water would remove the b u lk of th e dirt minimizing the contamination of the clean water and l eav ing it in better condition to clean off any remaining s tubborn dirt. Puttin g the dirty di s he s s traight into the clean wa ter would quickl y dilute and waste it s cleaning ability. This is equivalent t o having the countercurrent flow of s tream s in a liquid-liquid extraction or gas-liquid absorption co lumn The clean so lvent is be s t u se d to perform the final stage of cleaning, w hile the u se d so lvent i s s till able to perform so m e cleaning of the raw feed s tream as it enters the column. Student s see med to intuitively under s tand t his i ll ustration and it provides a non-graphical comp l eme n t to t h e us u al method of explaining the benefit s of co u ntercurre n t flow, which involve s showing how the average concentration (or t e mp e r at ure ) difference driving force differs between coand co untercurrent flo ws. Simon I veso n Univ e rsity of New c astle Callaghan NSW 2308 Australia cgsmi@ c c.newcast l e.ed u .au 257

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[ Gradua t e Educatio n ) INTRODUCING MOLECULAR BIOLOGY TO ENVIRONMENTAL ENGINEERS Through Development of a New Course DANIEL B. OERTHER University of Cincinnati Cincinnati, OH 45221-0071 H istorically applications of biology in chemical and environmental engineering have been approached from different perspectives with different goals For example, chemical engineering optimizes biochemical reac tions of pure cultures of microorganisms in highly controlled bioreactors used for manufacturing (e.g., fermentation) whereas environmental engineering employs mixed micro bial communities with minimum controls as least-cost pro cesses for meeting regulatory requirements (e.g., sewage treat ment). Although chemical and environmental engineering education often incorporates formal training in biology the motivation for course selection can be very different. Incre mental advances in biological knowledge that can be used to increase manufacturing capability or improve efficiency are useful in chemical engineering practice, and their integration into chemical engineering education is justified The same principle does not hold for environmental engi neering however. Once minimum regulatory requirements are met incremental advances in biological knowledge do not offer the significant cost savings for environmental bio logical unit operations that are needed to encourage the adop tion and integration of the new knowledge into environmen tal engineering education. Recently, development of 16S ribosomal ribonucleic acid (16S rRNA)-targeted technology provided researchers in en vironmental engineering with new tools to identify micoorganisms and to study micoorganisms in bioreactor environments As compared to classical techniques for iden tification and enumeration 16S rRNA-targeted technology allows in situ examination of the structure (i.e. who is present?) and function (i.e. what are they doing?) of micro bial communities without a prerequisite for isolating pure cul tures.111 For researchers in environmental engineering, 16S rRNA-targeted technology has been extensively tested, and current research activities have moved beyond the "proof of-concept state to widespread applications.12 31 In contrast integration of 16S rRNA-targeted technology within the en vironmental engineering curriculum remains to be fully de258 veloped. At the University of Cincinnati, the author has de veloped and pilot tested a "proof-of-concept" course titled Molecular Methods in Environmental Engineering. The course was designed to teach limited fundamentals of molecular biology in the context of quantitative engineering design and practice During its first offering fifteen graduate students in environmental engineering were exposed to "s tate of-the-art technology including hands-on laboratory exer cises following the full-cycle 16S rRNA approach." 1 1 1 Stu dents learned the importance of detailed understanding of microbial communities and microbial-mediated biochemical networks in biological unit operations, natural biological sys tems and the global biosphere. The format of the course in cluded a weekly lecture as well as a semester-long series of hands-on laboratory exercises designed to teach students to develop scientific questions, learn appropriate methodology, conduct careful experimentation analyze data and draw con clusions worthy of presentation to peers. Thus the final out come of the course included preparation of peer-review quality manuscripts by each team of students as well as one-on-one interviews with the instructor F ULL-CYCLE 16S rRNA APPROAC H Traditionally, the identification of microorganisms in en vironmental samples has relied upon semi-selective cultur ing or direct microscopic examination. These techniques have led to a rudimentary understanding of the role of microor ganisms in the global biosphere as well as the importance of microorganisms in public health and biocatalysis Recently, the techniques for determinative microbiology have been dra matically expanded to include cultivation-and-morphologic independent identification and enumeration of microorganDani e l B. Oe rth e r joined the Department of Civil and Environmental Engi neering at the University of Cincinnati in 2000 For ten years, he has been adapting methods from molecular biology to identify, enumerate and mea sure the physiology of microorganisms in biotechnology processes includ ing wastewater treatment and bioremediation. His research links the re sults of novel molecular biology assays with mechanistic modeling of bioreactor performance Copyright ChE Divi s ion of ASEE 2002 Chemica l E n g in ee rin g Education

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Collec t Samp l e E x t ra ct G e n o m e DNA c > Polymerase Cha i n Re a c ti on Denature Anneal Extend for E x ponential Growth C l o ni ng Ligation Transforma ti o n, Isola t e Recombinan t s c :, FISH and M i croscop i c E x a minat i on Fig ur e 1. Schematic of the principal steps in the "full-c y cl e 16S rRNA approach." Genetic material is isolated directl y from an environmental sample and the 16S rDNA genes are amplified in a PCR The product of the PCR is cloned and recombinants are isolated for extraction of plasmid DNA. Automated sequencing is used to pro v ide the primary nucleotide structure of the clones and probe design is ac complished using semi-automated proc e dures and readil y available software. Finally individual microbial cells are visualized through fluorescence in situ hybridization (FISH) with fluorescently labeled 16S rRNA-targeted oligonucle otide probes. Fall 2002 Graduate Education ] ism s in environmental samples. Arguably one of the most widespread families of new techniques for determinative microbiology targets rRNA Comparative st u dies of rRNA nucleotide sequences collected from a variety of microorgan isms led to the development of a uni versa! phylogenetic frame work for understanding the evolutionary history of microor ganisms. 1451 Subsequently, these comparative approaches were coupled with oligonucleotide probe hybridizations to study microorganisms in s itu without prerequisite culturing. 1 1 6 1 The full-cycle 16S rRNA approach" refers to the process of obtaining genomic information directly from an environ mental sample and then employing molecular methods to assay the abundance of nucleo t ide sequences directly wit h in an environmental sample The steps of the cycle, as applied in my course are briefly described and outlined in Figure 1. Genomic deoxyribonucleic acid (DNA) is extracted from an environmental sample using chemical and physical disrup tion of the microorganisms. Subsequently, a polymerase chain reaction (PCR) is used to selectively "grow-up" target genes from the heterogeneou s pool of genetic material. In our case, the target genes are 16S rRNA. The target genes, amplified in the PCR, are cloned into bacterial vectors and transformed into competent cells of Escherichia coli. The recombinant clones are cultured and plasmid DNA is extracted. The re sult s from commercial dideoxy terminal sequencing are used to de s ign an oligonucleotide hybridization probe purchased from a commercial vendor The fluorescently labeled probe is hybridized to a "fixed sample, and individual microbia l cells are identified using an epifluorescence microscope. For my class, commercially available kits were used to the extent possible to minimize the time spent by students and the teaching assistant in preparing reagents. Genomic DNA was extracted using an UltraClean Soil DNA Isolation Kit. 171 PCR was conducted using a model 2400 thermal cycler 1 81 and the Takara Ex Tag kit. 1 91 Cloning of the PCR products was accomplished with the TOPO TA Cloning kit version K2 1 1 01 and plasmid DNA was prepared using PerfectPrep Plasmid Mini preps. 11 1 1 Throughout the exercises a variety of equip ment was used including an ultra l ow temperature freezer, 1 1 2 1 a Mini Beadbeater-8, 1 1 3 1 a system for agarose gel e l ectrophore sis ,1 1 41 a Genesys !Ouv, 1 151 a constant-temperature rotary shaker 1161 and an epifluorescence microscope. 117 1 FORMAT FOR LABORATORY EXERCISES Step 1 Students arranged themselves into teams of three The selection of teammates was based both on a common interest in one environmental sample and on an effort to spread previous experience and expertise in molec u lar biology among the groups Step 2 Teams identified, eval u ated, and proposed an ap propriate environmental system for study Each system se259

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( Graduate Education ... w e plan t o e x pand the enrollment [in this course] to i nclude undergraduat e e n v ronmental eng i neering student s a s w ell as g r aduate and undergraduate s tudents f rom related d i scipline s, i ncluding chem ic al engineering and biomedical engine er ing. lected for the course was novel for the field of environmental engineering and possessed the capacity to stimulate a more extensive research question (e.g., supplemented a research question in an existing/developing MS or PhD degree, or pro moted a novel research direction generally underexplored .) A sample was obtained from the selected system. In all cases preference was placed on samples that were a part of a devel oping / ongoing research project with significant supplemen tary information generated from advanced process engineer ing and chemical/physical analyses (e.g., sample(s) from a novel bioreactor configuration or a bioreactor treating a novel waste stream). Ste p 3 Each team generated 16S rDNA sequence infor mation from their sample(s). Genomic DNA was extracted using an UltraClean Soil DNA Isolation Kitf 71 according to the manufacturer's instructions. Mechanical lysis of the samples was performed for one minute at the maximum set ting of a Mini Beadbeater-8 1 1 3 1 Genomic DNA was quanti fied using a Genesys 10uv 11 51 spectrophotometer assuming that an absorbance reading of 1 0 at a wavelength of 260 nm corresponded to a concentration of 50 mg DNAfl. T h e 16S rDNA genes of bacteria present in the samp l e were amplified by PCR using primer set S-D-Bact-0011-a-S-17 (5' to 3' sequence= gTI TgA TCC Tgg CTC Ag) and S-D Bact-1492-a-A-21 (5' to 3' sequence= ACg gYT ACC Tig TIA CgA CTI).r 1 8 1 The conditions for PCR included: 5 min. at 94 C; 30 cycles of 0 5 min at 94 C, 0.5 min. at 55 C, and 0.5 m i n. at 72 C ; 7 min at 72 C; and hold at 4 C. Each reac tion t u be contained: 1.25 U Takara Ex Taq polymerase, C 9 1 lx Takara Ex Taq reaction buffer, 200 M of each deoxy ribo nucleotide triphosphate (dNTP), 0.2 M of each primer, and 500 ng of genomic DNA PCR was conducted using a model 2400 t h ermal cycler. l 8 1 Agarose gel electrophoresis was used to check the quality of the PCR product. A 1 % (wt./vol.) agarose gel was pre pared in 1 x tris buffered EDTA (1 x TBE is 90mM tris borate and 2 mM ethylenediamine-tetraacetic acid [EDTA]) accord ing to the manufacturer's instructions r 191 Electrophoresis was cond u cted for two hours using a setting of 100 V for the power supply DNA fragments were visualized with a hand-he l d UV lamp after staining the agarose gel for ten minutes at room temperature with 50 mg / 1 of ethidium bromide. T h e PCR products were cloned into component cells of E. coli u sing the TOPO TA cloning kit, version K2 r 101 according 260 to the ma n ufacturer's instructions The blue/white screen with x-gal was used to detect the presence of insert in each plas mid, and the anti b iotic ampicillin was used to screen for the presence of plasmids in colony-forming units of competent cells. Ten clones were selected for each team of students, and plasmid DNA was prepared using Perfectprep Plasmid Mini preps 1 111 according to the manufacturer 's instructions Puri fied plasmid DNA was subjected to endonuclease restriction analysis using EcaRI.l 201 Digested plasmid DNA was electro phoresed on 2 % (wt./vol.) agarose gels and visualized using ethidium bromide staining and a hand-held UV lamp as de scribed above Two clones from each team were selected for commercial automated dideoxy terminal sequencing by the DNA Core Facility at the University of Cincinnati Sequencing primers included M13(-20) forward and Ml3 reverse 1101 as well as Bact-0343-a-A 15 (5' TAC ggg Agg CAg CAg 3'), S* 0519-a S-18 (5'gTA TIA CCg Cgg CTg CTg 3 '), S*Bact0907-a-A 20 (5' AAA CTC AAA TgAATI gAC gg 3') and S-*-Bact-a-S 16 (5' Agg gTT gCg CTC gTT g 3 ') .Cl 81 S t e p 4 An initial phylogenetic analysis was conducted, and t h e results were used to design oligonucleotide hybrid ization probes for fluorescence in situ hybridization ( FISH) Assembled sequences were compared to the Ribosome Da tabase Project (RDP) (available at rdp cme msu.edu) using Chimera Check and Probe Match Preliminary phylogenetic affi li atio n was co n firmed using a BLAST (Basic Local A l ign ment Search Tool) search of GenBank (available at www.ncbi.nlm.ni h .gov, fo ll ow the links to BLAST) The fluorescently labeled oligonucleotide probes were ordered from a commercial vendor. S t e p 5 Each team cond u cted fluorescence in situ hybrid ization (FISH) analysis of their original samples. Aliquots of the original sample were chemically fixed for one hour at room temperature with 4 % (wt./vol.) paraformaldehyde pre pared in 1 x phosphate buffered saline (1 x PBS is 130 mM NaCl and 10 mM sodium phosphate buffer). The samp l es were subsequently stored at -20 C in a 50 % (vol./vol.) mix ture of ethanol and 1 x PBS. The fixed samples were applied in a sample well on a Heavy Teflon Coated microscope slider 211 and air-dried FISH was performed as previously described. r 2 2 1 Briefly, each microscope slide was dehydrated in an increas ing ethanol series (50, 80, and 95 % [ vol./vol.] ethanol, one minute each) each sample well was covered with 9 l of Chemical Engineering Education

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hybridization buffer (20% [ vol./vol.] formamide, 0.9 M NaCl, 100 rnM Tris HCl [pH 7 .OJ, 0.1 % SDS), and fluorescently labeled oligonucleotide probe, 1 l (50 ng) was added to each sample well. Hybridizations were conducted in a mois ture chamber for two hours, in the dark, at 46 C. The slides were washed for 30 minutes at 48 C with 50 ml of prewarmed wash solution (215 rnM NaCl, 20 rnM Tris HCI [pH 7.0], 0.1 % SDS, and 5 rnM EDTA). Fixed hybridized cells were mounted with Cargille immersion oi)f 23 1 and a cover slip. Probe-conferred fluorescence was visualized with a model E600 upright epifluorescence microscope ,r2 41 and digital im B.A. B S M S Ph.D Hi g hest Degree M S Ph D 1 4 7 1 <23 2 3-26 27-30 30+ C u rrent D e g ree Field En v Eng En v Sc i. Eng i neer i ng O t her Highest Degree Field 4 En v En v 9 En v Sci. Graduate Education ] Quantitative molecular biology for Environmental Engineering versus qualitative molecular biology for Environmental Science Troubleshooting the laboratory exercises to improve the course for the subsequent year What is this "phylogeny stuff anyway? Historical development of molecular tools in Envi ronmental Science and Engineering Success stories for molecular tools in Environmen tal Science and Engineering Principles of microscopic examination N=13 Number of Previous Biologl Courses 0 < 2 4 < 5 5 <1 0 4 < 15 15+ N= 1 3 H o urs e;er week on course 5 1 < 6 2 < 8 5 < 10 N=13 <12 < 15 8 5 N=13 2 4 1 1 4 N=13 2 7 1 2 STUDENT FEEDBACK ages were captured using a Spot-2 charge coupled device (CCD) cam era. r 251 The results of the FISH analysis included determining the abun dance and spatial orga n i za ti on of phylo gentically defined mi crobial populations identified by unique oligonucleotide hybrid ization probes. The students learned the procedures for the laboratory exercises through Figure 2. Demographic of students enroll e d in th e pilot course as determined b y an anonymous in-class surv ey. Figure 2 summarizes the results of students responses to a demo graphic survey. Thirteen of the fifteen students enrolled in the course re sponded to the survey The class was divided almost equally between male and female stu dents with a median age a video series produced specifically for this course. They were given a laboratory manual at the start of the class, and videos of the laboratory exercises were distributed biweekly in VHS format. The manual outlined all of the procedures for the labo ratory and provided step-by-step instructions to complete each exercise. The videos gave the students an opportunity to view the instructor completing all of the steps of each exercise. The laboratory exercises were completed inde pendently by the three-student teams according to a sched ule arranged at the start of the class. Approximately the first fifteen minutes of the weekly lectures were dedicated to reviewing the progress of each team toward meeting the schedule for completion of the laboratory exercises. TOPICS FOR THE LECTURES Each week, approximately two hours were spent in a lec ture discussion format with the entire class. The nine topics that were covered in the pilot course included: Overview of methods including the value of differ ent methods and an answer to the question, "Why do Environmental Engineers need to learn molecular bi ology?" Measuring microbial community structure Measuring microbial community function Fall 2002 of 27-30 years old Five of the students had received significant formal training in biol ogy, previously participating in more than ten biology courses. The majority of the students had already completed their MS degree (eight out of thirteen) but more than 50 % of the stu dents had received their degree outside of environmental en gineering or environmental science Most students spent less than six hour s per week on the course but some students spent s ignificantly more time. Overall, the students enrolled in the pilot test of "Molecular Methods in Environmental Engineering could be categorized as mature students (i.e., in their late twenties working toward their doctoral degrees). Furthermore the clas s contained a significant number of stu dents with extensive previous experience in biology. Thus, the students enrolled in the pilot course were well prepared in maturity and previous biology experience to actively par ticipate in this novel course. As the course continues to be offered, I plan to track the success of the course in relation ship to the demographics of the enrolled students. In addition to collecting demographic information, at the end of the class the students were asked to respond to three open-ended questions. In response to the question, "In your opinion were the objectives of the course met?" students re sponded: Th e c ours e m e t som e of th e obje c ti ve s but som e students 261

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[ Graduate Education are not co nvin ced why we use molecular biology to id en t ify microorganisms in sys t ems that h ave been proved or h ave b ee n operating successf ull y. Y es. I am e quipp ed wit h knowledge about this approach, and I can int er pr et research r esu lt s and publications from this developing field. In response to the que s tion What was the be s t aspect of thi s course?" s tudents re s ponded : M ost of the procedures are basic/universal operations in m olecula r biology which means that we understand how to st ud y biology and biotechnology at the molecular level. Experimental wo rk -because it is through applications that a student ge t s a tight g rip on ideas a nd conce pts. In addition, the c hall enging experiments and the val u e of the final result make the wo rk more interesting. The lectures we r e interesting and informative. / learned a g r eat deal and my ideas about environmental engineering and science have been positivel y affected by the knowledge I ha ve gained. Your perspective We will nev e r see "c utting edge" deve l opments in a book The whole structure of the course is simila r to a research project. Th e best aspect was carrying the concepts from the classroom to th e lab in a mann er r e l evant to our field. A l so having a class that is new gives afresh perspective into the future of environ m ental enginee ri ng. In response to the que s tion What part of the course would you s ugge s t improving ?" s tud e nt s responded: More theoretical basis, especiall y for the background of molecular biolog y methods. From their re s pon ses to the open-ended que s tion s, it i s ap parent that the s tudents felt the pilot course was a success. It i s interesting to note that the s tudent s appreciated that the pilot course represented an effort to integrate research into the cla ss room. One of the greatest difficultie s for developing a role for molecular biology in an engineering curriculum is di scover ing a mechani s m for mo vi ng the se "s tate-of-the-art re se arch s kill s into a cla ss room se tting. In the futur e, we plan to expand the enrollment for Molecular Method s in Envi ronmental Engineering to include undergraduate environ mental engineering students as well as graduate and under graduate students from related di sc iplines including chemi cal engineering and biomedical engineering. CONCLUSIONS To address the growing national need for integrating genomics and molecular biology into the engineering cur riculum, the author developed and pilot tested a new course, Molecular Methods in Environmental Engineering ." Fifteen graduate s tudents were successfully introduced to molecular biology through lectures and hands-on laboratory exercises following the full-cycle 16S rRNAapproach. Although the 262 ) pilot course ca n be co n s ider e d a s u ccess, future offerings of thi s course mu s t be modifi e d to reduce the difficulty of co m prehendin g molecular biology by inexperienced engineering s tudent s. One of th e mo s t dauntin g challenges for thi s type of "s tate-of the-art course i s pro v idin g a s upportive yet in dependent learning e nvironm e nt. For hi g hl y motiv a ted g radu ate s tudent s, the author demon stra ted th a t the format for thi s co ur se i s s ucce ssfu l. T o offer this course to under gra duate s tud e nt s or poorl y prepared gra du a t e st ud e nt s repr ese nt s a future challenge. In up co min g course offerings, th e a uthor plan s to open e nrollment for Mol ec ular Method s in Envi ronmental Engineering" to und ergra duate s tudent s in envi ronmental engineering as well as s tudent s in chemical engi neering and biomedical engineering. As genomics and mo lecular biology be co me as common to an e ngineerin g cur riculum as chemistry and phy sics, engineering faculty need to take th e lead in dev e lopin g co ur ses that introduc e these topics from a n engineering perspective with a focus upon quantitative approaches and the app li ca tion of sc i e nc e to find cos t -effective so lution s to socie t y s problems. ACKNOWLEDGMENTS Thi s laboratory cour se would not have been pos s ible with out the commitment of s ignificant re so urce s from the De partm e nt of Civil and Environmental Engineering of the Uni ve r sity of Cincinnati. For the s u ccess of the pilot test, the a uthor is gra teful t o th e D e partm e nt. REFERENCES I Ama nn R ., W. Ludwig, a nd K H. S c hleifer Phylogentic Id e n tifica tion and ln Situ D etectio n of Indi v idu a l Microbial Ce ll s witho ut Cu l tivation," Microbial. R ev. (59) p 1 43, ( 1 995) 2 Rittman B "E dit orial : Molecular Understanding," Water Environ. R es ., ( 70) p. II 07 ( 1 998) 3 Stensel H.D. 200 1 Editorial: Probing the Bl ack Boxes, Wat e r Environ Res. ( 73 ) p. 259 (2001 ) 4. Woese C.R ., Bacterial Evo lut ion ," Mi c robi a l. R e v ., ( 51 ) p 22 1 (1987) 5. Woese, C. R ., There Must be a Prokaryote Somewhere : Microbiology 's Search for I tse l f Microb i al R e v ., ( 58 ) p I (1994) 6. Hugenholtz, P. B.M. Goebel, a nd N R. Pace Imp act of Culture-In dependent Studies on the Emerging Phylogenetic View of Bacterial Diversity J Bact., ( 180 ) p 4765 ( 1 998) 7 Catalog# 1 28 00 -100 MoBio Solano B eac h CA 8. Applied Bio sys t e m s, Foster City CA 9. P a n Vera Corp., Madison, WI I 0 In v itro ge n Corp., Car l sbad CA 11 Eppendorf Scientific Westbury NY 12. Model Ultima II Revco, In c ., Asheville NC 13. Bi os p ec Products Bartl esv ill e OK 14. Cata l og# CSSUl214 a nd ECI05 E-C Apparatus Corp ., H o lbro ok, NY 15 Spectronic Unicam, R oc h ester, NY 16 Model C24 New Brunswick Scientific Edison, NJ 17 Model E600, Nikon, In c M e l v ill e NY Ch e mi c al Engi n eering Education

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1 8. de l o s R eye s M.F. F.L. de l o s Reye s, M. H ernandez a nd L. R askin Quantification of Gordona amarae Strain s in Foaming Activated Sludge a nd Anaerob i c Dige s ter Sy s t em s with Oligonucleotide H ybrid izat i on P robes ," Appl En v ir o n. Mi c robi a l ., ( 64 ), p 2 503, ( 1998 ) 1 9 E-C Apparatus Corp., H o lb rook NY 20. P romega, ln c Madison, WI 21. Cel-Line Associates, New Field N J l8j 5 =i letter to the editor ) -----------To The Editor: Thi s letter i s motivated by th e pap e r An Undergraduate Cour se in Applied Probabilit y a nd Statistics" that appeared in the Spring 2002 i ss ue of Chemical Engineering Educa tion.111 Probability and s tati s tic s are difficult s ubject s to teach to engineering students, and Profe sso r Fahidy i s to b e con gra tulated on his effort s in thi s area. In thi s letter we would like to refer to the di sc u ssio n and examples related to regression a n a ly s i s Profe sso r Fahid y di cusses in detail the u se of numeric information (s uch as error variance, confidence inter va l s, correlation coefficient, etc.) for regre ss ion analysis, but doe s not mention graphic infor mation ( residual plots ) and phy s ical in s ight for regre ss ion analysi s. Using the example s pre se nted by Professor Fahidy 1 1 1 we would like to demonstrate th e importance of including graphical information and ph ys ical arguments in the r eg re s ion analysis. Let u s refer first to Example 4 in the pap er. In thi s example, the int egra l method of rate dat a a n a l ys i s is u se d for a (s up posedl y) first-order reaction. Nonlinear re gress ion can be u se d TABLE 1 R eg re ss ion Re s ult s for Example 4 in R e fer e nc e I Reaction Order l st Order Model logY=-k t k (va lu e ) 0 0039888 95 % Conf Interval 00 11 009 Y 0 ( or l/Y 0 value ) 95 % Conf. interva l R 0 7620164 Variance ( based o n Y ) 0.0023055 1 st Ord e r ()' Order Y=exp ( -k t ) Y=Y 0 +k t 0 003812 6 -0 004216 2 .00 1 0816 0 0015209 1.0329275 586582 0 7770 3 I 9 00 8362884 0 002271 rn 0 00 1 8759 2"' Ord e r IIY=I/Y 0 +k t 0 0059893 .0059893 0 9365288 .10 1 2594 0.7757433 0.0021994 0 Figure 1. R es idual plot for Example 4 in Fahid y pap e r .11 1 Fall 2002 22 Oerther D.B. J. Pernthaler A Schramm, R Amann, a nd L. R aski n Mon it ori n g Precur s or I 6S rRNA of Ac in etobacte r spp in Ac ti va t ed Sludge Wastewater Treatment Sy s tem s," Appl. En v iron Mi c robial. ( 66 ), p 2154 ( 2000 ) 23. Type FF, Cedar Grove, N J 24. Nikon In strumen t s, In c ., Melville NY 25 Diagnostic Instrument s lnc. S t er lin g H e i g ht s, MI 0 for finding the r eac tion rate coeff i c ient (k) u s in g concentra t io n (Y) ve r s u s tim e (t) data on th e regression mod e l Y = ex p ( -kt ). Alt e rnati ve l y, this eq u at ion can be lineari ze d to yield ln Y =-kt w here linear regression c an b e applied. The re s ult s of the line ar and nonlin ear re g re ssio n th a t were obtained u in g POLYMATH 5.1 are s hown in the first two columns of Table 1. Note that th ese re s ult s a re different from what i s pre se nted in [l] but they are correct and were confirmed by the author of the original article .r2 1 Lookin g at the numerical information pre se nted in T a ble l ( parameter values, confi denc e interv a l s, correlation coeff i c ient s, and variances) lead s to th e co nclu s ion th a t there i s no sig nificant difference be tween linear and nonlin ea r r eg re ss ion for determining k ( the varia nce s are almo s t th e sa m e, contrary to what i s argued in [l]). The s ame information may also lead to the conclusion that the model fit s the data rea so nably well. Thi s conclusion, however, i s contradicted by the r es idual plot shown in Figure 1. The re s idual s are not randomly di s tributed around a zero va lu e. Thi s ma y indic ate either lack of fit of the model or that th e underlyin g assumption of a random error di s tribu tion for the depend e nt varia bl es i s incorr ec t. Ph ysical in sig ht can s u gges t a lternati ve regression model s, but mor e information regarding th e reaction involved i s n ee ded Since no s uch information i s available, we will as s ume a homogeneou s reaction, ju s t for the sake of the dem onstration. A ss uming 0 th order reaction or 2 nd order reaction yields the model s s hown in the third and fourth columns of Table 1 re s pectively The numeri c information presented in the Tabl e point s on the 0 th order reaction as the mo s t appro priate one (s malle st variance va lue-note that in order to be on a unique sca le a ll the varia n ce calculations mu s t be ba se d on Y ) The residual plot for th e 0 th order reaction i s not s ig nificantly different however from that s hown in Figure 1 ; thu s, thi s model i s not s upported by the residual plot either. The conclusion from proper analysis of this example is that the data available are insufficient ( in quality quantity, or both) to determine in any certainty the order of the reaction it rep resent s. To obtain a more definite re s ult additional measure ment s must be made. In Example 5 a linear model Y=a+bx is fitted to data of mean fuel consumption rate ( Y) versus vehicle mass (x) The numerical result s that were obtained for this example, using POLYMATH, are: parameter values (including 95 % confidence intervals) a=-0.8695975.0733031 ; --------------Cont inu ed 0 11 page 277 263

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t~l!ll"b~arc=-:l;:a:--=s:--::s:-:r=o~o=-m=--------) A New Approach to Teaching TURBULENT THERMAL CONVECTION STUART w CHURCHILL University of Pennsylvania Philad e lphia, PA 19104-6393 A t AIChE 's annual meeting in 2000, I gave an oral presentation of an early version of a pair of new ex pressions completely free of explicit empiricism, for the prediction of fully developed turbulent thermal convec tion in all channels and for all thermal boundary conditions. At the s ame venue, In 2001 I also presented a greatly im proved version, although at the expense of a smidgen of em piricism. Both presentations prompted the same question from participant s: Is this approach being taught to current s tu dents and if not why not ?" I explained in both in s tances that thi s material is very new and i s not in any textbooks and furthermore, that it may not appear in textbook s for so me time to come since the authors of tran s port textbooks mu s t first become aware of the concept and its re s ult s, and then be convinced of it s educational (as well as predictive s uperior ity) over the method they are currently teaching Also, as Andersonr 11 has noted textbooks in chemical engineering seem to have a unique longevity and the more s uccessful of them are replaced or revised only after long intervals of time. Undoubtedly with these textbook characteristics in mind my mentor and departmental chairman Donald L. Katz, long ago made the suggestion (w hich to a young assistant profes so r wa s virtually an order) that every year I replace at least 20% of the graduate transport course content by embracing new developments in the literature. Throughout my career, that s ugge stion led to my use of notes incorporating these new segments, together with using a book or books as a supp lement rather than the other way around. I concl ude a full half -cent ury later that this process of annual s upplemen tation and revision has b y virtue of the associated forced self-study a nd se l f-learning in the fields of my teaching, more than compensated me (and perhaps my students) for the ef forts, and that it is a worthy complement of the new materials mo s t of us introduce periodically from our own research and consulti n g. I am here talcing advantage of the platform pro vided by Chemical Engin ee rin g Education to encourage and assist the process of supp l ementation for transport teacher s with respect to a new approach for the description and pre diction of turbulent thermal convection. In a previous CEE article,1 2 1 I pre se nted a new approach to the de sc ription and teaching of turbulent flow with the sa me objective. For that s impler and more re str icted topic it was pos s ible to include in the pre se ntation a virtually complete se t of supplementary note s for direct u se by any interested faculty member. For the much more complex proce ss of tur bulent thermal convection and the much more complex pro cess of development of the new model, however the presen tation of a worki ng set of s upplemental note s in thi s format i s s imply not feasible. Rather thi s article ha s the more Limited objective of outlining the new approach with the hope that faculty members who teach tran s port will be inspired to study the more complete documentation in the key reference s and make the effort to formulate their own supplemental notes Perhap s I will eventually find the time and motivation to pre pare a monograph on thi s topic but I do not recommend that anyone procrastinate with that as the excuse. When an analogue of the approach that was so si mple Stuart W. Church/II is the Carl V.S Patterson Professor Emeritus at the Univer sity of Pennsylvania where he has been since 1967 His BSE degrees (in ChE and Math) MSE and PhD were all obtained at the University of Michigan, where he also taught from 1950 to 1967 Since his formal retirement in 1990 he has continued to teach and carry out research on heat transfer and combustion He is also currently completing books on turbulent flow and correlation Copyr i g ht ChE Di v ision of ASEE 2002 264 Chemical Engineering Ed u cat i o n

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straightforward, and successful for turbulent flow was first attempted for the closely related topic of turbulent thermal convection, I anticipated that the path of development would closely parallel the previous one. While convection is inher ently more complex than flow in several respects, it is also simpler in the sense that it merely consists of the superposi tion of a scalar quantity the temperature on the flow The path of development that emerged after considerable trial and error proved to reflect the greater comp lexity that had been anticipated, and the final result s proved to reflect the antici pated greater simplicity. The predictive equations for turbulent thermal convection that are described in thi s paper are, by a s ignificant margin more accurate, fundamentally sound, and general than any prior ones. They also provide better in s ight into the relation s hip between flow and convection and a better co nception of thermal convection itself that more than compensates for the greater detail. This new material s hould therefore as s ug gested by audience members at the AlChE pre se ntation s, be given serious consideration for inclusion in the final portfo lio s of both our undergraduate and graduate st udent s. Apart from the merit of the predictive equations for turbu lent thermal convection that emerged the path of their de ve opment appears to have merit itself in an educational se nse. On the one hand it provides insight into a creative proce ss of co rrelation that i s within the capabilities of our st ud en t s. On the other hand it provides a per s pecti ve within which the s trength s and weaknesses of all forms of correlation can be evaluated, not only in flow and convection but also in every aspect of chemical engineering. Our s tudent s should be made to realize that whatever career they follow after graduation, they will s pend considerable time u s ing a nd/ or formulating correlations. I have a predilection for pre se ntation s in narrative an d hi torical contexts under the presumption that the personal char acteristics, as well a s the triumph s and failures of our prede cessors not only stimulate intere s t but also provide a mne monic for students. In this in s tance a description of the ser endipitous and irregular path of development of a completely new formulation in a relatively mature field may se rve a s imi lar role. Teachers who prefer a more orderly and skeletal ap proach are welcome to eliminate s uch diversionary material. Many detail s concerning origins proofs uncertaintie s, and limitations are deferred to the references, and in particular to Churchill and Zajic .l3 1 It is however essential that the teacher present these details or perhaps in the instance of graduate students, assign key references as required collateral read ing. In either event, students should be encouraged to ques tion the validity of the many assertions and s implifications in this article rather than accept them on faith." Undergradu ate students may require more guidance than do graduate s tu dents with respect to the new approach, but they have the Fa/l 2002 counterbalancing advantage of le ss to unlearn. THE NEW APPROACH FOR T URBULENT FLOW A thorou g h under standi ng by st udent s a nd faculty alike of th e new approach for the de sc ription and teaching of turbu l e nt flow, as previou s ly de sc ribed[2] i s an essential prereq ui site for the complementary new a pproach pre se nted here for turbulent thermal convection. Becau se of space limita tion s, howe ver, only tho se re s ult s that are directly applied or adapted for thermal convection will be reproduced here The time-averaged, once-integrated differential equation of conservation for momentum in the radial (negative-y) di rection in s tead y -on-the-mean full developed flow of a fluid of invariant den s ity a nd v i scos ity through a round tube can be represented by ( y \ du -, (I) 't wl l-;j = d y pu v Here tw i s the s hear s tre ss on the wall y i s the distance from the wall, a is the radius of the pipe, u is the time-aver aged ve locity and u' and v' are the fluctuating components of the velocity in the x and y direction s, respectively. The s uperbar de s ignate s the time-average of their product while a nd p are the dynamic viscosity and s pecific density of th e fluid. (Aside to teachers: The origin of thi s expression and the phy sica l meanin g of the seve ral variables and term s, including the s ign s of the latter s hould be de sc ribed or reviewed as a ppropriate. Any unea si ne ss of the students in this regard can be expected to per sist in what follows. Of course, this warn in g applies to some extent to s ubs e quent details as well.) Equation (1) can be rewritten in terms of the dimen s ion le ss wall var i ab l es of Prandtl namely ( ) 1/2 u + =uplt w Y+ = y( t w P) 1 12 I a+= a(t w P)l /2 / and one new variable, namely the fraction of the transport of momentum ( or the total s hear s tres s) due to the turbulent fl uc tu a tion s (u' v 'f+ = -pu'v' / t as ( y+ \[ -, ,++] du + ( 2 ) l -;;+ j I u V = dy + Equation (1), with y + Ja + replaced by 1-R, can be integrated formally to obtain the following expression for the radial dis tribution of the time-averaged velocity: I u + = a; J ,[ J -( u'v'f + }R 2 R (3) 265

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The velocity distribution can in turn be integrated over the cross-section to obtain, after utilizing integration by parts, the following integral expression for the mixed-mean veloc ity and thereby the Fanning friction factor : ( \ 1 /2 I + I ( fj =U~=Ju + dR 2 =-J[i-(u'vJ+]dR 4 (4) 0 0 Equations (1) through (4) are exact insofar as the restrictions mentioned above with respect to Eq. (1) are fulfilled. In or der to imflement Eqs. (3) and (4), an expression is required for ( u'v') + in terms ofy+ and a +. For this purpose ChurchiW 4 l proposed the following semi-empirical expression: [(",r r" +7l ;: lT I { -1 } 1 ( 6.95y + 11 -S/7 + exp 0.436y + 0.436a + l l+---;:;=-J (5) It is essential for the students to be aware of the origins and uncertainties of Eq. (5) since this expression has a critical role, both numerically and functionally in all of the develop ments that follow for both flow and convection. The third power dependence on y + for small values of y was originally postulated on the basis of asymptotic analyses but has since been confirmed by direct numerical simulations, which have also produced a theoretical value of approximately 7 x 10 4 for the numerical coefficient. The exponential term for mod erate values of y+, as well as the deductive term for y a + were both derived by speculative analysis, but the coefficients of 0.436 and 6 95 were determined from recent, improved experimental data for the time-averaged velocity distribution The power-mean form ofEq. (5) is arbitrary and the combin ing exponent of -8/7 is based on experimental data for u'v'. (See Churchill and Zajicl 3 l for further details, including com plete references.) Numerical integration ofEqs. (3) and (4) using ( u'vJ + from Eq (5) results in almost exact values of u and u~ owing to the smoothing associated with integration Such values of u may be represented with a high degree of accuracy for a + > 300 by the following expression that invokes no additional empiricism beyond that of Eq (5): ( 21 1 12 + 227 (50J 2 l + ( f) =um =3.2-7+ -;-+ + 0.4 36 Rn{a} (6) Equations (1) through (6) are the only ones for flow that will be referred to directly in the developments that follow for convection. It may occur to teachers and graduate students at this point that the relevant consideration of turbulent flow has been completed without any mention of the eddy viscosity or the 266 mixing length. One merit of the new approach which carries over to thermal convection is that the need to introduce such heuristic quantities is avoided completely bJ the more direct and simple development in terms of(u' v 'r AN ASIDE ONA GENERIC CORRELATING EQUATION Equation (5) is a particular application of the generic cor relating equation proposed by Churchill and Usagi 1 5 1 for two regions, namely (7) Here, y = y{x}, y 0 = {x O}, Y ~ = y{x 00 } and bis an arbitrary exponent. Either y 0 or Y ~ or both are necessarily functions of x rather than fixed values. For three regions, Eq. (7) can be extended either directly as ybq = (yg + l r + y ~ q or in staggered form as (8) (9) Here, Y; is an intermediate asymptote and q is a second arbi trary exponent. The reverse order of combination of y 0 Y; and Y leads to equally valid and, in general fundamentally different representations. Equations (7) through (9) have been introduced here to avoid interrupting the continuity of the development in which they are used. DEVELOPMENT OF A NEW FORMULATION FOR TURBULENT CONVECTION The analogue ofEq. (1), with the additional idealization of negligible viscous dissipation is kaT -T,, J =-+pc V ay and that of Eq (2) is (10) (I 1) Here j is the heat flux density in the y-direction, T is the temperature of the fluid, Land T w are their values at the wall T+ = k(-cwp f 2 (Tw -T) I jw T'v' is the time-averaged prod uct of these fluctuating quantities, (T'vJ + = pcT'v' / j is the fraction of the radial heat flux density due to the turbulent fluctuations, and k is the thermal conductivity of the fluid. The termsj/L and (T'vJ+ in Eq. (11) depend on two param eters, namely the Prandtl number Pr= c/k and the mode of heating at the wall, as well as on y and a. Ch e mi c al En g ine e rin g Edu c ation

PAGE 21

From an energy balance over an inner cylindrical seg ment of the fluid s tr ea m it follows that ( 1 2) Here T m i s the mixed-mean temperatur e of th e fluid s tre a m. As contrasted with 't / 'tw, which m ay b e inferred from Eq. (1) to vary linearl y with R ,j/j w varies n onlinearl y b eca u se of it s depend e nc e on the ve locity distribution and in some in sta n ces on the temperature distribution as wel l. Also as can be inferr e d from Eq ( 1 2), T varies with x as well as with y, even in fully dev e loped thermal co n vec tion whereas u var ie s only with yin fully developed flo w. Fully de ve lop e d ther mal convection i s ordinarily defined b y two criteria namel y where h = j j( T w Tm) i s the l ocal h eat transfer coefficie nt. Equation ( 11 ) ca n be put in a mor e tractable form for both formal and numerical so lution by introducing n ew varia bl es ( -)++ y and Pr defined as follows in pla ce of j/L and T'v' l+y =-:1-(~J=~ =~ J ~ ( oT!ox J ctR 2 ( 1 3) J w 't J w R R U m oTm I OX 0 and ( 1 4) The result i s (1 + y)R dT + ( (----,--;)++ ) = cty+ Pr l u v j l+ ++ Prt 1-( u' v') ( 15 ) The use of y, the perturbation of the he a t flux den s it y di s tri bution from that of the s hear s tr ess di s tribution was s u gges ted by Reichardt.C 61 The variable Pr was originally introduced in connection with modeling in term s of the eddy viscosity and eddy conductivity and accordingly by analogy with the cor re s ponding ratio of molecular quantiti es, was called the tur bulent Prandtl number. Although the redefinition of Pr in terms of ( u' v'f+ and (T'v'r + avo id s the se heuri stic var ables, the traditional name a nd sy mbol for thi s qu a ntity are retained herein out of respect for it s hi sto ri cal origin. It s hould be noted that Pr is not necessarily proportional to Pr s ince (T'v'f + i s, in g~neral a function of Pr Equation -=R can be integrated formally to obtain Fall 2002 T + -~ s ( 1 +y)dR2 2 ( (----,--;)++ 1 R 2 Pr l u v j l+ Prt 1( u 'v')++ ( 16 ) Then P weighted by u + / u ~, can be integrated over the cross sect i on to o btain 2a+ Nu=--= Tm + ( 17 ) For uni fo rm h ea tin g at the wa ll it fo llow s from th e cr iteria for fully d eve l oped thermal co n vec tion that aT I ax= aT m I ax It then fo ll ows from th e correspondingly reduced form ofEq. (13), to ge ther with Eqs. (3) and (4), that Y is a function o nl y of y a nd a Equation ( 17 ) ca n then by v irtu e of the sa m e considerations, be inte g rated b y parts to obtain ( 1 8) Equation (18) can be r e duced for three special cases. For Pr = 0 it ca n be ex pr esse d as Nu 0 = Nu{Pr= O} = o/ t (I +y) 2 dR 4 =8 / (1 + y)~R ( 1 9) while for Pr = Pr it can b e reduced by virtue of Eq. ( 4 ) to 8 Nu 1 =N u{Pr =Pr 1 } =--,1 --------J (I +y )2[ 1-(u 'v')++ ]ctR 4 0 (2 0) 2 6 7

PAGE 22

Here as can be inferred, (1 +y)~ 4 designates the integrated mean value over R 4, and (1 +y) 2 the integrated-mean ++ wmR value weighted by 1 (u'v') Both quantities may readily be evaluated numerically using Eqs. (3), (4), and (5), and the reduced form of Eq. (13). For Pr 00 the temperature fi~~ develops almost completely very near the wall where ( u' v') can be approximated by 0.7 (y+/10) 3 and y can be neglected. Equation (16) can then be integrated in closed form to obtain Nu ~ = Nu{Pr oo} = 3 312 (0.0007}" 3 a +( Pr/ Pr 1 )" 3 / n = 0.07343 Re( fl 2) 1 12 (Pr/ Pr 1 ) 113 (21) For uniform wall temperature, the criteria for fully devel oped convection require that (aT I ax) I (aTm I ax)= T+ /T:i Integration of Eq. (17) by part s i s no longer possibl e, but from the Limiting form of Eq. (16) for R = 0, it follows that (22) and Nu = 4 u; J T: 1 Re( ) 1 u t lT:i) 1 (l+y)wmR 2 (23) Here, T c i s the temperature at the axis of the pipe. Equation (21) remain s applicable as is. The determination of numeri cal values of y T:, and T,~ from Eqs. (13), (16), and (17) now requires iteration, but the functional forms of Eqs. (22) and (23) are adequate for the development herein. On the ba s is of the previous experiences with various as pects of turbulent flow I anticipated that Eqs (19) through (23) could be combined in appropriate pairings in the form of Eq. (7) to construct satisfactory correlating equations for Pr~ Pr and for Pr::=::; Pr,, or alternatively, in appropriate trip lets in the form ofEq. (8). All s uch attempts failed, however I then found (somewhat serendipitously) that a successful cor relating equation for turbule n t thermal convection cou l d be devised by using a particular analogy between momentum and energy transfer in which the exact solutions for three par ticular values of Pr occur in the form of Eq. (9). Accordingly, a brief and very selective review of such analogies is appro priate at this point. SELECTIVE ANALOGIES Reynolds l 7 1 postulated that the transport of both momen tum and energy between a turbulent stream and its confining surface occurred wholly by means of a mass flux of edd i es and t h ereby derived the equivalent of Nu= Pr Re(f / 2) (24) 268 Prandtl l 8 l improved upon the Reynolds analogy by postulat ing an added resistance due to linear molecular diffu s ion of momentum and energy across a viscous boundary layer of thicknes s 8 in series with transport by the eddies of Reynolds in the turbulent core, thereby obtaining the equivalent of Pr Re(f / 2) Nu----~-~---,-,= l+o + (Pr-l)(f/2) 1 12 (25) Equation (25) just as Eq. (24), is inapplicable for Pr < 1, owing to neglect of thermal conduction in the turbulent core, and also for Pr>> 1, owing to neglect of eddy transport within the viscous boundary layer Even so, it represents a great ad vance in that it correctly predicts a coupled, non-power de pendence on both Pr and Re in the latter case by virtue of the dependence off on Re Of the many analogies that have been proposed to eliminate the deficiencie s of the Prandtl a nalogy for large and small values of Pr (see, for example, Churchill 1 91 ), only two need to be examined here. Reichardt 1 61 eliminated dy + between the equivalents ofEqs. (2) and (15) and made several ingenious approximations that allowed him to integrate the resulting combined equation in closed form. Cburchill 191 assembled the fragments of this so lution into a si ngle expression for Nu and corrected the erro neou s expression u sed by Reichardt for the shear stress near the wall thereby obtaining (26) Equation (26) is limited in applicability to Pr~ Pr, by virtue of one of the simplifications made by Reichardt in order to be ab l e to integrate analytically ChurchiW I01 (also Churchill and Zajicl 31 ) followed a com pletely different path to derive an expression, which for Pr~ Pr ,, is exactly equivalent to Eq. (26) except for replacement of the term 1 Pr /Pr by 1 (Pr /Pr) 213 In retrospect the differ' l ence in these expressions is a consequence of the approximation of Reichardt of du + by dy + in the differential term lead ing to the right-most term ofEq. (26). FINAL FORMS The final predictive expressions for turbulent thermal con vection emerged from the various expressions above by means of the following lengthy series of insights postulate s, and inferences, all of which were essential. 0 Churchill, et al., 1III recognized ~a~~q (26) ":'~s equiva lent with T:i IT : evaluated at the lurutmg cond1t10ns, to Chemical Engineering Ed u cat ion

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(27) (D They further recognized that when Eq. (17) was rear ranged as -:-u-~----NN_uu.,_\ = /[ 1 + % ~ ~, [ P r~ r~ r J] it had the form of Eq. (9), with b= -q =l Yo=Nu, (28) The staggered independent variable, Pr/Pr 1, has the essen tial role of converting Nu, from a particular value to an as ymptote. According to Eq. (28), Nu goes through a s igmoi dal transition from Nu 1 to N a nuance of behavior that had previously been overlooked. In retrospect, correlation in terms of Eq. (7), that is direct interpolation between Nu and Nu I was doomed to fail. The relation s hip provided by the Reichardt analogy was essential to the derivation ofEq. (27) 0 The identification of Eq. (28) with Eq. ( 9) suggested that the analogue of Eq (28) in terms of Nu and Nu mioht 0 I e, be applicable for Pr~ Pr ,. That concept led to an expression with a discrete step in the derivative of Nu with respect to Pr/ Pr, at Pr= Pr,, but elimination of this di sco ntinuity by means of an arbitrary but ultimately vanishing coefficient resulted in Nu-Nu 0 =/[i+ Nu 1 (N u ~-N u 1 1( Pr 1 -Pr J] Nu 1 -Nu 0 7 Nu ~l Nu 1 -Nu 0 ) Pr (29) where Nu ~ = Nu ~ {Pr= Pr 1 }=0.07343 Re (f / 2)" 2 0 The absence of any allusion to geometry or to the ther mal boundary condition suggested that Eqs. (28) and (29) might be applicable for all geometries and all thermal bound ary conditions. Plots of numerically computed values of Nu versus Pr/Pr, for round tubes with uniform heating and uni form wall temperature, and for parallel-plate channels with equal uniform beating and with unequal uniform tempera tures, confirmed the validity of this spec ulation. These plots in logarithmic coordinates appeared to pro vide an excellent overall representation for all values of Pr/ Fall 2002 Pr ,, for all values of a + orb + (w here b i s the half-spacing of the parallel plates ) greater than 145, which is the lower limit for the existence of fully turbulent flow, for all geometries, and for all thermal boundary conditions. The more critica l test provided by arithmetic plot s, however reveal errors in Nu ofup to 20 % for both Pr/Pr = 0{ 10} and Pr/Pr = 0{0 01} After many attempted correctives, s ubstitution of the anal ogy of Churchill for that of Reichardt to obtain l ( Pr J 1 [ ( Pr J 2 3 ] l Nu= P; Nu 1 + INu ~ (30) was found to re s ult in an almost perfect representation for the dependence of Nu on Pr/Pr 0 The analogue of Eq. (30) ~or Pr~ Pr corrected as was Eq. (29) to remove the si ngularity in the derivative and with the arbitrary inclusion of the empirical factor (Pr,/Pr) 11 8 is (31) This expression re s ults in almost exact representations for Pr < Pr for all of the previous l y mentioned conditions thereby it is a complement in every respect to Eq. (30). IMPLEMENTATION The numerical calculation of values of Nu for specified values of Re and Pr and for particular geometries and boundary conditions require s numerical values or expres s ion s for f, Nu 0 Nu 1, and Pr For a round tube, values of f of s ufficient accuracy can be determined from Eq. (6) by noting that Re=2a + u ~. Values of Nu and Nu can be 0 I calculated from Eqs. ( 19 ) and (20), but an array of such value s ha s already been calculated for representative values of a+, and correlating equations have been devised for interpo lation. The slight inaccuracy associated with Eq. (5) is totally negligible when it is u se d in conjunction with Eq s (19) and (20). Equivalent expressions for f, and values and expres sio n s for Nu 0 and Nu are also available or can readily be derived and calculated for other geometries and thermal boundary conditions. Equation (2 1) is directly applicable as an asymptote for large values of Pr for all geometries and conditions. Current correlative and predictive eq u ations for Pr are quite uncertain (see, for example, Kays r 12 1 or Churchilll' 31 ). However, Nu as predicted by Eqs. (30) and (31) is fortuitously insen s itive to the expression used for Pr and ,, the following purely empirical equation Pr 1 = 0.85 + O.Ol 5 Pr (32) appears to be adequate for that purpose. The dividing value 269

PAGE 24

of Pr with respect to the u se of Eq. (3 0) or (31 ), that i s, the value of Pr for which Pr= Pr, is 0.867 according to Eq. (32). Ot h er correlating equations for Pr, give only slightly differ ent numerical value s for thjs pivotal value of Pr. Either Eq (3 0) or Eq. (31) can be used without se rious error for 0.45 < Pr< 1.7 which suggests that Eq (3 0 ) is a s ufficient expres sion for all fluids other than liquid metals SUMMARY Equations (3 0) and (3 1 ), together with Eq. (32), predict values of Nu within l % or 2 % of num er ically calculated val ues for all geometries and conditions i n the fully turbulent regime. This is to be compared with deviations of 10 % to 40 % on the mean for a ll expressions in current use ma n y of which are greatly re s tricted with re s pect to range and condi tions (see Churchill and Zajid 31 ). The remarkable improve ment in accuracy for Pr Pr ,, as provided by Eq (2 7) is a consequence of using the Reichardt analogy, w h jch is free of any explicit empiricism. This expression fails in exactness only due to so me minor mathematical s implification s made in it s derivation This s light inaccuracy i s in turn vi rtu a lly eliminated by use of the analogy of Churchill. On the other hand the g reatly improved accuracy of Eq (3 1) for Pr~ Pr i s a consequence of the identification of the structure of the analog y of Reichardt with th a t of the generic correlating equa tion of Churchill and Usagi for thre e regimes in s ta ggere d form, togeth er with a minor empiricism. Thjs same identifi cation revealed a virtual regime and a point of inflection for Pr Pr ,, and another s uch pair that h a d never before been recognized for Pr> Pr ,. Th e existence of these virtual regime s explains the numerical and functional failures of mo s t prior correlating equations. The generality of the new expressions for all geometries and thermal boundary conditions is a consequence of the rec ognition that the analogy of Reichardt could be expressed in terms of Nu 0 Nu, Nu ~, and Pr/Pr The s upplementary ex pression s for Nu 0 Nu ,, and Nu ~ whkh are exact in sofar as Pr is independent of y+, follow dir ec tly from formu l ation of the equations of conservation in t e rm s of the fraction of the transport due to the turbulent fluctuation s. They could have been derived u s ing eddy diffu s ional model s, but not so simply. Implementation of the new expressions for specified val ues of Re and Pr and for particular geometries and t h ermal boundary conditions, i s not onerous s ince the entire calcula tion can be preprogrammed. The path of development leading to Eqs. (3 0 ) and (3 1 ) could now be streamlined, but the de sc ription of the irregular path that was act u ally followed has educational value in that all s tudent s and practicing engineers s hould be concerned with the evaluation if not th e co n s truction of correlating equations. Although the proce ss of derivation of the new relation s hips for thermal convection i s much more complicated, and the relationships them se lve s are slightly more complicated to 270 e mplo y, these defi cie ncie s appear to be a s mall price to pay for their grea ter accuracy, so und er rationale, and broader ap plic a bility Student s should be prompted to que s tion any of the as se rtion s and non-ob v ious steps that were mad e i n the ab breviated de ve lopment herein and not expanded upon by the teacher. Ju s tifications may generally be found in the references REFERENCES I A nd erson, T.J ., "C hemical Proces s ing of E l ectrons and Holes ," Chem. Eng Ed 24 ( 1 ), 26 ( 1990) 2 C hur c hill S W., A New Approach to Teaching Turbulent F l ow," Chem. E n g. Ed 32 (2), 14 2 ( 1999 ) 3. C hur c hill S W. a nd S.C. Zajic The Pr ed icti on of Turbulent Con vect i o n with Minimal Exp li c it E mpiri cis m ," A1ChE J ., 48 ,927 (2002) 4. C hurchill S.W. "New Simplified Models an d Formulations for Tur bulent Flow a nd Co n vec ti o n ," A I ChE J. 42 11 25 ( 1 997) 5. C hurchill S W., a nd R. Usagi, "A General Ex pr ess i o n fo r the Corre la ti o n of Rat es of Transfer and Other Phenomena ," A!ChE J ., 18 1121 (1972 ) 6. Reichardt H ., Die Grundlagen des Turbulenten Warmeii bertraganges ," Archiv ges. Wiirmetechn., 2 129 ( 1 95 1 ): Englis h tra n l atio n The Principles of Turbulent Transfer ," Nat. Advisory Comm Aeronaut TM 1 408, Washington DC (1957) 7. Re y nold s, 0., On the Extent a nd Action of th e Heating Surface of Steam B o il e r s," Pro c. Lit So c Manchester, 14 7 ( 1 874) 8. P rand tl L. "E in Beziehung zw i sc h en Warmeaustaush und Strii mun gswide r s tand der F lii ssigkei t en," Ph ys. Z., 11 1072 ( 1 910) 9. C hur chill, S.W., Cr itiqu e of the Classica l A l ge b raic Ana l og i es be tween Heat, Mass, and Momentum Transfer ," In d. Eng. Chem. R es., 36, 3878 (1987) IO C hur chill S.W. New Wine in New Bottles: U n expected Find in gs in H eat Transfer. Part III. The Prediction of Turbulent Convection with Minimal Explicit Emp i r i c i s m ," Thermal Sci. Eng. 5 (3), 13 ( 1997 ) 11 C hur c hill S.W. M. Shinoda and N. Arai, A New Co n cept of Corre l at i o n for Turbulent Co n vect i on," Thermal S c i. Eng., 8 (4), 49 (2000) 1 2 K ays, W.M ., "T urbulent Prandtl Number: Where are We?" J H eat Transfer, Tran s ASME, 116 234 ( 1 994) 1 3 C hur c hill S.W. "A R einte rpr etatio n of the Turbulent Prandtl Num ber ," Ind. Eng Chem. R es in pre ss 0 .t~ 111111 ij ... ._1_e_tt_e_r_to_t_h_e_e_d_i_to_r ____ ) Dear Editor: Late last year, you published our Letter to the Editor re garding a survey we were carrying out on the use of Inher ently Safer Design ( ISD ), meant to make the proce ss indus try a lot sa fer. Se vera l of your readers downloaded o ur que s tionnaire and se nt their responses to us We got responses from eleven countries world wide The finding s of the s urvey have ju s t been publi s hed under the title Inherently Safer De s ign: Present and Future" in the Tran sact ions of the Institution of Chemical Engineers, Pr cess Safety and Environ men tal Pro gress, 8 0 Part B Ma y 2002. We are plea se d to e nclose a co py of the publication for Chemical Engineering Education

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your reference. Further th e following is a brief s ummar y of the s urv ey paper. It 's appearance would b e a fitting finale to the effort that s tarted with the initi a l publication of our lett e r in your journal. Summary A recent s urvey of the current u se of Inherentl y Safer De s ign ( ISD ) concepts attracted re s pon ses from 63 people in 11 co un tries. These included indu strialists, consultants, regulators, and academics. The salie nt results of the s ur vey are noted below in bullet form to focus atte nti o n followed b y recom mendation s to expedite the adoption and s pread of ISD. Almost everyone responding know s of ISD. Their knowledge stems from s pecialized lecture s, s hort courses, books conferences and training videos ISD ha s been practiced by so me for decade s, whereas others star ted only recentl y. ISD i s u sed in almost all stages of c h e mic a l proce ss development d es ign a nd operation. ISD i s u se d during the manufa c tur e of a whole range of product s Almo st all hazard s have been tar ge ted both on-shore and off-shore. The above attests to the univer sa lit y of ISD applica tion s There is a favorable impa ct o n b a l a n ce s h eets. It i s important to u se "Ma n age ment of Change" when implementing ISD to avoid introdu ci n g any new hazard s. There i s very little additional cost if implemented early. Payback i s fast. Some applications/practitioners have won awards. ISD i s included in l ectu r es at severa l in s titution s. More will do so now Many are not familiar with the current Inherent S afe ty ( IS ) indice s. Those familiar with them have used them s paringly A s imple reali st i c inde x i s needed that also s how s economic benefit s. Detailed examples of u se at different stages of proce ss development are nece ssary ISD concepts can influenc e R&D in various areas of chemical engineering and c hemi stry ISD s hould encompass inherent safe t y, health and environment ( ISHE ). ISD concepts, suitably modified can b e u se d for other branches of engineering s uch as minin g, construction, tran s port etc. Current regulations do not force the u se of ISD. Recommendations The sa d truth is that ISD i s applied when an ISD e nthu siast i s on the team and not otherwise. Implementation of the recom Fall 2002 mendations belo w might e n co ura ge th e uptake ofISD. Every c hemi s t and c hemic a l e ngineer s hould be trained in ISD Academics a nd profe ss ional bodie s should lead in thi s. Other sc ienti s t s and engineers s hould be given intro du ctory lecture s in ISD with examples from different industries. IChemE s hould make ISD a part of its approved degr ee sy ll ab u s Sub seque ntl y, it s hould persuade other engi n eering and science accredi ting soc ietie s to do lik ewise. There i s a need to teach IS to management and finan cial people also s ince their role i s crucial in encourag in g applications of ISD. D e dicated funding by gove rnment and industry for research and teaching in ISD will encourage many academics to take it up. Incentives by th e gove rnm e nt to cost s hare demonstra tion plants and provide tax break s for ISD. Expand ISD to e ncompa ss ISHE s ince the environment and occupational health a r e da y -to-da y concern s. It ma y eventually be exte nded to ISHEQ (Q for Quality ) si nce improvement s in SHE will decisively impact quality of product. Companies s hould provide exa mple s of ISD u se in various situatio n s a nd the eco nomi c benefits r ea ped in orde r to co n vi nc e other industries regulators, govern m e nt the media the public aca demic s, R&D funding age n cies, etc. In vo lve the main s tream print and audiovisual media to favora bly impact public opinion Amend regulation s to enforce the u se of ISD. In s i s tence by international agencies to include ISD in project s that they fund in the sa me way that the World Bank now in s i s t s on e nvironmental impact assessment s tudie s in proje c t s funded by it. Some expected results Tall columns of chemical plants will be reduced to one or two-story height s Thi s will improve the image of the c hemical industry. Increased in ves tm e nt in proce ss indu s try. L ess r es trictive regulations. Gr ea t er enrolments in UG and PG courses. Significantly enhanced funding for R&D Adoption of ISD b y other engineering discipline s, especially the more accident-prone ones such as construction, mining tran s portation, etc. J.P. Gupta David W. Edwards Loughborough University 27 1

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.ta_..5 11111 3.__ c_ u _rr_i_c_u_l_u_m ________ __,) NOVEL CONCEPTS FOR TEACHING PARTICLE TECHNOLOGY WOLFGANG PEUKERT, HANS-JOACHIM S CHMID Munich University of Technology 85748 Garching, Germany P article technology is an interdisciplinary subject deal ing with disperse systems, including all types of solid particles (aeroso l s, suspensio n s), liquid particles (drop lets emulsions), and gaseous particles (bubb le s). The main focus of our current research and curriculum, however, is on so lid particle s. The goal of particle technology is producing and handling disperse materials under economical and ecological con strai nt s. The material s are produced due to a s urplu s value of the product properties. Typical examples for these properties are the taste of chocolate the color of pigments, the stre ngth of concrete, or the electrical properties of semicond uctor s Consequently this is also a key point in our curricu lum In order to prepare a young engineer for his possible tasks in industry and research, we have organized the curric ulum to reflect the structure of the field (see Figure 1). The field can be struct ured generally in four level s. The first and most fundamental level covers the elementary processes, i.e., the physical fundamentals. They include the stat istical founda tions of particle technology, multiphase flow bulk mechan ic s and powder flow, interfacial phenomena and the interac tions of dispersed matter with electromagnetic radiation. On the second level we apply the fundamentals to machines and unit operations. In our curriculum, we concentrate on separa tion proce sses, further stre ngthenin g students' capabilities in multiphase flow phenomena. The third level considers whole processes. Here we teach the concept of product engineering, i.e., how to tailor product properties. Consequently, we have a close link to the applications which are actually very broad: Materials scie n ce (e.g., all ceramics manufacturing i s in fact applied particle technology ) Life science (e.g. protein s may be treated as small particles in some respects drug delivery ) Information technology (e.g., quantum dots clean room technology chemical mechanical polishing ) Environme ntal engineering (e.g., particle separation) How can the new areas be included in the curriculum without disregarding the conven tional ones? In our opinion, the only answer is that teaching the fundamentals is even more important, but the examples given to the students should change. Traditionally, chemical engineering has been taught in Germany using the unit-operation s concept. In most univer sities, teaching particle technology has followed the concept of Hans Rumpf, who stressed the physical fundamentals in the basic course, which is followed by courses in agglomera tion, solid-liq uid separation, or particle characterization, to n ame just a few. Unfortunately, in the USA particle technol ogy is taught extensively in only a few universities. Students l earn how to design machines and processes that either keep the particle size constant (i.e., se paration, mixing) or change the particle size (i .e., size reduction and size enlargement). In th e past, only mechanical mean s to produce and handle par,, ,. ~, Wolfgang Peukert got his diploma degree in Chemical Engineering (1984) and PhD (1990) at Karlsruhe University. In 1998 he became a full professor at Munich University of Technol ogy. He is the chair of solids and interface pro cess technology. He also leads the particle technology research group and teaches par ticle technology. Schmid got his diploma de gree in chemical engineering (1993) and PhD in mechanical process engineering {1998) from the University of Karlsruhe He is a re search assistant in the particle technology group at MUT. His main research interests are multiphase flows and particle character ization Co p yr i g ht C hE Division of ASEE 2002 272 Chemical Engineering Education

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tides were considered; therefore particles larger than approxi mately lm were mainly dealt with while the non-mechani cal method s of particle syn the sis (e.g., crystallization, gas phase processe s) that lead to s ubmicron particles were ne glected. By introducing product properties, we address the overall goal of a chemjcal proce ss, i. e., the production of well-de fined product properties under economical and ecological constraints. The concept of product engineering transcends educational traditions and recognizes the end value of dealFundamentals C 0 2 ~ -+Un it operations a, u 'e Main topics Statistical foundat i ons Multiphase flow Bulk mechanics Interfaces (Interaction w radiation ) Particle separation CFO l+P rocesses Property and process function Part icle formation Application and Characterization Sciences Particle consolidation Figure 1. Structure of particle technology curriculum and co urses offered at Munich University of Technology. 100 0 14 80 0,10 e u :?: application z c, property C 60 \----+ Cl) Ill ... handling\ 0 ,06 Ill i E 0 property \ \ Ill 0 ................... 'C u 40 ai 0 02 ;;:. ----------0 0 0,1 0,2 0,35 particle size x / m Figure 2. Property functions of a typical pigment. Showing the whole picture elementary -9! processes unit operations processes .f G------+------++ C;;J :S j B--+-----+------++ B particufate i 1 ntetta~~ part. formation and separation population balance Figure 3. Teaching concept and new topics (gray). Fall 2002 ing with process technology, i.e., the product property Al though thi s point of view i s not new it is largely neglected in the curriculum. Rumpf1 11 coined the expression "property function" for the end-product qualities as well as handling characteristics. The property function is defined as Product property= F( disperse properties and microstruc ture chemical composition) Disperse properties are particle-size distribution, particle s hape particle morphology, and particle-surface characteris tics. As an example, Figure 2 s hows the product quality of a pigment (in this case the color strength per unit mass of pig ments ) that improve s with decrea s ing particle diameter. The yield stress of the powder as an important handling property also increases with sma ller particles indicating prohibitive high re sis tance against powder flow Obviously there exists an optimum where both product and handling quality are ac ceptable. One solution to thjs problem may be to optimize powder formulation allowing both high product quality and acceptable handling properties. Of course, there are many other end-product qualities, such as taste (e.g., of chocolate), stre ngth (e.g., of concrete), activity (e.g., of a catalyst or a drug ), or the band gap (e.g., of a nanocrystalline semjcon ductor) Typical handling characteristics are flowability, dust development filtration resistance, ri sk of explosion, and abrasiveness, to name only a few. Polke and Krekel 1 2 1 intro duced the term process function to relate the disperse prop erties of the product to the production process and the educts Di sperse properties = F(process parameters, educts) Proce ss parameters include the types of machines and urut operations as well as their interconnection the operational parameter s. The art of chemical engineering in this context involves designing the best process for producing the correct djspersed properties leading to the desired product quality with a minimum of costs, including environmental costs. Thi s way, the product would achieve the highest profit since it is the most competitive. Our point of view in cludes both the economical aspects and a global perspec tive of environmental responsibility. EDUCATION IN PARTICLE TECHNOLOGY ATTU MUNICH Teaching Concept and New Topics The particle technology courses are a part of the chemical engineering and proce ss engineering ("Verfahrenstechnik" in German) curricula at the Munich University of Technology. On one hand the traditional education of chemical engineers prepares students for well-known applications such as the design of cyclones or heat exchangers, but many of the tradi tional applications have reached the point where their eco nomic success is decreasing. On the other hand, new oppor tunities are evolving in areas that are less farruliar to engi neers, e.g., information technology or various aspects of ma273

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terials science. The question is: How can the new areas be included in the curriculum without disregarding the conventional ones? In our opinion the only answer is that teaching the fundamentals is even more important but the examples given to the students should change 41 In Figure 3, our approach is shown schematically We explain the whole picture to the students by showing them the progression from molecular precursors to the whole process which actually covers many orders of magnitude in both geometrical dimensions and time scale. In other words, we pave the way from feed mate rials to end-product properties-this is the horizontal line. In the vertical, depth is gained by explaining certain as pects in a detailed way. By reflecting the first three lev els of Figure 1 we stress particulate interfaces (funda mental le vel) since we believe that this aspect has not been sufficiently covered in the past. Moreover with the advent of nanotechnology interfacial aspects have be come increasingly important. The second level compris ing unit operations is handled in a more-or-less tradi tional way although new aspects such as CFD model ing are included. On the process level, disperse systems have to be treated mathematically by means of population balance equations, which have so far not been covered in traditional particle technology curricula Courses The courses are organized into three levels The first and most fundamental level comprises a two-semester course in "F undamental s of Particle Technology" (see Figure 4). In this course, the important foundations (rang ing from statistics, motion of particles in fluids, fracture mechanics to dimensional analysis) and their implica tion in mechanical process engineering are covered. In addition, new elements such as population balances (which are increasingly used in industry) and interfacial phenomena are introduced. The latter comprise the fun damentals of interactions between molecules and par ticles, characterization of particulate interfaces and as pects of nanoparticle technology (e g coagulation and stabilization of colloidal suspensions). The second level stresses unit operations. Here, we concentrate on "Particle Separation" (see Figure 5). This course is principally organized in the traditional way, focusing on se paration of particles from gases as well as so lid-liquid separatio n. Different unit operations in gas sol id separation are introduced systematically by focus ing on common principles i.e., on transport mechanisms of particles to the collecting surfaces of the respective separators. In this way, various unit operations are treated very efficiently, which allows for introduction of new modem methods such as CFD and its use for optimizing suc h apparatuses. We also offer a complementary course 274 Statistical Foundations size distributions population balances separation mixing Multiphase Flow particle tracking separation / classification packed and fluidized beds pneumatic conveying Property Function Bulk Mechanics Particle Characterisation interaction with radiation Interfaces molecular interactions adhesive forces wetting and capillarity nucleation colloids/ DLVO friction and adhesion storage and powder flow agglomeration Figure 4. Fundamentals of Particl e Technolo gy course (particle c haracterization included in separate course). Gas solid separation (dilute systems) Fundamentals: CFO and particle tracking Solid liquid separation (dense systems) suspension rheology sedimentation filtration flocculation Figure 5. Particle Separation course ) Particle productiory )structure fo~!) process design top down grinding classification bottom up gas phase synthesis crystallization consolidation property function particle size and shape color crystallinity taste particle surface strength particulate systems (agglomerates, thin films ... ) Figure 6. Product Engineering course Chemical Engineering Education

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sampling l transport l Measuremen t Syste m system dynamics relevant physics constituing equations assumptions realization: arrangement sensor & signal how to calculate property distribution?(-> inversion) derive capability and limits of method practical aspects Figure 7. Particle Characterization co urs e. C promoting ti .9 C .l:I '1G understanding of a, Q; E .! u 'C ii 'i: principles and ., Q; :, a. I( 1: system .GI > I .a intercorrelations .!! ca physical-chemical foundation Figure 8. Methodologi c al approach. Technica l Skills Soft Skills Figure 9. Integrated approach of university education. Fall 2002 dealing with "Downstream Processing of Biot ech nolo gi cal Products" that focuses primarily on different unit op erations for separation, disintegration, and purification of bioproducts as well as their interaction s in the whole pro duction process In severa l aspects, bioproducts such as proteins can be regarded as nanoparticles although the lim its of this point of view should be kept in mind. A completely new course is being offered in product en gineering (see Figure 6) The key question is how to pro duce the physical properties that define the product prop erty, from the point of view of both handling and applica tion. Examples for property functions are presented to get her with various methods for producing the particles (e.g., comminution and classification, gas phase sy nthe sis of nanoparticl es, crysta llization and precipitation). Han dling and formulation topics round out this course. The s tudents learn key concepts for formation of struct ur ed solids, product design, and powder processing syste m s. In this context, the systems engineering approach is impor tant. There is also a course in particle characterization that teaches the main principles in characterizing particle prop erties, e g., concentration, size, s hape s urface and zeta potential (see Figure 7). The purpose of this course is to enable the students to choose an appropriate setup for ar bitrary particle characterization tasks. This is accom pli s hed by emphasizing the basic aspects of a measuring technique (e.g., physical principle signal recording, conditioni ng and evaluation) as well as a complete measurement syste m (i cluding sampling, transport and preconditioning). These principles are explained in conjunction with a choice of the most important measurement techniques. Whereas Fundamentals of Particle Technology I and II are mandatory for all c h emical engineering st udent s, Par ticle Separation is one of a group of three courses (toget her with Process and Plant Design and De sign of Thermal Pro cesses) from which the stude nt s must choose two. The re maining courses are e le ctive. Methodology and Didactics The course in particle technology follows several guide lines: The key item is the product property approach, i.e. particles have physical properties such as particle size distribution particle shape, or particle morphol ogy that are closely related to product properties. Although it is difficult to describe co mplete process chains, we enhance the st udent 's awareness of the complete process. From a methodological point of view, we believe that teach ing s hould follow a double-tracked approach. On one hand the teacher should stress the important physical fo u nda tions si nce excellent ski ll s in the fundamental principles will be esse ntial for the st udent s throughout their studies and their professional lives. This implies that a large num275

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ber of facts have to be taught, thus assigning an important ro l e to the teacher On the other hand, to promote the s tu dents' understanding of the underlying principles as well as to sharpen their view of the complete process active learn ing appears to be a key issue .13 5 6 1 We try to support this ac tive learning in different ways ( s ee Figure 8) Lab and virtual experiments are conducted so that student s can apply and transfer their acquired knowledge and get in volved with more realistic problems. Thi s i s accompli s hed by a mandatory lab course (one semester) as well as lab com ponents that are integrated into the courses described above. The lab experiments include a wide field of exemplary task s that include for example, dust s eparation in cyclone s filtra tion, mixing and particle characterization by laser diffrac tion as well as the investigation of the stability of colloidal s uspensions by dynamic light s cattering. Furthermore, a com pletely new virtual lab i s currently being established in the course Product Engineering, with computer simulation s of disperse systems (e.g. crystallization comrninution) based on population balances using commercial s oftware (e.g ., LabView and Parsival) We also encourage the students to take an active role throughout the courses wherever it is appropriate for example in the particle characterization course. After introducing the basic principles and the important characteristics of a mea surement systems ( e .g., as s essed equivalent particle size, sig nal recording, conditioning and evaluation necessary s ample preparation etc.) as well a s discussing their application to the most important measurement techniques the student s are arranged in small groups Each group is then assigned the task of analyzing one measurement technique that is s o far unknown to them They also have to prepare a presentation of their results that will relay the most important facts to their fellow students The groups are supposed to work autono mously, with the teacher playing a more passive role and only giving guidelines or help when asked. In this way several goals can be achieved. The students work and acce s s information autonomou s ly e g from literature in a foreign language The group work necessitates that students find their role s in a group and work together productively .111 Finally the students are given the chance to prepare and give a presentation. Even listening and assessing the presentation of other groups increases their ability in thi s respect. This is a capabi l ity that is not practiced enough 1 8 1 By actively preparing a s mall part of the course, the stu dents not only acquire valuable technical knowledge, but they also get a chance to increase their "soft skills Personal de velopment is often neglected in a university education. Stu dents should concentrate on both their technical skills and their personal growth (see Figure 9). This includes an ability for self-organization and focusing on defined targets intrin27 6 sic motivation to reach goals and an ability to communicate results On a deeper level internal self-reflection i s indis pensable for accepting personal strengths and weaknes s e s as well as those of other s Thi s is a precondition for all social skills. CONCLUSIONS Particle technology i s a much wider field than many people realize since it al s o comprises biochemical chemical, and thermal processes dealing with particles. Hence it i s not only of the utmost importance in the chemical industry where about 6070 % of all products are fabricated in disper s ed form, but also for a number of other field s, such a s material s s cience and information technology Product properties and the s ub sequently developed product engineering approach is at the center of our considerations. With a continuously growing number of applications for dispersed systems we feel a need to stress the fundamental aspect s even more With the gen erally observed trend toward finer particle sizes new topics such as particle interaction s and population dynamics have been included in order to prepare our students for newly de veloping areas such a s nanotechnology. The technical courses are complemented by various activities to strengthen the soft skills of the students. Recently, suggestions have been made by Cussler e t al. ,[9 1 on how to change chemical engineering curriculae. Consid ering the shift in industrial practice from large-scale processes producing commoditie s toward more s pecialized product design we feel that particle technology and particle design methods deserve a prominent place in the curriculum ACKNOWLEDGMENTS The authors would like to thank Professor Helmar Schubert from the University of Karlsruhe for very valuable discussions REFERENCES I Rumpf H Ober di e Ei ge n sc h a ft von Nutz s t ii uben Stab-R e inhalt,m g d e r Luft 27 ( 1 ) p 3 (1967 ) 2. P o Ik e R a nd J Kr e k e Qu a lit ii t ss i c herun g b e i d e r V e r f ahr e n se ntwi c klun g C h e m In g T ec h 64 ( 6 ), p. 5 28 ( 199 2) 3 J.L. C a n o, Gar ces A ., and S ae n z M.J O ra l Pr es entation s of Stu d e nt s in Product E n g in e erin g Lecture s." Int. J. En gg. Ed. 13 ( 3 ), p. 175 (1997 ) 4 Cu ss ler E.L. Do Chan g e s in the Chemical Indu s try Imply Change s in the Curri c ulum ?" Ch e m En g. Ed 33 ( 1 ) p 1 2 (1999 ) 5 Davi s R H Helpful Hint s for Effective Teaching Ch e m En g. Ed. 32(1 ), p. 36 (199 8) 6 Felder R.M D R Wood s, J E Stice and A Rugarcia The Future of Engineering Education Part 2 : Te a ching Method s that Work ." C h e m E n g. Ed 34 ( I ) p 2 6 ( 2000 ) 7 Humphr eys P ., V L o, F. Chan and G Dug g an De v elopin g Tr a n s ferable Group wo rk Skill s fo r En g in ee rin g Stud e nt s," Int J En gg E d. 17 ( 1 ), p 59 ( 2001 ) 8 Brostow W. "In s tru c tion in Materials Science and En g in e erin g: Modern Technology and the New Role of the Teacher ," Mat S c i and En g A3 0 2 p 181 ( 2001 ) 9. Cussler E.L. D W. Savage A.P.J Middelb e rg and M. Kind. Re focu s ing Chemical En g ineerin g," Ch e m En g. Pr og r., 98 ( 1 ), p 26S ( 2002 ) 0 C h e mi c al En g in ee rin g Edu c ation

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Letter to the Editor Continued from pag e 262. b=S.5164364.5315505; the error variance s 2 =0.467503 ; and correlation coefficient R 2 =0 953603 Professor Fahidy advises not to put too much faith in the linear regression model in spite of the relatively l arge R 2 va lue because of the extremely wide confidence in tervals on the parameter a. The fairly random di s tribu tion of the residuals (see Figure 2) suggests, however that the linear model may be the correct one. Further more, both physical considerations ( fuel consumption sho uld be zero for a zero mas s vehicle) and the wide confidence intervals on the free parameter a, indicate that the model can be improved by setting the free parameter at zero. Indeed carrying out the regre ss ion while setting a=0 yields: b=? .8 92916.3599903; s 2 =0.4641509 and R 2 =0.9481781. Thus this model is now acceptable, even wit h respect to the confidence interval values. One of Professor Fahidy's objectives in presenting this examp le was to warn against accepting relatively large R 2 values as proof of good linear relationship between the dependent and independent variables. The limitations of the R 2 statistics in this respect can be most strikingly demonstrated using residual plots Shacham et al. ,13 1 for examp le fitted vapor pressure data of 1-propanol with the two-parameter Clapeyron eq u ation. This regression yields the values: R 2 =0 99988 l 8 and s 2 = l.659E-05 (based on log P). Such a high value of R 2 can be interFigure 2. Residual plot for Example 5 in Fahid y paper .11 1 ~ m -~-~~~--,~ -------~--' ., Figure 3. Residual plot for vapor pressure data from Reference 3. Regression model: log P = 7.6380342-1622.8666/T Fall 2002 preted as a perfect fit. But the residual plot (see n in Figure 3) shows that the vapor pressure data set exhibits a curvature which is not predicted by the Clapeyron equation. Indeed using the four-param eter Riedel equation for representation of the same data y i e ld s: R 2 =1; s 2 =1. 327E-09 and randomly di str ibuted residuals. The last example, given in the Appendix of the article deals with a linear model for representing coded effectiveness indicators versus catalysts containing various coded platinum mass units Analysis of this example shows that if the free parameter, a, is set at zero (as suggested by the wide confidence intervals on a and physical con siderations) the linear model is appropriate to represent the data with B= 1.6437659.08459 l 7 R 2 = 0.88604 l 4, and s 2 =0 .8 508906. We can conclude that teaching statistical analysis of data and re gression models is very important but interpretation of numeric sta tistical indicators must be complemented by graphical analysis and consideration of the physical nature of the model in order to arrive at the correct conclusions. References Mordechai Shacham B en -Gurion University of the Negev Neima Brauner Tel-Aviv Universi ty 1 T.Z., "An Undergraduate Course in Applied Probability and Sta tistics, Chem Eng. Ed. 36 (2), 170 (2002) 2. Fahidy, T.Z ., Personal communication (2002) 3. Shacham M., N. Brauner, and M B. Cutlip, "Replaci ng the Graph Paper with Interactive Software in Modeling and Analysis of Ex perimental Data ," Comp. Appl. Eng. Ed., 4(1) 241 (1996) 0 Author's Response I am delighted at Professor Shacham's interest in my paper. I also fully concur with the argument that the residual plots are an impor tant and integral part of regression analysis. This is now sta ndard textbook material, and I do routinely discuss this s ubject in my course. Although my intention was to keep the article from being too lon g, in retrospect I should have spent a paragraph or two on residual analysis, and I regret the omission. In Example 4 it was stated that the reaction mechanism was first order irreversible, but perhaps not strongly eno u gh to imply an a priori knowledge of non-statistical origin, so that 0 th and 2 nd order models are beyond consideration With limited data and given a physically correct model, the method that provides regression pa rameters relating data to model with the smallest error variance may be acceptab l e in lack of something better, even if the residual plot does not show randomness of a desired degree. The quest for addi tional measurements is almost universal in the case of limited-size data. My views about R2 versus confidence intervals for true regression parameters do not fully coincide with the respondents ', but may I point out the redundancy of seven-digit values, computer printouts notwith sta ndin g An R2=0.8860414 is not more meaningful than R2=0.89 Thomas Z. Fahidy 277

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.ta ... 5 1111111 .._c_l_a_s_s_r o o m ____ ___ __,) GAS STATION PRICING GAME A Lesson in Engineering Economics and Business Strategies AARON SIN' ALFRED M. CENTER Cornell University Ithaca, NY I 4850 T he School of Chemical Engineering at Cornell Uni versity recently undertook an evaluation of its Mas ters of Engineering program to assess the curriculum and the amount of value added to the student's education by their participation in the program One conclusion that we reached was that students in a professional masters program were most likely to go on, at least initially to some kind of a position in a corporate environment. To increase the likeli hood of their success in those early years on the job, we felt that some level of knowledge of how a business unit works and how an engineer fits into such a unit would be of signifi cant importance to their career s. With this in mind we added a requirement that all M. Eng. Candidates take a course that would give them some insight into these areas. While there are a number of different courses at Cornell that deal with related topics, there was no one course that covered all of the areas that we thought were relevant. This led to the development of a new course, primarily for Masters of Engineering students, titled "Managing New Busi ness Development." The course is an attempt to explain the business develop ment process as it is likely to be carried out in a major corpo ration. It deals with concept development feasibility assess ment, front-end analysis to select the best implementation strategy, tactics to take the concept forward, implementa tion of the selected strategy, and ongoing improvement of the process once it is implemented to either increase or maintain profitability. The students are exposed to a number of different concepts. As the course advances, they are asked to demonstrate their knowledge through several case studies. The first case study involves producing plans for executing a feasibility study to introduce a new line of cosmetics in a newly opened over seas market. The second involves maximizing value from a feedstock that contains a number of different components. One of the concepts we found particularly difficult to get across to the students was pricing strategy. To provide a means for hands-on experience with this concept we developed what we call the gas station game ." Unlike most games in busi ness schools that generally involve multiple inputs and fo cuses at sitewide or businesswide optimization in a qualita tive manner, this is a quantitative pricing game that aims at illustrating market forces at work. Since most people in the U.S. regularly deal with the fluctuation of gas prices it is easy for the students to relate to it. We play this game every time the class meets. THE GAS STATION GAME In the game, students are divided into four groups with each of them managing a gas station. Operating under differ ent restrictions ("mom and pop versus "big chain"), students are asked to decide on their business goals and facility sizes which in tum lead to pricing structure and marketing tactics. We found that it is generally effective to have students perA ar on Sin received his B ChE. in 1998 from the University of Delaware where he was trained to become a practical engineer. At Cornell, he used this knowledge to design microfluidic devices for pharmaceutical testing with his research advisor Aaron is completing his Ph D. thesis and considering a career in academia Alf r ed Cen t e r is a registered professional engineer with over thirty years of experience in the petroleum industry He is now a senior lecturer in chemical engineering at Cornell teaching classes in unit operations labo ratory, senior design project management process control and busi ness development strategies Copyrig ht ChE D i vis i on of ASEE 2002 278 Ch e mi c al Engine e rin g Edu. c ation

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maximizing an individ u al player 's revenue did not necessar ily mean defeating the other s And in fact the mo s t favor able revenue picture is one in which all participant s were able t o s h are the market in some fashion We fou nd that wit hin approximately ten iterations the stu d e nt s were able to arrive at the conclusion that a s hared mar ket c r eated more revenue and that cutthroat competition was unlikely to succeed With this realization the student s go on to develop pricing strategies that allow each of them to sell close to their faci lit y s capacity and to maximize their in dividual revenues Figure 2 shows a typical adjustment process based on root mean-squared deviations in prices and revenues as compared to va lu es at the last it eration. At around the tenth iteration prices begin to converge to t h e range where a reasonable profit is s u stai n ed amo n g all s t ations The revenues continue to fluc tuate, on the ot h er hand since students often react to price cha n ges of the other station s after their demands have c h a n ged in s tead of anticipating the behavior of the oth ers. These fluctuations are likely to stabilize if we carry the game further. CONCLUSION We think this game provide s an easy way to teach pricing stra te gy in a fairly simplistic business model, and we are happy to pass along this game for your interest and use. ( __ AP_P_E_N_DI_X_A_) Assignment Sheet for the Gas Station Pricing Game There are fo ur gas stat i ons on Rt. 13 coming into Ithaca They are a b out a block apart, as indicated in the figure be low I II IV R t. 13 to I thaca III Figure 1A: Map of the four gas stations Preliminary market research indicates a demand of abo ut 1 20 cars/hr in the day and 20 cars/hr at night, at 10 gals/car While some percentage of the driver s go to the first gas sta tion in sig ht most make that decision based on things such as price, convenience (cre dit card/speed pass) and brand name. They also h ave the c hoi ce of gett in g gas from the next town if they feel prices are to o high. Your first task is to decide on the amount of investment, 28 0 "' ., u .!: C: 0 ., "' ;: ., C 1/) ::[ "" ., 2: 1. oi "" 15 00 % 7500 __,_Price 12 50 % Deviation _,, -Revenue Deviation 5000 10 00 % 7.50 % A 2500 I 5.00 % v\~ 0 2.50 % 0 00 % -2500 1 2 3 4 S 6 7 8 9 1011121314151617 Iterat i on "' ., ::, C: ., > ., "" ;;; C !: C: 0 ., "' ;: ., C 1/) ::[ "" Figure 2. The adjustment process: root mean squared de viation in pri ce s r e lative to final average price (left axis) and root mean squar e d deviation in revenues (right axis) plott e d against it e ration number. TABLE 1 Difference s between Mom/Pop Operation s and Chain Co mpanie s Investment Supply Cost Personnel Service M o rn/Pop $3 00 000 $ 1.45/ g al 1 @ $ 5/hr 12 hr Chain Unlimit e d $ 1.47/ g al 2 @ $5/hr Speed pass TABLE2 Gas Station Configurations and Costs Capacities 20 000 ga l 25,000 gal 30,000 gal 40,000 gal Capit a l C os t $2 00 000 $3 00 000 $ 400 000 $ 500 000 Op era ting C os t $ 56 / d ay $8 4 / d ay $ 111/day $ 138/day level of service and pricing strategy for yo ur gas statio n Your decision will depend on the nature of yo ur company (mom/ pop vs. chain), as listed in Tab l e 1. Table 2 lists the avai l a bl e gas station configurations The supply trucks come every seven days to refi ll the underground gas tanks. If you sell more gas than yo u r designed capacity, the extra gas will be available at 115 % x Max gas price in Ithaca. The goal of this exercise is to achieve the highest return on investment among all gro up s, wit h a minimum acceptab l e ROI at 12 % per year. You will be ab l e to c h ange yo ur prices (and o nl y prices) every week, depending on the market situation 0 Ch e mi c al Engin ee rin g Edu c ation

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form cash flow analyses for different scenarios. (The project ass ignment is s hown in Appendix A.) The cost parameter s are approximat e d and te s ted to produce realistic profit fig ur es in the end. Capital costs include the s torage tank mate rial and installation gas pump s, land requirement, engi neering costs, etc. Th e operating costs are estimated as 10 % of the cap ital inve s tm e nt assuming a ten-year project lifetime. When the s tudents are ready for the actual price bidding a s imulation i s used to determin e the demand in each station, ba se d on the four s tated price s (see Figure 1 ). The s imulation i s modified from the Monte Carlo Gille s pie algorithm from reaction kineti cs Simply, th e probability of customers visit ing each gas s tation is inver se ly proportional to the price di f ference between that particu l ar s tation and the minimum bid der. The s imul at ion then u ses a random numb er generator to determine the exact demand for each s tation An extra s t a tion with a fixed price i s added to mod e l gas s tation s from out si d e this town. To account for different level s of serv ic e provided by each statio n (e g., method of payment that is accepted), the price s are a dju ste d before the probabilitie s are calculated. The se adjustment amount s are ba se d on poll s conducted among s tu dent s r egar ding their own consumer preference s Th e s imu lation also include s so me proportion of cars that stop at the first gas s tation in sig ht in s tead of comparing price s, which again is det e rmi n ed u s ing a Gille s pie algorithm with a prede termined probabilit y. The profit of each co mpan y i s calculated ba se d on th e num ber of gallons so ld minu s operating costs of the gas sta tion A s mentioned before each group decide s in advance what the suitab l e underground s torage capacity will be, which gives rise to certain capital costs and operating costs. In the event that the gas s tation se ll s more gas than its capacity al low s, it will have to obtain extra gas at 115 % of the maxi mum price among th e four gas sta tion s. In thi s way, each gas s t a tion is e quall y profitable if the right price rel a tive to each other is found RESULTS AND DISCUSSIONS The results of the game are quite encouraging. We are try ing to teach the concepts of customer perception of product value, convenience, and price differentiation ba se d on tho se per ce ption s. We are a l so trying to s how that the s trategy of F ----~ o ~----P nadti Gu Sl.ltiOll M0Ate Cillo Simu~ .,_:: :,;.....;: :::........;::;;_ _______________ 11.43.l Fall 2002 Gu Stidon M o n~ CuLo: SWiOln 1 maon2 Wffl Ci:] (tllt' holll) 3 SfilldA 4 U T~ j HO~ I I l ~ M I j l.Alll. l c:;,, E l faigrtE] j Speed\;t! I cci!!it l I 121"! 12hr Ei' I I l,.hr 11 Z ti c ffl 201c ail? I m ol I iQK od1,,I I t @m M misi:l b,oa;i#! I I S::baiat '9'. I H P,11001 1 I Om R-fln Figure 1. The gas station game s imulation in action. ri17 8370 11 2':!m teOOtle $-f 440 1~96 fl960 12 15 4 2 188 1 3 $1 SOO 23(:e 23181 tJ4,620 466 2 7 9

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Random Thoughts ... SPEAKING OF EDUCATION Ill RICHARD M. FELDER North Carolina State University Raleigh, NC 27695 T here is a theory which states that if ever anyone discovers exactly what the Uni v erse is for and why it is here it will instantl y disappear and be replaced by something even more bizarre and inexplicable. There is another theory which states that this has alread y happened ( Dou g la s Adams) A lecture is a process by which the notes of the professor become the notes of the students without passing through the minds of either. ( R K. R a thbun) A teacher who is attempting to teach without inspiring the pupil with a desire to learn is hammering on a cold iron (Hora ce Mann) Teachers who cannot keep students in v ol v ed and excited for several hours in the classroom should not be there Richard M Felder is Hoechst Celanese Pro fessor Emeritus of Chemical Engineering at North Carolina State University He received his BChE from City College of CUNY and his PhD from Princeton He is coauthor of the text Elementary Principles of Chemical Processes (Wiley, 2000) and codirector of the ASEE Na tional Effective Teaching Institute (John Rou e che) If a professor can be replaced by a CD-ROM he / she should be (Ja c k Wilson) I'm sure the reason such young nitwits are produced in our school s is because they have no contact with anything of any use in everyday life (Petronius, d. ~66 AD) Times are bad Children no longer obey their parents and ev e ryone is writing a book. (Cic e ro) What's on your mind if you ll forgive the overstatement? (Fr e d All e n) Everything should be made as simple as possib l e but not simpler (Albert Einst e in) In theory there is no difference between theory and practice ; in practice there is (Chuck R e id) Copyr i g ht C hE D ivision of ASEE 2002 282 Ch e mical Engin e ering Edu c ati o n

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~S=i announcements ) ... _111111_..._ _______ CONFERENCE TEACHING ENTREPRENEURIAL ENGINEERING Monterey, California January /3-16, 2003 Engineering educators have done a great job of teaching students engineering science and engineering design. In ad dition, engineering schools are beginning to address the de velopment of "soft skills" such as communications, team work, and ethics. In the current environment, it is increas ingly important for the engineering education system to also find ways of teaching entrepreneurship and motivating stu dents toward such activities. This conference will set the stage for a continuing and fruitful dialog between engineering edu cators and the business community. The conference will assemble entrepreneurs, engineering educators, and business school faculty to discuss What are the attributes of successful entrepreneurs? What are models of successful programs teaching entrepreneurship to engineers? What is the culture at a university that fosters a spirit of innovation and entrepreneurship? How can engineering faculty become role models of innovation and entrepreneurship? The outcomes of the conference will be a set of recom mendations to engineering faculty, curricular integration op tions, model programs available for replication and contacts between academic and business that will be published in the journals of various professional societies. The Chairs of the Conference are Eleanor Baum of The Cooper Union and Carl McHargue of the University of Ten nessee. Additional information about this Conference and a regis tration form, can be found at the Conference's web site: Engineering Conferences International offices are located at 6 MetroTech Center Brooklyn, NY 11201 Telephone at 212-591-8 144 Fax at 212-591-8145 e-mail at bhconf@poly edu web at www.engconfintl.org. Fall 2002 CONFERENCE ENHANCEMENT OF THE GLOBAL PERSPECTIVE FOR ENGINEERING STUDENTS BY PROVIDING AN INTERNATIONAL EXPERIENCE Tomar, Portugal April 6-11 2003 This conference will provide a forum for exchange of ideas on methods of enhancing the global perspective of engineer ing students, identify the key obstacles, and discuss progress toward eliminating the obstacles The conference is jointly sponsored by Engineering Conferences International, Ordem des Engenheiros Portugal, and E4 (Enhancing Engineering Education in Europe). Thematic Network is financed by the European Commission under SOCRATES II and co-financed by the University of Florence Contact for more information or go to . The conference will focus on the recognition that exposure to other cultures brings personal enrichment to individuals and can be an important component of the educational expe rience. With the increased globalization of economies, the need extends beyond personal enrichment and has become an important asset to student mobility. Among the issues that must be addressed are compatibility of degree systems, ac creditation of courses and/or degrees, quality assurance, an accepted credit system, language of instruction, and legal and social issues such as visas, taxation, and financial suport. The Chairs of the Conference are Carl McHargue of the University of Tennessee and Eleanor Baum of The Cooper Union (New York, NY) The Co-Chairs are Antonio Salgado Baro s of the Orem dos Engenheiros (Portugal), G. Augusti of the University of Rome (LaSapienza, Italy), and C. Borri of the University of Florence (Italy). Additional information about this conference, and a regis tration form, can be found at the Conference's web site Engineering Conferences International (ECI) is the suc cessor to the United Engineering Foundation Conferences. ECI offices are located at 6 MetroTech Center, Brooklyn, NY 11201 Telephone at 212-591-8144,-Fax at 212-591-8145 e-mail at bhconf@poly.eduweb at www.engconfintl.org. 281

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To state a theorem and then to show examples of it is literally to teach backwards (E. Kim Nebeuts) Setting an example is not the main means of influencing another, it is the only means (Albert Einstein) Education is what happens to the other person not what comes out of the mouth of the educator. (Miles Horton) Education is the ability to listen to almost anything without losing your temper or your self-confidence. (Robert Frost) Lack of education is an extraordinary handicap when one is being offensive (Josephi n e Te y) Education is one of the few things a person is willing to pay for and not get. (W illiam Low e B, y an) Education is what survives when what has been learned has been forgotten. (B F. Skinner) A graduation ceremony is an event where the commencement speaker tells thousands of students dressed in identical caps and gowns that individuality is the key to success. (Robert Orb e n) There is a legend that the difference between classes of freshmen and post-graduates is that if you say "Good Morning" to the first, they reply "Goo d Morning." But the graduate students write it down (Donald Bligh) I used to keep m y college roommate from reading my personal mail by hiding it in her textbooks. (Joan Welsh) Predicting the future is easy It 's trying to figure out what's going on now that's hard (Fritz Dr ess l e r) lfl knew what I was looking for it wouldn't be research, would it ? ( Ri c hard Feynmann) If I accept you as you are, I will make you worse; however if I treat you as though you are what you are capable of becoming I help you become that. (Goethe) Teaching is the greatest act of optimism. (Colleen Wilcox) Try not to have a good time .. this is supposed to be educa tional. (Charles Schulz) All of the Random Thoughts columns are now available on the World Wide Web at http://www.ncsu.edu/effective_teaching and at http : //che.ufl.edu/~cee/ Fall 2002 283

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.t~ 111111 ij 1111111 311-c_l_a_s_s_r_o o_m ______ __.) MAKING PHASE EQUILIBRIUM MORE USER-FRIENDLY MICHAEL J. MISOVICH Rose-Bulman Institute of Technology Terre Haute, IN 47803 I believe phase equilibrium thermodynamics is the most conceptually difficult undergraduate chemical engineer ing class Even students who perform calculations sat isfactorily seem confused over the meaning of what they have learned Phase equilibrium is the single undergraduate chemical engineering class in which abstract concepts are presented to the near exclusion of practical applications. Table 1 gives examples of practical or physically intuitive subject matter found in classes that students typically consider abstract theo retical, or mathematical. These actually contain some bal ance of theory and practice, giving students a point of refer ence to physical processes and equipment. Calculations such as bubble and dew points are needed for practical design of course, but most phase equilibrium courses do not connect these to real processes or equipment. Practical applications of the material are taught as part of unit operations, mass transfer, or distillation courses. Students frequently have more intuition about the physical meaning of abstract quantities in classes other than phase equi librium. Heat transfer students could define the Prandtl num ber as CP / k, give a physical interpretation for all three variables, and potentially recognize related facts For example, "The Prandtl number could be derived by applying the Buckingham Pi theorem to a heat transfer problem," or "Larger Prandtl numbers result in larger convective heat transfer coefficients." They know that the Prandtl number for liquid water at 100 atm and 150C is unlikely to be 100 or 0.01. 100 atm and 150 C i s closest to 5 atm, 50 atm, or 500 atm? Most are at a complete loss when asked to apply abstract quantities such as activity coefficients to practical questions e.g., "Is ethanol more likely to form an azeotrope with hexane or n-octane ?" Lacking qualitative under s tanding their only approach for answering this question is detailed quantitative calculation. STRATEGIES F OR BUILDING I NTUITIO N Prausnitz et al., 111 describes the phase equilibrium prob lem as a three-step process Fir s t a real problem is translated into an abstract mathematical problem Second the math ematical problem is solved. In the final step, the mathemati cal solution is translated back into physically meaningful Mi cha e l Mis o vich will be Associate Profes sor in the Physics and Engineering Department of Hope College in August 2002. His research interests include thermodynamic property pre dictions from equations of state, physical chem istry of polymer solutions chemical engineer ing education and its assessment. TABLE 1 Content of "Theoretical" ChE Classes When phase equilibrium students define chemical potential, it is typically in terms of other abstract con cepts-free energy, standard states, fugacity, and ac tivity. They are unlikely to know whether a certain chemical potential is positive or negative, nor what practical significance its sign would have. Without doing a calculation, how many phase equilibrium stuFluid Mechanics Theoretical Concepts Shear stress tensor Dimensional Analysis Practical Concepts Pumps Valve s, Pipin g Mass Transfer Transport Phenomena Phase Equilibrium dents know whether the fugacity of liquid water at 284 Fluxes of a ll sorts Partial differential equations, Dimensionle ss Gr eek variables Chemic a l potential fugacity, activity Packed ab so rption towers Viscometers, Heat transfer with free convection, Wetted wall columns Bubble and Dew Points Flash Solubiliti es Copyright ChE Division of ASEE 2002 Chemical Engineering Education

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TABLE2 Common Intuition about Chemical Engineering Data Hi g h moleclar weight co mp o und s h ave hi g h b o ilin g point s A s ub s tance with a d e n s i ty order of m ag nitud e l ess th a n wa t e r is prob a bl y a gas A Re y n o ld s number in th e l a minar ra n ge fo r fl ow of wa t e r in typic a l proc ess piping i s n o t t y pi ca l Con vect iv e heat tran sfe r coefficients a r e ve r y lo w for gases as compared to liquid s TABLE3 Uncommon Intuition about Phase Equilibrium Data The f u gac it y of a l iquid i s approximately its vapor pr ess ur e, as l o n g as the pr ess ur e i s n o t ex tr eme l y hi g h Th e fugacity of a co mponent in an ideal gas mi x tur e i s it s partial pr ess ure Substances we consider nonconden s ibl e gases h ave fug acity coe fficient s lar ge r than o ne ; liquid s and co nd e n s ibl e va por s h ave fu gac ity coefficients s maller th a n o n e Substance s with larg e differ e n ces in b o ilin g p o int s are unlik e l y t o form azeotropes; s ub s tan ces w ith ve r y close boiling point s are almost ce rt a in to form th e m Act i v it y coefficients lar ge r than ap pr ox im a t e l y seve n indic ate that liquid -liqu id pha se se parati o n i s p oss ibl e The dilute componen t in e ith e r of tw o nearly immi sc ible pha ses obeys Henr y's Law up to it s so lubilit y limit TABLE4 term s. Typically, thi s ste p consists of transformjng highly abstract variables into phy s ically s ignificant ones C h e mical Potential Fugacity Activity Composition Each transformation re s ult s in a le ss abstract variable than the previous s tep Students do not see m to recognize this perhaps because we do not teach it explicitly Instead, they see c hemical potential, fugacity, and activity as equally nebu lou s a nd a b strac t concepts upon which a rote series of math ematical operations will hopefully produce a physically mean ingful varia ble s uch as compos ition pres s ure, or temperature. One of my princip a l goa l s in teaching phase equilibrium thermodynamics is to help s tudent s develop an intuitive un derstanding of the topic. I point out to them in the beginning that this class d ea l s with t ec hnjque s for ge nerating data to u se in other classes to the nearl y total exclusion of applica tion s. Since st udent s w ill not be a ble to rely on proces ses or equ ipm e nt to provide intuition I emphasize under s tanding the data and its significance. This type of intwtion about data rather than equipment occurs in other classes as the Prandtl number exam pl e above and as similar examples in Table 2 indicate. To promote thi s, I e mpha size calculation and use of data ha vi n g an obvious ph ys ical interpretation e g temperature pr ess ur e, vo lume vapor pressure, composition, and entha lp y When co ncept s s u c h as free energy, chemical potential fugac ity a nd activity are pr ese nted the focus i s partl y on their u se in s olving for the more ph ysical variables. Whenever po s ible I e ncourage s tudent s to examine how the abstract vari a ble s affect the ph ys ical variables, and thu s to develop some intuition about the s i g nificance of the abstract variables. Examples are given in Table 3; these are so metime s Comparison of Graphical Figure Use in ChE Textbooks present but not frequently emphasized in phase equi librium text s. No ng raph Graphs per P e r cen t Graph T ex tbook Graah Figures Figures Pag es JOO aag es Figur es Introduction to Che mi ca l Eng in eeri n g Therm o d y n a mic s 1 21 1 07 44 568 1 9 71 (C h a pt e r s I 0-15) (57) ( 11 ) (199) (29) (84) Chemical and Proc ess Thermod y namic s' 3 11 6 60 541 2 1 66 (C h apters 9-13) (62) (6) (253) (25) (9 1 ) Tran s port Ph e nomena 1 4 1 69 105 7 11 10 40 Element a r y Principles of C h emical Processe s 151 1 7 15 587 3 53 ( Chapter 6 ) (8) ( 0 ) (7 1 ) (11 ) ( 10 0) Momentum Heat and Ma ss Tran s fer 1 6 1 15 9 106 773 2 1 60 (C h a pter s 35, 3 7-40 ) (63) ( 19 ) ( 1 43 ) (44) (77) Gr a ph fi g ur es includ e all twoa nd thr ee-di m e n s i o n a l coo rdin ate pl o t s a nd n o m ogra ph s Any figure that included both gra phical and n o n gra phi ca l in fo rm a ti o n was treated as a gra ph figure. Onl y numbered ca pti o n ed fi g ure s in t ex t and exa mpl es were co unt ed ; figures w ith problems and in ap p end i ces were excl uded Page s includ e all t ex t, ex ampl es, qu es tion s a nd prob l ems but exclude a pp e ndi ces. Fall 20O2 More so than in many chemical engineering classes phase equilibrium data are most useful and un derstandable when pre se nted gra phically. Thi s i s evi dent from observations g iven in Table4 of how fre quently graphical material is pre se nted in textbooks. Thermodynamics and unit operations text s contain more gra ph s and a higher proportion of figure s that are gra ph s, as opposed to s chematic diagrams and other drawings. Within each text the chapters more 285

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closely related to phase equilibrium have a higher proportion of graphs than the text as a whole, as indicated by the num bers in parentheses in Table 4. Furthermore, many students have a vis ual learning style. These students may struggle with equations and textual in formation, especially in an abstract context, and it is crucial that they see data presented graphically and also learn how to prepare data in a format that is most comprehensible to them. Hence, students need to make the connection between calcu lations and equations di sc ussed in class and graphical pre se ntation of phase equilibrium data To assure they are ca pable of both understanding and generating graphical data I assign a significant number of computer problems requiring this, as explained in further detail later in this article. Com puter spreadsheets have been previously suggestedl 7 ,s J for use in solving phase equilibrium and equation-of-state calcula tions, and they are well suited both for the calculations and for subsequent graphical pre se ntation. One recent text l 9 1 includes a number of example s pread s heets that may be u se d for applications simi lar to those described in thi s article, although I prefer to have stu dents write their own spreadsheets. DETAILS OF PHASE DIAGRAM COMPUTER ASSIGNMENT As an illustration of s uch assignments, consider the construction of a binary Pxy diagram for an ideal so lution at some constant temperature. Figure 1 is an example generated by repetitive dew point pressure and bubble point pre ss ure calculations. Taking liquid mole fraction x 1 as the independent variable, and as s uming component vapor pressures Pi5 and p 2 at are known, Eqs. (1-3) allow calculation of all dependent variables in the problem. To generate the diagram, al low x 1 to vary over the range 0.0 to 1.0. These calcu lations are easily done using computer spreadsheet sof tware 1200 11 00 1000 800 700 600 -ently makes solving the problem too time-consuming.) Fill the remaining three columns in the middle rows of the spread sheet with formulas given by Eqs. (1-3). If these formulas are entered correctly in the first of the middle rows, a single copy/paste command generates the entire table through the remaining middle rows. There may be one complication in producing a graph from these results. In a conventional Pxy diagram pressure is taken as the vertical coordinate twice With liquid composition as the horizontal coordinate a bubble point curve is produced, then with vapor composition as the horizontal coordinate, a dew point curve is produced. To do this on the spreadsheet, a single y-coordinate must be paired with two different x-co ordinates. At one time, few spreadsheet packages included this capability, but many recent versions (including Microsoft Excel) now allow it. If using an older package without this P-x-yOiagramatT= 100degC Methyl i&opropyl ketone ( 1) Diethyl ketooe{2) I,/ .,/ I/ I / .,/ / / 1....--------------------,,.,, r------~ ,/ V I ~ 1/ V ,.,---:: 0 1 0 .2 0.3 0.4 0.5 x1, yl 0 6 0 7 0.8 0 9 Figure 1. Px y diagram prepared using spreadsheet. Headings and Cons tants (1) (2) (x values) X psat Y1 =1 1 p (3) Figure 2 shows the general organization of thi s spread s heet. The upper rows contain heading s and constants suc h as the vapor pressures. The middle rows are used for calculations. The leftmost column is initially filled with values between O and 1 at intervals of0.01, or a su itable small increment. (This should be done using sprea dsheet commands or formulas; occasionally, a st udent will attempt to enter the number s manually and become frustrated that using the computer appar286 0.00 0.01 O.o2 x 2 values P values y, values (Blank) 0.99 1.00 Copy of ( Blank) Copy of y val u es P values Figure 2. General structure of spreadsheet for P xy diagram. Chemical Engineering Educa ti on

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capability, set up the lower rows of Figure 2 as shown, then define the first column as the x-coordinate for graphing and each of the two columns containing pressure val ue s as sepa rate y-coordinates. The lower rows of Figure 2 can be omit ted when using current versions of Excel and other s pread sheets that allow multiple xy pairs to be graphed. ADDITIONAL COMPUTER ASSIGNMENTS Table 5 lists other thermodynamic data graphs prepared using computer s pread s heet s. A very brief discussion of each follows. Many were prepared by st udents as homework as signments u s ing techniques similar to tho se outlined for the Pxy diagram. Copies of these assignments are available upon TABLES Grap h s Prepared Using Spreadsheets for P h ase Equilibrium Class Binary phase diagrams for ideal so lution s P xy Txyb xy ll Fugacity versus pressure Numerical integration of PV data b Generalized viral coefficient b Redlich-Kwong equation of state b Volumetric properties of binary non ideal so lutions Excess vo lum e Partial molar excess volumes Activity coefficie nt s in binary solutions versus composition Margules Van Laarh Wilson Infinite diluti on activi ty versus temperature Wilson Phase diagram for nonideal azeotrope forming binar y mixture Pxyb Txyb xy ll Excess free e n ergy of h o mo geneous azeotrope forming binary mixture ve r s u s composition Experimental data Margules equatio n (fit to azeotrope data )' Margules equatio n (best fit to VLE data) Wilson equation (literature consta nt s)b Excess free energy of h eterogeneous azeotrope forming binary mi xture versus composition Experimental data Margules equation (best fit to VLE data) Margules equation ( b est fit to LLE so lubilit y data) Prepared by st ud en ts as hom ework assig nm e nt h Pr epare d by instructor for c l ass discu ss ion Fall 2002 request. Some graphs were not assigned but were generated by the instructor and presented during class discussion. The same spreads heet data u sed to produce a Pxy diagram as described above could be u se d to plot an xy diagram at constant temperature. Pxy and Txy are the predominant rep resentations of VLE data in phase equilibrium classes, but xy is probably the most frequently used format of the phase equilibrium data in other classes, e.g., distillation, absorp tion mass transfer. Using the method described above, generating Pxy data for an ideal binary syste m at constant temperature does not require trial and error. Calculation of a single Txy datum for an ideal binary syste m at constant pre ss ure requires iteration or trial and error si nce the vapor pressures are functions of temperature. But generating a Txy diagram for such a system -the locu s of dew and bubble point temperatures for all pos sible compositionsdoes not require trial and error. Taking temperature as the independent variable rather than liquid composition, all other variables can be calculated directly by Eqs. (1-3) Selecting a range of temperatures in increments be tween the pure-component boiling points generates the diagram. Plotting y versus x instead of T versus y and T versus x pro duces an xy diagram at constant pressure from the same data. For nonideal binary mixtures, activity coefficients are func tions of liquid composition and possibly temperature Pxy and xy diagrams at co n s tant temperature are generated in a straig htforward fashion without iteration si nce temperature is fixed and liquid composition i s taken as the independent variable for generating the table as de sc ribed above. Iteration cannot be avo ided when generating Txy and xy diagram s at constant pressure for nonideal binaries To find activity coefficients and vapor pressures, liquid composition and temperature are needed Only one can be assumed. Di rect calculation of liquid composition from vapor pressure as in the ideal case is not possible If temperature is used as the independent varia ble as suggested for the ideal case, a unique composition may not result becau se azeotropes are possible. I recommend using liquid mole fraction as the in dependent variable ranging from O to 1, as in the Pxy dia grams. Iteration can be performed by circular recalculation on the sp read s heet. Unfortunately, spreadsheets vary signifi cantly in their implementation of circular recalculation, even from version to version and it is difficult to give a "rec ipe" that works in all cases. Often particular rearrangements of equations or ordering of the columns is necessary No matter what package was being used however I have always been able to find some method that eventually worked. Thermodynamics textbooks common l y contain graphs of excess and partial excess properties such as volume and en thalpy for binary so lutions In the volumetric properties as sig nment s tudent s generate similar graphs for ethanol-water using density data as a function of composition taken from ---------------Continued on page 291. 287

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.ta ... 5.3a._la_b_o_ra_to_r....:y:...._ _____ __ ) CHEM-E-CAR DOWN UNDER MARTIN R HODES Monash University Melbourne, Victoria 3800 Australia T he Chem-E-Car competition has been run for under graduates by the AIChE for the past three years with finals at the AIChE annual meetings. The idea i s for teams of undergraduate students to design and build a small car powered by a chemical reaction. The objective is for the car to travel a certain distance and then stop The distance to be traveled and the weight to be carried by the car are not announced until the day of the competition. The emphasis is on control of a chemical reaction with a keen eye on safety and the environmental impact of the design. The winner is the team whose car stops nearest to the required distance. In addition to designing and building the car, each team must make a poster that describes the car 's operation and include a safety and environmental assessment. Having witnessed the enthusiasm of the participating stu dents and spectators at the AIChE Chem-E-Car Competition finals held in Dallas and Los Angeles I decided to organize a Chem-E-Car competition here in Australia. Early in 2001 I contacted all chemical engineering departments in Australia and New Zealand, sent them copies of the rules (for the AIChE competition) and invited them to join Six departments re sponded enthusiastically, and within a couple of months teams of students were working away. The original plan was to have l oca l competitions within each department, with these com petitions generating finalists for the grand Australasian final. University work and the difficulty of the Chem-E-Car task took its toll, however Several teams fell by the wayside in cluding the team from my department. As time went on, it Mart i n Rhodes is Professor in the Depart ment of Chemical Engineering at Monash University in Melbourne, Australia He has a keen interest in chemical engineering educa tion and specializes in particle technology, a subject on which he has written an under graduate textbook. His research interests in clude f/uidization gas-particle flows interpar ticle forces and particle mixing Copyright ChE Division of ASEE 20 02 288 became clear that the grand final would be a fight between five teams-four from Australia and one from the National University of Singapore, who, upon hearing about the com petition, asked if they could take part. The grand final was held on day three of the World Congress of Chemical EngiFigure 1. The NUS c ar (a) with bodywork removed to r eve al the inn er detail and (b) in motion. Figure 2. Th e UNSW car drifting throu g h its self ge nerated mist C h e m i c al Engine e rin g Edu c ation

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neering at the Melbourne Exhibition Centre in l ate September. THETEAMSANDTHECARS National Un i versity of S i ngapore (NUS ) The NUS car ( Figure 1 ) used the decompo si tion of 15 % hydrogen peroxide solution with dilute potassium perman ganate so lution as a catalyst to generate oxygen, which was stored in the stai nles s s teel reactor. Openin g the ball va l ve at the rear of the reactor released the contents in short order, propelling the car along. The car was s topped by friction. The distance traveled was controlled by adjusting the quanti ties of reactant used and the time for reaction. During the test run s prior to the competition, thi s car an nounced itself with a loud bang and blew away the plastic sheeting that had been specially erected as a s plashguard be hind the star t line. Race helper s hurriedly modified and re erected the splashguard. The va l ve on the rear of the reactor was equipped with a lengthened handle Starting the car inFigure 3. The Sydn ey University three-wheeled, twoce ll car. valved swing ing an oversized pair of laboratory tongs, golf iron style, to hit the handle and swiftly open the valve. The swipe with the tongs only happened at the precise time, dic tated by the reaction countdown. On it s fast official competition run, the team member wield ing the tongs was either a little too enthusiastic or had poor aim; the result was that the car turned onto its side within a few meter s of the start line. University of New South Wales (UNSW) The UNSW car named Cold Power ," was powered by a l.5-3V electric motor running from an electrochemical cell. The cell u sed solutions zinc sulfate and copper sulfate with zinc and copper electrodes. The electrodes were made from 1 mm sheet, totaling around 200cm 2 for each metal. The dis tance was controlled u si ng a sw itch that involved measuring the s peed of s ublimation of so lid carbon. A quantity of solid carbon dioxide was placed in a container on one side of a pulley On the other s ide were a number of counterweights Figure 4. The Newcastle One team car experiencing terminal technical problems. s uch that the solid carbon dioxide con tainer rested on a metal electrode, which completed the circuit. As the s olid carbon dioxide vaporized, the weight on that side of the pulley de creased until it was outweighed by the counterweights. Once this occurred, the solid carbon dioxide container lifted off the electrode and cut the power to the motor. The amount of so lid carbon dioxide initially placed in the container (anywhere from 20g to 50g) was determined by the dis tance to be traveled. The UNSW car was interesting to observe as it glided along in a white cloud generated by Figure 5. The Newcastle Two team 's car a) running without its sparkler timing device and b) in full sparkling glory. Fall 2002 the subliming carbon di oxide (see Figure 2). Sydney University The Sydney University car (see Figure 3) was de signed and built by a team of first-year engineering students (mechanical and chemical). It was driven by an electric motor pow ered by an electroc h emi cal cell comprised of 1.8M sulfuric acid and potassium dichromate so lution (1g/100ml) with zinc electrodes. This car had three wheels and a low center of gravity. It 289

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was able to travel well in a straight line. The inventory of acid was only 5ml, and the cell was enclosed to minimize spillage problems in the event of a crash. The first run of the Sydney team was good, but unfortunately, it started without the required weight. Newcastle University Team One The Newcastle Team One car was driven by a small 3 5V IA motor and powered by a zinc/copper copper sulfate bat tery, using IM copper sulfate solution and IM sulfuric acid This car made a promising start getting third closest to the line on its first run. Technical problems (a broken electrical connection to the motor), however, prevented it from leaving the starting line on its second run (see Figure 4). Newcastle University Team Two The Newcastle Team Two car (see Figure 5) was driven by a 3V electric motor via a six-speed gearbox. The motor was powered by a battery of four cells each producing l .45V-two cells in series with another two cells in series. The cell used was an alkaline battery, very similar in chemistry to com mercial batteries. A children's sparkler was used as a timing fuse to stop the car When the sparkler burned to the end, it melted through a section of solder wire incorporated into the cell wiring and disconnected the power supply from the car motor. The length of the sparkler determined the running time of the car and was decided according to the results of previous trials. Spar[ Chem-E-Car University of Newcastle The Cell The car Is powered by an electrolytic reaction taking place In a dry cell The cell used 11 an alkaline battet'y very similar In chemistry to commercial batteries T Ionic Reaction In Cell : 'I Zn Zfl2+ + 2e Mn'+ e Mn>+ 3mm Features Car dimensions 225 200 mm (lop Yiew) Weight (tare) a 1028 g : car=320g zinc =4 x 37 g Stoel: 4 X 105g EMD = 4 x 35g 3Vmotor -~ 6 apeed gearbo~ ~ ., ;, = ~ ., ::.., ... ... )~ : t) 1 Individual coils pn,dlJce 1.45 V wthln the car, coll& ""' .. below to """' -~: Safety MSDSI consulled 5Nled container uaed ID hold cell High purity chemlcal1 mNn negligible by-produclo Minimal intefference required with cell u componenta last for --i runa klers were found to be remarkably consistent and had a burn ing rate of around 0.28 emfs. Extensive safety testing had been carried out on sparklers used indoors to ensure mini mum smoking or sparking. With the sparkler burning away as the car rolled along it was pleasing to the eye In practice on home turf, it had man aged to consistently s top only a few centimeters from the desired distance. On this day it was the most consistent car and eventually achieved second place. THE RESULT Team Newcastle Two won the poster competition with a concise, informative display (see Figure 6). The performance competition winner was the team from the National Univer sity of Singapore; after a crash on its first run, their car stopped only 135cm short of the 20m designated distance on its sec ond and final attempt. Team Newcastle Two took second place when their car stopped I80cm after the line The trophy a polished Plexiglas CSTR on wheels, was made by the work shop staff at Monash University and is now in the hands of the NUS team. Reports from faculty involved in supervising the local de partment competitions suggested that the students benefited greatly from the experience. To get to the start line with a car that was competitive and worked according to the rules, each team had to solve the series of specific engineering prob lems. Several teams went beyond mere functionality and con] The Stopping Mechanism Commen:ial tpantJers.,. UMd tini,g tuNs to stop fie car Wwtn the lt)wtier bums to the end II melts through HCtion of solder wn incotpofllrtd into the ce1 wimg and dboonnects the power .upply from the car molar The length of tie IJ>lrtdtf' detemines the running time Of the car and Is de0IMd aocorctlng to the results of previous trials The apert!er la lloated from the envin:>nrMnt to entunlrnaxi'nl.maaf9ly Sperttler bumng rate 0.28 ems Extwllive Akty sting has been caried ou, on 1P1i111Jers l'ldoors, to .ntUl"I minimum smoking or Safely procedures developed for 1par1der use : No tl mmabfe materials within spark radius of 450 mm Spar1
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User-Friendl y Phase Equilibrium Continued from page 287. handbooks .1 10 11 1 By doing this assignment, students can de velop a better intuitive understanding of the meaning of such excess property data because they see where the data came from. Additionally, the magnitude of the variation of ac tivity coefficient with pres s ure is related to the partial molar excess volume Using these results students can prove to themselves why activity coefficients are typi cally assumed pressure-independent. Before using activity coefficients in VLE calculations, s tu dents prepare a few plots of activity coefficient versus com position or of infinite dilution activity coefficient versus tem perature When they produce graphs s imilar to those in the textbook students reinforce their concept of what "s hape these functions should have. Also, by plotting results from sev eral different equations on one graph, s tudents see that it makes little difference which correlation is chosen in most cases. For subsequent VLE and LLE calculations, they typically use the Margule s equation because it is the most simple mathematically. In conjunction with VLE pha se diagram s, stu dent s produce plots of excess free energy functions. These plot s can be used to determine constants in an activity coefficient correlation. For example, a plot of QE/RTx? 2 versus x 1 can be u se d to determine Margules equation parameter s by a straight-line fit. When constants determined by several methods are used to plot an xy diagram students learn the fit of the data is as important as which equation i s used Phase separation and LLE are analyzed with graphs of free energy of mixing versus liquid composition. For LLE it is the shape of these curves--convex or concave-that is the determining factor in phase stability. As with the VLE data students generate plots of these functions from experimental data points and, by fitting activity coefficient correlations in various ways, compare the results. Phase equilibrium and chemical reaction equilibrium are often taught in one course. I have also s uccessfully used com puter spreadsheet assignments or demon stra tions for class dis cussion in the reaction equilibrium portion of the course. It is a fundamental belief of mine that students will choose to use the computer and specific software in cases where it makes a problem easier to solve. When I assigned these prob lems, I did not require the use of specific software. (In fact, I did not require the use of a computer at all, but with the avail ability of computing resources and the s tudent s' general fa miliarity with computers, no hand-plotted solutions have been submitted in about ten years!) I typically discussed how to structure a spreadsheet for the assignment and frequently had the students work through a hand calculation for a single data point as an in-class exercise. Fall 2002 The majority of s tudent s "follow the path of least resis tance and complete the assignment u s ing the standard spread sheet package, currently Microsoft Excel. The specific choice of sprea dsheet ha s little effect. Student s have solved the prob lem s u s ing Quattro Pro Lotus 1-2-3, SuperCalc, and the Smart Spread s heet in past years. Moreover, it is unnece ssary to u se a spreadsheet as a few students h ave demonstrated by solv ing the problems using programming languages (FORTRAN, C), graphics package s, and math so lver s (Mathcad, Maple). All s tudent s eventually gravitated to spreadsheets by the end of the class, however The only warning I give to students who use nonstandard computer software is that I may not be able to assist them with computer-related problems if they are u sing a package with which I am unfamiliar CONCLUSIONS In teaching phase equilibrium thermodynamics, I have at tempted to promote under sta nding and intuition of the course material. Initial explanation that the goals of the class relate mainly to data handling and generation, unlike other chemi cal engineering classes, prevents confusing expectations from developing. Meaning and consequences of data are empha sized, particularly for abstract quantities s uch as activity co efficients for which interpretation is not necessarily explicit. Widespread presentation and students u se of graphical data is made convenient u si ng computer s preadsheet software ACKNOWLEDGMENTS The se computer assignments were developed over a series of courses taught at Michigan State University and Villanova University. REFERENCES I. Prau s nitz J.M. R N Lichtenthaler and E.G. de Azevedo Molecular Thermod y nami cs of Fluid-Phas e Equilibria, 2nd e d ., Prentic eHall lnc, Englewood Cliffs NJ p. 4 (1986) 2. Smith J.M. H.C. Van Ness, and M.M Abbott Intr oduc ti on to Chemi cal En ginee rin g Th e rmod y nami cs, 5th ed McGraw-Hill New York ( 1996 ) 3. Kyle B.G ., Chemical and Pro cess Thermod y namics, 2nd ed., Prenti ce Hall Englewood Cliff NJ ( 1992) 4. Bird, R.B ., W E Stewart, and E.N Lightfoot, Transport Phenomena, John Wiley & Son s, New York (1960) 5 Felder, R M. and R.W. Rousseau Elementary Principles of Chemical Pro cesses, John Wiley & Sons, New York ( 1986 ) 6 Bennett C.O ., and J E. Myers Momentum Heat, and Mass Transfer, McGraw-Hill New York (1985) 7. Savage, PhilJip E ., "Sprea d s heet s for Thermodynamics In s tru c tion ," Ch e m. Eng. Ed. 29 (4) p. 262 ( 1995 ) 8. Pratt R M. "T hermodynamic Properties In volv ing Deriv a tive s: Us ing the Peng-Robinson Equation of State ," Chem. Eng. Ed., 35 (2), p. 112 (2 001 ) 9 Elliott, J R., and C.T. Lira lntr oducto 1 y Chemi c al Engineering Ther mod y nami cs, Prentice Hall PTR (I 999) 10. Green D W. and J.O Maloney, eds, P e rry 's Chemical Engineers Handb ook, 7th ed., McGraw-Hill, New York NY (1997) 11 Weast R .C., ed. CRC H andbook of Chemistry and Ph y si cs, 60th ed. CRC Press Boc a Raton FL D-227 ( 1979 ) 0 291

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.ta .. 5_.._ i a b o r. a t o r .:.y ________ ) ON IMPROVING THOUGH T WITH HANDS G.K. SURESHKUMAR, K.C. KHILAR Indian Institute of Technolog y, Bomba y India 400 076 L aboratory exercises are essential 11 2 J toward the dev e opment of a good chemical engineering graduate with desirable sk ill s such as independent learning inter dependent learning problem solving, critical thinking, cre ative thinking interpersonal ski lls teamwork lead ers hip integration communication and change management. 1 3 1 The standard laboratory exercise in chemical engineering, how ever, revolve s around an apparatus that remains unchanged for several years and can lead to unethical practices among st udents 11 4 1 s uch as s ubmission of data/report s from previou s years. Moreover the application of thought which is crucial for laboratory work and developing the skills mentioned above, is almost nonexistent in the standard laboratory exer cise From an in s tructional-objective s viewpoint, rsi most labo ratory exercises are designed to be at Bloom lev e l 2 (co m prehension ) out of the possible six level s, Thi s leads to se vere resentment toward laboratory work among stu dent s and professors alike. Students consider lab courses as a formality to be completed while faculty treat them as poor cousins of theory courses, relegating the entire re s ponsibility for lab courses to lab supervisors or teaching assistants, We believe that if students are challenged to think criti cally on laboratory exercises and encouraged to be creative, their interest in and respect for laboratory work would im prove, and in tum, the faculty would be further motivated to offer better laboratory courses/projects, With thi s belief a laboratory course consisting of both duals tep laboratory ex ercises and a recommendation/innovation exercise was con ceived and assigned to third-year Uunior) undergraduate stu dents taking the fluid mechanic s laboratory at the Indian In s titute of Technology Bombay Our laboratory guidelines state that the overall aim of this laboratory course is to inspire students to appreciate the un derlying themes of the experimental aspects/approaches to engineering/science with fluid-flow aspects as a model sub ject. The goal is to develop students abilities to "think with their hands, Another purpose of this course is to improve understanding of fluid-flow principles, to develop a physical feel for some fluid-flow situations, to develop a familiarity 292 with so me commonly u se d fluid-flow equipment, to incul cate a concern for safety, to improve communication of ex periment a l results to improve the quality of analysi s and in quiry and to kindle the spirit of discovery in s tudent s, Fur ther we expect th e exercise to develop so me of the above mentioned ski ll s in a chemical engineering grad uat e, T H E L A B ORA TORY E XER C I SE S The activities for the laboratory consisted of dualstep labo ratory experiments (perfor med by student groups) and a recommendations report (a n individual activity) The Dual-Step Laboratory Exercise Each laborato ry experi ment was conducted over two lab sess ion s Durin g the first session, stu dent gro up s were ex pected to follow the procedures g i ve n in the manual to carry out the experiment. Students were expected to be come com fortable with the equipment and the experiment, and the first sess ion experiments were de sig ned accordingly After the first sess ion s tudent s were required (as home work) to analyze th e data taken during the lab session based on the theoretical prin c iple s in the lab manual/fluid mechanic s text book/note s and examine whether the results obtained were as G K Sureshkumar (G K,) is currently Associ ate Professor in the Chemical Engineering De partment at Indian Institute of Technology, Bombay He received his BTech. in Chemical Engineering from Indian Institute of Technol ogy, Madras and his PhD from Drexel Univer sity His research interest is free radical-based improvements in the productivity of bioreactors. He can be reached at Karlie C, Khilar is currently Professor in the Chemical Engineering Department at Indian Institute of Technology, Bombay He earned his BTech degree in Chemical Engineering from Indian Institute of Technology, Kharagpur, and his PhD from University of Michigan He and his students work in nanoparticle produc tion and colloid-associated contaminants transport in porous media Copyr i gh t C hE Division of ASEE 2002 Chemical Engineering Education

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expected. The following ensued: a) If th e ex perimental r es ult s m atc h ed the expecte d r es ult s, s tudent s were expected to think of a dditi o n a l experi m e nt s, preferably n ew o n es that co uld b e done wit h the sa m e (or s li g htl y modified ) set up But the a dditi o nal experiments ne e d to b e done within the time frame of the seco nd lab sess i o n We believe that worki n g with these pra ct i cal co n strai nt s wo uld h elp students acquire "s tr eet s mart s," which are u sefu l in h a ndlin g real-world problems. b) If th e experimen t a l results did not match the expected results, st ud ents were required to form hypotheses based on the r es ult s and design ways to experime nt a ll y (with certai n calculations) prove or disprove their various h y poth eses in th e seco nd lab sessio n. The emp h asis was on th e t ec hnic a l/ sc i e ntifi c rigor in pro ofs The s tud en t s were a l so warned th at th eir theories co uld be pro ved false b y their ex p erime nt s and that it was acce pt ab l e to a dmit th ey did n ot und e r stand the reasons for disagree ment within th e tim e ava il ab l e to th e m a nd therefore a dditi o n a l s tud y would be required. After th e seco nd J a b sessio n each stu d e nt gro up was ex pected to s ubmit a s ingle report in th e regular format, i.e., (a) Aim and Objecti v e s, ( b ) Methodolo gy, (c) R es ult s and Di s cussion (w hi c h was requir e d to be s i g nifi ca nt ), ( d ) Conclu s ions and (e) the original data s heet s. The report s were gra ded on the following ba ses: If the actual results matched the expected results: Ability t o follow pro ce dur es IQ % Dat a analysis ( 1 s t sess i o n ) l S % Di sc u ss i o n (1st sess i o n ) lS % Creativity/originality aspects (2nd sess i o n ) 20 % Dat a a n a l ys i s (2 nd sess i o n ) lS % Di sc u ss ion (2 nd sess ion ) 1S % Pre se nt at i o n ( mainl y co mmuni catio n ) 10 % Report s that addressed n ove l as p ects to st ud y in their sec ond session were rewarded h a nd some l y in gra din g th e cre ativity/originality criterion (see the s tudent examp l es pr e se nted later ). If the actual results did n ot m atch the expected results: Ability to fo llow pro ce dur es IQ % Dat a a n a l ys i s (1st sess i on) LS % Di sc u ss i o n JS % Clarity in thought a nd s itu atio n/probl em analysis (2 nd sess i on) 20 % Ri go r (2 nd sess i o n ) LS % Di sc u ssio n (2 nd sess i o n ) I S % Pre se nt at i o n ( m a inl y co mmuni catio n ) 10 % Report s th a t were well de ve loped on both the po ss ible rea so ns for the di sag reement between ac tual and expected data and the experiments to pro ve or di s pro ve th e m were give n high mark s for the clarity-in-thought criterion. The diffi c ult y le ve l in problem analysis was also recognized in that cri te rion-report s that fully analyzed a difficult s itu a tion received hi g her mark s th a n tho se that, as a m a tt e r of chance, analyzed Fall 2002 a si mpl e, easy -to-identif y s ituation Al so, report s that un e qui voca ll y pro ve d or disprov e d their points received high m ar k s for the rigor crite rion. Oth er cr iteria s uch a s data analy sis, di sc u ssio n and presentation, carry their u s ual weight. The Recommendations R eport O ve r the duration of the course, each s tudent was expected to think about an experiment or a se t of ex periment s that could be don e in the fluid mechanic s Jab. Students were encour age d to b e as creative as po ss ible. Near the end of the course (a week before the l ast day of c la sses), each s tudent was ex pected to s ubmit a d etaile d r epo rt on thi s experiment ( or se t of experiments) and the equipment and instruments needed. The reports were evaluated on the following bases: Creativity/originality aspects 3 0 % Clari t y in th o u g ht 2 0 % D etai l 3 0 % Doability 10 % Presentation (main l y com muni catio n ) I 0 % The du a lste p exercises evaluated through the report s carried a 70 % weig ht a nd the recommendation report carried a 30 % weight toward the final grade. IMPLEMENTATION OF DETAILS /RATIONALE In the beginning of the se me s ter before the experiments began the in s tructor met the class and discussed the exer c i ses and recommend e d s trategie s. In addition to experimen tal det ai l s for the first sessio n the laboratory manual carried information on safety procedure s for the lab, error analysis, technical writing, and the unacceptability of academic di hone sty, all of w hich were se riou s l y di sc u ss ed in the initial m ee ting Th e instructor al so emphasized the need for s afety procedure s whenever he observed lap s e s during the Jab ses s ion s Student groups were asked to se lect their own leader s who would assign dutie s for the group members and be gener ally re s pon s ible for the group's activities. This ensured that an ave nu e for the d eve lopment of t eamwor k and leader s hip skills exis t e d Also, on many occasions, the in s tructor communicated to the groups through their l ea d e r s. Befor e the start of the first sess ion the groups were ad vise d to familiarize themselves with the detail s for each ex periment u s ing th e lab manual and the textbook The first sess ion ex periment s were de s igned as s horter ver s ions of the experime nt s given in the u s ual lab course and students were e n co ura ge d to s p e nd the additional tim e becomin g comfort a ble with the setup and th e vario u s equipment u se d For ex am ple th e in str uctor encouraged the s tudents to raise ques tions regarding the e quipment or the reasoning behind the various experimental s tep s, which the students normally took for granted. The s tudents took the first session seriously be ca u se the y knew the y had to consider the se tup, the experi mental m e thod s, and the underlying theory in order to have a n intere s tin g seco nd sess ion During the experiment (both sessio n s), groups we re advised to record the data in duplicate 293

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using a carbon s heet and the members were asked to sign each data sheet. The dupljcate copy was s ubmitted to the instructor at the end of each sessio n, and nonsubmi ssio n would result in a grade of zero for that session. The in s tructor ha s never had to give a zero over the past two years for thi s reason. After the d ata analysis for the first sessio n the groups were required to meet the instructor to dj sc u ss their plans for the seco nd sessio n. Thi s meetin g was not to guide the students on what they could do in the secSAMPLES FROM STUDENT EXERCISES Samples from the Dual Step Laboratory Exercises Agreement Betw een Actual and Expected Dat a An ex periment for the lab involved studying the relation s hip b e tween Power number and Reynold s number in an agitated syste m. One of the groups found good agree ment between actual and ex pected data and therefor e had to think of additional experiments to do on the ond sess ion but for the in s tructor to li s ten and comment on the po ssi bility of doing the experi ments. Thjs meeting was norm a lly sc heduled a few day s before the second sess ion primarily to address a ny s pecial requirement s for the ex periment that needed to be communicated to the lab s uperintendent. Also thi s meeting helped the instructor ensure that the seco nd sess ion experiments were of proper scope ( nei ther too large nor too s mall ) and rea so nably well thought out, especially if the actual data matched the expected data in the first sessio n In addition, it was communicated to the s tu dents at the beginning of the se me s ter that no complete di s mantling of the set -up s would be .. the overall sa me setup. They decided to compare the rela tionsrup between Power and Reynolds number s for an aqueous sys tem with and without a s ur factant. They found that the Power number for the corresponding Reynold s number was lower for the syste m with s urfactant than for plain water. Therefore, they concluded that th e power requirements for an aqueous sys tem with s ur factant are lower than that for plain water. They also provided qualitative explanations for the observed results from a molecular viewpoint. aim is ... to improve the quali ty of anal ys is and i nqui ry, and to kindle the spirit of disco v e ry in students. Another experiment involved st udyin g two phase flow characteristics in a vertical transpar ent tube s uch as the relationships between s lu g length a nd s lug velocity a nd between pressure drop and void fraction, etc. The group that ob allowed, except in rare cases. This encouraged the students to trunk of "non-invasive" means for testing their theories Also, thj s precaution was nec essary because so me piping network s in our lab had packing s to prevent leak s that would be difficult for an inexperienced person to reassemble. The lab report s for the duals tep exercises were due before the start of the next experiment; the in str uctor gra ded them and offered constructive criticism and feedback witrun a week of s ubmi ss ion. Students appreciated the timel y feedback The grading of the recommendations report was time con s uming (three to four consecutive, full day s) As long as grades are important, so me s tudent s may cheat to get the be st grade; 16 71 therefore a s ignificant amount of time was spent establisrung the originality of s ubmitted reports. This was acrueved through one-on-one interviews with st udent s who had submitted doubtful reports. Durin g an interview, it was easy to ascertain whether cheating had taken place by ask ing relevant qu es tion s mo st of which were on the experi ment s ubmitted All experiments were run on existing equipment; therefore thi s duals tep exercise doe s not require additional funds for equipment. It can be run anywhere even in the face of fund crunches It also provide s a greater prob a bility for di sagree ment between actual and expected data and thu s help s s tu dents develop lateral-thinkin g abilities while forming hypoth eses for the di sag reement. Therefore th e duals tep labora tory exercise provides a way to tum a see ming disadvantage in running an ex i s ting laborator y co ur se into an a dvantage of improving thought in s tudent s 294 tained result s as expected decided to s tudy the relationsrup between the radius of curvature of the s lug s leading e d ge and it s length. They developed a theory ba se d on geometri cal considerations for the variation of the leading-ed ge cur vature with slug length ; they also showed correspondence between the theoretically expected resu l t s and mea sure d d a t a Disa g r eement Be tween Actual and Expected Data An other ex periment in vo lv e d a piping network with various types of pipes fittings, and valves. The objectives for the first ses sion included determination of the frictional los ses across the pipe fitting s and valves. The experiment required recordin g readings from manometer s attached to the pre ss ur e taps across relevant fitting s or valves and determining the water flow rate u s ing the pre ss ure difference mea s ured across the orifice meter. The first gro up that worked on the exper iment found th a t the friction lo ss constants obtained for the vario u s fittings on the network were rugher by almost an order of ma g nitude than Uterature values. Therefore the group first postulated that scale formation led to higher loss constants. To test the po s tu l ate, they arranged for the network to be cleaned thor oughly a nd repeated the experiment in the seco nd session. Thi s did not yield s i g nificantl y differ e nt lo ss co n s tant s, thereb y partly disproving the po s tulate th a t the sca l e forma tion alone resulted in the di sc repancy Students in one of the other groups that worked on the experiment po st ul ate d that the water-flow rate measurements u s in g the calibration c urv e for the orifice meter ma y not have been correct; they noticed Chemical Engi n e e ring Edu c ation

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a di sc repanc y between flow rates mea s ur ed u si n g a measur ing jar/ s top watch arrangement a nd the orifice meter read ings. So the students prepared a fresh calibration graph for the orifice meter and found it to be differ e nt from the ex i s t ing erroneous calibration chart. Th ey also pro ve d that th e fric tion lo ss constants obtained u si n g th e new calibration gra ph were comparab l e to the values found in th e literature. Sa m ples from the R ecommendations Report A s tudent named Nikhil Agarwal s u ggeste d an inexpen s ive s imple method for d e t er minin g the v i scos it y of a so lu tion b y allowing it to flow over a smoo th inclined flat plate from a re se rvoir and takin g m eas urement s Using s uitable balance s, Nikhil expressed the viscosi ty as a function of me a surable parameter s (with origins from th e thickne ss of the l iquid layer 8 l) as: pg8 3 cos ~ = 3Q where p i s the fluid den s ity g is th e acce l era tion due to grav it y, 8 i s the film thickne ss, is the angle bet wee n th e plate and the ve rtic a l and Q is th e flow rate. He carefu ll y consid ered the detail s and limit ations of the expe riment a l proce dure and s ugge s ted a method to s tud y the var i atio n of viscos ity with temperature u s ing th e sa m e setup. Another s tudent Sikand e r Siraj, u s ing i nput from a frie nd in electrical engineering, s u ggeste d a photoelectric diode based ( PED) device for the m eas ur ement of s lug len g th s in the two-pha se flow experiment. The idea had it s origins in th e bur g lar alarm principle. For the measurement h e u se d the deviation caused by the refraction of th e infrared beam when it pa sses through m e dia of di ffe r e nt refractive indices. STUDENT AND STAFF FEEDBACK The s tudent s were asked to se nd their comments through e-mail to their class repre se ntative who removed detail s per tainin g to the authors of the comments, com pil e d without ed iting and forwarded the co mment s as a si n gle file to the in str uctor For the improved version of the l a b comme nt s from 82 out of 83 s tudent s were received a nd a ll exce pt nine ex plicitl y sta ted that the lab was useful to th em. The y sai d that their learning included fluid-m ec h a ni cs principles, ap pli ca tion of thought to a lab le aders hip qu a litie s, thinkin g cre atively, and working in a group. Som e po s itive comment s over the past two years include Du e to thi s lab alone I can say that I know so me 'c hemi ca l e n g ineerin g,"' and Thi s i s the first time I feel what a lab course is a ll a bout. Al so, many s tudent s s ugge s ted minor c han ges in equipment, etc ., to im pro ve th e lab. Of the nin e st ud e nt s w h o did not sta t e their likin g for the lab seve n were n e utr a l a nd the o ther two said that the lab was not u se ful to them. The s taff associated with th e lab were enthusiastic about fulfilling the requirements of the lab They also said that they Fall 2002 enjoyed setti n g up th e vario u s ex periment s although it in vo l ved a dditional time INITIAL CHALLENGES The first time it was offered, almost all s tudent s expressed that the lab demanded a lot of their time. We believe this was b eca u se s tudent s compared it with previou s editions of the same co ur se. In a ddition the same experiments that were g i ven in pr ev iou s edi t io n s were p ackage d into a two-ses s ion ( dualste p ) format, significa ntl y increa s in g the work. There fore in the next edition of th e course, the experiments were consolidated into half the original number, with all other de tails unchanged Afterwards, th e r e were very few comments (3 out of 83) that ther e was too much work. The first time the course was offered, the groups were as s i g n ed accor din g t o st ud e nt roll number s, which the students h ated The n ext time th e st udent s were asked to form their ow n gro up s with the average cumulative performance index (C PI ) of the group member s being clo se to the cla ss average CPI; thi s incorporat es coo perati ve learning elements. Com plaints about un s uit a bl e gro up s were almost eliminated. The remaining c hallen ge i s gro up s ize Six students in a gro up is nonideal and s hould be reduced. We intend to re duc e the numb e r b y runnin g the experiments more frequently in the future. Th e l ogistics constraint ne e d s to be addressed first h oweve r. In s hort a focus on d eve lopin g the critical thought process in st udent s made the l a bor atory course interesting to both students and instructors and also dev e loped s tudent s' respect for experime ntal work ACKNOWLEDGMENTS We wo uld lik e to thank th e s tudent s of CL333 for their e nthu sias tic participation in the exercise as well as O S. Sawarkar V.B.V. Nair V. Ramachandran and A.D Kadam for their contributions REFERENCES I Middleber g, A.P.J., Laboratory Projects : Should Students D o Them or Design Them ?" Chem. Eng. Ed 29 (1), p. 34, (199 5 ) 2. Jones, W.E., B as i c Chemical Eng in eer in g Experime nt s," Che m Eng. Ed. 27 ( 1 ), p. 18 8, (1993) 3. Ru garc i a, A. R .M. Felder D R Woods, and J.E Stice The Future of Engineering Education. I. A Vision fo r a New Ce ntur y ," C h e m E n g. Ed 34 ( 1 ), p. 1 6, (20 00 ) 4. Macias-Machin A., G. Zhang and 0. L eve n s pi e l The Unstructured St ud en t-D esig n ed R esearc h Type of Laboratory Experiment ," Chem Eng. Ed., 24 (2), p 78 (I 990) 5. Felder R.M. D R. Woods J .E. Stice, a nd A Ru ga rci a, The Future of Engineering Educatio n ll Te ac hing Methods that Work ," C h em. Eng. Ed. 34 (1), p 26 (2000) 6. Felder R .M., "C he a ting : An Ounce of Pr eventio n ... Or th e Tra g i c Tale of the D yi n g Grandmother, Chem. Eng Ed 19 ( 1 ), p. 1 2 ( 2000 ) 7. Sureshkumar, G.K., A Choose-Foc u s -An a l yze Exe rci se in ChE Un dergraduate Courses ," C h e m. E n g Ed., 35 ( 1 ), p 80, (200 1 ) 8 McCabe W.L. J.C Smith, and P Harriott Uni t Operations of Chemi ca l Engineering, McGraw-Hill, Singapore, 6th e d ., (2000) 0 295

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.tA 11111 ij.._..__c_u_rr_i_c_u_l_u_m __ _____ __,) THE EARTH'S CARBON CYCLE Chemical Engineering Course Material ROGER A. SCHMITZ University of Notre Dame Notre Dam e, IN 46556 0 n three occasions in recent years, I have taug h t an elective course at the University of Notre Dame for chemical engineering se nior s titled "To pics on Ecol ogy and the Environment." I de ve loped the course because I felt it was important for our students (and myself as well) to have a greater appreciation-from a chemical engineer's per spective-for the workings of Earth 's natural processe s, both biotic and abiotic, and a knowledge of how human and in dustrial activities are di s turbing or might di s turb them. One of the significant components is a module on the car bon cycle-the subject ofthis article. In gathering and devel oping material for thi s module and others in the course, I was struck by these observations: Many of the Earth's processe s, including the carbon cycle, though fundamenta ll y very complex in detail, can be represented b y simp l e models that are useful for study purposes and even for quantitative estimates, at least as a first approximation. The developm e nt analysis, and application of models are well within the sco p e of an undergraduate c hemical engineering curricul um Th e sub j ect matter, or bits and pieces of i t, can be integrated advantageousl y, straightforwa rdl y, and nearl y seamlessly into core chemical engineering courses. My objectives in this article are to demonstrate all of thi s, using the carbon cycle as the means and to provide conve nient material for others who may be per s uaded by my third observation. Of the biogeochemical cycles of the six m ajo r life ele ments C N P S 0 and H the carbon cycle receives the lion 's s hare of the attention in the literature. That 's no s ur pri se inasmuch as mo s t of our energy need s are met by the burning of carbon-based fuel s and inasmuch as the conse quent increa s ing level of atmospheric carbon dioxide and it s potential effect on the Earth's climate is a frequent focus of attention in technical and nontechnical publication s. What's more chemical engineers will have opportunities to play a prominent role in any steps taken to moderate that level whether those steps be toward alternate energy sources or toward sequestering or otherwise preventing emissions di rectly into the atmosphere. THE CONCEPTUAL MODEL Carbon is found in all of Earth s compartments or reser voirs-in the biota and in the atmosphere hydrosphere and lithosphere. Mathematical model s describing the cyc l e ac count for the movement of carbon among and within those reservoirs and for anthropogenic disturbances, which are prin cipalJy due to fossil fuel burning and deforestation (i.e., mainl y burning of removed trees) for land use changes. Figure I presents a sc hematic diagram of a conceptual model of the carbon cycle consisting of s ix reservoirs, num bered one through six. (A seventh reservoir for fossil fuels e nter s dynamically into the model later only as a di s turbance to the six-reservoir natural cycle.) Other reservoirs, includ ing sediments, marine biota and lakes rivers, and streams, are omitted for reasons given later. In one way or another, all models are based on this s tarting picture which is sometimes Roger Schmi tz is the Keating-Crawford Prol:ll"ll~mT-=c:1nT fessor of Chemical Engineering at the Univer sity of Notre Dame He received his bachelor 's degree from the University of Illinois and his PhD from the University of Minnesota both in Chemi cal Engineering His current interests are in the modeling and analysis of environmental and ecosystem dynamics. Copyrigh t ChE Division of ASEE 2002 296 Chemical Engineering Education

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modified to include one or more of the omitted reservoirs Models differ primarily in the extent of detail and correspond ingly in the objectives of the modeler. For example, highly detailed climate studies employ general circulation models based on fundamental transport equations to describe pro cesses in the atmosphere and/or ocean reservoirs and several types of vegetation to describe the atmosphere-biota ex change [ 1 1 At the other extreme, so-called box (or com partment or lumped ) models that are intended to give es timates of global averages of carbon in major reservoirs, are based on spatially aggregated descriptions, often with no more detail, sometimes even less than that shown in Figure 1 ATMOSPHERE I F6I F15 Fs1 F, : (50)(100 (50 I I I 5 I I M1 (612) F12 (57) 2 The number s in parentheses beside the arrows in Figure 1 represent estimates in petagrams of carbon per year (PgC/ y) of the transport ( commonly termed "fluxes in the rel evant literature ) of carbon between reservoirs. Such fluxes are estimates adju s ted so that each box is balanced at a steady state, where it would remain unless disturbed There is no common agreement on the values of the reference pre-indus trial masses and fluxes or even on the reference year (gener ally between 1800 and 1860) but the variation from one ref erence source to another is of little significance. The values shown in Figure 1 are in line with those used in the references cited above. I F21 F13 F3 1 (58) (19) (18) 3 l,[ 2 7 1 Except to allude to the structure of high-end models and their purposes (and sometimes to compare re sults), I choose to work with simple box models in the course In short, as tools for study, they have suited my purposes Further if prop erly calibrated and tuned they have proven useful for quantitative purposes so long as the principal interest is in global averages, par ticularly in atmospheric car bon dioxide levels. terrestrial warm ocean cool ocean M 1 the mass of carbon in the atmosphere reser voir can be taken to be en tirely in the form of CO 2 The 612 PgC in that reser voir corresponds to a CO 2 concentration of286 ppmv (parts per million by vol ume) -the concentration unit used in most illustra tions to follow (The con version factor of 2.128 PgC/ppmv is based on a total atmosphere mass of 5.14 x 10 6 with a molecu lar weight of 29.) biota surface waters surface waters M5 Fd M2 M3 (580) _.J (730) ( 5 7) (140) F56 F 24 F42 F34 F43 (50) (12) ( 7 0) (100 (42) 6 4 soils & deep detritus ocean waters j M6 M4 (1500) (37000) fossil fuels, Fr The conceptual model rep resented in Figure 1 and the mathematical description to follow are amalgamations of several box models that I have studied and used in the course The version pre sented here is closely pat terned after but not identi cal to, that described in a re cent publication by Lenton .r3 1 I usually have the students go through the development LITHOSPHERE HYDROSPHERE Notice the notation in Figure 1 M; stands for the mass of carbon in box i; Fij for the flux of carbon from box i to boxj. The anthro pogenic disturbance flux F r moves carbon from a nonrenewable fossil fuel Figure 1. Schematic diagram of a six-box model of the car bon cycle Values shown for reservoir masses (M ;, in PgC) and fluxes (F ;, in PgC /y ) ar e representative of the pr e -in dustrial st e aJ y state (-1850). reservoir to the atmo sphere .* The other anthro pogenic disturbances Fd and F ,, take carbon from of other models as complementary outside work. THE REFERENCE PRE-INDUSTRIAL STATE The quantities shown in parentheses in the boxes in Figure 1 represent estimates of the pre-industrial distribution of carbon (i.e the mass of element C in all of its compounds) in petagrams (PgC, 1 Pg= l0 1 5 g.) These are typical reference values presumed to represent the balanced (steady-state) conditions around the year 1850-early in the industrial revolution when there was little or no observable change from year to year. Fall 2002 the renewable terrestrial biota reservoir to the atmosphere (deforestation) and from the atmosphere to the terrestrial biota (reforestation), respec tively (There is increasing interest in sequestering part of F r by redirecting it to cavities in the lithosphere and/or to the deep ocean. 1 8 9 1 Those slight but interesting variations to the model will be mentioned in suggested exercises near the end.) The following list gives a succinct description of the other fluxes: Actuall y, F r a ccounts for all carbon emissions to the atmosphere except those due to defor e station It i s commonly termed emi s sions due to fo s sil fuel buming -a term that I s hall use throughout. Other industrial sources such as cement manufacturing account for only a few percent of the total. 2 9 7

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F 1 2 F 2 1 F 1 3 and F 3 1 are simply mass transfer rates for the exchange of carbon (as carbon dioxide in this case since nearly all atmospheric carbon is in that form) between the atmosphere and the ocean waters. Basically the rates are described by the product of a mass transfer coeffi cient and a concentration driving force, but the nuances involved in using that description warrant further attention later. F 23 represents the advective flow of carbon from the warm to cool surface ocean reservoirs. This flow which accounts for most of the ocean mixing results from the downflow of cool surface water at high latitudes and the corresponding upwelling to the warmer surfaces at low latitude s. There is also an eddy-mixing component contained in the fluxes between the surface and deep ocean waters. The model could be further simplified without affecting results noticeably by lumping boxes 2 and 3 into a single box F 1 5 i s the rate of photosynthetic uptake of carbon from the atmosphere by terrestrial vegetation. This flux, assumed often in model s of this type to be describable by a single overall rate expression, gets special attention later. M 5 is the total carbon in terrestrial biota but we might think of it as being the mass of vegetation since about 90 % of it is in forests. F 56 is the flux of carbon in litter fall-mostly dead leaves and the like but generally including all dead and waste products from the terrestrial biota F 5 1 and F 6 1 are the fluxes of carbon mostly as carbon dioxide with small amounts as methane and other compounds, to the atmosphere by biotic respiration As mentioned above, a more complete box structure would include additional elements for aquatic biota; sediments; and rivers, streams, and lakes. Such additions are more suited for discussions and assigned work than for incorporation into a working model for the following reasons: The inventory of carbon in aquatic biota and in rivers, streams, and lakes is negligibly small; sediments, the largest of all reservoirs with a total carbon mass of about l 0 8 PgC, are the most sluggish by far; the small fluxes (~0.3 PgC/y) into and out of the sedi ments lead to a first-order time constant of the order of sev eral hundred million years! For the reservoirs represented in Figure 1 first-order time constants, calculated as the ratio of the mass of carbon in a reservoir to the flux of carbon out of it, range from 1.19 years for the cool surface waters in box 3 to 330 years for the deep ocean waters in box 4 For the at mosphere box 1, it's 3.48 years. The illustrations in simula tions to come will cover time spans up to 250 years, over which time the sediment reservoirs are virtually steady. THE EQUATIONS The mathematical description of the box model of Figure 1 consists of a set of carbon balance equations. For the atmo298 sphere, box 1 for example dM 1 dt F21 -F12 +F 3 1 -F13 +F51 -F15 +F61 +(Ff +Fct -F,) (I) In general dM i = (FF ) + disturbances (2) dt L, JI IJ J = I If a particular Fij does not appear in Figure 1 its value in Eq. (2) is zero. The disturbances, as represented in Figure 1, ap pear only in the balances for boxes 1 and 5 To keep account of the fossil fuel supply, a seventh box is added, an out-of-cycle, nonrenewable reservoir of the car bon in fossil fuels. The following balance describes the deple tion of that reservoir: dM7 --=-Ff dt (3) All terms in these equations have units of petagrams of car bon per year (PgC/y). The initial conditions are the reference pre-industrial res ervoir levels in 1850. I use 5300 PgC for the initial value of M 7 somewhat arbitrarily, but based on rather common state ments that while the total carbon stored in fossil fuels is about 10 000 PgC only about half of it can actually be recovered for use Since most of the reservoirs undergo relatively small changes over periods of interest, as later simulations will show, the fluxes can be related to the reservoir masses by first-or der processes. That is (4) Such relationships are frequently employed in box models of the biogeochemical cycles, including the carbon cycle, with three exceptions: F 1 5 F 2 1 and F 3 1 For the others, the numeri cal value of k can be obtained readily from the reference IJ data given in Figure 1. If the carbon in the ocean were present simply as carbon dioxide in aqueous solution, we would expect all four of the F's connecting the ocean surface waters to the atmosphere to be describable by Eq. (4)-under the safe assumption that Henry's law applies to the dilute CO 2 solution. The situation is complicated however by the fact that CO 2 in aqueous so lution enters into equilibrium chemical reactions involving carbonate and bicarbonate forms. Therefore, while the fluxes F 21 and F 3 1 can be related linearly to aqueous CO 2 they are not linearly related to the total C; that is, to M 2 and M 3 The relationship to the total carbon in solution is complicated. It is affected by all of the factors that affect acid-base equilib rium in ocean water-total alkalinity, salinity, temperature and dissolved salts of weak bases, such as boron. A rigorous treatment requires linking a set of equations for ocean chemChemical Engineering Education

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istry dynamics to the above se t. Some studiesC 3 5 J have fol lowed that procedure, as have I in s ome instance s Others l 2 A 7 1 have opted for a simp ler empirical approach that use s the following relationships : F 21= k 2 1M~ 2 F 3 1=k 31 M ~ 3 (5) Values of the exponents ~ 2 and ~ 3 called buffer factors or Revelle factors, can be obtained from charts of the type given in the book by Butcher et al. 1101 The y can also be obtained b y delving into the intricacie s of ocean chemistry dyn a mic s and corre latin g result s of calculations. I used the latter approach to obtain the values shown later but to save space and to s tay on track I s hall spare further d e tail. My testing ha s s hown that results of computations u s ing constant val u es of the 's hardly differ from tho se obtained by appending detail e d ocean dynami cs to the model so long as c han ges in M 2 and M 3 are relativel y s mall generally le ss than 5 % The numerical values of~ range between 9 and 15 ; the nonlinearity is s urpri s ingly s tron g Notice that with val ues of ~ 2 and ~ 3 given, num er ical va lue s of the rate con s tants ~ 1 and ~ 1 can be determined from the reference con ditions given in Figure 1. The rate of photosynthetic uptake F 1 5 of carbon from the atmosphere cannot be represented reali s tically as a linear func tion of M 1 The basic reason i s that the function s hould ac count for a sa turation effect with regard to th e nutrient CO 2 That is the rate increases with increa si ng CO 2 but approaches a limit. For small changes in M 1 th e function ma y be ap proximated by a linear relation s hip but as a later illu s tration will show c han ges in M 1 are l arge over the period s of interest. There seems to be no clear consensus as to w h at form to use for F 1 5 in model s of this type Whatever the specific form a common feature i s a dependence on atmospheric carbon that suggests an ultimate sa turation. The particular o ne cho sen does not s eem to be a critical matter so long as the con stan t s are ca librated or tuned to fit existing data Neverthe less this is a fertile item for cla ssroo m discussion debate and out s ide work. Here I s hall u se th e form employed by Lenton C 31 (6) where y is the threshold value of M 1 (I used Lenton 's value of 62 PgC.) r i s a sat u ration parameter ( Lenton used it as a tuning parameter and arrive d at a value of 1 94 PgC. By methods de s cribed l ater, I arrived at a va lu e of 198 PgC. ) k s i s a rat e coeffi cient to b e calc ul a ted from th e reference state M g i s a function th at depend s on the di s turbance s F a nd Fd as exp l ained and described below In s hort it acco unt s Fall 2002 for c han ges in th e Earth s ca p aci t y for terrestria l biota. The role of the function M 8 i s important but not obviou s at first gla n ce, and definition s and explanations do not come easily. Let me first define it by way of the following equation and then offer brief explanations w h ere k d i s th e fraction of fo r es t e d area o r ma ss (o r forest capacity) that ca nn o t b e refore s ted ( i s not ava il ab l e for r egrow th ) fo llowin g deforestation activities-for exa mpl e, forest areas cleared for urban d eve lopment. (7) k is that fraction of the reforested area o r ma ss that increases the Earth s capacity for terrestrial biota (T hi s i s somet im es termed afo r es tati o n as o pp ose d to r efores t tion that directly renews d efo r es t ed areas M s r e f i s a n or mali zi n g factor in se rted arbitrari l y to make M g dimensionless. I tak e it to be the initial va lue of M s Lenton u se d thi s form but did not include k and F explic itly in hi s formulation. Reforestation can be acco u~ted for without tho se factors ifF d i s allowed to h ave negative values. I prefer to s how F and F d se parately for clarity in sim ulation s lat er. Simply s tated the integral in Eq. (7) accounts for perma nent effects of the di s turbances F d and F Were that integral not included the model e quation s wou ld lead to the follow ing ill ogical conc lu s ion among other s : If F f = 0 and if F d and F eventually se ttle to zero, the ultimate s teady state of carbon in the reservoirs would be identical to the starting ref erence s tate ; the effects of the temporary nonzero values of the di s turbance s would die away, according to the model. But obviously the effects of so me land u se changes must per sis t -fo r examp l e, if forest areas are cleared an d urbani zed with no off se tting refor es tation. With the integral included in M 8 with k d '# 0 a nd F, = 0 s uch land u s e change would per manently affect the di s tribution of carbon, not its total amount. Other illustration s can be given to ju s tify the form of M 8 but perhap s further explanation if needed i s better so ught in stu dent exercises l ater. An alternate form of the integral equation above is this dif fere ntial eq uation: dM kF-kF -8 = r r d d withinitia1conditionM 8 (!850)= I (8) dt M s ref The numerical va lu e of the coefficient k 1 5 in Eq. (6) can be calculated from the reference va lu es s h own in Figure 1, given values for r and y and taking M 8 = 1 ( it s initial state). With Eq (8) added to the material balance equations th e co mplete mathematical model consi s t s of the following set of eight ordinary differential equations : 299

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dM1 =-(k12 +k13)M1 -k,sM s M1 -y +k 2 1M~ 2 dt M 1 +r + k 3 1 M~ 3 + ks 1 Ms + k 6 1 M 6 + Fr (t) + Fct (t)F,(t) dM2 =k1 2 M1 -(k23 +k24)M 2 -k 2 1M~ 2 +k4 2 M4 dt dM3 ~ 3 --=k13M1 +k23M2 -k 3 4M 3 -k 3 1M 3 +k4 3 M4 dt dM 4 = k24M2 + k34M 3 -(k4 2 + k 43 )M4 dt dMs =k,sM s M1 -y -(ks1 +ks 6 )Ms-Fct(t)+F,(t) dt M 1 +r dM 6 -=ks6Ms -k61M6 dt dM7 =-Fr(t) dt dM g -[kctFct(t)-k,F,(t)] / dt / Ms r e f (9) Numerical values for the constants are given in Table 1. Determining the values of the k's as described earlier cali brates the model to the data for the reference year 1850. The value for Y is taken from Lenton 's model. The value for kd is somewhat arbitrary and could be adjusted by tuning the model, but I have taken it to be constant throughout at 0 23. (Lenton used a value of 0.27.) I have arbitrarily chosen a value of unity fork ,. My method for determining the value for r the only tuning parameter, will be described in the next section The values for ~ 2 and ~ 3 were determined as described earlier. Implicit in this development is the assumption that the car bon cycle is independent of all other state variables or that all others are constant, such as temperature, moisture, and other nutrient levels. That assumption is frequently invoked, but it may be an oversimplification if the model results are to be applied to global climate dynamics for example In the aforementioned work ofLentonr 3 J the carbon cycle is coupled to the Earth's energy balance, and in that of Ver et al.r7 1 to other nutrient cycles. TUNING AND TESTING WITH HISTORICAL DATA Extensive historical records are available for testing and tuning the model. Figure 2 shows data on emissions due to fossil fuel consumption, Ff' taken from Marland et al., 111 1 and deforestation F d taken from Houghton and Hackler, 1 12 1 as well as the total of the two over the period 1850 through 1990. (I used 1990 as the endpoint because the deforestation data given 300 by Houghton and Hackler are not tabulated beyond that year. We can safely assume that reforestation, F,, has been negligi bly small in the past.) The dramatic increase in fossil fuel emis sions since the middle of the twentieth century is evident. The solid curves in Figure 2 show my empirical fit of the reported data In order to get a rather precise representa tion I used separate functions over four segments of Fr and over six segments ofFd. This detailed fitting may seem to be overkill. I simply wanted to eliminate an inaccurate TABLE 1 Numerical Values and Units for Model Constants symbol value units k ,, 0.0931 y k 0.0311 y k, s 147 y ~I 58 ( 7 3 0 ~ 2 ) P gd ~ 2 )y l ~ 3 0.0781 y ~ 0.0164 y k, 1 8( 1 4 0 ~ 3 ) P gd l -~3) y ] k,. 0 714 y k 0 00189 y k 0.00114 y k 0 0862 y k s 0.0862 y k 0 0333 y ~ 9.4 ~ 3 10.2 y 62 0 P g C r 198 P g C k 0.2 3 0 k 1.0 9.0 -,------------------~ 8 0 t----------------__qlf 5.0 l -----------" ~-"-,------f~ '-1 : i 4.0 E 3 0 +----------_,_,__ _,,._,,, ,.___._ __ --I 1.0 0.0 1850 1870 1890 1910 1930 1950 1970 1990 year Figure 2. Historical record of carbon emissions to the at mosphere Symbols represent reported data / 11 1 21 solid curves are empirical fits. Chemi c al Engineerin g Edu c ation

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representation of the disturbance record as an explana tion for any model failure. With this representation of the historical disturbance s and the model constants in Table l the system of ordinary differ ential equations in Eq (10) can be solved readily by numeri cal routines available in a number of software package s, to obtain a model-generated record of carbon in the reservoirs from 1850 through 1990 (I used Mathcad for this particular exercise and extensively throughout the course.) The solid curve of Figure 3 shows the result for atmospheric CO 2 ; the data points are reported estimates or measurements from the Worldwatch Institute database J 1 3 1 The good agreement be tween model results and reported data was assured over a portion of the curve, at least by my method of determining the value of r. Its value of 198 PgC as given in Table 1 was determined by an iterative search aimed at minjmizing the total squared difference between model results and reported data over the period 1980-1990. Admittedly the good agree ment over the early years was also virtually assured because model constants were calculated to give a perfect fit of the >' 380 370 360 -f-TA B LE2 Model Computed Quantities for 1990 I 753 2 744 3 143 4 37071 5 577 6 1489 7 5086 8 0.952 Units of M, are PgC, except M 8 which has no unit s. /. [ 350 ,e, -model calculations I 340 Q -'C 330 I 320 CJ CJ 'l:i 310 -a hoo 1;j 290 280 1850 1875 reported data 1111 I / -~ ,p ---~ 1900 1925 year 1950 1975 2000 Fig u re 3. Reported and model-calculated records of atmospheric carbon dioxide since 1850 Fall 2002 reference data of 1850. Over the other years, the maximum disagreement which occurs around 1925, is less than 1.3 %. All s uch things considered, this test of the model lends legiti macy to its u se in predicting carbon distributions through some years ahead. Table 2 li s t s the calculated 1990 level s of carbon for all reservoir s. Notice that changes in the five of the six reser voirs have been relatively s mall over the 140-year period, according to the model. The terre s trial biota in box 5 increased only from 577 to 580 PgC owing to the offsetting effects of decreases by defore s tation and increases by atmospheric CO 2 fertilization. The atmospheric reservoir increased by 23% by 1990 and i s obviously destined to go higher but changes in others have amounted to about 2% or le ss A total of 214 petagrams of new carbon was injected into the cycle from the fossil fuel reservoir and distributed among the other reservoirs over the period 1850 through 1990 Even tually most of that will reside in the deep oceans, box 4, but by 1990 that reservoir has increased by only 71 petagrams. Atmospheric carbon increased by 141 petagrams. Some of that redistribution of carbon, but not any of the increase in the total is due to deforestation with a nonzero value of kd. In the simulations to follow, the ending values of the M's for 1990, given in Table 2, are used as the initial state. SIMULATIONS The simulations described in this section engage the stu dents in the use of the model and exhort them to learn about current trends, issues, and possible future actions-and to become informed about likely consequences regarding fu ture di s turbance s to the carbon cycle. The principal interest is in the prediction of atmospheric carbon dioxide levels through the 21 st century. Such predictions based on models of varying degrees of complexity, have been reported in a number of recent s tudies. l 1 3 5 7 1 4 l Disturbance Scenarios Postulated scenarios for future carbon emissions over a century of time when human activities, worldwide econo mies and international politics are involved are naturally laden with uncertainty, the effects of which, in fact, probably over shadow the effects of the assumptions and simplifications in the model itself. Notwithstanding such, predictions through simulations require inserting the disturbance functions F r, F ct, and F into the model equations. The most commonly employed scenarios for carbon emis sions are those in a set of five that were suggested in a 1992 report to the International Panel on Climate Change, IPCC. l 3 1 51 Th e list given in the R efe rences section is only a small sample. Th e int er es t e d r eader will be l e d to a mu c h larger assortment of m ode ls and related s ubj ects simply by en t e rin g the keyword "ca rbon on a web browser. 301

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Known by the names IS92a, IS92b .IS92e, they are based on likely or possible trends in population changes economic growth, energy supplies, etc. in developed and developing countries There is also a Kyoto protocol which if en acted according to Article 3 of the agreement, would call for a worldwide decrease in emissions to 95 % of the 1990 level by the year 2012. 1 1 6 1 Shown in Figure 4 are slightly modified versions of three of the IS92 scenarios for total carbon emissions for 1990 on ward, including the most pessimistic (IS92e) and the most optimistic (IS92c) cases, and what s usually referred to as the "busines s -as-usual scenario (IS92a) : The latter is the most commonly used version and as its description im plies is based on the assumption that carbon emissions can be predicted from current trends with no major changes in policies and practices. Also shown in Figure 4 is a representation of the scenario for the Kyoto protocol based on the assumption that emis sions would be held constant after 2012. (Ver et al., used a similar representation l7l ) The IS92 scenarios break down the anticipated emissions into fossil fuel use and deforestation All of them use the same deforestation pattern, which de clines to zero by 2100 A curve showing the modified defor estation scenario is also included in Figure 4 The differences between that curve and the others in the figure are the fossil fuel components Reforestation is not included in the sce narios as a separate disturbance. Some Results I use two different approaches for simulations each hav ing certain advantages over the other. One is a straightfor ward numerical solution of the differential equations using Mathcad-basically similar to the method used to generate the historical curve in Figure 3. It's the workhorse that I incorporate into classroom presentations and the major tool used by the students for assigned work I constructed the other using Lab VIEW .. to give a convenient user interface a vir tual laboratory, for certain classroom demonstrations and stu dent experiments It provides the user with hands-on control of the disturbances during a simulation showing effects of manipulations "live" on virtual strip-chart recorders and digi tal displays. (Actually I've used the LabVIEW simulation for classroom demonstration at the very beginning of the I modified the IS92 scenarios for both th e fossil Ju. e l and deforestation co mponents in order to brin g the 1990 values of th e s ce narios in a g ree m e nt w ith th e data a c tuall y r e ported for that ye ar .1 11 11 1 This amount e d to adding 0 1 P g C to all o f th e IS92fos s ilfu e l quantiti e s and incr e a s in g all of the defor es tation valu e s b y about 50 %. These m o difi c ations are more for refinem e nt and fastidiousn e ss than/or an y signifi c ant eff e ct on c al c ulati o ns ** Lab VIEW, d eve lop e d b y th e National In s trum e nts C o rporation in Aus tin T exa s is g raphi c al pro g rammin g softwar e dev e lop e d mainly for data a c quisition and in s trum e nt c ontrol. It al s o se rve s as a p o w e rful tool f o r co nstru c tin g v irtual laborat o ri es. 302 module because it is illustrative and serves to introduce goals and whet the appetite for learning about model development and simulations. ) Space limitations prohibit a full descrip tion of the Lab VIEW simulator and its operation here, but the gist of it is shown in the photo of the user's panel in Fig ure 5 and the brief description in the caption. Notice that those features afford the user an option of sequestering carbon by reforestation and by capturing a fraction of emissions, Ff' in the deep ocean and geologic reservoirs. Figure 6 presents an example of the results of Mathcad simulations using the four scenarios of Figure 4. (For those simulations, I used linear interpolation between the data points shown in Figure 4 for the period 1990-2100.) The results in Figure 6 are based on the parameters listed in Table I ex cept that here the values used for ~2 and ~3 are l 1.0 and 12.3, respectively. (As I mentioned above, tho s e values depend on the total carbon in the surface ocean reservoirs. I used the 1990 values of M 2 and M 3 given in Table 2 as a basis for the new values for the period 1990-2100.) F i s taken to be zero Notice that the model predicts atmospheric CO 2 would in crease to 702 ppmv by the year 2100 if the IS92a business as-usual scenario were followed. Based on that scenario pre dictions by models used by othersr 1 3 1 41 range between 697 and 724 ppmv. Over the entire 110-year period, the maxi mum difference in atmospheric CO 2 between any two of the four models (the three cited above and the present one) is about 4 % an observation that buttresses confidence in dis cussions of quantitative results from the model at hand. No tice the wide range of predicted CO 2 level s in 2100 resulting from the different scenarios for carbon emissions. The high est is nearly twice the lowest ; both are probably unrealis tic extremes. Business-as usual would result in nearly doubling the 1990 CO 2 level by the year 2100 according to the model prediction. G' 3 5 -i= ===============---=== ==== ========:----::=: ::]I Historical total to 1990 3 0 .....,_1S92a total (modified) --1S92c 2 5 ----1s92e --+Kyoto protocol total g, 2 0 A Historical deforestation to 1990 --1S92 deforestation (modified) ~ 1 5 ~ .. 10 t---------.. .. /' _,,,,, 0 1850 1 8 75 19 00 19 25 19 5 0 19 75 2000 2 0 25 2 050 2 0 7 5 2 100 year Fig u re 4. Carbon e missions to the atmosphere ; historical data and possible future scenarios Chemi c al En g ine e rin g Edu c ation

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1 990 s i mulat i o n year s u rface water (Pp C ) 8 87 918 Additional Work U s ing Mathcad and Lab VIEW si mul a tion s stu d e nt s o b v ously can be in vo lved in ex aminin g all sorts of que st ion s, model variations, and p arame ter effects H e r e is a partial li s t of exercises that I ha ve u se d so m e of w hich require co n su lt ing outside references. Extend s imulation s beyond 2 100 to a ddr ess a number of que st ion s raised about the ultim ate s t ea d y state (Act ally, I ask the s tudent s to u se the s tead y state forms of the equations to address some of the se.) Wh a t would that u l timat e s tate be if emissions were halted immediately? 950 932 0 reported da ta to 2000 i 850 -----for IS92e sce nario from 1990 1 > I592a E a. 750 / .s I592c I 702 'C Kyoto I ;a 650 0 / '6 C 0 550 u 499 l 472 450 350 ---0 0 0 0 0 0 0 0 0 0 0 0 250 J---~--1---'----' ---~----' 18 50 19 00 1950 2000 2050 2100 y ear F i g u re 6. Atmosp h eric carbon dioxide levels ; reported hi storica l data and model predictions. Fall 2002 atm o s p heric CO2 ( p pmv) ',;;:::.-: E:= w i th IS92a s this simulation 1850 level 3 (288 ppm v) 2s w i th I59 2 a sc enario total fossil fuels deforestation with manuall y adjusted scenario 1990 2100 em i ssi o ns (PgC/y) 1990 2100 Figure 5. __ The user's panel forLabVIEW si mulation s The e l e m e nts wit h black arrows are for u ser inputs adjustable as th e sim ulati on proceeds. The numb er to the l e ft in eac h reservoir box i s th e initial va lu e give n in Tabl e 2 ; that on the right, the c urr e nt value. The two s urfa ce water boxes of Figure 1 are combined into one for these sim ulation s What wo uld it b e if a ll car bon in the fossil fuel re servo ir were eve ntuall y u sed? How long will it take to a pproach a s t ea d y s tat e i f car bon e mi ssio n s to th e atmosphere are halt ed at a certain time? Carry o ut s imul a tion s t o clarify if n ecessary, the role s and effects of kd k ,, and M 8 -o r to te s t entirely different forms of F 1 5 th e ra te of photo sy nthetic uptake of car bon. Wh a t i s a reali s ti c mathematical de sc ription for the di s turbance F ,, if refore s tation begin s with new tree s that require a numb e r of years for maturation ? Examine th e pr edicte d changes in the s trength s of the terrestrial and oceanic s ink s ( or s ource s?) of atmo s pheric carbon over th e 21 ce ntur y. It i s so metime s s ugge s ted th a t the mo s t realistic goal that can be achieved regarding the control of atmospheric CO 2 is t o "sta b i li ze" it at twice the pre-indu s trial le ve l by the year 2100. Tr y to ac h ieve that goal b y manipulating the emissions (o r by fa bricatin g an emissions scenario) in s uch a way that atmospheric CO 2 line s out at about 1224 PgC ( 572 ppmv ) b y the year 2100. ( Thi s is an ideal exer cise-even an entertaining one-for the Lab VIEW simu lator. In fact, the data s hown on the digital displays and charts in Figure 5 are the end s tate s of thi s exe rcise. ) Noti ce that th e difference b e tween the emissions level so achieved in 2100 and that dictated by the IS92a sce---------------Co nrinu ed on pag e 309. 303

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.tA.-5-3._la_b_o_r._a_t_o_r.:.y ________ ) DETERMINING THE FLOW CHARACTERISTICS OF A POWER LAW LIQUID JAMES R. HILLIER DAL E TING, LI SA L. KOPPLIN, MARGARET KocH, SANTOSH K. GUPTA University of Wisconsin Madison WI 53706 N on-Newtonian liquids present unjque problems with re s pect to their flow behavior. These problems are seldom addressed in undergraduate co ur ses in chemi cal/mechanical engineering and are pos s ibly covered only through a sing l e experiment in one of the laboratory courses Tjahjadi and Guptal' 1 extended the work of Walawender and Chenl 2 1 and developed an experimental scheme that illustrates how the apparent viscosity, TJ of a p se udoplastic liquid (di lute aqueous s olution ofNa-CMC) decrease s with increasing shear rate, y They also suggested performing additional experiments after adding some sodium chloride to the CMC so lution, to observe a dramatic decrease in TJ and re lat e it to the contraction of the polyelectrolyte molecule s in an ionic medium Although the results had considerable educational value the equations used were quite complex and cumbersome to use with the result that a student obtained littl e insight into the method of ana l ysis-this limits the value of their experiment. In the present work (developed as part of the informal experiments l 3 1 at the Summer 2000-1 laboratory at the Unj versity of Wisconsin-Madi so n), a much simpler experiment has been developed that uses the easily under s tood macro sc opic energy balance (the engineering Bernoulli equation 141 ) to obtain experime ntal results A 0.07 % (by weight) solution of a sodium salt of carboxy methyl cellulose (Na-CMC ; weight average molecular weight = 7 x 10 5 ; DS = 0.9; Aldrich Chemicals Milwaukee WI ) in deionized water was used for our study CMC was selected because of its pseudoplastic nature over a range (1 10 5 s 1 ) of shear rates. In addition, CMC is an inexpensive, nontoxic, biodegradable water-soluble polymer commonly used in mining applications, food thickeners, adhesives, and textiles. 304 The results obtained could also be compared to existing val ues in the literature 111 for consistency. EXPERIMENTAL SET-UP The experimental se t-up i s s imilar to that used for studying the flow characteristics of Newtonian liquid s, as described by Crosby .l5 1 Flush-mounted glass capil l aries (in one case a copper tube ) of different diameters and lengths are used with a drain tank ,l5 1 as shown in Figure l Two different kinds of experimental unit s were made so as to vary the s hear rate over a reasonable range. The detailed dimensions are pro vided in Table I. PROCEDURE The CMC so lution to be used in all the experimental runs was prepared using laboratory-grade carboxymethy l cellu lose powder. A solution of 0 07 wt % CMC in deionized waJames R. Hillier received his BS degrees from the University of Wiscon sin-Madison in Chemical Engineering (2000) Biochemistry (2000) and Molecular Biology {2000) He is currently the Plant Engineer for Equistar Chemicals in Fairport Harb or, OH while working on a master s degree in polymer engineering and a diploma in disaster management. Dale Ting received his BS in Chemical Engineering from the University of Wisconsin-Madison in 2000 He is currently working in process develop ment at The Procter and Gamble Co in Cincinnati OH Lisa Kopplin received a BS in Chemical Engineering from the University of Wisconsin-Madison (2000). She is currently serving as a Project Engi neer for General Mills Inc in their West Chicago manufacturing facility Margaret R. Koch graduated from the University of Wisconsin-Madison with a BS in Chemical Engineering in 2000 She is currently working in Process Development at S C Johnson & Son Racine WI Santosh K Gupta received his BTech (1968) from I I. T. Kanpur, and his PhD (1972) from the University of Pennsylvania-Philadelphia. He has been on the faculty of I .I T. Kanpur, since 1973 and has also been a Visiting Professor at the University of Notre Dame National Uni versi t y of Singapore and the University of Wisconsin-Madison His research inter ests include polymerization engineering and optimization using Al tech niques Copyrig ht C hE Division of ASEE 2002 Ch e mi c al Eng in eer in g Edu c ation

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ZR Buret tube ,__ ___ ZR --------i CD } Rubber stopper Pyrex cap i ftory {al Epoxy -j I--Zr 0 lb) Capillary Figure 1. Experim e ntal se t-ups for Phas es 1 and 2 a and b, 50 ml gra duat ed tube (buret with l ower end c ut ] co n n ecte d to aligned g lass ca pillari es, flush -m ounted to minimiz e e ntran ce lo sses. c, 5 lit SS tank ( diameter 0.158 m] with sight g l ass to measur e h, u se d. Glass or Cu cap illa ries/tubes u se d. Detail s provid e d in Tabl e 1. TABLE 1 Details of the Ex perimental Runs Set-Up Approx Run Use d 10' ( 2r ) h ( 1=0 ) Rang e of K No. ( Fig. o ) m L,m m y,s' n Pas" l a 0 532 0.541 0.0705 69-122 0.809 0.054 2 l a 1 040 0 .2 05 0.20 288 -55 2 0 .64 0 0.0884 3 l a 1 .04 0 0.205 0 20 268-436 0.858 0 7913 4 l a 1.040 0.120 0.20 347 733 0.86 0 .04755 5 l a 1. 040 0 .1 01 0.20 3771 0 1 3 0.62 0. 1 048 6 la 1.040 0. 101 0 .20 358-857 0.70 0 .066 53 7 l a 1 .040 0.055 0 20 5351 6 1 9 0.701 0.06599 8 l a 1 .040 0.055 0 .20 518-1608 0 685 0 07466 9 l a 1. 040 0 055 0.46 515-5628 0.639 0 100 10 l a 1.040 0.055 0.46 530-5924 0.633 0.1018 II lb 1.536 1. 2 1 6 0 565 3 1 8-483 0.839 0.031 1 2 lb 1.5 36 1. 2 1 6 0.57 3 1 2-442 1.00 0 012 1 3 l e (g l ass) 2.200 1 .279 0 57 513 563 0.311 0 79 1 1 4 I e (g l ass) 2.720 1.27 9 0 0705 6 5 8 683 0.857 0.026 15 I e (g l ass) 2 720 2 279 0.0705 892-965 0.355 0 6 11 16 l e (C u )* 3.176 0.610 0 0652 10 981 216 0.507 0.229 Gla ss ca pilla ry o r Cu tub e u sed + S ee Figur es I a c ++ Se e Eq. (2) Fall 2002 ter was prepar ed we ll in adva n ce to guarantee the ho mogeneity of the s olutions. 111 The solution was heated to 30-50 C for about 4 to 8 hours and stirred for over 24 hour s. Homo ge neity of the solution was con firmed b y observing it s clarity agai n st a very bright li g ht so urce. 11 61 In eac h experimental run a s p ec ified amount of pol mer s olution was adde d to the holding tank. The ini tial va lue s, h 0 of the l eve l of so lution in the tank (see Figure 1) are given for the different experimental run s (Ta ble 1 ) Flow was starte d and data on h was recorded over time t s tartin g at the calibration mark. Thi s al lo we d flow pattern s to establish so that data would not b e a ltered b y flo w d eve lopment. Experimental run s were s topped pri or to co mplete efflux of the liquid from the tank so as to reduce the s ignificance of e nd effects THEORY Since CMC so lution s b e ha ve like p se udoplastic s, th eir apparent viscositie s, 'Tl decrea se with increasing s hear rates, y Th e genera l dependence of 'Tl on y i s quit e complex but over s mall ranges of the s hear rate, y the following pow er law model 14 6 71 i s followed quite well : 't = Ky" (I) w h ere -r i s the s hear s tre ss. In Eq. (1), the constant, K i s referred to as th e co n s i ste nc y index and the expo n e nt n is the power l aw ind ex The appare nt viscosity i s th e n g i ve n b y 't K n 1 'Tl =-:-= y y (2) A macro sco pi c ( m ec h a ni ca l energy balance for thi s sys t e m 17 Eq. s.zo1 l eads to (see Appendix 1 for details) ( L h) 2 KL ( 3n + I ')" v" p g+1-) \ n rr' (3) In Eq (3), p i s the den si ty of the s olution r 0 and Lare the (i nner ) radius and l e n gt h of the capillary ( Figure 1 ), h is the h e i g ht of th e s olution above the capi llar y e ntrance at tim e, t g i s the acceleration due to grav it y, and v is th e ma ss-average velocity inside the ca pillary at time t. Th e mas s -avera ge velocity of the so lution inside the ca pillary can be obtained u s ing the contin uit y equa tion (4) where R is the inner radiu s of the drain tank. A s econd 305

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The primary advantage of the present study is that analysis of the raw data can be performed using equations that are easily understood by juniors in chemical engineering, and standard computer packages can be used ... or third degree polynomial can be fitted to data on h (t). Thi s gives excellent values of the coefficient-of-determination of about 0 .999 and higher This polynomi a l is then used with Eq. (4) to obtain v. Eqs. (3) and (4) can be combined and integrated for Newtonian fluids (n = 1 ) to give the standardl 4 J equation for the efflux time for a vertical tank-pipe assembly under laminar-flow conditions. The s tudent s find these deri vations easier to comprehend (in fact, they can make the derivation s them se lves) than the equations described by Tjahjadi and Gupta .r 1 1 The validity of the assumption of laminar flow should be confirmed by calculating the Reynold s number for the pseudoplastic liquid u s ing[ 7 ; Eq 5 .so1 ( J n n 2 n R e= z3 n _n_ D p v 3n + 1 K (5) For pseudoplastic flows present in the laminar region, as in this study, the sudden contraction/entrance losses are expected to be negligible. r 1 2 1 In the more general case where the en trance los ses are important, the Bagley correction 1 8 91 can be used. This could be a possible avenue of further study for a student. Equation (3) can be rewritten as [ 2KL(3n+li"] lo g [pg(L + h)] = lo g ro'+I l -n) + n lo g (v) (6) An appropriate log log plot of Eq. (6) gives n ( = slope). K can then be obtained u si ng n and the intercept a., using K = exp 0 _n_ [ a.rn +l ( J"] 2L 3n+l (7) Once values are obtained for both n and K the shear rate (at the wall of the tube, r = r) can be evaluated using r 4 7 ; App 11 = p g(L + h )r 0 [ ] 1/n y 2LK (8) The apparent viscosity, Tl can then be evaluated (at this wall s hear rate) using Eq. (2). Equation (8) assumes that the power law dependence is valid, and so the value of y obtained is inferred from the data-fitting procedure Unfortunately u se of the power law assumption, though helpful in s implif y ing the experiment at the undergraduate level, can give a false idea of the complexity of the method of analysis routinely used by profes s ional non-Newtonian 306 rheologists (who commonly u se the Rabinowit sc h tech nique 16). An alternative procedure of data analysis that is not as difficult and that can be attempted by an undergradu ate student, is the use of the Schummer approximationt 101 (de scri bed in Appendix 2). Such an analysis pre serves, to some extent, the physic s of mechanical energy balance and clo se ly follows the s tep s that would be employed in the professional rheological evaluation of non-Newtonian v i scos it y One set of experimental d a ta ge nerated herein i s analyzed later to compare the results u sing the power law and the Schummer approaches. RESULTS AND DISCUSSION Details of the severa l experimental set -up s and run s are given in Table 1. The se experiments were de sig ned and per formed in two pha sesRun s 1 and 11 through 16 in Table 1 comprising the first phase, fo)lowed b y Runs 2-10. The re s ults of the first phase were analyzed and u sed to help im prove the designs for Phase 2. Figure 2 shows data from Phase 1. It demon stra tes the decrease of the apparent viscosity with increasing shear rates. Although the viscosity vs. s hear rate diagram is incomplete, the s hear-thinning effect characteris tic of pseudoplastic fluids is quite evident. The straig ht-line seg ment s on thi s lo glo g plot confirm the validity of the power-law model over small ranges of s hear rate. The data overlap in some regions which confirms the accuracy of the results. The value of the power law index varies from about 0.3 to 1.0 (see Table 1 ) The range of shear rates covered extends over almo s t two decades and the data appears to fall 0001 ,0 ..... -,. .... S h 1 uR1t1 (1 /1) ~, R.1 11 Figure 2. Apparent viscosity vs. shear rate for a 0.07 wt% Na-CMG aqueous solution, assum in g power law behavior of the liquid. Phas e 1 results shown with Runs indicated. Results from Ref. 11 also shown for comparison. T empera ture= 23 G. Chemical Engineerin g Education

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on a smooth curve over this range. The data is also found to be consistent with some earlier work [ 111 performed using the same solution, using a stainless steel tank with a copper tube, similar to that used in Run No. 16 Our data is also consistent with the earlier data t 1 1 on a 0 07 wt % Na-CMC solution having a slightly larger weight-aver age molecular weight of 7.5 x 10 5 (the apparent viscosity at 1000 s 1 was about 7 cP earlier, and is about the same in Figure 2). The replicability of our results was found to be excellent. It should be mentioned here that an interesting activity would be to confirm the experimental results obtained here with those using more sophisticated capillary-flow or Couette viscom eters available in research laboratories. Use of the former would also illustrate the use of the more exact Rabinowitsch technique of analysis_[l 9 J The experimental results shown in Figure 2 were then used to design a few additional experiments (Phase 2) so as to ex0 1 l 1 0 001 +----------'ii, i 10 1 00 Shear Rate (1fs) 1 000 10000 Figure 3. Results for Phase 2, assuming power law behavior of the liquid. Run Nos. 2,3 x; 4, :; 5 ; 6 --; 7 o; 8 +; 9, ; 10 ; Temperatur e = 23 C. I Schummer I 0 Powerlaw .t!' 0 01 ;;; 8 "' 5 0.001 +-----<--+---+--+-+-+--+-+-~---+---+--+--+--<--+-~ 100 1000 Shear Rate (1/s) 10000 Figure 4. Comparison of 11 vs y obtained assuming power law behavior of the liquid with that using the Schummer correction. Set 9 (Table 1) data used. Fall 2002 tend the range of shear rates. The corresponding plot for the apparent viscosity vs. shear rate for these runs is given in Figure 3 and the values of Kand n in Table 1. It was found that the data for the two sets of experimental runs, in the range of s hear rates of about 300 to I 000 s 1 superposed very well (these have not been shown since the data points get too cluttered). It is interesting to observe that Runs 9 and 10 give data over a very large range of shear rate, and one could as well use just one or both of these set-ups for a routine laboratory experiment. It should be emphasized that Eq. (3) is applicable only over small ranges of shear rate (and so over a small range of t as the meniscu s falls). A log-log plot of this equation does not s how straight line s for some cases, and one must exer cise so me judgment to fit the points. Moreover, the viscos ity of CMC (a polyelectrolyte ) solutions in deionized water is very sensitive to the concentration of small amounts of salts that may be present. (1 1 The addition of small quantities of NaCl to the solution could help improve the reproduc ibility of the result s s ubstantially and would help if one were to compare the results obtained by different groups of stu dent s taken over severa l weeks. Figure 4 shows one set of data (Run 9, Table I) that has been analyzed u s ing both the power law assumption for the solution as well as the more accurate Schummer technique. The results s uperpose quite well but a shift in the curves is quite evident, as di sc ussed in Ref. 10 CONC LU S I ON S A simp l e experimental se t-up was developed to study the decrease of the apparent viscosity of a 0 07 % (by weight) aqueous solution ofNa-CMC with increasing shear rate. Two experimental units were found that covered a reasonably large range of shear rates of 500 to 6000 s 1 The primary advantage of the present study is that analysis of the raw data can be performed using equations that are easily un derstood by juniors in chemical engineering, and standard computer packages (e .g., Excel etc.) can be used for this purpo se Additional experimental data can easily be taken after adding sodium choride to the CMC solution, to study the effect of molecular contraction of the polyelectrolyte .r 1 1 The results obtained using the power law assumption are com pared to more elaborate methods of analysis, and a few ad ditional experiments have been suggested for the more enterprising student. (..._ ____ A_P_P_EN_D_1_x_1 ____ __,,) Details of the Derivation of Eqs. (3) and (8) The macroscopic mechanical energy balance [ 41 is applied 307

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between points 1 and 2 (Figure la) with the following a s sumption s : Th e c olumn is vertical The kinetic en e rgi e s of the liquid at 1 and 2 ar e n e gligible Entrance or other loss e s ar e negli g ibl e and the onl y lo s s es are due to vis c ous e ff e cts in th e c apilla ry This leads to ( ~p ) g(L+h)= P ca pill ary (Al.I) where 'to is the shear stress at the capillary wall, r = r 0 and (Af>) 11 is the pressure drop acros s the length L of the cap ary capillary A force balance over a control volume of radius r, and hav ing a differential length dz gives r 41 -dP 2 't d z r (A 1.2) or -dP ( ~P I 2 -r o dz l L )ca pill a r y f o (AJ.3) Equations (Al.2) and ( Al.3) give (Al.4) Using the following variation of Eq (1 ) (Al.5) where u is the axial velocity at location r in Eq (A 1 .4), we obtain ( ) -du 't o J i n [ J l /n yr=--r dr r 0 K (Al.6) T h is can be integrated from r = r 0 ( 't = 'to) tor= r ( 't = 't) to give u(r)= ro -r [ J l / n 1 / n + I 1 / n + I Kro I+ _!_ (Ai.7) n Equation (Al.7) can easily be integrated over O :=; r :=; r 0 to give the mass average velocity, v, as which can be rearranged (and Eq. Al.1 u s ed) to give 3 08 (Al.8) Kv" (3 n + I J 0 r o ( ~P i 't o =T l _n_ ) =z l T )ca pill a r y (Al.9) Equation (Al.9) can be combined with Eq. (Al.l) to give Eq (3) Equation (Al.6) can be s implified to give y(r) = [ ~ J l / n ( p g (L + h) r J l / n Kr o 2LK (ALIO) which leads to Eq. (8 ) (with r = r 0 ) ( ______ A_P_P_E_N_o_1x_2 _____ ) Details of the Schummer Approximation' 101 The apparent s hear rate Y a p and the apparent vi s cosity 'll a p are defined r 1 01 by 4 v 4Q y =-=a p r 0 nr J -r 0 r J pg(L+h) 'llap =-. 8 L Y a p V (a) (b) (A2 l) Schummer s tates that the true s hear rate, y, corre s ponding to Y a p (at which the vi s cosity is equal to 'll a p) is given by = 083 =3.32v Y Y a p ro (A2.2) The experimental data can be u s ed to give the average veloc ity v in the capillary as a function of time. This can be used with Eq s. ( A2 lb) and (A2.2) to evaluate 'll a p and the "true" (or the corresponding) shear rate y to give a more accurate plot of 11 v s y. REFERENCES I. Tjahjadi M. and S K. Gupta C h e m En g E d ., 20 84 ( 1986 ) 2. Walaw e nder W.P ., and T.Y. Chen Ch e m E n g. Ed. 9 10 (1975 ) 3 S a th e r GA a nd J C oca C h e m En g. Ed ., 22 140 ( I 98 8) 4 Bird R B ., W E St e w a rt a nd E N Li g htfoot Tran s p o rt Ph e n o m e na 2 nd e d ., J o hn Wil ey a nd Son s, New York NY ( 2001 ) 5 Cro b y, EJ. E x p e rim e nt s in Transp o rt Ph e n o m e na D e partm e nt of Ch e mi ca l En g ine e rin g, Univ e r s ity of Wi s con s in Madison WI ( 1961 ) 6. Kumar A ., and S K Gupta Fundam e ntals o f Pol y m e r S c i e n ce and Engin ee rin g, T a ta M c Graw Hill New Delhi lndia (197 8) 7. McC a b e, W.L. J.C. Smith and P. H a rriot U n it Op e rati o n s of Ch e mi c al En g in ee rin g, 5th e d. McGraw Hill N e w York NY (199 3) 8. Ba g l ey, E B. J Appl. Ph ys., 28 6 2 4 ( 195 7) 9 McK e lvey J.M ., P o l y m e r Pro cess in g, John Wiley a nd Son s, N e w York, NY ( 1962) 10 De a ly J M ., and K F. Wi s brun M e lt Rh e ol ogy and It s Rol e in Plasti c s Pr oces sin g, v an No s trand R e inhold New York NY ( 1990 ) 11 Zh a n g, J ., J J e nkin s, B Lind e n and A Kri s top e it U W M a di so n Tran s p o rt Lab M e m o M a di so n WI ( 2000 ) 0 C h e mi c al En g in ee rin g Edu c ati o n

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The Earth s Carbon Cy cl e Continued from page 303 nario (i.e., the differen ce between the end point s of curves of the lower s trip chart of Figur e 5) i s the amount of carbon that would ha ve to b e replac e d by an equivalent energy so urce. Follow up que s tion s for co n s ideration and/or further si mulation s: What alternate sou r ces of e ner gy might fill the gap? Could it be filled by seques tering ca rbon in the t e rr estrial biota ( reforestation ac tivities) ? ... in geologic storage? ... in the deep ocean wate r s? Would those possibilities lead to a permanent sta bili z ation? What is the trend of the fab ri cated emis s ions c ur ve in 2 100 ? What is its ultimate fate if atmo s pheri c CO 2 is to s ta y l eve l at 572 ppm v? Start from the beginnin g with an alternative model th a t presumably improve s on thi s one (e.g b y a ddin g la yers to the ocean or atmosphere, a s p at ial variation to th e t e restrial re se rvoir s) Calibrate tune and test the model against the resu l ts s hown here CONCLUDING COMMENTS Many of the Earth's biogeochemical proc esses can be st ud ied and mode l ed within the co ntext of th e u s ual chemical engineering curricular material. The carbon cycle, the focu s of this article, i s a particularly apt example be ca u se, thou g h ba s ically complex, it can b e u sefu ll y d escri bed b y a s imple mathematical model. Addition ally, it is b ei n g disturbed and altered by human ac tivitie s, po ss ibl y t o the ex tent of causing global warming and other climat e c han ges, and i s therefore a s ubject of current interest and concern A s ide from s tudent s learnin g about thi s parti c ular s ubject important and timely as it is, in my view another wort h w hile outcome i s th a t they gain confidence in th eir ability to ana lyze phy s ical s ituation s that may not be on th eir u s ual bill-of fare and to apply their chemical e n gi ne eri n g tool s to th e for mation of a mathematical d escri ption. Never mind that the description is s oaked with s implific a tion s and assumptions s uch as perfectly mixed boxe s for oceans, s ingl e -rate expres sions for all of the Earth's photo sy nthe s i s, and so on. A great deal is l earned by pondering inve s tigating and debatin g the bases for such simplifications a nd assumptions This article describes my coverage of the s ubject in a course devoted to topic s on ecology and the environment. The cov erage is scalab l e-dow n ward to a brief treatment and selected h omework assignme n ts integrated into some of t h e usual core course offeri n gs or upward to the development of more so phisticated models and t h e application of more advanced de script i ons of t h e ra t e processes mathematical analysi s, and comp ut ational met h ods. Whatever the scope, st u dents ben efit from the broadening experience of applying their chemi cal engineering tools in a quantitative way to an important subject o u tside the mainstream Fall 2002 R ea d ers who wo uld lik e to h ave a n e l ectro nic copy of thi s module w hich co n sis t s of a s lid e s how with link s to s pre a s heet s, sim ul a tion s, etc including the Lab VIEW s imulator s hould contact m e at . ACKNOWLEDGMENT D eve lopment of th e material for thi s article was part of a proj ec t s upport e d by the CRCD pro g ram ( Grant EEC9700537-CRCD) of the National Scienc e Foundation. REFERENCES I. Cox P.M. R.A Bett s, C.D. Jones S A. Spa ll and l.J Totterdell "Ac ce l eration of Global Warming Due to Carbo nCycle Feedbacks in a Co upl ed C lim a t e Model ," Na tur e, 408 1 84 (2000) 2. C ham eides, W.L., and E .M. P erd u e, Biog eoc h e mi ca l Cycles: A Co m puterInt erac ti ve Study of Earth System Science a nd Global C h ange Oxford University Pre ss ( 1 997) 3. Lenton T.M., Land and Ocean Carbo n Cycle Feedback Effec t s o n G l obal Warming in a Simp l e Ear th System Model Tellus, 52B 11 59 (2000) 4. Rodhe H ., and A. Bjork s trom Some Co n se quences of Non-Propor tiona lit y Between F lu xes a nd Reservoir Co nt ents in Natura l Sys tem s," T e l/u s, 31 269 (1979) 5 Schnoor J.L. Environmental Modelin g: Fate and Transport of P o l li 1t ants in Wat er, Air, and Soil, Jo hn Wiley & Son s ( 1 996) 6. Siegenthaler, U a nd F. Joos Use of a Simple M o d e l fo r Studying Oceanic Tracer Di s tributions a nd th e Global Car bon Cycle ," Tel/us 44B 1 86 (I 992) 7. Ver, L.M.B., F.T Mackenzie and A. Lerman Bi ogeoc h emica l R spo n ses of the Carbon Cycle to Natura l and Human Perturbations : Pa st, Pre se nt and Future," Am. J of Sci., 299 ,762 ( 1999 ) 8. Herzog H ., B. Eliasson, and 0 Kaar s tad "Capt urin g Gree nh ouse Gase s," Sci Am., February 2000, 72 (2000) 9. Kane R .L. a nd D.E. Klein "Car bon Seq u estra ti on: An Option for Mitigating Global Climate Change," Chem. Eng. Pro g., Jun e 200 1 44 (200 1 ) I 0. Butcher S S ., R.J. Charlson, G.H. Ori ans, a nd G.V. Wolfe (eds), Glo bal Bio geochemical Cycles, Academic Press ( 1992) 11. Marland, G., T .A. Boden R.J A nd res, A.L. Brenkert, a nd C.A. Johnston Tr e nds : A Compendium of Data on Global C han ge, Ca rb o n Dioxide Information Analysis Ce n ter Oak Rid ge National Labora tory, Oak Ridge TN ( 1 998) 12 Houghton R A., and J .L. H ackle r Trends: A Compe n dium of Data on Global Change, Carbon Dioxide Information Ana l ys i s Cente r Oak Ridge National Laboratory Oak Rid ge, TN ( 1 998) 1 3. W or ld watc h C D-ROM Worldwatch In sti tut e, Washington DC (200 1 ) (T hi s C D-ROM and downloadable datasets are ava i l a bl e a t < http :/ / www.wo rld watch.org/pubs/> 14 H o u g ht on, J.T. L.G. Meira Fi lh o, B.A. Ca ll a nd e r N H ar ri s, A. K attenberg and K. Ma s kell (e ds ), Climate C h a n ge / 995: The Scien ce of C l imate Change, Comribution of Workin g Group I to th e Seco nd Assessme nt R epo rt of the In tergove rnm e nt al P anel o n Climate Change See Figure 5 of Te c hni ca l S u mmary ( Pub l i s h e d for t h e Inter gove rn mental Panel on Climate Change, IPCC) Cambridge Univ e r s ity Pre ss ( 1995 ) ( Thi s aod other r eports of the IPCC are ava il a b l eon l in e a t 15 Leggett J. W.J Pepper and R.J Swart "E mi ss ion s Sc e nario s of th e IP CC: An Update, Climate Change / 992: The S11pplementa1y R port to the IP CC Scientifi c Assessme nt ( J T. Houghton B .A. Ca ll ander, and S K Varney e d s), p. 69-95, Cambridge University Pr ess ( I 992) 16. United Nations Framework Convention on Climate Change COP 3 R eport, Document FCCC/CP/ 1 997 /7 /Add I (T h e full text of thi s r p o rt i s avai l a ble a t < http : //www.unfccc.int/resource/protintr.html> 0 309

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e.;_5_3._a_s_s e s s m e n t _____ __ ) PORTFOLIO ASSESSMENT I n Introductory ChE Courses SURITA R. BHATIA University of Massachusetts Amherst, MA 01003-9303 A s defined by Feuer and Fulton,[ 1 J performance-based assessment refer s to assessment techniques that re quire students to create a final product such as a written report, oral presentation, or portfolio of their work, as opposed to the mor e conventional assessment techniques of written quizze s or exams. Performance assessment can also be defined as an assessment method that evaluates a st udent s ability to perform a s pecific procedure or ta s k ;[ 21 in this con text the assessment mu s t contain a performance task, a stu dent-re s ponse format, and a sco rin g sys tem. Examples would include judging a st udent' s ability to manipulate laboratory equipment or respond to an open-ended prob lem Y1 Slater s ugge s t s designing a performance ta sk that is "so mewhat undefined complex and has multiple entry and exit point s;" that is, a t ask that ha s more than one correct solution path .[ 2 1 The advantages of performance-ba se d assessment tech niques have been documented by severa l studies in the edu cational literature. [ 1 61 Many st udie s emphasize the real world" nature of performance assessment; 1 31 student work is evaluated in a manner that is much closer to what will be encountered in the work environment. Perhap s most impor tantly, research has s hown that alternative assessment help s in the evaluation of students with various learning s tyle s and educational backgrounds promoting excellence among a more diver se student population.f 41 These "alternative assessment" techniques [3] are not new to engineering education. Traditional performance-based as sessment is often used (although not often acknowledged as such) in juniorand senior-level courses in the form of labo ratory experiments written lab report s, design projects and oral presentations ; and the ABET EC 2000 guidelines have brought increased attention to outcomes-based assessmentP 81 But alternative assessment i s not widely used in the freshmanand sophomore-level courses for a variety of reasons. Educators may worry that freshmen a nd so phomores do not have the depth and breadth of knowledge to complete a de sign project or written paper, or that there is sim ply not enough class time to have st ud e nt s give oral presentations ... after all, there is barel y enough c la ss time to teach these st dents mass and energy balance s and thermodynamic s. There is another mean s of implementing performance based assessment in these courses, however -one that ha s remained largely under-u se d in engineering education: student portfolio s. WHAT IS A P ORT FO LIO? Portfolio s are collections of stu dent work, typically se lected according to guidelines set forth by the instructor .r3 1 These guidelines may have a one-to-one correspondence with the course objective s, or an in st ru c tor may choose to highlight particular course objectives An example of required items from the freshman chemical engineering course at UMass which I will di sc u ss in more detail below i s given in Table 1. Along with each item, s tudent s are asked to submit a state ment of why the item was chosen This element of se lf-analy sis or self-reflection is crucial if portfolios are to be more than just "s tud e nt folders. "[ 91 For comparison, the course obSur it a R. Bhatia is an assistant professor in the ChE Department at the University of Massachu setts. She received her BChE from the Univer sity of Delaware, her PhD from Princeton Uni versity, and held a postdoctoral position at the CNRS / Rhodia Complex Fluids Laboratory Her research interests are associative polymers rhe ology, shear-induced structure and structured cell encapsulation materials. She has taught mass balances and heat transfer at the under graduate level and coteaches a graduate course on colloidal dispersions. Copyrig ht ChE Division of ASEE 2002 3 /0 Chemical Engin ee ring Education

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TABLE 1 Required Portfolio Entries for Freshman Course in Chemical Engineering Fundamentals 1. A p ro bl e m with a n o n ro utin e so luti o n w h e r e s tu de nt s h a d t o e mpl oy n ew s tr a t eg i es o r m e th o d s of so luti o n 2. A h o m ewo rk probl e m th a t in vo l ve d t ea m wo r k or gro u p work 3 A p ro bl e m th at gave th e s tud e nt a goo d sense of r ea l -wo rld a ppli ca ti o n s 4. A probl e m in vo l v in g d a t a ana l ysis o r d a t a fitti n g 5. A p ro bl e m i n vo l v in g th e u se of M a th CAD 6. A p ro bl e m i n vo l v in g th e u se of Microsof t Exce l 7. A se l f-a n a l ys i s o f th e st ud e nt 's s tr e n g th s a nd weak n esses w ith r ega rd s t o co n ce pt s le ar n e d in c l ass 8. R e fl ec ti o n s o n c h e mi cal e n g in ee r ing, thi s class, an d a n y th o u g ht s o n caree r c h o i ces TABLE2 Course Objectives for Freshman Course in Chemical Engineering Fundamentals At the e11d of this course, stude11ts should [] Und e r s t a nd co n ce pt s o f e n g in ee rin g ca lcul a ti o n s, in cl udin g s i g nifi ca nt fi g ur es a nd dim e n s i o n a l ana l ysis, a n d b e a bl e t o p e r fo rm unit co n ve r s i o n s [] Und e r s t a nd p rocess fl ows h ee t s, know h ow to draw and l a b e l a fl ows h ee t an d b e a bl e t o cl ear l y defi n e s ub systems wi thin pro cesses t o se t up c on se rv a ti o n e qu a ti o n s [] Und e r s t a nd co n serva ti o n o f m ass a nd b e ab l e t o so l ve m a t er i a l b a l a n ces on s t ea d y proce sses [] Under s tand th e rm o d y n a mi c qu a ntiti es s u c h as int e rn a l e n e r gy e nth a lp y, and h e at cap ac it y [] Und e r s t a nd th e co n ce pt b e hind di s till a ti o n a nd b e a bl e t o p erfo rm s impl e v apor-liquid e quilibria ca l c ul a ti o n s u s in g R ao ult 's L aw a nd H e nry 's L a w [] Und e r s t a nd co n se r v ation of e n e r gy a nd b e a bl e t o se t up s impl e e ner gy balan ces [] B e a bl e t o u se so ftwar e p ac k ages (fo r i n s t ance Mi crosof t E xce l or M a thCAD ) t o se t up a nd s ol ve e n g in ee rin g ca lcul a ti o n s an d aid in d a t a an a l ys i s [] Be a bl e to u se th e prin c ipl es and t oo l s l ea rn e d in thi s co ur se t o s olv e problem s not c overed in d e t a il as p a rt of th e co ur s e and to continue learnin g related materi a l a s ne e d e d in the futur e Fall 200 2 Many studies emphasize the "real-world" nature of performance assessment; student work is evaluated in a manner that is much closer to what will be encountered in the work environment. j ec ti ves ar e I.i s l e d in T a bl e 2. A widel y cited b e n e fit of portfolio a ss e ss ment i s an im pro ve ment in c ommunication s kills and creative thinking s kill s, particularl y in mathematic s and s cience two disciplines w h e r e s tud e nt s o f ten ha ve difficult y communicating their re s ult s. 1 3 4 91 Th ese assess ment technique s a l s o promote stu d e nt se lfassess m e nt a nd reflection. Thi s a llow s s tudent s to b eco me b e tter a t s el ec ting and pre se nting their best work which help s them gain confidence in their abili ti es 141 Studie s in colle g e ph ys ic s clas s e s 161 have s hown that port folio s m ay se r v e to h e lp s tudent s or g anize work and internal i ze co n ce pt s; ho weve r preliminary s tudie s of portfolio us e in undergraduat e c h e mi s tr y cour s e s 1101 indi c ate that there is a di sco nn e ct b e t w een s tudent performance o n exam s and in port fo lio entri es w ith r e gard to s pecific cour s e objective s. E du c at o r s in c hemi c al en g ineerin g may feel uncomfortable with th e c oncept of "s tudent s elf-refle c tion "; a fter all we are here to teach s tudent s, not to ask them how they "feel about engineering ri g ht ? We prefer hard numbers and are more accu s tomed to quantitative a s sessment methods But the util it y o f portfolio s h as b e en demon s trated in s everal science mathematic s, a nd en g ineering cour s e s. 1 4 6 1 0 1 6 1 Many state s requir e u s e o f portfolio s in all s ubject areas for grades four throu g h twel ve, 14 51 and portfolio s ha v e been s ucce s full y u s ed in under g raduate ph ys ic s, chemi s try and geol og y c our ses. 1 6 91 The chemical engineering program at the Colorado School of Mine s ha s relied heavily on portfolio as s essment for over a de c ade and Old s and Miller 141 give an excellent descrip tion of the u s e of portfolio s in the ChE curriculum. Both Alverno Colle g e 1151 and Ro s e-Hulman Institute of Technol ogy 1161 have implem e nted an electronic portfolio s y s tem for all s tudent s Preliminary results from the Rose-Hulman project indicat e th a t s tudent s find the electronic portfolio sy s tem easy to u s e and th a t u s e of a web-based s y s tem reduced some of the di s advanta g e s of conventional portfolio s, including stor age u s er acce ss, and availability. l 16 1 It i s important to keep in m.ind the difficulties and limita tion s a ss oci a ted with portfolio a s ses s ment. Portfolio s are not appropriate for a ss e ss ing factual knowledge or recall abili tie s ; thus, they should be used in conjunction with conven tional, quantitative assessment techniques. 1 91 Portfolios can be difficult to manage and time-consuming to grade, which 3 11

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Perhaps most importantly, research has shown that alternative assessment helps in the evaluation of students with various learning styles and educational backgrounds promoting excellence among a more diverse student population. makes them easiest to implement in courses with small to medium enrollments. Slaterf 9 1 and Wink r 101 have reported tech niques to extend the use of portfolios to large lecture courses however. Although there has been an emphasis on the use of portfo lios in upper-level "capstone" courses, such as senior design and the unit operations laboratory,r' 4 1 I focus on their use in introductory chemical engineering courses. I believe portfo lio assessment has unique benefits to beginning engineering students, as described further in the following paragraphs. GRADING PORTFOLIOS Implementing innovative assessment i s all well and good but how are we going to evaluate a nd grade student portfo lios? Since the portfolio entries have pre s umably been graded as part of a homework assignment or exam earlier in the se mester, it does not seem fair to me to place the students in "do ubl e jeopardy by basing the portfolio grade on whether or not the problems are correct. I chose to grade portfolios by giving equal weight to three criteria: Completeness and organization Quali ty and style of writing L eve l of thought, analysis and r eflectio n in eac h entry The first two criteria are easy to evaluate. The first refers to w h et h er students have all the required item s, including a table of contents and page numbers The sec ond criterion refers to writing s tyle and grammar, again fairly straightforward to evaluate. The third criterion is a little more s ubjective and requires so me planning on the part of the instructor I evaluated the level of thought and analysis by judging the extent to which each entry addressed two to three thought questions ," which are listed in Table 3. Students were given these questions at the start of the semester to help guide them through the self analys i s process. Slater1 9 1 recommends developing a "sc oring rubric, whereby the portfolio grade i s based on the extent to which s tudents demonstrate mastery of the required number of ob jectives. For example, you may require students to have at least eight entries, each of which i s related to a specific course objective. A simple scoring rubric could then be an "A" grade for demonstrating adequate mastery in seven or more objec3 1 2 tives (as evidenced by the portfolio entries), a B grade in five or more objectives, and so on. More detailed examples, developed for a unit operations course, are given by Old s and Miller; 1 1 41 see also the examples given by Slater_ 1 9 1 EXAMPLE Portfolios in the Introductory ChE Course In the spring of the freshman year, students at UMass take a course titled Chemical Engineering Fundamentals. The co ur se content covers units and dimensions mass balances s imple reactive systems (i.e., CSTR s and PFRs) and forms of energy. The typical enrollment i s 40-50 s tudent s, mo s t of whom are engineering major s with an interest in chemical engineering. After completing the fre s hman year require ments, s tudents can apply for admission into the chemi cal e n gineering major. Thus, many stude nt s in the ChE Fundamentals course are still unsure of their choice of major. I c ho se to implement portfolio assessment in this course as an optional assignment. The portfolio assignment could be u se d to replace a low grade on either of two midterm exams or a low homework grade, but not the final exam. Many in s tructors give students the option of"dropping" one low grade, so I did not feel that the use of portfolios would cause grade TABLE3 Questions for Student Self-Analysis in Portfolio Entries [] Wh at co nc e pt o r topic was involved with this problem ? What s kills did you u se in so lvin g it ? [] How did thi s problem help you l ea rn so m e thing new? [] Did you learn anything about yo ur se lf your thought proce ss, or your stre ngth s and weaknesse s as a re s ult of this activity? [] Wh a t stra tegie s did you u se? What were you thinking a s yo u worked the problem ? [] Would you do a nythin g diff e r en d y if you had more tim e? [] Can you describe any connection s b etwee n th e act ivity a nd other concepts s ubject areas, or r ea l life s ituati o n s? [] Doe s the problem represent a s p ec ial achievement for you a se n se of accomplishment at h av ing learned a particular co ncept or a se nse of improvement over time ? Chemi c al Engineering Education

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TABLE4 Student Evaluation Survey 0. Did you comp l ete the optiona l portfolio assig nm ent for this class? I. (If Yes" to the first question) I enjoyed completing the portfolio assignment. 2. ( If Ye s" to the first question) I felt that I l earned more abo ut myself and m y st ren g th s and weaknesses in c h emica l engineer ing and problem so l ving as a result of comp l et in g the portfolio 3. (If "Yes" to the first question) My written communication skills have impro ved as a r esu lt of completing the portfolio assign ment. 4. I feel th at the use of both qualitative (e.g written reports, oral r epo rt s, a nd portfolios) a nd quantitative ( e .g. exams a nd homework ) m et hod s of assessment were appropria t e for this c la ss. 5. I di s like qualitativ e m e th ods of assessme nt (e.g., wr itt en reports, oral reports, and p o rtfolio s) because I feel that they are subjective. 6 I feel that quantitative methods of assessment (e g. exams and homework ) are most appropriate fo r e n g in ee rin g and sc ien ce classes. 7. I would lik e to see qualit at iv e methods of assessme nt (e.g., written reports, ora l reports a nd portfolios) incorporated int o other sc i e nc e and e n g in eer in g classes. 3 C 8. 2 0 2 3 4 5 6 7 Question number Figure 1. Results from student surveys after co mplet ing course. Respon ses to questions are as follows: 1 Strongly agree; 2-Agree; 3 -No strong opin ion ; 4 Disagree ; 5 Strongly disagree Columns and error bars represent th e average and stan dard deviation for eac h question from a sample size of 13 surveys for questions 1-3 and 28 s urv eys for ques tions 4-7. Question numbers correspo nd to those given in Table 4. Fall 2002 inflation. On the first day of class, I gave s tudent s a handout de sc ribing the portfolio assignment, including the informa tion in Table s 1 through 3, and a s ummary of the grading protocol for portfolio s I also held a s hort class discus sion on what portfolio s are and why they were being used for this course. Students were required to have at least eight portfolio en trie s, which are li s ted in Table 1. Six of these entries were related to course objectives or outcomes, with a focus on objectives that are difficult to assess using conventional exam technique s (i.e., the u se of Micro soft Excel, data-fitting tech niques etc.). The se entries were expected to be copies of prob lems either from the homework or exams. Student s were re quired to attach a copy of their solution to the problem and a short (o ne paragraph to one page) explanation of why the problem was chosen In addition, two one-page essays (the last two items in Table 1 ) were required. I also handed out a li s t of questions to keep in mind as they wrote their portfolio entries (listed in Table 3). Finally, students were asked to organize their entries, num ber each page, and include a table of contents in the portfo lio Periodically throughout the se mester I reminded students to work on the portfolio assignment and to come see me if they had questions on the assignment. RESULTS Student Feedback and Assessment Survey The clas s enrollment was 41 s tudent s. Forty-one percent of the students ( 17 students) completed the portfolio assign ment. Grades on the portfoljo s were roughly in the low C to high "A" range. For most students, the portfolio grade was used to replace a low homework grade, but the difference in the final grade for the course with and without the portfolio was never more than a letter grade. I was somewhat distressed to find that several students counted on the portfolio to bring up their low homework grade and thus did not s pend as much time on the homework as signments throughout the se me s ter as I would have liked. I have since altered the portfolio guidelines to allow students to replace a low midterm exam grade, but not the final exam or a low homework grade. I found that grading of the portfolios was time consuming, but I did not feel that it took lon ger than grading exams. The time commitment is similar to that required for evaluating written reports and I made comments on all portfolios re garding grammar and writing style. Students were asked to complete a survey upon comple tion of the course, and the survey questions and student re sponses are given in Table 4 and Figure 1, respectively. 3 1 3

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Portfolios can be particularly useful for beginning chemical engineering students, who often do not have class projects that require them to synthesize concepts and present their results in a written format. These are preliminary results ; obviously, data need to be taken on a larger sample size before conclusions can be drawn. The results also may be biased due to wording of the survey questions. This needs to be addressed before definitive conclusions can be reached, and I am currently updatin g and redesigning the survey questions for future classes On th e whole, the response from students was quite posi tive. The strongest and most uniform response was to Ques tions 2 and 4 ; 86% of students who completed a portfolio strongly agreed or agreed that the portfolio helped them to learn more about themselves and their stre n gths and weak nesses in chemical e n gineering and problem solving, and 89 % of all students felt that the use of both quantitative a nd qualitative assessment methods were appropriate in the course. It remains unclear whether or not the portfo lio assignment helped students improve their written com munication skills. Several of the written comments that accompanied port folio entries were quite encouraging, and I have li s ted some of the more memorable comments in Table 5. There were also comments both positive and negative that were useful to me as an ed uc ator. Students were very honest about com ponents of the class that they liked and disliked. Most of these comments were made in response to Item 8, Table 1 reflections on chemical engineering and the class Examples of these comments are also given in Table 5. CONCLUSIONS AND RECOMMENDATIONS Portfolios can be particularly useful for beginning chemi cal engineering students who often do not have class projects that require them to synt h esize concepts and present their results in a written format. Interestingly students did not feel as though the assignment improved their written communi cation skills, but the portfolio assignment did see m to give these incoming students an opportunity to reflect on their a bilitie s and their choice of major. Portfolios can also be used to assess course objectives that are difficult to evaluate using traditional techniques Ba sed on my experience, I have some guidelines and rec ommendations for implementation of portfolios: Be prepared to read up on assessment tech niques. Several of the references listed contain 3 14 excellent examples of student entries and grading schemes_ r 4 5 9 11 1 I found the National Institute of Science Education Field-Tested L earn in g Assessment Guide website particularly useful. (Found at .) Be clear about expectations for portfolios at the start of the semester You may want to give students sample entries. Remind students that they should be saving hom ework sets and collecting problems for entries in their portfolio This is extremely important for freshman-level students who are still learning how to organize their coursework. If you allow students to u se a portfolio grade as a replacement, make sure their expectations are realistic One fabulous portfolio assignment will not pull a final D grade up to an "A"-as I mentioned above, the overall effect on the final grades in the course was never more than a letter grade. It is worth noting that implementing portfolio s as a replacement" for a poor exam could allow a student to bring a failing grade up to a D ." Instructors need to decide for themselves whether this is permissible and to develop their own guidelines accordingly For examp l e, I specified that if stude nt s received a zero grade on an exam or homework due to academic dishonesty this grade could not be replaced under any circumstances. One could imagine extending this rule to any failing grade to prevent the above scenario. Finally, I found that it was problematic to allow students to replace a low homework average with the portfolio grade. Create a grading sc h eme that places emphasis on what you think is most important whether this is good writing, clear organization, self-reflection, Chemi c al Engine e ring Edu c ation

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TABLES Sam pl e Comments from Student Portfolios New Strategies of Problem Solving (/tem 1) a11d Self-A11alvsis (Item 7) I now have more co nfid ence knowing th a t if I can' t so l ve a problem u s in g th e accepted method of so lut ion, I will be able to come up with a new method perhap s so methin g nonroutine in order to so l ve the problem ." ''T hi s problem showed me that I s hould have more confidence in my ability to find a sol uti o n when it doesn t s imply pre se nt itself after a se ri es of steps." I could apply things I had l ear n ed in a comp l e t e l y different context to ot her s ituation s. This i s act uall y quite comforting, as I've always wondered if I' II be ab l e to u se the thing s I learn now l ater on in life when I might actually n eed them ." I've had trouble [with] time management as I have usually been ab l e to understand the problem s but h ave n ot left m yse lf enough time to gat h er it all in a presentabl e format." My weakness is that every time I hit a wa ll I tend not to do anything about it. I can on l y blame myself for not attempting [but] I already mad e m y c hoi ce in s taying in thi s major and it i s all up to me in keeping th a t c hoi ce Reflections 011 Chemical E11gi11eeri11g and The Fu11dame11tals Course (/tem 8) All in all I enjoyed the class, I enjoy b ei n g a c h e mi ca l engineering s tudent and I l ook forward to the da y when I am employed as a fab ul ous c h emical e n gi n eer I dislike comp ut ers a nd I dreaded u s in g them for this class. I probably wo uld h ave stuck w i th thi s major if it were not for MathCAD and Excel. I do not think being taught [MathCAD] for one cla ss period i s enoug h c l ass time. Since th e class i s a lm os t over, I feel a real se n se of accomplishme nt. I know that it is only a fre s hm an level cla ss, but I put a great deal of effort a nd time into the c l ass ... lt makes me pro ud to say that I'm a c h emica l enginee rin g m ajor when people ask me ." I fee l lik e I' ve go tt e n a mu ch better idea about what c h emica l e n g in ee r s do through the vario u s assignments a nd from the ora l presentations of my peers. I fee l that we did not [spend] much time on u s ing the compu t er." B efo r e Laking this class I wasn't positi ve that chemical e n gineering was the right major for me I fe lt that perhaps I would n ot be ab l e to hand l e th e workload or grasp all of th e material that I needed to know. Howe ve r I now feel that I am actua ll y ca pab l e of becomin g a n e n g ine er." I l ove go in g t o my c h e mi ca l e n gineering classes, they are the only o n es that I don t purposely skip." A s a result of thi s class I am much more confident about my choice of major and the preparation ii will give me to s ucceed in the career I want to pursue. Fall 2002 or assessment of a s pecific course objective. Make s ure yo ur grading sc heme i s clear to the s tudents at the s tart of the semester. ACKNOWLEDGMENTS I would like to ac knowledge my Ch e mical Engineering Fundamentals s tudent s for participating in this work Pro fessor Donald Wink (C hemi stry, University of Illinois at Chica go) provided me with a copy of hi s recent ACS pre sentatio n on portfolio assessme nt and s uggested several of the works cited in thi s article, which was greatly appreci a ted The manu sc ript reviewers, particularly Reviewer #3, made severa l useful and constructive comments. Mrs. Kanak Bhatia (Ed.D. candidate, University of Delaware) a l so s uggested several helpful references and made com ment s on the manu sc ript. REFERENCES I Feuer M.J and K Fu lt on, The Many Faces of Performance A sess ment ," Phi D e tra Kappan 74 473 ( 1993 ) 2. Slater T.F. Perform a nce Assessmen t ," in Field-Tested Learning Assessment Guid e, Nationa l In stit ute of Science Education (2000) (access ed 6/6/02 ) 3. H e rm an, J.L. P.R. Ashbach and L. Winter s, A Pra ctical Guide to Alternative Assessment Association for Supervision and Curricu lum D eve lopment A l exa ndria VA (1992) 4. Lambin D.V ., and V.L. Walk e r Planning for Cla ss room Portfolio Assessment," Arithmetic Teacher, 41 3 I 8 (1994) 5. Abruscato J ., "Ea rly R es ults and Tentative Impli ca ti o n s from th e Vermont Portfolio Project ," Phi D e lta Kappan 74 474 ( 1993 ) 6. Slater, T.F. The Effectivenes s of P ortfo li o Assessme nt s in Science ," J. Coll. Sci. T eac h. 26 3 15 ( 1 997) 7. Shaeiwitz J.A. Outcomes A ssess ment: It s Time H as Come ," Chem. Eng. Ed. 33 (2), I 02 ( 1 999) 8. DiBia s io D .A., "Outco m es Assessment: An Unstable Proces s?" Chem Eng. Ed 33 (2), I I 6 (1999) 9. Slater T.F., Portfolios ," in Fi e ld-T es ted Learning Assessment Guide Nat i o n a l In stitute of Science Ed u catio n (2000) < htp: // www.wee r. w i sc.e du / ni se /cl 1/flag/cat/perfass/perfass l htm > (ac cessed 2/ I 5/02 ) 10 Wink D.J ., "Portfo li o Assessme nt in Large Lecture Class ," Ab s tra cts of Pap e rs of th e ACS, 220 49 ( 2000) 11. Johnson, J M ., Portfolio A ssess ment in Mathematics: Lessons from the Field ," Th e Computing T eac h e r, 21 22 ( 1994 ) 1 2. Adamchik, Jr. C.F. The De sig n and Assessment of Chemistry Port folios ," J. Chem Ed., 73 528 (I 996) I 3. Ph e lp s, A.J. M.M. LaPorte a nd A. Mahood, Portfo l io Assessment in Hi g h School Chemistry : One Teacher's Guidel i ne s J Chem. Ed., 74 528 ( 1997 ) 14 Old s, B.M ., and R L. Miller "Us in g P ortfolios to Assess a ChE Program ," Chem. Eng. Ed., 33 (2), I 1 0 (1999) 15 "A l ve mo 's Diagno stic Di g ital Portfolio ," (accesse d 6/6/02) 16 Ro ge r s, G.M., and J. Williams Building a Better Portfolio ," PRISM, 8 (1999) 0 3 15

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~SQ curriculum ) -----------ASPECTS OF ENGINEERING PRACTICE Examining Value and Behaviors in Organizations RAMON L. ESPINO University of Virginia Charlottesville, VA 22904-4741 S ince 1995 the School of Engineering and Applied Sci ences at the University of Virginia has offered an elec tive course that examines human values and practices in engineering organizations The course is available to all fourth year engineering students and is taken by 40 to 50 stu dents each year. It i s taught by the Brenton S. Halsey Visiting Professor of Chemical Engineering who is s elected annu ally from individuals with high-level experience in ind u stry. Support for the Chair comes from a generous endowment by The James River Corporation in honor of its founding CEO, Brenton Halsey Previou s Halsey Professor s and their affilia tions are given in Table 1. The details of the course content and execution are left to the discretion of the Halsey Professor but it s core objective i s to provide engineering students with s ignificant insight into the professional and nontechnical aspects of engineering prac tice. The intention is to better prepare the University of Vir ginia engineering graduates to succeed in the business and technical world that they will be entering after graduation. This paper describes the course materials assignments and assessments for the spring semester of 2001, which is repre sentative of recent offerings DEVELOPING THE COURSE The teaching experiences of previous Halsey Professors contributed significantly to the current course content. Al though the objectives have remained the same, there is now more emphasis on the students reading and analyzing infor mation prior to class. Thi s information is generally in the form of Harvard Busine s s School (HBS) Ca s es and Notes The resu l t of this approach is more in-depth discussion in class I built the course s yllabus around the HBS Cases and Notes. Harvard Business School Publishing l 1 1 offers an Index of Cases and Notes available for purchase. I suggest one HBS Th e objecti v e of the course wa s t o increase s tudent a w areness of th e non t echn ic al c ompet e ncies the y shou l d pos s e ss in ord er t o s uc c eed in th e w o rk w orld Case and two HBS Note s per week, requiring about nine hours of homework (reading and writing a summary) per week Lectures to reinforce and elaborate upon the major themes of the course are strongly recommended We have found that many of these should be given by outside speakers from busi ness and government in order to emphasize the broad appli cability of the concepts being discussed. Finally additional reading material can be used to round out the course. COURSE STRATEGY AND TEACHING METHOD I developed the syllabus to follow the chronological order of the professional and business career of an engineering graduate. Selecting the first employer is the starting point followed by early career assignments and culminating with the complex organizational personal and business challenges of a senior manager. HBS Cases provide a well-written platRamon L. E spino received his BS degree from Louisiana State University in 1964 and his Doc tor of Science degree from the Massachusetts Institute of Technology in 1968 both in chemi cal engineering He joined the faculty at the Uni versity of Virginia in 1999 after twenty-six years with Exxon Mobil His research interests are in fuel cell technology and methane conversion to clean fuels and chemicals Copyrigh t C h E Div i s i on of ASEE 2 00 2 3 16 Ch e mi c al En g in ee rin g Education

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form that describe s s pecific s ituation s with no dir ec t answers or outcomes. The additional reading assig nm e nt consisted mainly ofHBS Notes, which provided a conceptual framework for th e s tu dents to a nalyze the cases with so m e knowledge of b as ic co cepts on bu si ne ss practice s, interpersonal behavior, and hu man values. The s tudent s were all ex pe c t e d to read two book s: Getting to Yes 1 21 and The Seven H abits of Hi ghly Effective P eo pl e 131 The cla sses were de s igned to be hi g hly interactive wi th the bulk of the time s pent di sc u ssi n g the HBS Cases and Note s. In addition, there were lecture s on S ty l es of commun i c ating and int erac ting Ind ividual com p etencies Year 1995 1 996 1997 1 998 1 999 2000 200 1 2 002 Title Kevin Simp so n E li zabe th Fi s h e r Lisa Benton TA B LE 1 Ha l sey Professors at t h e University of Virginia Name Co111pa11y/Positio11 N H Prater Mobay/CEO J M Trice Jr Mon sa nto / Director-HR R .A Moore Jr Internation a l P aperN P D L. Ashcraft Te mpl e-Is l a n d/VP J.D. Stein BASF/CEO V.A. Ru sso Scott PaperNP R L. Esp in o Exxon/R&D Manager A. R Hir s ig ARCO Chemical/CEO TABLE2 HBS Cases Topic Int e r viewing a nd se lectin g yo ur employer Dual career decisions Conflicts in yo ur fir s t assignment Amelia Rodgers Anne L ivi n gs t o n Te c h Transfer a t... Thur good Mar s hall... Conflic t in a diver se ... Fir s t gro up-l ea der assignment C h a n gi n g job s and new leader s hip role Conflict betwe e n development and production Leader of middle-level m a n agers H arassment and soc i a l conflict David Fletcher Hiring your ideal busine ss team MOD IV Product.. Effect i ve teamwork PPG-D eve l op in g... Ri s k s a nd r ewards of empowe rm e nt John Smithers at Sigtek Leading a quality process initiati ve J enssen Shoes Managing a diversity co nfli c t Coming Glass W orks Leadership du rin g a bu si n ess downturn Fa/12002 Conflict management Teams and team performance Strategi c planning D eve l op in g a personal ca r eer plan Six outside s peaker s led discussions on various aspects of their bu si ne ss careers The se included Managing fami l y and business lif e H ow to improve leadership skills Conflict management and negotiation Working wit h consult in g com pani es Attending business sc ho ol R einfo r cing o r ganizational va lu es A d etai l e d outline of th e co ur se is pre se nted in Table 3 ( next page) The two 75-minute class p erio d s each week allowed adequate tim e for di sc u ss ion of the Case and the Notes as we ll as for the lecture s give n by the Hal sey Professor or by invited s peaker s LEARNING THROUGH THE HBS CASES Th e Case Method is b ase d on r ea l-life s ituation s that rep resent the kind of challenges that engineers and manager s are lik e l y to face durin g their work life The cases helped s tudent s s harpen their analytical ski lls their ability to com munic a te clearly a nd forcefully, a nd mo s t import a ntly helped them to de ve lop their problemso l v ing abilities. Table 2 indi ca te s the topi c b ei ng di sc u sse d in each case. The st udent s were assigned the HBS Case a week in ad va nce They were required to write a 3-to-4-page summary of their assessme nt of the s ituation and their propo se d so lution (s) Th ey were a l so asked to document the key learn ings they had d erive d from the case It was gratifying to ob se rve th e ir increa s ing so phistication in analysi s and problem so l v in g durin g the course of the se me s ter Ther e were a number of intere s ting observations that re s ulted from discu ss ion of the HBS Ca ses. The students paid a lot of attention to the interper so n a l style of the protagonist s a nd were quite se n si ti ve to antisocial behavior. They were to m y s urpri se, expecting to experience s uch behavior in the workplace This applied eve n to hara ss ment situations. An other cla ss-wi de a ttitude was to view most conflicts as rooted in poor communication, and it took a lot of di sc ussion for them to see poor co mmunication s imply as the external mani festation of a more profound conflict. LEARNING KEY CONCEPTS THROUGHTHEHBSNOTES The course provides an introduction to a number of critical competencies engineer s need in order to succeed in organi zations The se were provided mainly through reading and di sc us s ion of HBS Note s. The Notes were also given to the s tudent s a week in advance of the clas s discu ss ion There 3 1 7

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was a close coupling between the teaching s in the Note s a nd the Case being discus s ed in parallel. This worked well as confirmed by the frequent reference s to concepts pre se nted in the Notes in the students' analyses of Cases It is unr ealis tic to expect the stu dent s to fully master all the concepts, but it was clear that they became very aware of their importance. The hop e i s that when they are confronted with s imilar situa tio n s, they wi ll refer to these Note s for guidance. We di sc u sse d the difference s between management and l ea d ership and the many comp l ex and ambiguous issues that mo st mana ge r s face. We s p e nt very productive time on the influenc e of culture a nd hi s tory on s ubtl e but important dif ferences in manager s' beh av ior in th e USA, Europe, Japan, India, China and Latin America. Ha v in g some students from outside the USA gave immediacy to these discussions. A s expected, i ss ue s of bu s ine ss ethics grabbed the s tudent s' attention and e li cited stro ng and quite varied opinions. In fact, I was s urpri se d at the diver sity of viewpoints, how s tron g ly they were held and that ther e was no correlation with ge n der race, or economic back gro und. TABLE3 Course Outline Week 1 H omework/Class Di scuss i o n HBS Notes on "Learni n g by the case m e thod an d How t o c hoo se a l eade r s hip p attern" Lecture Indi v idual and team co mp ete n c i es Week2 H omework/Class Dis c u ss i o n HBS Notes on "U nd e r s t a ndin g context" a nd "Co nfli ct in g responsibilities HBS Case K evin Simpson Lecture Styles o f co mmuni ca tin g and int eract in g Week3 H omework/C la ss Dis c u ss i on HBS Notes o n M a n ag in g yo ur career HBS Case "E lizab e th Fisher Lect ur e In v it e d Speaker-Managing family a nd busine ss life Week4 H omework/Class Discussion HBS Notes o n "Power dynamics in organizations" HBS Case "Lisa B e nt o n Lect ur e Th e seve n h a bit s of hi g hl y effect i ve people WeekS H o m ewo rk/ Class Dis c u ssion HBS Notes o n "Ma na gi n g your bo ss" and Exercising influ e n ce" HBS Case "A melia Rod ge r s" Lecture Invit e d Speaker-Improving yo ur l ea d ers hip ski ll s Week6 H o m ework/C l ass Dis c ussion HBS Notes o n "Eva lu at in g an action plan an d Understanding co mmuni ca ti o n s in one-to-o n e relationships HBS Case "A nn Livingston a nd P ower Max Systems" Lectur e The seve n habit s of hi g hl y effec ti ve people Week7 H o m ework/C l ass Discu ssion HBS Not es o n B eyo nd the myth of a p erfect mentor and Man ag in g n e twork s" HBS Case 'Techno l ogy tran s fer a t a d efe n se co ntra c t o r Lec tur e In v it ed Speaker-Conflict management a nd n egot iati o n Week8 H o m ework/Class Di scussion HBS Notes o n Power dependence a nd effective m a na ge m e nt a nd Influ e n ce t ac ti cs" HBS case Thurgood Marshall High School" Lec tur e Co nfli ct m anagement s t y l es 3 1 8 Week 9 Hom ewo r k/Class Dis c ussion HBS Notes o n Int egr it y m anageme nt a nd Managing a ta sk -force HBS Case Managin g conflict in a diverse environment" Lecture Invited Speaker-Workin g in a co n s ultin g company Week 10 H omework/Class Discussion HB S Notes o n Barriers and gateways t o co mmun icatio n s" and "On goo d co mmuni ca ti o n s" HBS Case D av id Fletcher Lec tur e In v it ed Speaker-Should yo u ge t a n MBA? Week 11 Hom ewo rk/Class Dis c us s ion HBS No t es o n 'T h e power of talk" and The disc iplin e of team s" HBS case Mod IV prod u ct development t eam" Lecture Getti n g to Yes Week 12 H omework/Class D iscussion HB S Notes o n "The c hall enge of comi trn en t and "A n ote o n high-commitment wo rk systems" HBS Case PPG-Developing a se l f-directed workforce" Lec tur e Strategic planning Week 13 H o m ewo r k/Class D iscussion HB S No t es o n Organization structure ," Organization effec ti veness," and 'T h e c h a ll e n ge of c h ange HBS Case John Smit h e r s at Sigtek" lee/Lire Invited Speaker-Reinforcing o r ga ni zatio n a l va lu es Week14 H omework/Class Di sc u ssion HB S Notes o n Bu s in ess e thic s: th e view from th e trenches ," "Eth i cs w ith o ut a sermo n ," and Ways of thinking a bout a nd across differences HBS Case J e n sse n Shoes Lec tur e Developing a personal career pl a n Week 15 F in al H omework: A personal career plan Analysis of the Most ad mir ed co mp a n y ... Group report of HBS Case "Co rnin g Glass Works Chemical Engineering Education

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I was disappointed in the students lack of interest in learn ing about team building, task-force management, and build ing commitment in the workplace The students felt that they knew about these topics and that they were already profi cient. I do not believe I ever convinced them there was a lot for them to learn and that success in these areas requires skills they actually did not possess. OTHER FEATURES O F TH E CO URS E The students were given a three-part final homework as signment. One element was a personal mi s sion statement with an associated five-year career development plan The plan could also include other facets of their life such as family, health, religion, community involvement, etc. For each of the elements they were encouraged to follow a disciplined aping change. The companies chosen reflected the students' wide range of career interests and included among others, enter tainment communications financial, computer technology, oil and chemicals, consumer products. They were asked to suggest the future direction the company needed to take to improve performance. A majority suggested expanding glo bal reach and more technology investment, while only a few focused on improving cost competitiveness. STUDENTASSESSMENTANDFEEDBACK During the semester the students were asked to provide feedback on course content and to assess its effectiveness The data are summarized in Table 4 and show that the major ity of the class found the course very useful. They rated the discussions of HBS Cases and Notes my work experiences and personal stories, and the outside speakers the highest. proach that included short-term (6 months), midterm (2-3 years), and long-term (5 years) plans. For each time period, they were asked to state goals and specific objectives and to define strategies and action steps. They were initially unen thusiastic about this task but the final product indicates that TABLE4 They were less enthusiastic about the other reading mate rial perhaps because they were not used to this amount of reading in an engineering Course Assessment ot Useful 2 3 F e bru a r y % M arc h % A pr i l % they thought hard about it and put together a realistic and credible plan. The second element of the final homework was a team project. Groups of four students were asked to analyze a fairly complex HBS Case of a Coming Glass Works Division un dergoing a change in management during a bu s iness down turn. They were asked to devise strategies and specific action plans for the division as well as a self-assessment of their team petformance. The reports indicated a wide range of team performance, with the key problems being an inability to agree on an action plan finding time to work together and uneven participation by team members. This assignment came at the very end of the semester which was too late to refute their earlier assertions that teamwork wa s something they knew how to handle." The third element of the final homework was an analysis of a company's performance during the last four years. Each student selected a company from those reviewed by Fortune Maga z ine in its annual publication of America s Most Ad mired Companies." L 4 5 l They were asked to analyze the per formance of the company they chose to identify reasons for any change in rankings during the four-year period, and to forecast future trends. The objective of this exercise was to allow the students to apply to a specific company-wide situation what they had learned about effective management, leadership, and managFall 2002 4 5 6 7 8 Very Userul 2 5 45 3 0 29 5 0 1 8 2 5 37 3 5 course. SUMMARY The objective of the course was to increase student aware ness of the nontechnical competencies they should possess in order to succeed in the work world. It is unrealistic to ex pect that at the end of a semester they would have mastered all these competencies but it was evident that they were much more sensitive to the importance of such skills and had grasped the essentials. Also, they were left with an excellent collec tion of HBS Cases and Notes that could serve them well when confronted with similar situations. As I frequently indicated to them, I wished that I had such a learning experience in my engineering schooling and early career. The main reason for writing this article is to encourage other colleges and universities to consider offering a course along the general outline that I have described. I also encourage experienced business practitioners to teach such a course. The Halsey Professors are unanimous: it was an exciting and grati fying experience to share what you have learned with the next generation of engineering and business leaders REFERENCES I Har v ard Bu s ine ss School Publi s hin g, 60 Harvard Way Boston MA 0216 3 2. Fi s her R. W. Ury and B. Patton, G e llin g to Y es 2nd ed. Penguin Book s 3 Covey S.R. Th e 7 Habits of Highl y Eff ec ti v e People Simon and Schuster 4. Fortun e Maga z in e, March 6 1997 5 Fortun e Maga z ine February 21 200 I 0 319

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I N D E X GRADUATE EDUCATION ADVERTISEMENTS Akro n U ni ve r s i ty of. ........ . .. .. .... .... .......... 32 1 Iowa Sta t e U ni ve r sity ................ .. ... .... .... .. 360 P e n sy l vania State U ni vers i ty ......... .............. 395 A l a b a ma Unive r sity of .................... ........... 322 Jo hn s H o p k in s Un i vers i ty .. .... .. ... .... .......... 36 1 Pi tts bu rg h Universi t y of ................ ............. 396 Alabama Hun tsv ill e; U ni ve r sity of.. .... .. .. 323 Kansas, Unive r sity of ...................... ...... .. .... 362 Po l ytechnic U n ivers i ty .. .... .. .. ... ....... .. .. 397 A lb e rt a, U ni versi t y of .. . .. .. .. ... .. .. .... .. 324 Kansas S t a t e Unive r s i ty ............. .. ... ... . .. 363 Princeton Univers i ty ............... ..... .. . . .. .. .. 398 Ar i zo n a, U n ive r sity of ..... .. .. .... .. ... .. .. ... ...... 325 K ent u cky, U ni ve r s i ty of .................... .... .. .... 364 P u rd u e Univers i ty .. ...... .. .. .. .... .. .. .. .. 399 Ar i zo n a S t ate Un i versity ..... .. .. . .. .... ......... 326 La m ar Un i vers i ty .. .. .... .. ..... .. ... .. .... .. .... 430 R ensse l aer Po l y t ec hni c I n s t it u te ... .. .. .. .. 400 A uburn U ni vers i ty .. ... .. .. .... .. ......... .. .. 327 L ava l U ni ve r s it e .................. ... ........ .. .... .. 365 R h ode I s l a n d, Un i ve r s i ty of.. .. ... .. .. .... .. 435 B ri g h a m Y o un g U ni vers it y .......... .. .. .. .. 427 Le h igh U ni ve r s i ty ..... ......... ............... .. .. ... 366 Rice University .. ....... .. .. .. ... ..... .. .. .. .... .. .. 40 I B riti s h Co lumbi a, U n ivers it y of ...... .. .. ... 427 Lo u isiana, Lafaye tt e: U ni ve r s i ty of .. .. ... 367 R oc h es t er Univers i ty of .. .. .. .... .. .... .. .. 402 B row n U ni vers i ty .. .. .. .. .. .... .. .... .... . .. .. 44 1 Lo u isi an a Sta t e Un i vers i ty ........ ... .. ... ... .. 368 R ose Hu Im an In s t i tut e of Tec hn o l ogy ....... .. 435 B u ck n e ll U ni vers i ty ............. ... ....... .. .. .... .. 428 Lo ui s i ana T ec h U ni ve r sity .. .. .. ... .. ......... 430 Rowa n Univers i ty ..... .... ..... ........ . ............. 403 Ca l gary, U ni ve r s i ty of .................. .. .. .. ... .. 328 Lo ui sv ill e, U ni ve r sity of .. .. .. .. .......... ... .. .. 43 1 R ut gers University .... ..... .. .. .... .. .. .. .. . .... .. .. 404 Ca li fo rni a B erke l ey ; U ni ve r s i ty of .............. 329 Manha tt an Co ll ege ................. .. ........... .... .. 369 Saska t c h ewan, U n ivers i ty of .. .. ... .. .. .. .. 436 Ca li fo rni a, D avis; U ni vers it y of ... .. .. .. .. 330 M ary l a nd U ni ve r si t y of ................. . .. .. 370 Singapo r e, Natio n a l Univers it y of .. .. .. .... 405 Ca li fo rn ia, Ir v in e; Un i ve r si t y of ... .. .. .. ...... 33 1 M ary l a nd B a ltim o r e Co un ty; U ni ve r s i ty of 37 1 So uth Caro l ina, U n iversity of .. .. .. .. .. .... .... 406 Ca li fo rni a Los A n ge l es; U n iversi t y of ..... : ... 332 Massac hu se tt s, L owe ll ; U ni vers i ty of .......... 44 1 So u t h F l o rid a, U ni versity of ......................... 437 Ca l ifo rn ia, R i ve r s id e, U n iversity of ......... .... 333 Massac h use tt s, A mh e r st; U ni ve r s i ty of .. .. .. 372 So uth ern Ca li forn i a, University of .. .... ..... .. 436 Califo rni a, S a n ta B arbara; Un i versity of .. .. 334 Massac hu se tt s In st i t u te of T ec hn o l ogy .. .. ... 373 Sta t e U n ive r sity of New York .... .. .. .. .. .. .. ... 407 Ca li fo r nia In sti tu te of Tec hn o l ogy ... .. .. 335 McGi ll U ni ve r sity ................ .. .. .. ................... 43 1 Stevens In sti tut e .. .. .. ... .... .. .. .. ... .. .... .. .. 408 Carneg i e-Me ll o n U ni ve r sity ......... .. .. .. .. .. 336 McMas t er U ni ve r s i ty . ....... .. .......... . .. .. 374 Syd n ey, U n ivers i ty of ... .... .. ......... ... .. .. .... 437 Case W es t e rn R ese r ve U ni ve r s it y .. .... .. ... . 337 M ic h iga n U ni ve r s it y of ......... ....... .. .. .. 375 Syrac u se U n ivers i ty of .. .. . .. ... .... .. .. .... 438 C in c inn a ti U ni ve r sity of .. .. .. ... .. .. .... .. . 338 Mic hi gan St a t e U ni versi t y ................. .. .. 376 Te nn essee, U ni ve r s i ty of .... .. .. .. .. .. .... .. 409 C it y Co ll ege of New Yo r k .. .. .. .. .. ..... .... 339 M ic h igan T ec hn o l ogica l U ni ve r s i ty .. .. .. ... 432 Texas, University of ... . .. .. .. .. . .. .... .. .. 4 1 0 C l eve l a nd S t ate Un i versity .. .. .. ... .... .. ..... 340 M inn esota, U ni versity of .. .. .. .. .... .. .... .. 377 Texas A&M Un i ve r s i ty .... .. .... .. ... .. .... .. ... 4 1 1 Co l ora d o B o uld er; U ni vers i ty of .. .. .... .. .... 341 Mississ i p pi S t a t e U ni versity .......... ....... .. ... 378 Texas A&M U n ivers i ty, Ki n gsville .. .. .. . 438 Co l ora d o S c h oo l of M in es ............. .. ..... ... 342 Misso u ri Co lumbi a; U n ive r sity of ............... 379 Toledo Unive r s i ty of .................................... 4 1 2 Co l ora d o St a t e Unive r s i ty ............. .. .. .. ... 343 M i sso u ri R o ll a; U n ive r s i ty of ............ .... .. 380 T u f t s Unive r s i ty .. .. .. .... .. .. ... .. .. ......... 4 1 3 Co lumbi a U ni ve r s i ty .......................... ........ 428 Mo n as h U ni ve r s i ty .. .. ... .. .. .. .... .... ............ 432 T ul a n e U ni vers i ty ................ ............. ......... 4 1 4 Co n nec ti c ut U ni ve r s it y of ................. .... ...... 344 Mo nt a n a S t a t e Un i ve r s it y .. .. .... .... ..... .. 433 T ul sa, Unive r s i ty of ........................... ......... 4 1 5 Co rn e ll U ni ve r s i ty ...................... ...... ........ 345 Nebraska U ni ve r sity of .. .. .. .. ...... ............ 38 1 U t ah, U ni ve r sity of .. . .. .. .. .. ....... ......... 439 D ar tm o uth Co ll ege .. ......................... .. ......... 346 Nevada, R e n o; Un i vers i ty of .. .. .... .. ........... 433 Va n de r b ilt U n ivers i ty ......................... ......... 416 De l aware U ni ve r s i ty of .... .. .. ........ .. .. ........ 347 New J ersey In sti tu te of Tec hn o l ogy .. .. .. .. 382 Villanova Un i vers i ty ......... .. .. ...... .... .. .. .. 439 D r exe l U ni vers i ty .................... .. ....... .. ..... .. 348 New Mex i co, U ni vers it y of .. .... .. ... .. ........... 383 Virg i n i a, U n ive r si t y of ...................... ........... 4 1 7 Eco l e P o l y t ec hn i qu e M o n trea l .. .. .. .... ... .. 349 N ew M ex i co St ate U ni vers i ty .......... .. ... .... 384 Virg ini a Tec h .................... .. .. .. ... .... .. .... .. 4 1 8 E n g in ee rin g R esearc h Ce n te r ............. ......... 429 New So uth W a l es, U ni versity of .......... ... ... 434 Was hin g t o n U n ivers it y of .. .. .......... ......... 4 1 9 F l o rid a, U ni ve r s i ty of . .. .. .. .. ... .. .. .. .. 350 No rth Car olin a St a t e U n ive r sity ................ 385 W as h i n g t o n S t a t e U ni versi t y .. .. ... ... ... ..... 420 F l o rid a A&M/F l or id a Sta t e Un i ve r s it y ....... 351 No rth D ako t a, U n ive r s it y of .. .. ...... ... .... 434 Was h ing t o n U ni vers it y ....................... ......... 42 1 Flo rid a In s titu te of T ec hn o l ogy ... .. .. .. .... 352 Northeas t e rn Un i ve r s i ty ...................... .. .. .. 386 Wa t e rl oo, Unive r s i ty of .. .. .. .. ... .. .... .. .. 440 Georg i a I n s titut e of T ec hn o l ogy ...... .. .. .... 353 No rth wes t e rn U ni vers i ty ....... .. . .. . .. 387 Wayne State U n iversity .. .. .. .. ... ... ... .. .. 422 H o u s t o n Un i ve r s i ty of .. .. .. .... .. .. .. .. .. .. . 354 Notre Dame U ni ve r sity of ..... ...... .. .. .. .. 388 West Virgi n ia University . .. .. .. . .. .. .. .. 423 H owar d Univers i ty .. ..... .. .. .. ... .. .. .. 355 O hi o Sta t e U ni vers i ty .............. ...... .. .. .. .. 389 Wisconsin Unive r s i ty of . .. .. .. .... .. .. .... 424 I d ah o U ni ve r si t y of ....... .. .. .. .. .. ........ .... 429 O hi o U ni ve r s it y ............................ . .. .... .. .. 390 Wo r ces t e r Po l y t ec hni c In s ti t ut e .. . ............. 425 Illin o i s C h icago; U ni ve r s it y of .... .. ..... 356 O klah o m a U ni ve r s it y of ........ .......... ....... 39 1 Wyoming U ni ve r sity of ..................... ...... .. 440 Illin o i s, U rb a n a-C h am p a i g n ; U ni ve r si t y of 357 O klah o m a S t a t e Un i ve r s i ty .. .. .... .. .......... 392 Yale U ni versi t y ...................... .. . .. .. .. .. 426 Tllin o i s In s titu te of Tec hn o l ogy .......... .. ...... 358 O r ego n Sta t e University .... .. .. .. .. .. .. ..... 393 Iowa U ni vers i ty of .............................. ........ 359 P e nn sy l va ni a U ni ve r s i ty of .. .. .. . .. ... .. .. .. 394 320 Chemical Eng i neering Education

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Graduate Education in C hem i c al E ngineering Teaching and res earc h assistantships as well as industrially sponsored fellowships available up to $20,000 In addition to stipends, tuition and fe es are waived. PhD students ma y get some incentive sc holarship s. The deadline for assistantship applications lS April 15th For Additional In formation, Write G.G.CHASE Mu ltiph ase Pro cesses, Fluid Flow l nterfacial P henomena, Filtration Coalescence H.M.CHEUNG Nanocomposite Materials, Sono c h emica l Proc essi ng Pol y meriz a tion in Nanostrucrured Fluid s, Supercritical Fluid Pro cessi n g S.S. C. CHUANG Catalysi s, R eac ti o n Engineering Environment a lly Benign Synthesis J. R. ELLIOTT Molecular Simul atio n Ph ase Behavior Ph ysica l Prop e rti es, Pro cess Modeling E.A.EVA s Materials Pr ocessi ng and CVD Modeling Chairman, Graduate Committee L. K.JU Bi oc h emica l E n g in eeri n g, Environmen t al S. T.LOPINA Bi oMate ri a l Engineering a nd P o l yme r Engineering B .Z.NEW BY Surfa ce Modifi ca ti o n P olymer Thin film H.C.QAMMAR Nonlinear Control Chaotic Pro cesses P. WANG Bi ocata l ys i s a nd Bi omateria l s De p art m ent of Chemical Engineering The University of Akron Akron, OH 44325-3906 Phone (330) 9727250 Fax (330) 972-5856 www.ecgf. uakron. ed u/~ch e m Fal/ 2002 32 1

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THE UNIVERSITY OF ALABAMA Chem i cal Engineering A dedicated faculty with state of the art facilities offer research programs leading to Doctor of Philosophy and Master of Science degrees. Research Areas: Biomaterials, Catalysis and Reactor Design, Drug Delivery Materials and Systems, Electrohydrodynamics, Electronic Materials, Environmental Studies, Fuel Cells, lnterfacial Transport, Magnetic Materials, Membrance Separations and Reactors, Microelectro Mechanical Systems, Nanoscale Modeling, Polymer Processing and Rheology, Process Dynamics, Self-Assembled Materials, Suspension and Slurry Rheology, Transport Process Modeling For Information Contact: Director of Graduate Studies Department of Chemical Engineering The University of Alabama Box 8 7 0203 Tuscaloosa, AL 35487-0203 Faculty: G. C. April, Ph.D. (Louisiana State) D. W Arnold, Ph.D. (Purdue) C. S. Brazel, Ph.D. (Purdue) E. S. Carlson, Ph.D. (Wyoming) P. E. Clark, Ph.D. (Oklahoma State) W. C. Clements, Jr., Ph.D. (Vanderbilt) R. A. Griffin, Ph.D. (Utah State) D. T. Johnson, Ph.D. (Florida) T. M. Klein, Ph.D. (NC State) A. M. Lane, Ph.D. (Massachusetts) M. D. McKinley, Ph.D. (Florida) S. M. C. Ritchie, Ph.D. (Kentucky) L. Y. Sadler III, Ph.D. (Alabama) J.M. Wiest, Ph.D. (Wisconsin) M. L. Weaver, Ph.D. (Florida) Phone: (205) 348-6450 An e qual em plo yme nt I e qu a l e du cati onal oppo r t u ni ty ins titu ti o n 322 C h e mi ca l E n g in ee rin g Edu c ati o n

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Chemical & Materials Engineering Graduate Program T he Department of Chemical an d M a t eri als Engineerin g a t the University of Ala bama in Hunt svil le offer s yo u the oppor tunit y for a so lid and rewarding gra duate career that will lead to further success a t the forefront of academia and indu s try. We will provide graduate program s that educate and train students in advanced areas of chemical engineering material s sc i e nc e a nd e n g ineerin g and biotechnology Option s for a n MS a nd PhD de gree in Engineering or Materials Science are available. Our faculty are dedicated to international l ead ership in re searc h. Project s are ongoing in Mass Tran s fer Fluid Mech a nic s Combustion Biosparation s, Biomateri a l s, Microgravi ty Mat rials Proce ss ing and Adhesion. Collaborations have been established with n ea rb y NASA/ Mar s h a ll Space Fli g ht Center as well as leadin g edge biotechnolog y and engineering companies. We are also dedicated to innovation in t eac hin g. Our classes incorporate advances in computational method s and multi media presentations Department of Chemical Enginee ri ng The University of Alabama in Huntsville 130 Engineering Building Huntsville, AL 35899 Fall 2002 FACULTY & RESEARCH AREAS Ramon L Cero Ph.D. (UC-Davis) Professor and Chair Capillary hydrodynamics mult i phase flows enhanced heat transfer surfaces (256) 824-7313 rlc@che.uah.edu C h ien P. Chen Ph.D. (Michigan State) Professor Multiphase flows spray combustion, turbulence modeling, numerical methods in fluids and heat transfer (256) 824-6194 cchen @ che uah edu Krishnan K. Chittur Ph.D. (Rice) Professor Protein adsorption to biomaterials FTR / ATR at solid-liquid i nterfaces biosensing. (2 56) 824-6850 kchittur @c he uah edu Do u glas G. Hayes Ph.D. (Michigan) Associate Professor Enzyme reactions in nonaqueous media separations involving biomolecules lipids and surfactants surfactant-based colloidal aggregates. (256) 824-6874 dhayes @c he.uah edu James E. Smith Jr. Ph D. (South Carolina) Professor Kinetics and catalysis powdered materials processing combustion diagnostics and fluids v i sualization using optical methods ( 256) 824-6439 jesmith@che.uah.edu Jeff r ey J. Weimer Ph. D (MIT) Associate Professor Joint Appointment in Chemistry Adhesion biomaterials surface properties thin film growth surface spectroscopies scanning prode microscopies. (256) 824-6954 jjweimer@matsci.uah edu The Univers i ty of Alabama in Huntsville An Affirmati v e Actio n /E qual Opponunity In s titut i on Web page: http://cheme n g.uah e d u Ph: 256 824 6810 FAX: 256 6839 323

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University of Alberta Chemical and Materials Engineering The University of Alb e rta is well known for its commitment to exce llence in teach ing and research. Th e Department of Chemical and Materials Engineering has 37 professors and over 100 graduate students. Degrees are offered at the M.Sc. and Ph.D. levels in Chemical Engineer ing, Materials Engineering, and Process Control. All full-time graduate students in the res earch program s re c ei v e a stipend to cover living expenses and tuition. 324 For further information contact Gradu a t e Pr ogram O ffice r D epa rtm en t of C h e mi ca l an d M ate r ials E n g in ee r i n g U n ive r s i ty of A lb er t a Ed m o nt on Al b e rt a Ca n ada T 6 G 2 G 6 PHO NE (78 0 ) 4 921 8 2 3 FAX (78 0 ) 49 22 88 1 e -m a il : c h e mi ca l. e n g in ee r i n g@ua lb e rt a ca we b : www .ualb e r ta c a /c h e m e n g M. BHUSHAN Ph D ( I.l. T. B o m bay ) Se n so r Lo ca ti o n Fault Di ag n os i s Pr ocess S afety R.E. BURRELL P h D ( U ni ve r s it y of W a t e rl oo ) Na n os tru c /llr e d Bi o m a t e r ia l s Dru g D e li very Bi o film s 1is s u e l 11t eg rati o n w ith Mat e rial s P. CHOI Ph D (U ni ve r s it y of W a t e rl oo ) M olec ular M ode lin g of P o l y m e r s Th e rmod y nami cs of Pol y m e r S o l wion s a nd Bl e nd s K. T. CHUANG Ph D. ( U ni ve r s i ty of A lb e rt a ) Fu e l C e ll C ata l y si s S e parati o n Pro ces s es P o lluti o n C o nt ro l I. G. DALLA LANA Ph.D (U n iv of Minn e s ota ) E MERIT U S Ch e mi c al R e a c tion En g in ee rin g H e t e ro ge n eo u s Cat a l ys i s J. A. W. ELLIOTT, P h.D ( U ni v e r s i t y ofToro n to ) Th er m o d y nami cs St a ti s ti c al Th e rm o d y nam ics b u e rfa cia l Ph e n o m e n a D. G. FISHER Ph D ( U ni ve r s i t y of M i c hi gan ) EME RIT US P rocess D y nami cs a nd C o ntr o l R ea l-7im e Co mput e r A ppli c ati o n s J.F. FORBES Ph.D ( M c M as t e r U ni ve r s it y ) C HAIR R e al 7im e Op t imi z ation S c h e dulin g and P l ann i n g M. R. GRAY Ph D (Ca li fo rni a In s t. of T ec h ) Bi o r e a c tor s C h e mi c al Kin e ti cs Bi 111 m e n P roce s s i n g R. E. HAYES Ph.D. ( U ni ve r s i ty o fB a th ) N um e ri c al A n a l ys i s R e a cto r M o d e lin g Co mp u tati o na l Fluid D y n a mi cs B. HUANG Ph D (U ni ver s i ty of A l berta ) Co m r o ll e r P erfo rman ce A ssess m e nt Multi va riabl e Co 111ro l S tati s ti c s S. M. KRESTA P h D ( M c Ma s t e r Unive r s i t y ) Tur b ul e nt & Transiti o n a l Fl ows Multiph ase Fl ows C FD S. LIU, Ph D. (U ni vers i ty of A lb ert a) Flu i d P arti cle D y n ami cs Transp o rt Ph e n o m e na Kin e ti c s D. T. LYNCH Ph D (U ni ve r s it y of A lb e rt a) D EAN OF EN GlN EE RIN G Catal ys i s Kin e ti c M o d e l in g N um e r i c al M e th o d s P o l y m e ri z ati o n J. H. MASLIYAH Ph.D. ( U ni ve r s i ty of Briti s h Co lumbi a) T ra n s p o rt Ph e n o m e n a Co ll o id s Particl eFluid D y n a mi cs Oil Sand s A. E. MATHER Ph D ( U ni ve r s i ty o f M i c hi ga n ) Ph ase E qu i li b r ia Flui d P roperties a t H ig h Pr ess ur es Th e rm o d y nami cs E. S. MEADOWS P h D ( U n iv e r s i t y of Tex as) P rocess C o 11tr o l Fu e l Ce ll M o d e lin g and Co ntrol Optimi za ti o n W. C. MCCAFFREY Ph D ( M c Gi ll U ni ve r s i ty ) R eac t i o n K in e ti c s H e a vy Oil Up g radi n g P o l y m e r R ecy clin g Biot ec h nol ogy K. NANDAKUMAR, Ph D ( Prin ce t o n U ni vers it y) Tra n s p o r t Ph e n o m e na Di st ill ati o n Co m p u tatio n a l F lu id D y n a mi c s A.E. NELSON Ph D. ( Mi c hi ga n T ec hn o l og i ca l U ni ve r s it y ) H e t e ro ge n eo u s Cata l ys i s U H V Surfa ce S c i e n ce Ch e m i c al Kin e ti cs M. RAO Ph.D. ( Rut g e r s U ni ve r s it y ) Al l111 e ll ige 111 Co m ro l P rocess Co nt ro l S. L. SHAH Ph D (U n i v e r s it y of A lb erta ) Co m p w e r P rocess Co 11tr o l S ys t e m l d e 11tifi ca ti o n Pro cess a nd P e rf o m ra n ce M o ni tor in g J.M. SHAW Ph D (U ni ve r s i ty of B ri t i s h Co lumbi a ) P e trol e u m Th e rm o d y nami cs Multip h as e M ix in g P ro ces s M o d e lin g U. SUNDARARAJ Ph D. (U ni ver s i ty of Minn eso t a) P o l y m e r Pr o ce ss i n g P o l y m e r Bl e n ds /nt e rfa c i al Ph e n o m e n a H. ULUDAG Ph D (U ni ve r s it y of T o ront o) B i o m a t e ri al s Tiss u e E n g i n ee r in g D ru g D e li ve ry S. E. WANKE Ph D ( U n ive r s it y of Ca li fo rni a D av i s) H e t e r oge n eo u s Catal ys i s Kin e ti cs P o l y m e ri za ti o n M. C. WILLIAMS Ph D ( U ni ve r s i ty of Wi s co n s in ) EMERIT US R h eology P o l y m e r Ch a ra c t e ri za ti o n P o l y m e r P rocess in g Z. XU, Ph D ( Vir g ini a P o l y t ec hni c In s titut e a nd St a t e Uni ve r s it y) S u rf ace S c i e n ce & E n g in ee rin g Min e ral P rocess i n g Wa s t e Mana ge m e nt T. YEUNG Ph.D. (U ni ve r s it y of B ri t i s h Co lumbi a) E m u l sion s /n terfacial P h e no m e n a M i c ro m ec han i cs C h e mi c a l E n g in eer in g Ed u c a ti on

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FACULTY/RESEARCH INTERESTS CHEMICAL AND ROBERT G. ARNOLD, Profe ssor (Ca lTech ) Mi c r obio lo g i ca l Ha za rdous Waste Treatm e nt, M e tal s Speciation and Toxicity ENVIRONMENTAL ENGINEERING PAUL BLOWERS, Assistant Pro fessor (Ill in o i s, Urbana-Champaign) Chem i ca l Kin et i cs, Catalysis, Surface Ph enomena JAMES C. BAYGENTS, Associate Professor ( Prin ceton) Fluid M ec hani cs, Transport and Colloidal Phenomena Bi oseparations WENDELL ELA Assistant P rofessor ( Stanford ) Parti c l eParti cle In teract ion s, Environmental Chemistry JAMES FARRELL, Associate Profes so r ( Stanford ) Sorption/desorptio11 of Or g ani cs in Soils at THE JAMES A. FIELD, Associate Professor (Wage ni gen Agricultural Un i v.) Bior e m e diation Mi cro biolo gy, Whit e Rot Fungi, H aza rdous Waste ROBERTO GUZMAN, Associate Profe sso r ( North Carolina State ) Affinity Prot e in Separations, P o l y m e ri c Su,face Science ANTHONY MUSCAT A ss istant Profe sso r ( Stanford ) Kin e ti cs Surface Chemistry Surfa ce Engineering, Semiconductor P rocessing Mi croco11 taminati on KIMBERLY OGDEN, Associate Profes so r ( Colorado) Bi o r eac tors, Bi o r eme diation, Organics R e moval from Soils THOMAS W. PETERSON, Pro fessor and Dean (Ca !T ech) Aeroso l s, Ha z ardous Waste In c in erat i on, Mi croco ntamination ARA PHILIPOSSIAN, Associate Prof essor (T uft s) Chemical/Mechanical Polishin g, Semiconductor Pro cess in g JERKER PORA TH, Re search Profes so r (U pp sala) Separation S cie 11 ce EDUARDO SAEZ Associate Profe sso r (U C Da v i s) Rh eo l ogy, P oly m er Flows, Multiphase R eactors FARHANG SHADMAN, Pro fessor (Berke l ey) R eac tio11 E11gi11 ee ri11g, Kineti cs, Catalysis, R eac ti ve Membranes, Mi c r oco ntaminatio11 JOST 0. L. WENDT, Profe ssor a nd H ead (Jo hn s Hopkin s) Combustio11 Gen e rat e d Air Polluti o n, ln c i11 era ti o n Waste Ma11agement For further information, write to h1tp :llwww.c he arizona.edu or write Clzairma11, Graduate Study Committee Department of Chemical a11d E11viro11me11tal E11gi11eeri11g P.O. BOX 210011 The U11iversity of Arizona T11cso11, AZ 85721 The University of Arizona i s an equa l opponu nit y educatio n a l institution/equal oppon unit y emp l oye r. Women a n d minoritie s are e n couraged to app l y. Fall 2002 The Chemical and Environme ntal Engineering Department at the University of Arizona offers a wide range of research opportunities in all major areas of chemical engineering and e n vi r o nmental engineering and gra duate courses are offered in most of the research areas li sted here The department offers a fully acc redited und ergrad u ate degree as well as MS and PhD graduate degree s. Strong interdisciplinary programs exist in bioproces si ng and bioseparations microcontamination in electronics manu facture, and environme ntal process modification. Financial support is available through fellowships, government and industrial grants and contracts, teaching and research assistantships Tucson has an exce llent climate and man y recreational opportunities. It is a g rowing modern city that r etains much of the old Southwestern atmosphere. 325

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ARIZONA STATE UNIVERSITY Department of Chemical and Mater i als Engineering A Distinguished and Diverse Faculty Chemical Engineering Jonathan Allen, Ph D. MIT. Atmospheric aerosol c hemistry s in g le-particl e measurement techniques, e n vironme ntal fate of organic pollutants Stephen Beaudoin Ph.D. North Carolina State Semiconductor material s processing, environ ment a lly-beni g n se miconductor processing particle and thin-film adhesion chem i cal mechanical polishing, pol y m er di e lectrics James Beckman Ph.D., Arizona Unit operations, app lied mathematics energy-efficient water purification, fractionation, CMP reclamation Veronica Burrows Ph.D Prin ce ton Surface sc i ence environmental se nsor s, se miconductor processing, interfacial chemical and phy s ic a l processes in sensor proce ssi ng Ann Dillner Ph.D. Illinoi s, Urbana-Champaign. Atmospheric particulate matt er (aerosols) c h e mi st r y a nd physics, ultra fine aerosols, light sca tterin g, climate and health effec ts of aeroso ls Chan Beum Park Ph D POSTTE C H South K orea. Bioprocess in ex tr e mis novel cell-free prot e in sy nthe s i s, biolab-on-a-chip t ec hnol gy Gregory Raupp Ph.D. Wisconsin. Gas-solid surface reactions mechanisms a nd kinetics interacti o n s between s urfac e re act ion s and sim ultaneou s tran s port proce sses, semiconductor material s processing thermal and plasmae nhanced chemica l vapor depo s ition (C VD ) A nneta Razato s Ph.D Texa s at Austin. Bacterial adhesion, co ll oid int eractions, AFM biofilm s, ge n e tic e n gi ne e rin g Daniel Rivera Ph.D., Caltech. Control sys tem s engineering, dynamic modeling via system identification robust con trol com puter-aided control system de s ign Michael Sierks Ph D., Iowa State. Protein engineering, biomedical eng in eering, enzyme kinetics antibody engi neerin g Materials Science and Engineering James Adams Ph.D Atomistic s timulation of metallic surfaces, adhesion, wear, and a ut omotive ca tal ys t s he avy m e tal toxicity Terry Alford Ph .D Cornell Electronic materi a l s, ph ys ical metallurgy, e l ectronic thin film s Nikhilesh Chawla, Ph D. Michigan. Lead-fr ee so ld ers composit emater ial s, powder m e tallur gy Sandwip Dey Ph.D., Alfred Electro-ceramics, MOCVD and ALCVD diel ec tri cs : leakage l oss me c hani s m s and mode lin g Ste phen Krause Ph.D Michigan C haracteriz atio n of st ructural changes in pro cessi ng of semico nductor s A multi-disciplinary research environment w ith opportunities in electronic materials processing biotechnology processing, characterization, and simulation of materials ceramics air and water purification atmospheric chemistry process control Subhash Mahajan (C hair ), Ph.D Berkeley. Semicond u ctor defect s high temp era ture semiconductors s tructural materi a l s deformation James Mayer Ph.D. Purdue Thin film proce ss in g ion beam modification of materials Na te Newman Ph.D Stanford Growth, characterization, and modeling of so lids tate material s S. Tom Picraux Ph.D. Caltech. Nanostructured material s, epitaxy, and thin-film e l ectronic m ate rial s Karl Sieradzki Ph D. Syrac u se. Fracture of so lids thin-film deposition and growth, corrosion Mark van Schilfgaarde Ph.D. Stanford Methods and applications of electro ni c s tructure theory dilute magnet i c se mi co ndu c tors GW approximation For details concerning graduate opportunities in Chemical and Materials Engineering at ASU, please call Marlene Bolf at (480) 965-3313, or write to Subhash Mahajan, Chair, Chemical and Materials Engineering, Ariwna State University, Tempe, Ariwna 85287-6006 (smahajan@asu.edu). 326 Chemi c al Enginee ri ng Edu c a tion

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F a /1 2002 Faculty Robert P. Chamber s Uni ve r s it y o f Californi a Berk e l ey Harr y T. Cullinan Carn eg i e M e llon Un i ve rsity C hristine W. Curti s Fl o rida Stat e U ni ve r s i ty Steve R. Duke Uni ve r s i ty o f Illin o i s Said Elnashaie Uni ve r s i ty of E d i nbur g h Jame s A. Guin U ni ve r s i ty of T exas, Au s tin Ram B. Gupta U n ivers i ty of T ex a s Au s tin Gopal A. Krishnagopalan University of M a in e Y. Y. Lee Io w a Stat e U ni ve r s i ty Glennon Maple s Oklah o ma Stat e U ni ve r s i ty David R. Mills Wa s hin g ton St a t e Uni ve rsi ty Ronald D Neuman Th e I n s titut e o f Pap e r Ch e mi s tr y Stephen A Perusich Uni ve r sity o f Ill in o i s Timoth y D Placek Uni ve r si t y o f K e ntu c k y C hristopher B. Roberts Uni ve rsit y o f N o t r e D am e A. R. Tarrer P u r du e Uni ve r s i ty Bruce J. Tatarchuk U ni v er s i ty o f Wi sc on s in f Research Areas Biochemical En~neering Pulp and Ptper Process Systems En~neering Integrated Process Design Environmental Chemicru En~neering Cat~is and Reaction En~neering Materials Fblymers Surface and lnterfacial Science -~=Thermodynamics Supe[ritical Fluids Electrochemical En~neering Transport Phenomena Fue l CellTechnolo~ Microfibrous Materials Nanotechnolow Di ~c tor of Graduate Recruiting DepartmentofChemicalEngineering Auburn University AL 36849 Phon e ( 3 3 4 ) 844 4827 Fax ( 334 ) 844-2063 http://www.eng.aubum. u mail: chemical@eng.ad6urn.edu 3 27

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DEPARTMENT OF CHEMICAL AND PETROLEUM ENGINEERING FACULTY R G Moore Head (Alberta) J. Azaiez (Sta n fo r d) H. Baheri (Saskatchewan) L.A. Behie (Western Onta r io) C. Bellehumeur (McMaster) P.R. Bishnoi (Alberta) P J Farr e ll (Calgary) R. A Heid e mann (Washington U ) J M. Hill (Wisconsin) A. A Jeje (MIT) M. S. Kallo s (Calgary) A Kantza s (Waterloo) B B. Maini (Univ. Washington) A K M ehrotra (Calgary) S A. Meht a (Calgary) B J M ilne (Calgary) M Pooladi-Darvi s h (Alberta) A. Settari (Calgary) S. Sriniva s an (Stanford) W. Y S v rcek (Alberta) M A Trebble (Calgary) H. W. Y arranton (Alberta) B Young (Canterbury, NZ) L. Zanzotto (Slovak Te c h. Univ. C z echoslovakia) The D epartment offers graduate programs l e ading to t h e M .Sc. and Ph D. degrees in Chemi c al Engineering (fu ll -time) and th e M.Eng. degree in Ch e mi ca l Engineering, Petro l eum R e servoi r Engin ee ring or Engine e ring for th e Env i ronme n t (part-time) in th e following areas: Biochemical Engineering & Biotechnolog y Biomedical Engineering Catalysis and Fuel Cells Environmental Engineering Modeling, Simulation & Control Petroleum Recovery & Reservoir Engineering Polymer Processing & Rheology Process Development Reaction Engineering/Kinetics Thermodynamics Transport Phenomena F ellow s hip s and Re searc h Ass i s tant s hip s a r e a v ail a bl e t o a ll q u a lifi e d a ppli ca n ts Fo r A ddi tio n al J 11fo rma tio11 Write Dr. W Y. Svrcek Assoc i ate Head Graduate Studies Department of C h emical and Petro l eum Engineering University of Calgary Calgary Alberta, Canada T2N I N4 E-mail: gradstud @ uca l gary ca The University is lo c ated in the City of Calgary, the Oil capital of Canada, the home of the world famous Calgary Stampede and th e 1988 Winter Olympics Th e City combines the traditions of t h e Old West with the sophistication of a modern urban center. B e autiful Banff National Park is 110 km west of the City and the ski resorts of Ban.ff, lake Louise .a nd Kananaskis areas are readily accessible. In th e above photo the University Campus is shown in the foreground. The Engin ee ring complex is on the l eft of the pictur e, and the Ol y mpi c Oval i s on the right of the pi c ture 328 Chemi c al Engin e ering Edu c ation

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University of California, Berkeley The Chemical Engineering Department at the University of California, Berkeley one of the pre eminent departments in the field, offers graduate pro grams l eading to the Master of Science and Doctor of Philosophy. Stude nt s also have the opportunity to take part in the many cultural offerings of the San Francisco Bay Area a nd the recreational activities of California's northern coast and mountains. FACULTY N it ash P. Balsara Elton J. Cairns Harvey W. Blanch Douglas S. Clark Arup K. Chakraborty Enrique Iglesia David B. Graves Jay D Keasling A l exander Katz Roya Maboudlan C. Judson King John S. Newman Susan J. Muller Clayton J R a dk e John M. Prausnitz David V. Schaffer Jeffrey A Reimer Rachel A Segalman Alexis T Bell Chairman: Arup K. Chakraborty BIOENGINEERING Blanch Clark Keasling Schaffer Chakraborty Muller Prausnitz & Radke KINETICS THERMODYNAMICS TRANSPORT PHEl\OMENA QUANTUM& STATISTICAL MECHANICS SPECTROSCOPY POLYMERS & SOFT MATERIALS Balsara Chakraborty Muller Prausnitz, Radke Reimer & Segalman CATALYSIS & REACTION ENG Bell Chakraborty, Iglesia Katz & Reimer ELECTROCHEMICAL ENGINEERING Cairns Newman & Reimer ENVIRONMENTAL ENGINEERING Bell Graves Iglesia, Keasling & King MICROELECTRONICS PROCESSING & MEMS Graves, Maboudian Reimer & Segalman FOR FURTHER INFORMATION PLEASE VISIT OUR WEBSITE: http://cheme.berkeley.edu/index.shtm I Fal/ 2002 329

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University of California, Davis Department of Chemical Engineering & Materials Science Offe rin g M .S. a n d Ph D deg r ee p r ogra m s in bo th C h e m ical Eng in ee r i n g a n d M ateria l s Scie n ce and E n ginee rin g ------------F a culty------------Da v id E Block Ass i stant P rofesso r Ph D Univers i ty of Min n esota, 1992 I ndust r ial fennenrarion biochemical processes in phannaceurical industr y Roger B Boulton Professor P h D ., Univers it y of Me l bo u rne, 1 976 Fem,entarion and reaction kinetics c rystall i zation Stephanie R. Dungan Assoc i a t e P rofesso r P h. D ., Massac hu setts In s titut e of Tec h no l ogy 1 992 Mice/le transport colloid and interfacial science in food p r ocessing Roland Faller Assistant Professor P h. D Max-P l anck I nsti t u t e for Poly m er Researc h 2000 Molecular modeling of soft condensed matter Bruce C. Gat es, Professor P h D University of Washi n g t on Seatt l e, 1966 Catalysis solid superacid catalysis ,eolite catalysts bimetallic catalysts catalysis by metal clusters Jeffer y C. Gibeling Professor P h. D S t a n fo r d U ni versity 1 979 Defomwtion fracture and fatigue of metals, layered composites and bone Joanna R. Groza P rofesso r P h. D ., P o l y t ec hni c In s titu te, Bu c h ares t 1 972 P lasma activated s i111 ering and processing of nanostrucru r ed mate r ials Brian G. Higgins P rofessor Ph D. U ni vers it y of Minn eso t a 1 980 Fl u id mechanics a n d i nterfacial phe n ome n a sol gel p r ocessing, coating flows David G. Howitt P rofesso r Ph D ., Unive r s i ty of Ca l ifo rni a Berke l ey 1976 Forensic and failure anal y sis electron mic r oscopy ignition and combustion processes in materials Alan P Jackman Professor Ph.D Un i versity of Mi n nesota 1968 Protein production in plant cell cultures bioremediation Ton y a L. Kuhl Assistan t Professo r P h.D. Un i vers i ty of Cal i fornia Santa B arbara 1996 B iomareria/s, membrane i111eractions i111ennolecularand i111ersurface forces in complex fluid systems Enrique J. Lavernia P rofe so r Ph D ., M assac hu se t ts In s titut e of T ec hn o l ogy, 1 986 Sy n t h esis of s tru c tu ral m a t e r ials and composites; w,ws t rucrured materials and compos i tes, t hennal spray p r ocessing Jiirg F. Loffler Assis t a n t Professor Ph D Swiss Federa l In sti tut e of Tec hn ology (ETH) Zlirich 1 997 Nanostructured and amorphous materials; magnetic, structu r al and them,ophysica/ properties, neutron and x-ray scattering Marjorie L. Lon g o Assistan t P ro f essor Ph .D. U n ivers i ty of California, Sa m a B arbara, 1 993 H ydrophobic protein design for active comrol, surfa c ra111 microst m ctu r e and interact i on of proteins and D NA with biological me m branes Karen A. McDonald P rofesso r Ph D ., U ni vers it y of Mary land Co ll ege P ar k 1 985 P lant cell c u lt u re biop r ocess in g algal c ell c u ltures Ami y a K. Mukherjee P rofesso r D Ph il., University of Oxfo r d 1962 Superplasticity of inte n netallic alloys and ceramics, high temperature creep defomw tion Zuhair A. Munir Professo r P h.D. U ni versity of Califo rni a B e r ke l ey, 1 963 Combustion synthesis, multilayer combustion systems, ft111ctionall y graded mate r ials Alexandra Navrotsk y, Prof esso r Ph D U ni ve rsit y of C hi c a go, 1 967 T h ermody n a mi cs a n d solid state chem i stry; high te m pe r atu r e calo r ime t ry Ahmet N Palazoglu Pr o f esso r Ph D R ensse l ae r P o l y t ec hni c I ns titut e 1 984 P rocess con t rol a n d p r ocess des i gn of env ir onme 111 ally ben i gn p r ocesses Ronald J. Phillips Professor Ph D Massac h use t ts I nsti tut e of Tec hn o l ogy 1 989 Transport processes in bioseparations, Newtonian and non Newtonian suspension mechanics Robert L Powell Professor P h D. Jo h ns H opkins University 1978 R heolog y, suspension mechanics, magneti c resonance imaging of suspensions Subhash H. Risbud Professo r a n d C h ai r Ph D ., U n ivers i ty of California B e r ke l ey 1 976 Semiconductor quantum dots, high T superconducting ceramics p o l y m e r composites for opt i cs Dewey D.Y. Ryu, P rofessor Ph D M assac hu se t ts In s titut e of Tec hn o l ogy 1 967 B iomolec u lar process eng in ee r ing and recombinant bioprocess tec h no l ogy Julie M Schoenung Assoc i a t e P rofesso r Ph D ., M assac hu se tt s In s tit u t e o f T ec h no l ogy, 1 987 M aterials sys t ems analysis; poll w ion preventio n and waste m in imization; process economics James F. Shackelford Professo r Ph D ., University of Califo rni a, B e r ke l ey, I 971 Structure of ma t erials, biomaterials, nondestrncrive testing of engineering materials J.M. Smith, Pr ofesso r E m eri tu s Sc .D. M assac hu se tt s I ns titut e of Tec h no l ogy, 1 943 C h emical k i netics a n d reacto r design Pieter Stroeve P rofessor Sc. D M assac hu se t ts In s t i tu te o fT ech n o l ogy, 1 973 Membrane separa t ions, lnngmui r Blodgett films, colloid and s u rface science Stephen Whitaker, Pro f esso r Ph D U ni versity of De l aware 1959 Multiphase transpo r t phe n omena 33 0 The mu l tifaceted grad u ate s tud y experie n ce in the D e p art m e n t of C h e m ical E n gineering a n d M at e ri a l s Sc i ence a ll ows s tu de nt s t o c h oose r esearc h proj ec t s a n d th es i s adv i sers fro m a n y of o ur fac ult y w ith expe rti se i n chemical engineering b i oc h e mi ca l e n g in eering and/ o r materia l s science a n d e n gi n eering. O ur goal is 10 prov i de the fi n ancial a n d acade m ic s up po rt fo r s tud e n ts 10 co m p l e t e a s ub sta n t i ve r esearc h p ro ject w ith i n 2 years for th e M S an d 4 years fo r th e Ph .D. LOCAIIONs 5oc:rammto: 17 m11a Son p,_.,.,, 72 mlla Lalre Tahoe: 90 m11a Davis is a small bike-friendly university town located 17 miles west of Sacramento and 72 miles northeast of San Francisco, within driving distance of a multitude of recreational activities in Yosemite, Lake Tahoe, Monterey and San Francisco Bay Area. For information about our program, look up our web site at http://www.chms ucdavis.edu or contact us via e-mail at chmsgradasst@ucdavis.edu Graduate admi.tsions on-line applications and printabk forms availabk at http://gradstudies ucdavis edu/b4apply.htm Gradua~ Admission Chair Professor Jeffery C. Gibeling Dept. of Chemical Engineering & Materials Science Univtrsity of California, Davis Ont Shields Av e nue Davis CA 956/6-5294 USA Phonl! (530) 752-7952 Fax (530) 752-/03/ C h e mi ca l E n g in ee rin g E du ca ti o n

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UNIVERSITY OF CALIFORNIA Graduat e Studi es i n JRT TJNE Chemi c al Enginee r in g Y .. and Material s S c i enc e and Engineering for Chemical E ngin ee r i ng En g in ee ring and Material s Sci e nc e M ajo rs Offering degrees at the M S and Ph.D. levels R esearch in frontier areas in chemical engineering, biochemical engineering biomedical engineering and materials science and engineering Strong physical and life science and engineering groups on campus. F A CULTY Ying Chih Chang (Stanford Univers i ty) Nancy A. Da Silva (California I nstitute of T echnology) James C. Earthman (Stanford University) Ste v en C. George (University of Washington) Stanle y B. Grant (California Institute of Technology) Juan Hong ( Purdue University) Enrique J La v ernia (Massachusetts I nstitute of Technology) Henr y C Lim (Northwestern Univers i ty) Jia Grace Lu (Harvard University) Martha L. Mccartne y ( Stanford University) Farghalli A. Mohamed (University of California, Berkeley) Frank G. Shi (California I nstitute of Technology) V asan Venugopalan (Massachusetts I nstitute of Technology) Joint Ap p ointments: G. Wesle y Hatfield ( P urdue University) Noo Li Jeon (University of I llinois) Sunny Jiang (University of South F l o r ida) Roger H. Rangel (University of Ca l ifornia, B erkeley) William A Sirignano ( P rinceton University) Ad i unct P rofessors Ru s sell Chou (Carnegie Mellon Un i versity) Andrew Shapiro (University of Ca l iforia, I rvine) Victoria Tellkamp (University of Ca l iforia I rvine) The 1,510-acre UC Irvine campus is in Orange County.five miles from the Pacific Ocean and 40 miles south of Los Angeles. I rvine is one of the nations fastest growing residential, industrial and business areas Nearby beaches, mountain and desert area recreational activities, and local cultural activities make 1 rvine a pleasant city in which to live and study. For further information and application forms, please visit h tt p: // www.e n g. u ci e du/ c b e/ or contact Department of Chemical Engineering and Materials Science School of Engineering Univer s ity of California Irvine CA 92697-2575 Fall 2002 / B i omedic a l Engineering Bioreactor Engineering Bioremediation Ceramic s Combu s tion Compo s ite Materials Control and Optimiz a tion Environmental Engineering Interf acial Engineering Material s Proce ssi n g Mechanical Propertie s Metabolic Engineering Microelectronics Processing and Modeling Microstructure of Material s N anocry s talline Material s Nucleation, Chrystallizat i on and Glass Transition Process Polymers Recombinant Cell Technology Separation Processes Sol-Gel Processing Two-Phase Flow Water Pollution Control 331

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CHEMICAL ENGINEERING A T RESEARCH AREAS Aerosol Science and Technology Biochemical Engineering Combinatorial Catalysi s Complex Systems Engineering Electrochemistry Membranes Molecular and Cellular Bioengineering Pollution Prevention Polymer Engineering Process Design, Optimization Dynamics, and Control Reaction Kinetics and Combustion Semiconductor Manufacturing FACULTY J.P. Chang P. D. Christofldes Y. Cohen J. Davis ( Vi ce C h a n ce ll o r fo r In fo rmati o n Tec hn o l ogy) S. K. Friedlander R. F. Hicks E L. Knuth ( P rof E m e ritu s) J.C. Liao V. Mano u sio u thakis H. G. Monbouquette K. Nobe L.B. Robinson ( P rof E m e ritu s) S.M.Senkan Y.Tang W. D. Van Vorst ( P rof E m e ritu s) V. L. Vilker ( Pr of E m erit u s) A R. Wazzan PROGRAMS -----------3 32 UCLA s Chemical Engineering Department offer s a program of teaching and research linking fundamental en gineering science and industrial practice. Our Department has strong graduate research programs in Bioengineering, Energy and Environment, Semiconductor Manufacturing Engineering of Materials and Process and Control Sys tems Engineering. Fellowships are available for outstanding applicants intere s ted in Ph.D. degree program s. A fellow s hip in cludes a waiver of tuition and fees plus a s tipend. Located five miles from the Pacific Coast, UCLA's attractive 417-acre campus extends from Bel Air to Westwood Village. Students have access to the highly regarded s cience programs and to a vari ety of experiences in theatre music art, and sport s on campu s. CONTACT Admissions Officer Chemical Engineering Department 5531 Boelter Hall UCLA Los Angeles, CA 90095-1592 Telephone at (310) 825-9063 or visit us at www.chemeng.ucla.edu Ch e mi c al En g in ee rin g Edu c ati o n

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University of California, Riverside Department of Chemical and Environmental Engineering The Graduate Program in Chemical and En vironmental Engineering offers training lead ing to the degrees of Master of Science and Doctor of Philosophy. All applicants are r e quired to submit scores from the general apti tude Graduate Record Examination (GRE). For more information and application mate rials, write: Graduate Advisor Department of Chemical and Environmental Engineering University of California Riverside CA 92521 Visit u s at our website: http://www.engr.ucr.edu/chemenv Faculty ng1neer1ng Wilfred Chen ( Cal T ec h ) Environmental Biotechnolog y, Microbial Engineering Biocatal ys is Da v id R. Cocker ( Caltech ) Air Quality Systems Engineering Marc Deshu s se s ( ETH Zuric h) Environmental Biotechnology, Bioremediation, Modeling Robert C. Haddon ( Penn Stat e) Carbon Nanotubes, Advanced Materials Eric M.V. Hoek (Y ale ) Environmental Membrane Processes Collodial and Inte,fa c ial Phenomena Mark R. Mat s umoto (U C Da v i s) Water and Wastewater Treatment, Ha za rdous Waste, Soil R emediation Ashok Mulchandani ( McGill ) Bioengineering, Biomaterials Biosensors, Environmental Biotechnology Joseph M. Norbeck ( Nebraska ) Advanced Vehicle Technolog y, Air Pollutants, Renewable Fuels Mihri Ozkan (U C Sn Diego ) Biomedical Microdevices Bio-MEMS and Bio-Photonics Anders 0. Wistrom (U C Da vis) Particulate and Colloidal S yste ms Jianzhong Wu ( UC Berkele y) Molecular Simulation, Theory of Complex Fluids, Nanomaterials Yushan Yan ( CalTech ) Zeolite Thin Films Fuel Cells, Nanostructured Materials, Catalysis T h e 1,2 0 0-acre R iversi d e campus of the University of California is located 50 miles east of Los Ange les w i t hin easy d rivi n g d is t a n ce to mos t of t h e m ajor cult u ral a n d recreationa l offe rin gs i n S o u t h ern C a li fornia. I n a ddi tion it is virtua ll y equi d ista nt from t h e desert, the mo un tai n s, an d the ocea n Fall 2002 333

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334 UNIVERSITY OF CALIFORNIA SANTA BARBARA ERAY S. AYDIL Ph D ( H ouston) Microelectronic s and Pla s ma Pro cess in g SANJOY BANERJEE Ph D (Waterloo) Environmental F luid D y n am i cs Multipha se Fl ows Turbulenc e, Comp ut a tional Fluid D y namic s BRADLEY F. CHMELKA Ph D. (U.C. B e rk eley) Molecular Material s S cie nce In organ i c -Or ga nic Composites Porou s Solids NMR Pol y me rs PATRICK S. DAUGHERTY Ph D. (Austin) Protein Engineering a nd De s i g n Library Technologi es MICHAEL F. DOHERTY Ph.D (Camb rid ge) D es i gn and Synth es i s, Separations, Proce ss Dynami cs and Co n tro l FRANCIS J DOYLE III Ph D (Caltech) P rocess Co ntr o l Syste m s Biolo gy, Nonlinear Dyn a mi cs GLENN H. FREDRICKSON Ph D (Stanfo rd ) Statistical Mechanic s, Gla sses Polymer s, Compos it es, A ll oys G.M. HOMSY Ph.D ( Ill inois) Fluid Mechanics In s t abil iti es, Porou s Media Int erfacial Flow s, Convective Heat Tran sfe r JACOB ISRAELACHVILI Ph D. (Ca mbridg e) Co ll oidal and Biomol ec ul ar Int eractions Adhesion and Frictio n EDWARD J. KRAMER Ph.D (Carnegie-Mel l on) Fracture a n d Diffu s i on of Polymer s, Pol y m er Surfaces a nd In terfaces L. GARY LEAL Ph.D. (Stanford) Fluid Mechanics, Phy s i cs a nd Rh eo l ogy of Complex Fluids, in c ludin g P o l y m ers, S u spe n s ion s, and Emulsio n s GLENN E. LUCAS Ph D. (M .I T.) Mechanics of Materials, Structural R e liabilit y. DIMITRIOS MAROUDAS Ph D (M. I .T.) Theoretical a nd Computational Materials Science, Electronic and Structural Material s ERIC McFARLAND Ph D (M. I T.) M.D. ( Harvard ) Combinatorial Materi a l Science Environmental Catalysis, Surface Science DUNCAN A. MELLICHAMP Ph D ( Purdu e) Computer Co ntr ol, P rocess Dynamic s, R eal-Time Co mputin g SAMIR MITRAGOTRI Ph D. (M. I. T.) Drug Deli ve r y and Biomaterial s DAVID J. PINE Ph D (Cornell) (C hair ) Pol y mer, Surfactant and Colloida l Phy sics, Multiple Light Scatt e rin g, Photonic Crystals ORVILLE C. SANDALL Ph D (Berkeley) Tra n s port Phenomena Separation Pro cesses DALEE. SEBORG Ph D (Princeton) Pro cess Control Monitoring a nd Identific at ion MATTHEW V. TIRRELL Ph D (Massac hus etts) Pol yme r s Surfaces, Ad h esion Biom ateria l s T. G. THEOFANOUS Ph.D. (Minnesota) Multipha se F l ow, Ri sk Assessment and Mana geme n t JOSEPH A. ZASADZINSKI Ph D (Minnesota) Surfac e a nd lnt e rfa c ial Phenomena Biomaterial s PROGRAMS AND FINANCIAL SUPPORT The D epart m ent offers M.S. and Ph.D. degree programs Finan cial aid, including fe ll owships, teaching assistantships, and re search assistantships, is avail able. THE UNIVERSITY One of the wo rld' s few seashore campuses, UCSB is located on the Pa cific Coast J OO miles n o rth west of Los Angeles. The student enro llm ent is over 18 000 Th e m et r opo litan Santa Barbara area has over 1 50,000 residents and is famous for its mild even climate For additional information a nd applications write to C hair Graduate A dmi ssio n s Co mmitte e Department of Chemical Engi ne e rin g U n iversity of California Santa Barbara CA 93106 Chemical Eng in ee rin g Education

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Chemical Engineering at the CALIFORNIA INSTITUTE OF TECHNOLOGY '~t the Leading Edge" Frances H. Arnold Anand R. Asthagiri John F. Brady Mark E. Davis Richard C. Flagan George R Gavalas (Emeritus) Konstantinos P. Giapis Julia A. Kornfield Aerosol Science Applied Mathematics Atmospheric Chemistry and Physics Biocatalysis and Bioreactor Engineering Biomaterials Biomedical Engineering Bioseparations Catalysis Chemical Vapor Deposition John H. Seinfeld David A. Tirrell Nicholas W Tschoegl (Emeritus) Zhen-Gang Wang Combustion Colloid Physics Fluid Mechanics Materials Processing Microelectronics Processing Microstructured Fluids Polymer Science Protein Engineering Statistical Mechanics For further information write __________________ Fa/12002 Director of Graduate Stud ies Chemical Engineering 210-41 California Institute of Technology Pasadena California 91125-4100 Also v isit us on the World Wide Web for an on-line brochure : http :// www.che.caltech.edu 335

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I ........ ... .... ......... ... . .... .............. . . : -: : :-: : : : .. :.: : -: -: ._ ... :-:-:-:-: ......... ...... .. .. .. ....... .. NelJer fettr~ui&ed bLJ tt f acultLJ thttt rules the e,l)orld of chernicttl er1qir1eerir1tJ, carr1eqie Mellol'I trttiflS LJOU tO &ecorne tt suverfiero il'I LJour oc.lJl'I riqfit.

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Case Western Reserve University M.S. and Ph.D. Programs in Chemical Engineering Research Opportunities Advanced Energy Systems Fuel Cells and Batteries Hydrogen Infrastructure Membrane Transport Sensors Microfabrication Biomedical Engineering Transport in Biological Systems Biomedi cal Sensors and Actuators Wound Healing Inflammation and Cancer Metastasis Neural Prosthetic D ev i ces Advanced Materials and Devices Diam ond and Nitride Synthesis Coatings Thin Films and Surfaces In-Situ Diagno stics Fine Particle Science and Processing Polymer Nanocomposites Electrochemical Microfabrication Fall 2002 For more information on Graduate Re search, Admission, and Financial Aid, contact: Graduate Coordinator Department of Chemica l Engineering E-mail: grad@cheme.cwru.edu Web: http: //www.cwru.edu/cse/ec h e Faculty John Angus Harihara Ba skaran Robert Edwards Donald Feke Jeffrey Glass Uziel Landau Chung-Chiun Liu J. Adin Mann Heidi Martin Ph ilip Morrison Peter Pintauro Syed Qutubuddin Robert Savinell Thomas Zawodzinski Case We ste rn Reserve University 10900 Euclid Avenue Cleve l and, Ohio 44106-7217 337

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Opportunities for Graduat e Study in Chemical Engin eer in g at the 338 UNIVERSITY OF CINCINNATI M.S. and Ph.D. Degrees in Chemical Engineering Faculty Carlos Co Joel Fried Rakesh Govind Vadim Guliants Daniel Hershey Chia-chi Ho Sun-Tak Hwang Yuen-Koh Kao Soon-Jai Khang William Krantz Jerry Y. S. Lin Neville Pinto Peter Smirniotis The Unive r sity of Cincinnati is committed to a policy of non-discrimination in awarding financial aid. For Admission Information Director Graduate St udi es Chemical Engineer in g PO Box 2 10171 University of Cinci nn at i Cincinnati, Ohio 45221-0 I 71 Email: mcarden@alpha.che uc.edu or jlin@alpha.che .u c.ed u The faculty and students in the D epartment of Chemical Engineering are engaged in a diverse range of exciting r esearch topics. Assistantships and tuition scholarships are available to highly qualified applicants to the MS and PhD degree programs. Advanced Materials In organ i c membranes nanostructured materials microporous and mesoporous materials, advanced materials processing, thin film technology, fu e l cell and sensor materials, self assembly Biotechnology (Bioseparations) Novel bioseparation techniques affinity separation, biodegradation of toxic wastes, con trolled drug delivery, two-phase flow Catalysis and Chemical Reaction Engineering H eterogeneous catalysis, environmental catalysis z eolite catalysis novel chemical reactors modeling and design of chemical r eactors Environmental Research D esulfu ri zation and denitrication of flue gas, new technologies for coal combustion power plant, wastewater treatment removal of volatile organic vapors Membrane Technology Membrane synthesis and c h aracte ri z ation, membrane gas separation, membrane reactors, sensors and prob es, pervaporation, biomedical, food and enviro nm ental applications of membranes, hi g h-t emperatu r e membrane technology natural gas processing by membranes Polymers Thennodynamics, polymer blends and composites high-temperature polymers hydrogels pol y mer rheolog y computational pol y mer science pol y meri z ation technolog y Separation Technologies Membrane separation, adsorption, ch r omatography separation system synthesis, chemical reaction-based separation pr ocesses Chemical Eng in eering Educat i on

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Chemical Engineering at The City College of New York CUNY (The City University of New York) A 154-year-old urban University the oldest public University in Ameri c a on a 35-a c r e Gothic and modern campus in the greatest c ity in the w orld FACULTY RESEARCH : 0 Andreas Acrivos* 00 ~ Rh eo l ogy of co n ce ntrated s u s p e n s i o n s; Di e l ec tr ph o r es i s in fl ow in g s u s p e n s i o n s; D y n a mi ca l sys t e m s th eo r y a nd c h ao ti c p a rticl e moti o n s Alexander Couzis: P o l y m o rph se l ec ti ve t e m p l a t e d c r ys t a Hi z ati o n ; M o l ec ularl y thin o r ga ni c b arrier l ayers; Surf ac t a nt fac ilit a t ed we ttin g of h y d ro ph o bi c s urfa ces; sof t ma te ri a l s 0 Morton Denn oo~ : P o l y m e r sc i e n ce a nd rhe o l ogy ; n o n-N ew t o ni a n fluid m ec h a ni cs Lane Gilchrist: Bio e n g in ee rin g w ith ce llular m a t e ri a l s; Spectro sco p y g uid e d m o l ec ular en g in ee rin g; Stru c tu ra l s tudi es o f se lf -asse mblin g pr o t e in s ; Bi o pro cess in g Robert Graff: Coa l liqu efac t io n ; P o llution pr eve nt io n ; R e m ed i a ti o n Leslie Isaacs: Pr e par a ti o n a nd c h arac t e ri zat i o n of n ove l o pti ca l m a t e rial s; R ecy clin g o f p av ement m a t e rial s; Appli c ation o f th e rm o-a nal y tic t ec hnique s in m a t e ri a l s r ese ar c h Jae Lee: Th e ory of reactiv e di s tillation ; Pro c e ss de s i g n and control ; Separ a ti o n s ; B i o p rocess in g ~ Char l es Ma l darelli: lnt e rf ac ial fluid m ec hani cs a nd s t a bilit y; Su rf a ce t e n s i o n dri ve n flo ws a nd rni cro fluidi c a ppli ca ti o n s; Surfa c t a nt ad so rpti o n ph ase b e h a vior and nan os tru c turin g a t inter fa c es Irve n R in ard: Proc ess de s i g n m e thodol-o g y ; D y nami c process s imulation ; Micro-rea c tion te c hn o l ogy; P rocess co nt ro l ; Biopro cess in g David Rumschitzki: T ra n s p o rt a nd r eac ti o n as p ec t s of arte ri a l di sease; F a ll 20 02 lnt e rf ac i a l fl ui d mec h a ni cs and s t a bili ty ; Cata l ys t d eac ti va ti o n a nd r eac ti o n e n g in ee ri ng Reuel Shinnar 00 : Ad va n ce d pro cess d es i g n m et h o d s; C h e mi ca l r eac t or co ntr o l ; Spin o d a l d eco mp os i t i o n of binar y so l ve nt mi x tur es; Pro cess eco n o mi cs ; En e r gy a nd env i ronme nt sys t e m s Carol Steiner: P o l y m e r so luti o n s a nd hydrogels; Sof t bi o m a t eria l s Co n tro ll ed re l ease tec hn o l ogy Gabriel Tardos: P ow d e r t ec hn o l ogy; Gra nul a ti o n ; Fluid p art i c l e sys t ems, E l ec t rosta ti c effec t s ; A ir p o lluti o n Sheldon Weinbaum 00 : Fluid m ec h a ni cs, Bi o t ra n s p o rt i n l iv i ng ti ss u e; M o d e l i n g of ce llul ar mec h a ni s m of bo n e grow th ; bi o h ea t t ra n s f e r ; kidn ey fu n c ti o n Herbert Weinstein: Fl u idi zation a n d multiph ase flows : multi p h ase c h e mi ca l r eac t o r a n a l ys i s a nd d es i g n Multiph ase r eac t o r a n a l ys i s a nd d es i g n ASSOC I ATED FACULTY : 0 Jimm y Feng: ( Mec h a n ica l Eng.) L iq u i d crys t a l s 0 Joel Koplik: (P h ys i cs ) F lu id mec h a n ics; M o l ec ul ar m od e lin g; T ra n s p o rt in ran do m m ed i a 0 Hernan Makse: ( Ph ys i cs ) G ra nul a r m ec h a ni cs 0 Mark Shattuck: ( Ph ys i cs) Experime nt a l gra nul ar rheo l ogy; Co m p u ta ti onal granu l ar fl u id dy n amics; Experimenta l spatio-temporal control of pa n e m s 0 Le vic h I nstitut e Narional A ca dem y of Sciences oo Nationa l A c adem y of Enginee r in g 5Americcm A c ade m y of Arts and Scie n ces CONTACT INFORMATION: D e partm e nt o f C h e mi ca l E n g in ee rin g C it y Co U ege of New Y o rk Co n ve nt Ave n ue a t 140 th Stree t New Y ork NY I 0031 www-che engr.ccny.cu n y.edu c he h r @ao l. co m t \.J.l ... J ..... .............. ~-; .,,~-""J'l:J 1, ___ f, ..... ..... ... .; I ": l ... r .. .. .,,.. ----~ ..... -...... t ...... .... .. ,, ... ..... ... ,_., .. .. ....... .. .... ., ;;, ... -~ .... ., -.'~I I' l l ~' ~. .. .. --\~ :, ..... : \. t. .. ~:J ..J.! ...... .,, !--.-,.,,!.! -' I I I 11 ., ., It It f :r .. ,: .. .... ... .. I I I ... ... ~: *:-~ ... ..... ,..... ,, .. ,.. ... .; ........ ;.,,,. ... .. ..... --. ..... ,,..: t, ..... f I ,,., ..... .... ~........ ,.. .... .. :::..... : : .,:.,.;11 ,,.. ........ :. ,,,.,: .. .. '' ..... ........ .. ..... ........ ... ............. -~ .-:. .. .. . .. . -... .... ... .... ... .. .. I .......__ __. ", .. ..: .. : .. ... "', .,. J..,; ..... ,. c. --. ~~ ..._.... .. :h-f i .. I ,, 339

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Cleveland State University Graduate Studies in Chemical and Applied Biomedical Engineering Engineering Degr~e~e~s~-----Fenn College has more than 75 years of experience in provid M.Sc. D.Eng. D.Eng. Chemical Engineering Applied Biomedical Engineering Chemica l Eng ineerin g CSU Faculty A Annapraga da (U niver s it y o f Michi g an ) J.M. Belovich (Univ e r s it y of Michig a n ) G. Chatzimavro udi s (Georgia Institute of Technology) G A. Coulman ( C as e We s tern Reserv e Univ e r s it y) J.E Gatica ( Stat e Uni v er s it y of New York a t Buffalo ) B. Ghorashi ( Ohio State Univer s ity ) E.S. God leski ( Cornell Univer s ity) R. Lustig ( In s titute of Thermo and Fluiddynamic s of the Ruhr-Univer s ity Bochum Germ a ny) D.B. Shah ( Michigan State University ) 0 Talu ( Arizona St a t e Univer s ity ) S N. Tewari (Purdu e Univer s it y) S. Ungara la (Michig a n Technological Univer s ity ) CCF Collaborating Faculty J. Arendt ( Ohio State Univer s ity) B. Davis (Pennsylvania State Univer s ity ) K. Derwin (Univer s ity o f Michigan ) A. Fleischman ( Ca s e We s t e rn Reserve Univer s ity ) M. Grabiner (University of fllinois) S Halliburton ( Vanderbilt University ) G. Lockwood (University of Toronto Canad a) C. McDevitt (Univer s ity of London U.K. ) S Roy ( Ca se West e rn Reserve Univer s ity ) R. Shekhar ( Ohio Stat e Univer s ity ) W. Smith ( Cleveland State University ) A. van den Bogert (Univer s it y of Utrecht The Netherland s) I. Vesely ( University of W es tern Ontario, Canada ) G. Yue (University of Iow a ) For more information. write to: ing outstanding engineering education. Graduate Studies in Chemical and Applied Biomedical Engineering at Cleveland State University 's (CSU s) Fenn College of Engineering offers a wealth of opportunity in a stimulating environment. Research opportunities are available in collaboration with the Bio medical Engineering Department of the renowned Cleveland Clinic Foundation (CCF), Cleveland's Ad vanced Manufacturing Center local and national industry, and Federal agencies, to name a few. Assistantships and Tuition Fee Waivers are available on a competi tive basis for qualified students Cleveland State University has 16 000 s tudents enrolled in its academic pro grams. It is located in the center of the city of Cleveland, with many outstand ing cultural and recreational opportuni ties nearby. RESEARCH AREAS Adsorption Processes Agile Manufacturing Artificial Heart Valves Biomechanic s Bioreactor Design Bioseparation s Blood F l ow Combustion Computational Fluid Dynamics Drug Delivery System s Environmental Pollution Control Materials Synthesis and Processing Medical Imaging MEMS Technology Orthopedic Devices Process Modeling and Control Reaction Engineering Statistical Mechanics Graduate Program Coordinator Department of Chemical Engineering Cleveland State University Cleveland, OH 44115 Surface Phenomena and Mass Transfer Thermodynamics and Fluid Phase Equilibrium Tis s ue Engineering Tribology Ventricular Assist Devices Telephone: 216-687-2569 E-mail : ChE@csvax.egr.csuohio.edu http://www csuohio.edu/chemical_engineering/ Zeolite s : Synthesis Adsorption, and Diffusion Assistantships and Tuition/Fee Waivers are available on a competitive basis for qualified students 340 Ch e mi c al Engine e rin g Edu c ation

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University of Colorado at Boulder The Boulder campus has a controlled enrollment of about 22 000 undergraduate s and 5 000 graduate s tudent s. The beautiful campus has 200 buildings of rough-cut sandstone with red-tile roofs. The excellent educational opportunities and beautiful location attract outstanding s tudent s from e ver y part of the United State s and 85 countrie s. The University of Colorado ha s it s main campu s located in Boulder an attracti v e community of 90 000 people located at the base of the Rocky Mountain s Boulder ha s o v er 300 day s of s un s hine per year with relativel y mild and dry s easons The city i s an active and innovative town that provide s a rich array of recreational and cultural acti v iti es .---Department of Chemical Engineering Faculty and Re search Interests Fal/ 2002 Kristi S. Anseth Pol y m e rs Biomaterial s, T iss u e En g in eeri n g Chr i stopher N. Bowman Pol y m e r s, M e mbran e M ate rial s David E. Clough Pr ocess Control Appli e d S ta t i st i cs Robert H. Davis Fluid M ec hani c s Bi o t ec hn o l ogy, M e mbr a n es John L. Falconer Catal y sis Zeolite Membran es R. Igor Gamow Bioph y sics High Altitud e Ph ys i o l Og) \ Human P e rf o nnan ce, Di v in g Ph y siolo gy Steven M. George Surfa ce Ch e mi s try Th i n Film s, Nan oe n g in ee rin g Doug Gin Pol y m e rs R y an Gill Biote c hnolog y Christine M. Hrenya Fluidi z a t i on Granul a r S ys t e m s, Fluid M ec hani cs Dhinakar S. Kompala B io t ec hn olog y A nimal Ce ll Cultur es M e tab o li c E ng in ee rin g J. Will Medlin H eteroge n eo u s C a ta l y sis So lid St ate S e n so r s Co mp u ta t i o nal Ch e mi st r y Richard D. Noble M e mbran es, S e parati o n s W. Fred Ramirez Pr oc e ss C o ntr o l Bi o t ec hn o l ogy Theodore W. Randolph B io t ec hnol ogy Sup e r cr iti ca l Fluid s Robert L. Sani Tr a n spo r t Ph e n o m e n a Appli e d M a th e mati cs Daniel K. Schwartz Int e ,f ac ial and Coll o id S c i e n ce A lan W. Weimer C e rami cs, En e r gy, R e a c ti o n En g in ee rin g Graduate stud e nt s ma y parti c ipate in th e int e rdis c iplina ry Bi o t ec hnolog y Trainin g Pro g ram and the interdis c iplinary NSF In dustr y /Uni ve r s i ty Coop e rati ve R e s e ar c h C e nt e r f o r Membrane Appli e d S c i e n ce and T e chnolog y and th e C e nt e r fo r Fund a m e ntal s and Appli cat i o n s of Ph o t o pol y m e ri z ati o n s For information and application Graduate Admi ss ion s Committee Department of Chemical Engineering Univer s ity of Colorado Boulder CO 80309-0424 Phon e (303) 492-7471 Fax (303) 492-4341 E-mail chemeng @s pot.colorado edu http : //www.Colorado EDU/che/ 3 4 /

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Faculty R.M. Baldwin (CSM, 1975) A.L. Bunge (Berkeley, 1982) A.M. Dean (Harvard, 1971) J.R. Dorgan (Berkeley, 1991) J.F. Ely (Indiana, 1971} D.W.M. Marr (Stanford, 199:J) C. McCabe (Sheffield, 1998) J :r. McKinnon (MIT, 1989) R.L. Miller (('SM, 1982) E.D. Sloan (Clemson, 1974) J.D. \\Tay (Colorado, 1986) C.A. Wolden ('.VIIT. 1995) D.T. Wu (Berkeley, 1991} Visit http://\\"\Tw.n1incs.edu 342 Colorado School of Mines E volving from its origins as a school of mining founded in 1873 CSM is a unique highly-focused University dedicated to scholarship and research in materials energy and the environment. The Chemical Engineering Department at CSM maintains a high quality, active and well-funded graduate research program According to the NSF annual survey of research expenditures our department has placed in the top 25 nationally each of the last 5 years Research areas within the department include: Materials Science and Engineering Organic and inorganic membranes (Way Baldwin) Polymeric materials (Dorgan McCabe Wu) Colloids and complex fluids (Marr Wu) Electronic materials (Wolden) Fuel cell membranes (Way) Theoretical and Applied Thermodynamics Natural gas hydrates (Sloan) Molecular simulation and modelling (Ely McCabe) Transport Properties and Processes Dermal absorption (Bunge) Microfluidics (Marr) Space and Microgravity Research Membranes on Mars (Way, Baldwin) Water mist flame suppression (McKinnon) Reacting Flows Flame kinetics (McKinnon Dean) Reaction mechanisms (Dean McKinnon) High-T fuel cell kinetics (Dean) tl :; ~-~ ''! ,i ~ l lll l ,IIJ i-4' tJ. !!illn .~ .... .c-;;il'\ -, .. ~ -!"-Finally located at the foot of the Rocky Mountains and only 15 miles from downtown Denver, Golden enjoys over 300 days of sunshine per year. These factors combine to provide year round cultural, recreational and entertainment opportunities virtually unmatched anywhere in the United States. Chemical En g ine e rin g Edu ca tion

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Fa/12002 M.S. and Ph.D programs in chemical engineering RESEARCH IN .. Advanced Proce ss Control Biochemical Engineering Biomedical Engineering Chemical Thermodynamics Chemical Vapor Depo si tion Computation al Fluid Dynamic s Environmental Biotechnology Environmental Engineering Magnetic Re so nance Imaging Membrane Separations Metabolic Engineering Polymeric Material s Porous Media Phenomena Thin Film s Tissue Engineering FINANCIAL AI D AVA I LABLE Teaching and re se arch assistantships paying a monthl y s tip end plus tuition reimbursement. F or app li cations and further information, write Graduate Advisor, Departm ent of Chemical Engineering Colorado State University Fort Collins CO 80523-1370 tate University CSU is located in Fort Collins, a pleasant commu ni ty of 100 ,000 p eople with the s pirit of the West th e v itali ty of a growing metropolitan area, and th e friendliness of a sma ll town. Fort Collins is lo cated about 65 miles north of D enve r and is adjacent to the foothills of th e R ocky Mountains. Th e climate is excellen t with 300 s unn y da ys per ye a,; mild t em peratures, and low humidity. Opportunities for hik ing, biking camping, boating fishing, and skiing abound in the immediate and nearby areas. Th e ca pu s is wit hin easy walking or biking distance of the town '.s shopping areas and its Center fo r the Per forming Arts. FACULTY Brian C. Batt Ph.D. University of Colorado Laurence A. Belfiore, Ph.D. University of Wisconsin David S. Dandy, Ph.D. California In stitute of T ec hnolog y M. Nazmul Karim, Ph.D. University of Manchester James C. Linden, Ph D Iowa State University Vincent G. Murphy, Ph.D University of Massachusetts Kenneth F. Reardon, Ph.D. California Institute of Technolog y Kristina D. Rinker, Ph. D North Carolina Stat e University A. Ted Watson, Ph.D. California In st itute of Technolog y Ranil Wickramasinghe, Ph. University of Minnesota 343

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University of Connecticut University of Connecticut 191 Auditorium Road Unit 3222 Storrs CT 06269-3222 Tel: (860) 486-4020 Fax: (860) 486-2959 www.engr.uconn.edu/cheg cheginfo@engr.uconn.edu 344 Chemical Engineering Department Graduate Study in Chemical Engineering [] B i o c h e mical E n g in ee rin g a nd Bio te chn o l ogy James D. Br ye rs Ph.D. Ric e University (Joint Appointment) Biochemica l Engi n eering, Biofilm Processe s, Biomaterials Rob e rt W Coughlin, Ph D. Cornell University Biotechnology Biochemical and Environmental Engineering Catalys i s Kinetic s, Separations, Surface Science Ranjan Srivastava, Ph.D., University of Mar yland Experimental a nd Computationa l Biology Biomole c ular Network Analysis Stochastic Biologic a l Phenomena Evolutionary Kinetic s Th omas K. Wood, Ph.D ., North Carolina State University Microbiological Engineering, Bior e mediation with Genetically-Engineered B ac teri a, Enzymatic Green Chemistry, Biochemical Engineering, Biocorro s ion [] P o l y m e r Scie n ce Patrick T. Mather, Ph D. University of California Santa Barbara Pol ymers, Mi cros tru ct ur e and Rh eology, Liquid Crystalinity, Inor ga nic -Orga nic Hybrid s Ri cha rd Parna s, Ph.D ., University of California Los Angeles Composites Biomaterial s Montgomer y T. Shaw, Ph.D. Prin ce ton University Pol y mer Rheolo gy and Proce ss ing Polymer-Solution Thermodynamics Rob e rt A. Wei ss, Ph.D ., University of Massachusetts Polymer Structure-Property R e lation s hips Ion-Containing and Liquid Crystal Polymer s, Pol y mer Bl e nd s L ei Zhu, Ph D., University of Akron Polymer Pha se Transitions, Structures of Morphologie s of Block Copo l ymers, Polymeric Nanocomposites Biodegrabable Block Copolymers for Dru g Deli very [] Co mpu ter A id e d Mo d e lin g Luk e E.K. Achenie, Ph D. Carnegie Mellon University Modeling and Optimization Molecular De s ign, Artificial Intelligen ce, Fl exi bility Analysis Thomas F. Anderson, Ph.D., Univesity of California at Berkele y Modeling of Separation Proce sses, Fluid-Phase Equilibria Douglas J Cooper, Ph.D., University of Colorado Process Modeling Monitorin g a nd Contro l Michael B Cutlip Ph.D., University of Colorado Kinetic s and Catalysis Electrochemical Re act ion Engineering Numerical Method s Suzanne Schadel Fenton, Ph.D., University of Illinoi s, Urbana-Champaign Computational Fluid D y n amics, Turbulence Two-Phase Flow [] E n vi ronm e nt a l a nd E n e r gy E n g in eer i ng Can Erkey, Ph.D. Te xas A&M University Supercritical Fluids, Catalysis, Nanotechnology Jam es M. Fenton, Ph D ., University of Illinois Urbana-Champaign Electrochemical a nd Environmental Engineering, Mass Transfer Proce sses, Electronic Materials Energy Systems Fu e l Cells Jos e ph J. H e lbl e, Ph.D. Massa c hus etts Institut e of Technology Air Pollution Aerosol Science, Nanoscale Mat e rial s Sythesis a nd Characterization, Combustion E m erit u s P rofessors C.O. Bennett J P. Bell A.T. DiBenedetto, G .M. Howard H.E. Klei, D.W. Sund s trom Chemical Engineering Education

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CORNELL UN IV FR SIT Y ,l ., .. I J ,t ... :, \ ... ~ 1 \ ,, ,' :., ,. : ,. At Cornell University graduate students in chemical engineering have the flexibility to design research programs that take full advantage of Cornell s unique interdisciplinary environment and enable them to pursue individualized plans of study Cornell graduate programs may draw upon the resources of many excellent departments and research centers such as the Biotechnology Center, the Cornell Center for Materia l s Research, the Cornell anofabrication Facility the Cornell Supercomputing Facility, and the Na n obiotec hn o l ogy Center Degrees granted include Master of Engineering Ma s ter of Science and Doctor of P h ilosophy. All Ph.D. student s are fully funded with tuition coverage and attractive stipends. Re se ar c h A r e a s Advanced Materials Processing B ioc h emica l and Biomedical Engineering Fluid Dynamics, Stability, and Rheology Mo l ecu l ar T h ermodynamics and Computer Simulation Polymer Science and Engineering Reaction Engineering: Surface Science Kinetics and Reactor Design Situated in the s c enic Finger Lakes region of New York State the Cornell campus is one of the most beautiful in the country. Students enjoy sailing, skiing, fishing hiking, bic y cling, boating, wine-tasting and man y other activities. For furth e r information w rit e : Che1111, ul oll(/ 1/1011//1/, 11/m /,11g111, 1 Ill'-: ,0 A. Brad Anton Lynden A. Archer Paulette Clancy Claude Cohen .::: "' Lance Collins T Michael Duncan ; J runes R. Engstrom b.o Fernando A. Escobedo s:: .... .... "' Emmanuel P Giannelis Peter Harriott Yo n g Lak Joo Donald L. Koch Kelvin H. Lee Leonard W. Lion Christopher K. Ober William L. Olbricht David Putnam Ferdinand Rodriguez Michael L. Shuler t i Paul H. Steen Larry Walker Ulrich Wiesner t m e mb e ; Nati o nal A c ad e m y of En g ine e ring t m e mb e r, Am e ri c an A c ad e m y of Art s & S c i e n ce Director of Graduate Studie s School of Chemical Engineering Cornell University 120 Olin Hall Ithaca, NY 14853-5201, e-mail: DGS@CHEME.CORNELL.EDU or "visit our World Wide Web server at: http://www.cheme.comel l. edu Fall 2002 345

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Graduate Stud y & R esearch in Chemical Engineering at Dartmouth's Thayer School of Engineering 346 Dartmouth and its affiliated professional sc h oo ls offer PhD degrees in the full range of science disciphnes as well as MD and MBA degrees. The Tha ye r School of Engineering at Dartmouth College offer s an ABET-accredited BE degree as well as MS Masters of Engineering Management, and PhD degrees. The Chemical and Biochemical Engineering Pro gram features courses in foundational topics in chemical engineering as well as courses serving our areas of re sea rch s pecialization: Biotechno lo gy and biocommodity engi n eering Environmental science and engineering Fluid mechanics Materials science and enginee rin g Process design and evaluation These important research areas are representative of those found in chemical engineering departments around the world. A di s tinctive feature of the Thayer School is that the professors students, and visiting scholars active in these areas have backgrounds in a variety of engineering and scie ntific subdisciplines. This intellectual diversity reflects the reality th a t boundarie s between engineering and scientific subdisciplines are at best fuzzy and overlapping. It also provide s opportu nities for s tudents interested in chemical and biochemical engineering to draw from several intellectual tradition s in coursework and resear c h. Fifteen full-time faculty are active in research involving chemical engineering fund a mental s. Faculty & Research Areas Ian Baker (Oxford) Structure / property relationships of materials, electron microscopy John Collier (Dartmouth) Orthopaedic prostheses, implant/host interfaces Alvin Converse (Delaware) Kinetics & reactor de sign, enzymatic hydrolysis of cellulose Benoit Cushman-Roisin (Florida State) Numerical modeling of environmental fluid dynamics Harold Frost (Harvard) Microstructural evolution, deformation and fracture of material s Tillman Gerngross (Technical University of Vienna) Engineering of glycoproteins, fermentation technology Ursula Gibson (Cornell) Thin film deposition optical materials Francis Kennedy (RPI) Tribology surface mechanics Dani el R. Lynch (Princeton) Computational methods oceanography and water resources Lee Lynd (Dartmouth) Biomass processing pathway engineering, reactor & process design Victor Petrenko (USSR Academy of Science) Phy sica l chemistry of ice Horst Richter (Stuttgart) Thermodynamics, multiphase flow energy conversion, process design Erlan d Sc hul so n (British Columbia) Physical metallurgy of metals and alloys Charles E. Wyman (Princeton) Biomass pretreatment & hydrolysis cellulase synthesis & kinetics, process design For further information, please contact: Chemical Engineering Graduate Advisor Thayer School of Engineering Dartmouth College Hanover, NH 03755 http://thayer.dartmouth.edu/thayer/research/chem-biochem Chemical Engineering Edu ca tion

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University of Delaware www.che.udel.edu/ Faculty Mark A. Barteau ( R o bert L. Pi gfor d P ro f essor ; C h a ir ) S urf ace C hemi s tr y Ca t a l y s i s Kin e tic s Spec t rosco p y, Sca nn i n g Pr obe Micro sco p ies, M a t e ri a l s Antony N. Beris Fluid M ec h a ni cs, Vi scoe l as ti ci t y, N o n e quilibrium T h e rm ody n a m ics N u me ri ca l M e th ods, P ara ll el Co mp u t i n g Douglas J. Buttrey O x i des Th e rm ody n a m ics, Cr ys t a l G row th S t r u c tur e Ca tal ys i s S uper co nduct o r s Jingguang G. Chen ( Materi a l s S c i e n ce a nd En g in ee rin g ; Dir ec t o r Ce nt e r fo r Cataly ti c Sc i e n ce and T ec hn o l ogy) Na n oscale Mi croe l ec t ro ni c D ev i ces, C at a l y ti c M a te r i a l s, E n v ir o nm e nt a l Ca t a l ysis Costel D. Denson M a t e rial s, P o l y m e r s, Co m pos it es Tra n sport Sep ara ti ons Prasad S. Dhurjati Bi o t ec hn o l ogy, Bi o reactor s, M od elin g Bi oinfo rm a ti cs F a ul t D iag n os i s Expe rt Sys t e m s F a ll 2002 The Department of Chemical Engineering The U ni versity of Dela w ar e offers M.Ch.E. and Ph.D. degrees in Chemical Engineering. Bo th degr ees involve research and course work in engi n eering and related sciences. The Delaware tradition is one of st rong interdisciplinary r esea rch on both fundamental and appli ed problems. Jeremy S. Edwards Quan ti ta t ive Ana l ys i s of Metabo li s m a nd Ce llul ar Fate Pr oc e sses; B ioi n fo rm a ti cs a n d Ge n o m ics; Bi o t ec hn o l ogy a n d Metabo l i c E n gi n ee rin g Eric M. F urst Mi cro rh eo l ogy of Co m p l ex Flu ids, Ce llul ar M ec h a ni cs a nd M ove m en t St ru ct ur e a nd D ynamics of Co ll oi d al C r ysta l s, l n t erfacia l Ph e n o m ena Eric W. Kaler (Elizabeth I nez Kelley Professor ; D ea n Co ll ege of E n gi n ee rin g) Co ll oi d s S u rfacta nt s P o l y m e r s T h e rm ody n a mi cs B i o m o l ec ul es Jochen A. Lauterbach co mb i n atoria l cata l ysis a n d hig t hr o u g hp u t sc r eeni n g fabricat i on of co n duc tin g p o l yme r n a n o film s, n o n-li nea r ph e n o m e n a in h eterogeneo u s ca t a l ysis s pectral imaging of diffusio n p r ocesses i n polymers Abraham M. Lenhoff P ro t e i n Bi op h ys i c s, Separati o n s, Co ll oi d s T h e rm ody n a mi cs a nd Tra n spo rt Raul F. Lobo Ad so rpti o n Ca t alysis, Z eo lit es, Mi croporo u s M a t eria l s In o r ganic M a t e ri a l s S y n t h esis Babatunde A. Ogunnaike Pr ocess Con t ro l M ode lin g a n d S i mul a t ion Syste m s B io l ogy A pp li ed St atis ti c s Christopher J. Roberts Kin etics a nd Stati s ti cal T he rm odyna mi cs of L i q ui d s A m o rph o u s So lid s ( Gl asses) P ro t ei n s ; Kin e t ics a nd Th e rm odyna mi cs of P ro t ei n D egra d at i o n ; Pr e di ctio n of Ph ys i c a l and Chemica l S t ab ili ty of Pro t ei n s Anne S. Robinson Bi oc h e m ica l E n g in ee rin g Bi o m o l ec ul e Int eractio n s Bi oreac t o r Co n tro l M olec ul a r E n g in ee r i n g Ce llul a r E n g in eer in g T.W. Fraser Russell ( A ll an P Co l b urn P rofess o r of C h e mi ca l E n g in ee rin g ; Vi ce P rovos t fo r R e s earc h ) P h o t ovo l taic s, M ult ip h a s e F l uid M ec h anic s Stanley I. Sandler (Henry B e lin duP o nt C h ai r ; D irector Ce nt e r fo r M o l ec ul ar and E n gi n ee rin g Th e rm ody n a mi cs ) The rmod y nam ics Sta ti stical Mec h anics Comp u tatio n al C h e mi s try E n v i ro nm e nt Separa t io n s Bi osepara ti o n s Annette D. Shine El ec t ro rh eo l ogy, P o l y m e r Pr ocess in g, Rh eo l ogy S u pe r cri ti cal Fluid s Dionisios G. Vlachos S u rface C h e mi s t r y, Co m b u s ti on, P o ll ution A b a t e m e nt R eac t o r D es i g n ; N u cl eat i o n a nd G ro wth of N a n o pha se Mat e ri a l s and M e mbr a n es; Num e ri ca l M et h ods B i fur ca t io n Th eory P atterni n g of M a t e r ials Norman J. Wagner Co ll o i d a nd P o l y m er Sc i e n ce Rh eo l ogy Sta ti s ti cal M ec h a ni cs o f Com p lex Fl u ids, The r modynam i c s B iopo l y m ers Brian G. Willis C h e mi ca l Ph ys i cal M ec h a n is m s of Co pp e r Meta li za ti o n a nd Se mi co ndu c t or I n t e r co nn ec t Mate ri a l s Co mput a ti o n al C h e mi s tr y Mo d e l s ofC VD G ro wth Mec h a n is m s Ma t e ri a l s P rocess in g r esea r c h of Co mpound Se mi c ondu c t o r Materials fo r Sys t e m o n a C hip I n t egrat i on Richard P. Wool P o l yme r s, Compos it e s, Ad h es i o n Int e rf ace s Ma t e r ia l s fr o m Rene wa bl e R eso ur ces Bi odegra d a bl e Pl as ti cs 34 7

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DREXEL UNIVERSITY M.S. and Ph.D. Programs in CHEMICAL ENGINEERING RESEARCH AREAS Biochemical Engineering Biomaterials Biomedical Engineering Colloids and lnterfacial Engineering Molecular Dynamics Simulations Plasma Processing Polymer Science and Engineering Process Control and Dynamics Rheology and Fluid Mechanics Safety Engineering Systems Analysis and Optimization Tissue Engineering Transport Phenomena ABOUT DREXEL: Full financial support available FACULTY Charles Weinberger, Head (University of Michigan) Cameron Abrams ( University of California) Richard Cairncross (University of Minnesota) Donald Coughanowr (University of Illinois) Nily Dan (University of Minnesota) Elihu Grossmann (University of Pennsylvania) Cato Laurencin (Massachusetts Institute of Technology) Young Lee (Purdue University) Anthony Lowman (Purdue University) Stephen Meyer (Clemson University) Rajakkannu Mutharasan (Drexel University) Giuseppe Palmese (University of Delaware) George Rowell (University of Pennsylvania) Masoud Soroush (University of Michigan) Margaret Wheatley (University of Toronto) Steven Wrenn (University of Delaware) Department is experiencing a dramatic growth in research funding. Drexel is located in downtown Philadelphia with easy access to numerous cultural activities and major pharmaceutical, chemical and petroleum companies. FOR MORE INFORMATION WRITE TO: Professor Tony Lowman alowman@drexel.edu Department of Chemical Engineering Drexel University, Philadelphia PA 19104 Or visit us at: http://www.chemeng.drexel.edu

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.., ECOLE POLYTECHNIQUE MONTREAL Mic h ael D Buschmann Associate P rofesso r, P.En g., Ph.D (MJTI Ti ss u e Eng in ee rin g Biome c hani cs Carti l age Ph ysio l ogy Arthritis R esearc h E-mail : mik e@grbb.po l ym tl. ca Pierre J. Carreau Profe sso r. P.En g., PhD (Wisconsin, Madison ) H ead: Center o n Applied Re sea r c h on P o l y m ers (U RL: www.crasp.po l y mtl.c a) Rh eo l ogica l Prop e rtie s of Su spe n s ion s in P o l y mer s a nd P o l y m e r Blend s Mod e lin g of P o l y m e r Pr ocess in g Mixin g of Nonewtonian Fl uid s E-mail : pierr e carrea u @ mail.pol ym tl. ca Jamal C h aouki Prof essor, Ph D (Polytechnique ) H ead: E n v i ronmental and Bi otec hn o l og i ca l Pr ocess E n g in ee rin g Re sea rch Centre (U RL : www biopro .po l ym tl. ca) Chemical R eact i o n Engineering Multiph ase R eac t o r s Particle Tracking Tomography Fluidization of P owders E-mail : c h ao uki @ bi opro. p oly mtl. ca Louise Deschenes A ssis tant Profe sso r P.En g. Ph.D ( I NRS-Eau ) C o-C hair NSERC Indu s trial Chair on Sit e Biorem e di a tion Intri nsic Soil Bi oremedia ti o n Underground Water Tr eat m e nt E n viro nmental Microbi o lo gy Eco t oxico lo gica l Ri sk Asses s m en t Email : d esc h e n es@ bi opro.po l ym tl. ca Char l es Dubois A ss i sta nt Profe sso r P .Eng Ph.D. (U. Laval ) Rh eo l ogy and Impl e m en t a ti o n of R eac ti ve Med ia P o l ymeriza t ion/Compo undin g E-mail: c harl es. dub ois@polymtl.ca Basil D Favis P rofessor, Ph.D. ( McGill ) Pr ocessi n g-Mo rph o l ogy -Propert y R e l a t io n shi p s in P o l y mer Bl ends Int erface C har ac t e ri za ti o n in Multiph ase S ys tem s E-mai l: favi s @c himie po l ymt l. ca Miroslav Grmela Senior R esearc h A ssoc iate Ph.D. (Prague) Th e rmod y n a mi cs of Irr ev ersible Pr ocesses Mol ec ular Rh eo l og i cal Mod e llin g Flow of Vi scoe l astic Fluid s Pol y m e r Proce ss in g E -m ail : g rm e la @c h i mi e.po l y mtl. ca Marie C laud e Heuzey Assista nt Prof essor, P .E n g., Ph.D ( McGill ) Rh eo l ogy P o l y m e r Pr ocessing E-mail : m c h e u zey@c himie .po l ym tl. ca Chr istoph e Guy Pr ofessor P.En g PhD. (Polytechnique ) Dean of R esea rch Natura l Ga s T ec hn o l ogies Odors Tr e atm e nt of Solid Wa s te s a nd Emis s ion s Multiphase R eacto r s E mai l : c hri s t op h e.g u y@ mail.pol y mtl.c a Mario Jolicoeur, A ss i s tant Profes so r P Eng PhD (Polytechnique ) Bior eac tor E n g ineerin g M yco rrhi za l Fungi-Plant Symbi os i s Metaboli c En g in eeri n g Pharmaceutical En g in ee rin g E-mail: mari o.jo li coe ur @ polymtl.ca I lll 1u1thL1 111!,nm,1111111 uint.tLI u, Danilo Klvana P rofessor Ph.D ( Pra g u e) He ad: G as Technolog y R esearch Group (URL : www .po l y mtl. ca/ udr 7 .htrn ) Catalytic Ga sSolid H ydroge n atio n St orage of Methane Catalytic Combustion Pr e pa rat i o n of Catalysts an d Electrocatalysts E-mai l : d a nilo klv a na @ m a il.p o l ymt l. ca Pierre G Lafleur Pro fessor P En g., PhD (McGill ) D epartmen t C hairman P o l y mer Pr ocessing Comp ut er-A id ed Design Engineering and Manufacturing E-mail : pierre.laneur @ mail.polymtl.ca Robert Legro s, P rofessor P .Eng PhD ( Surre y ) S ol id W aste In c inerati o n Fluidized-Bed Combu s tion Fluidized-Bed D ryi n g Spouted B ed H ydrodynamics Expa nd ed B ed Bi oseparatio n E-mail: robe rt l egros@ m ai l.pol ym tl. ca Jean R Paris Profes so r P .E n g Ph D (Nort h wes t e rn ) H ea d : R esearc h G ro up on Pulp a nd P ape r Scien ce a n d Engineering (URL : www .g re s ip.polymtl.ca ) Pr ocess D esign and A n a l ys is Pr ocess Integrati o n Sy s t em C l os ure in M ec hanic a l and Chemical Pulp Mills Pin c h Analysis Pr ocess Simulation E-mail: jparis @g p apet ier .po l ym tl. ca Miche l Perrier P rofessor In g. PhD ( McGill ) D ynamics and Control of Chemical and Bi ochemical R eactors D ynamics a nd Control of Pulp and P aper Proces ses (URL : www. ur cpc.polym tl. ca/-perrier) E-mail: mi c h e l /pe rrier @ ur cpc. pol y mtl. ca Rejean Samson Profe sso r Ph.D. (Lava l ) NSE R C Indu s trial Chair for Sit e bior e mediation (U RL : www bi op r o polymtl.ca/bioremediation ) Environm e nt al Bi o t ec hn o l ogy W aste Treatment Air P o lluti o n E-Mail: s arn so n @ biopro po l y mtl. ca Henry P. Sc hreib e r S e ni or R esearc h Associate, Ph D (Toronto) Compo si te Material s Surface and Int erface P olymer Properties Microwave Pl as ma Surfac e Treatment E-mail : chreibe r @cras p polymtl.ca Paul Stuart A ssoc i ate Pr ofessor P En g Ph.D (McGill ) NSERC In dustrial Chair Pr ocess Int egration i n th e Pulp in Paper Indu s try E nvironment a l Engineering Pulp and Paper Pro cesses Pr ocess Int egra tion L ow Sludge Produ c tion Waste Water Treatm e nt E-mail: p s tu art@po l y mtl.ca Philippe Tanguy Profe sso r P Eng ., PhD (Laval ) NSERC/Paprican Indu s trial C h air o n Paper Coating (U RL : www urpei pol y mtl. ca) Mi x in g o f Rh eo l og i ca ll y Complex Fluid s Coating Pr ocesses Surface Treatment of Paper E-mail: tanguy @ urpe i po l ym tl. ca lk J1,II llllL lll ill ( lh..'lllll,tl ll~llll'l'I Ill~. I u ik I\ ih ILL lllllLjlll' P() H11\ (10~l) "il,1111 l]l ( l !llll \Ilk \It lll(lL',tl {)LIL hl'L ( d!l,td.1 11 ( '\ PI Hllh..' +I .:;11 ~ -W -Hd; l .t\ +1.::.1--.+ t.+04]",lJ I 111.111 LllL'IIIIL,ilLll!..'.llh...'L'llll~((l111.11l1)(lhlllllL.t \ 1"11 ( llll \\Lh,11L ,ll \\ \\ \\ !..'.L ll pt ih 11111 l ,l Fall 2002 349

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UNIVERSITY OF FLORIDA Graduate Studies in Chemical Engineering leading to M.S. and Ph.D. degrees TIM ANDERSON semiconductor processing thermodynam i cs SEYMOUR S. BLOCK Professor Emeritus biotechnology JASON BUTLER complex flu i ds fluid dynam i cs surface phenomena ANUJ CHAUHAN flu i d mechanics interfac i al phenomena bio materials OSCAR D. CRISALLE process control semiconductors pulp and paper polymer process i ng RICHARD B. DICKINSON cellular enginee ri ng biomedical engineering ARTHUR L. FRICKE Professor Emeritus polymers pu l p & paper characterization GARHOFLUND catalysis surface science semiconductors LEWIS JOHNS transport phenomena applied mathematics DALEKIRMSE computer -aided design process control DMITRY KOPELEVICH multi-scale and molecu l ar modeling TONY LADD statistical mechanics, fluid mechanics biomechanics ATULNARANG kinetics of microbial growth env i ronmental bioengineering RANGA NARAYANAN transport phenomena, applied mathematics low gravity processes MARKE ORAZEM electrochemical engineering CHANG-WON PARK fluid mechanics, polymer processing RAJ RAJAGOPAI.AN colloid physics, particle science FAN REN semiconductor device fabrication and characterization DINEIH. SHAH surface sctences, biomedical engineering lmOSSVOIIONOS Wlll8Wller lr8almenl, particle separations process control JAION F. WEAVER heterogeneous catalysis, dynamics of solid

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Florida A&M Univeristy a,:id Florida State University JOINT COLLEGE OF ENGINEERING GRADUATE EDUCATION AND RESEARCH IN CHEMICAL ENGINEERING and Program in Biomedical Engineering Fall 2002 MS/PhD in CHEMICAL ENGINEERING Advanced Polymers and Materials Process Control and Optimization Reaction Engineering Bioengineering Computational Engineering ar;-id Transport Processes MS/PhD in BIOMEDICAL ENGINEERING T i ssue Engineering Cellular Transport Processes Imaging and Spectroscopy Biointerfacial and Biomedical Engineering Computational Biomedical Engineering For more information contact: Department of Chemical Engineering FAMU FSU College of Engin e erin b g (850) 41 0-6 149 Or visit our websit es: http://www e ng .fsu edu/cheme http : //www.eng fsu edu/bme 35 1

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Graduate Studies in Chemical Engineering Join a small, vibrant campus on Florida 's Space Coast to reach your full academic and professional potential. Florida Tech, the only indepen dent scientific and technological university in the Southeast, has grown to become a university of international standing. Faculty P.A. Jennings, Ph.D. J.R. Brenner Ph.D. D.R. Mason, Ph.D. (emeritus) M.E. Pozo de Fernandez, Ph.D. R.G. Barile Ph.D. M.M. Tomadakis Ph.D. J.E. Whitlow Ph.D. Research Partners NASA/Kennedy Space Center Florida Solar Energy Center Florida Institute of Phosphate Research Department of Energy Florida Space Grant For more information, co nta c t Florida Institute of Technology College of Engineering Dept. of Chemical Engineering 150 West University Boulevard Melbourne, Florida 32901 -6975 (321) 674-8068 Graduate Student Assistantships and Tuition Remission Available ... _, -c ;Wllf .1 1111 Research Interests S1>acecraft Technology Alternative Energy Sources Materials Sl'iem:e Environmental Engineering Expert S:vstems www.fit.edu/ AcadRes/engsci/chemical/chemical.html

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A S Abhiraman: polyme r science and engi neer i ng ; Pradeep K Agrawal : hetereoge nous catalysis surface chemistry reaction kinetics ; Sue Ann Bidstrup Allen : microelec tronics polymer processing ; Andreas Bommarius : biocatalysis b i oprocessing ; L. Victor Breedveld: complex fluids high throughput materials characterization microfluids ; Charles A Eckert: molecular thermodynam i cs chemica l kinetics sepa r t i ons ; Larry J Forney : mechanics of aerosols buoyant plumes and jets ; Martha E Gallivan : process control i nterfac i al sci ence microelectronics ; Denn i s W Hess : microelectronics processing thin film sci ence and technology plasma processes ; Clifford Henderson: microelectronics pro cessing patterning imaging materials thin films ; Jeffery S Hsieh: pulp and paper ; Christopher Jones : catalyst deve l opment for po l ymer synthesis organometallic chem istry ; Paul A Kohl : photochemical process ing chemica l vapor deposition ; William J Koros: structure-permeability relationships for polymers ceramics polymer-ceramic hybrid substrates formation of composite and integrally skinned asymmetr i c mem branes ; Jay Lee : process control integrated sensing and system i dentification ; Charles L. Liotta : synthesis and propert i es of poly meric materials computer modeling of chemical processes ; Peter J Ludovice : molecular modeling of synthet i c and b i olog i cal macromolecules ; J. Carson Meredith : colloid and polymer science and technology related to th i n films and nanotechnology ; John D. Muzzy: polymer engineering energy conservation economics ; Sankar Nair : novel functional materials and nanoscale systems ; Robert M Nerem : biomechanics mammalian cell structures ; Mark R. Prausnitz : bioengineering drug delivery tissue permeabilization ; Matthew J. Realff : optimal process design and scheduling ; Ronald W. Rousseau : separation processes crystallization ; Athanassios Sambanis : bio chemical engineering mic r obial and an i mal cell structures ; Robert J Samuels : polymer science and engineering ; F Joseph Schork : reactor engineering, process control poly merization, reactor dynamics ; Arnold F Stancell : membranes, polymers process econom i cs; Daniel W. Tedder : process syn thesis and simulation chemical separation waste management resource recovery ; Amyn S Teja : thermodynam i c and transport properties phase equilibria supercr i tical extraction ; Mark G White : catalysis kinet ics reactor design ; Timothy M Wick : tissue engineering bioreactor design cell cell interactions biofluid dynamics; Ajit P Yoganathan: biofluid dynamics, rheology transport phenomena Georgia Deru@~ ulliJu@ @uTech[ru@D@@W School of Chemical Engineering Graduate Degree Programs Doctor of Philosophy, PhD Master of Science in Chemical Engineering, MS Doctor of Philosophy in Bioengineering, PhD Master of Science in Bioengineering, MS School Home Page www.che.gatech.edu On-line Graduate Application www.grad.gatech.edu/admissions Contact Information Dr. Ronald W. Rousseau, Chair School of Chemical Engineering Georgia Institute of Technology Atlanta, Georgia 30332-0100 ronald.rousseau@che.gatech.edu

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UNIVERSITY of HOUSTON Chemical Engineering Graduate Program Faculty and Their Research ,:~:-N. R. AMUNDSON (CULLEN PROFESSOR) _____ c he mica/ reactions ; Transport ; Mathematical modeling :~:. V. BALAKOTAIAH ____ C hemica/ reaction engineering ; Applied mathematics :~... A. T. CAPITANO Tissue Engineering ; In Vitro Toxicology a:~V. M. DONNELLY Petroleum engineering; Energy D. J. Eco NO MOU (JOHN & REBECCA MOORES PROFESSOR) Electronic materials ; Composites and ceramics F, M. P. HAROLD (Dow PROFESSOR, CHAIRMAN) Chemical reaction systems 1-_. E. J. HENLEY (EMERITUS) Reliability engineering ; Biomedical engineering "'cZ;_ R. KRISH NAMOO RTI Polymeric materials; Biom ateria/s ~: D. Luss (CULLEN PROFESSOR.) . Houston Dynamic Hub ot Chem i cal Engineering Houston offers the educational, cultural business, sports and entertainment advantages of a large and diverse metropolitan area with significantly lower costs and crime rates than average Houston is also the increasingly dominant hub of the US energy and petrochemical industries as well as the home of NASA s Johnson Space Center and the wor/d ren owned Texas Medical Center The Chemical Engineering Department at the University of Houston offers excellent tac iii lies, competitive fin an cia/ support and an environment conducive lo personal and professional growth For more information www. che e uh. edu grad-che@uh.edu Graduate Office Chemical Engineering University of Houston Houston, TX 77204-4004

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Chemical Engineering at Howard University Where modern instructional and research laboratori es, to gether with computing facilities, support both student and faculty research pursuit s on an eighty-nine acre main cam pus three miles north of the heart of Washington DC. --Faculty and Research Interests------Mo bolaji E. Aluko, Professor and Chair PhD University of Californi a, Santa Barbara R eactor modeling crystalli z ation microelectronic and ceramic materials pro cessing process control reaction engineering analysis Joseph N. Cannon, Profe ssor PhD University of Colorado Transport phenomena in environmental systems computational fluid mechanics heat transfer Ram esh C. Chawla, Profe ssor PhD Wayne State University Mass transfe r and kinetics in environmental systems bioremediation incineration air and water pollution control William E. Co llin s, Associate Professor PhD University of Wiscon s in-Madison P o l ymer deformation, rheology, and surface science biomaterials bioseparations materials science M. Gopala Rao Profe sso r PhD University of Washington, Seattle Adsorption and ion exchange process energ y systems radioactive waste management remediation of contaminated soils and g round wate r John P. Tharakan, AssociateProfessor PhD University of California, San Diego Bi oprocess engineering protein separations biological ha z ardous waste treatment bio-environmental engineering Robert J. Lutz Visiting Profe sso r PhD University of Penn sy lvania Biomedical enginee rin g hemodynamics drug delivery pharmacokinetics Herbert M Katz Professor Emeritus PhD University of Cincinnati Environmental engineering For further information and a ppli cations, write to M.S. Program Director, Graduate Studies Chemical Engineering Department Howard University Washington, DC 20059 Phone 202-806-6624 Fax 202-806-4635 Fall 2002 355

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UIC The University of Illinois at Chicago Department of Chemical Engineering MS and PhD Graduate Program FACULTY ========= Kenneth Brezinsk y, Professor and Head P h.D City University of New York, 1978 E-Mail: Kenbrez@UIC.EDU John H Kiefer Professor Emeritus P h.D Cornell University, 1961 E Mail: Kiefer @ UIC.EDU Andreas A Linninger Associate Professor Ph .D Vienna University of Techno l ogy, 1992 E Mail: Linninge @ u ic edu G Ali Mansoori Professor Ph D ., University of Oklahoma, 1969 E Mail: Mansoori @ UIC.EDU Sohail Murad Professor Ph D., Cornell University, 1979 E-Mail: Murad @ UIC.EDU Ludwig C. Nitsche Associate Professor Ph D. Massachusetts Instit u te of Technology, 1989 E-Mai l : LCN @ UIC.EDU John Regalbuto Associate Professor Ph D. University of Notre Dame, 1986 E-Mail: JRR @ UIC.EDU Salish C. Saxena Professor Emerit u s P h D Calcu tt a University, 1956 E-Mail: Saxena@UIC.EDU Stephen Szepe Associa t e Professor P h .D., Illi n ois I n sti tu te of Techno l ogy, 1966 E-Mail : SSzepe @ UIC.EDU Christos Takoudis Professor Ph.D ., University of Minnesota 1982 E Mail: Takoudis @ UIC.EDU Raffi M. Turian Professor P h .D ., University of Wisco n sin, 1964 E-Mail: Turian @ UIC.EDU Lewis E. Wedgewood Associa t e Professor P h .D., Uni ve rsity of Wisconsin, 1988 E-Mail : Wedge @ uic ed u RESEARCH AREAS T ransport Ph e nom e na: Tran s port propertie s of fluid s, s lurr y tran s port a nd multiphase fluid flow Fluid me c h a ni cs of pol y mer s and other viscoelastic m e dia. T h er mod y namic s: Mo l ecu l a r s imu l ation and s ta t i s ti ca l mec h anics of liq u id m ix t ures S u pe r ficia l fl ui d extraction/retrograde condensation, asp h a l tene c h aracte ri zation Kinetics and Reaction E n g in ee rin g: Gasso lid reaction kinetic s Energy tra n sfer p rocesses la se r d i agnostic s a nd combustion c h emistry. Env i ronmen t al tec h no l ogy su r face che mi stry a nd opti mi zatio n Cat a l yst preparation and c h aracteriza ti o n s u pported m eta l s. Che mi ca l ki n et i cs i n a u to m otive e n g in e emissio n s Biochemical E n g in ee rin g : Bioin s trumentation Bio se para t ion s. Biodegrada bl e po l y mer s Nonaqueo u s enzymo l ogy Optimi za tion of mycobacter i a l fermentations. Materials: Microelectronic material s and proce ss ing heteroepitax y in group rv materials and in s itu s urface s pe c tro sco pie s at interface s. Comb u stion sy nthe s is of ceramics and s ynthe s i s in s upercrit i ca l fluids. Product and Proce ss D evelopment and design co mpu t er-aided m o delin g and s im u lation po ll ution preve nt ion -------For mor e information writ e to Director of Graduate Stu d ies Department of Chemical Engineering University of Illinois at Chicago 810 S C l inton Chicago I L 60607-7000 (3 12 ) 996 3424 Fax (3 12 ) 996-080 8 URL : http : / / www uic.edu/dept s/ chme /

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Chemical and Biomol ecular Engineering at the University of Illinois at Urbana-Champaign Th e combination of distinguished faculty, outstanding facilities and a di vers i ty of research interests results in exceptional oppo r tunities for gradu ate education. The c h emical and biomolecular engineering department offers graduate programs l eading to the M.S. and Ph.D degrees. Richard C. Alkire E l ectrochemical E n g in eer in g Richard D Braatz Advanced Proces s Control Steve Granick Polymer s a nd Biopolymer s Nanorheologyrrribology, and Surface Spectro sco pie s V ina y K. Gupta lat erfac i a l Phenomena: Structure and D y nami cs in Thin Films Jonathan J. L. Higdon Fluid Mechanic s and Computational Algorithm s Paul J. A. Kenis Microreactor s, Microfluidic Tools and Microfabrication Sangtae Kim Bioinform atics, Microfluidic s /Nanofluidics Mark J. Kushner Pla s ma Chemi s try and Plasma Materials Pro cess in g Deborah E. Leckband Bio e n gi n ee rin g and Bioph ys i cs Jennifer A Lewis Colloidal Assembly Complex Fluids and Mesoscale Fabrication Richard I. Masel Kinetics Cataly s i s, Microfuel Cells and Microchemi ca l System s A nthon y J. McHugh Polymer Science and Engineering Dani e l W. Pack Biomolecular Engineering and Bi otechnology Nikolaos V. Sahinidis Optimi zat i on and Proces s Sy s tems Engineering Kenneth S. Schweizer Macromolecular Colloidal a nd Complex Fluid Theory Edmund G. Seebauer Microelectronic s Proce ss ing and Nanotechnology Michael S. Strano Nanofabricated Materials, Molecular Electronic s, and Fullerene Nanotechnology Huimin Zhao Mo l ec ul ar Bi oe n g i neering a nd Biotechnolo gy Charles F. Zukoski Colloid and lnt erfac i al Science Fall 2002 For information and application forms write: D epartment of Chemical and Bi omolec ul ar Engineering University of Illinoi s at Urbana -Champ aign 114 R oger Adams Lab Box C-3 600 S Mathew s Ave. Urbana, Illinoi s 61801 3792 http://www.chemen g. uiu c.e du CREATING YOUR FUTURE 357

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GRADUATE STUDY IN CHEMICAL AND ENVIRONMENTAL ENGINEERING AT Illinois Institute of Technology THE UNIVERSITY Private, coeducational and research university 1700 under graduate students 3000 graduate students Campus recog nized as an architectural landmark Three miles from downtown Chicago and one mile west of Lake Michigan THE DEPARTMENT Among the oldest chemical engineering programs in the nation Merger of chemical and environmental engineering departments in 1995 created state-of-the-art, interdisciplinary research and educa tion program s M S., Professional Master and Ph.D. degrees in chemical and environmental engineering New food process engi neering program New double Master 's degree program in chemi cal engineering and computer science Fellowships and assistant ships available APPLICATIONS Graduate Admissions Coordinator Chemical and Environmental Engineering Department Illinois Institute of Technology 10 W. 33rd Street Chicago IL 60616-3793 Phone : 312-567-3533; Fax: 312-567-8874 http: // www chee.iit.edu / e-mail: chee@iit edu FACULTY AND RESEARCH AREAS Chairman Hamid Arastoopour Associate Chair for Undergraduate Affairs Fouad Teymour Associate Chair for Graduate A ff airs Salish Parulekar Javad Abbasian; separation processes gas cleaning, air pollution Nader Aderangi; unit ope rati o ns c h emical processes Paul R. Anderson; precipitation kinetics eva luati on of oxide adsorbents for water and wastewater treatment Hamid Arastoopour; computational multiphase flow, fluidization, material processing particle technology fluid -particl e flow Barry Bernstein ; computational fluid mechanics, material properties, polymer rheology Donald J Chmielewski; process control, pollution prevention Ali Cinar; chemical and food process control, nonlinear input-output modeling sta tisti cal process m onitoring Dimitri Gidaspow; hydrodynamics of fluidi z ation using kinetic theory, gas-solid transport Henry R Linden;fossil fuel technologies energ y and resource economics energy and environmental policy Dem e trio s J. Moschandreas; ambient and indoor air pollution statistical analysis, enviro nm ental impact assessment Allan S. Myerson; crystallization from sol uti on, nucleation, molecular modeling Kenneth E. Noll; ai r resources e n g in eering, air pollution meteorology, ha z ardous waste treatment Krishna R. Pagilla ; wate r and wastewater enginee rin g, envi ronm e ntal microbiology, soil remediation sludge treatment Satish Parulekar ; biochemical engineering chemical reaction engineering Victor H. P e re z-L un a; biomedical and tissue engineering Jai Praka s h ; sol id state che mi st y, mat erials sy nth esis and c hara cterizat i on for e n ergy co nv ersion and storage applications Jay D. Schieber; kinetic theory, pol y m e r rheology predictions, transport phenomena, non-Newtonian fluid mechanics J. Robert Selman ; applied e l ect ro c h emistry and elect r oc h emical eng in eer ing, battery and fuel cell design Eugene S. Smotkin; FTIR spectroscopy of electrode surfaces, electroc hemi ca l mas s spectroscopy, fuel ce ll s combinatorial catalyst screening Fouad A. Teymour ; polymer reaction engi n ee rin g, math e mati cal model in g, nonlinear d yna mi cs David C. Venerus; polymer rheology and processing, transport phenomena in pol ymeric syste ms Darsh T. Wasan; thin liquid films; int erfacia l rheology ; foams, em ulsion and dispersion, environmental technologies Research Faculty and Lecturers Said AI-Hallaj Michael Caracotsios E lli s Fields William Franek Ted Knowlton Harold Lindahl Robert Lyczkowski Zoltan Nagy Alex Nikolov Ali Oskouie Giselle Sandi Charles Sizer Hw a-Chi Wang

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Graduate program for M.S. and Ph.D. degrees in Chemical and Biochemical Engineering F A CULTY Gary A. Aurand North Carolina State U 1996 Supercritical fluids / High pressure biochemical reactors Stephen K. Hunter U. of U tah 1989 Bioartificial organs / Microencapsulation technologies David Rethwisch U. of Wisconsin 1985 Membrane science / Polymer science / Catalysis Audrey Bu t ler U of Iowa l 989 Chemical precipitation processes Julie LP Jessop Michigan State U. 1999 Polymers / Microlithography / Spectroscopy V.G.J. Rodgers Washington U. 1989 Transport phenomena in bioseparations / Membrane separations Greg Carmichael U. of Kentucky 1979 Global change / Supercomputing / Air pollution modeling Robert Li nhardt Johns Hopkins 1979 Biopolymers and pharmaceutical applications Alec B. Scranton Purdue U 1990 Photopolymerization / Reversible emulsifiers / Polymerization kinetics f & -, '+:;.,' Vicki H G r assian U. of California Berkeley 1987 Surface chemistry / Heterogeneous processes David Murhammer U. of Houston 1989 Insect cell culture / Bioreactor monitoring Ramaswamy Subraman i an Indian Institute of Science 1992 Structural enzymol ogy/Structure function relationship in proteins C. Allan Guymon U. of Colorado 1997 Polymer reaction engineer ing / UV curable coatings/ Polymer liquid crystal composites Tonya L. Peeples Johns Hopkins 1994 Bioremediation/ Extremophile physiology and biocata l ysis John M. Wiencek Case Western Reserve 1989 Protein crystallization/ Surfactant techno l ogy For information and application: THE U NIVE RS ITY O F I O W A Graduate Admissions Chemica l a n d Bioc h emical E ng i n eeri n g 4133 Seama n s Center Iowa City IA 52242 1527 1-800-5 5 3-IOWA (l-800-5 5 3-4692) chemeng@icaen.u io wa e d u www eng i neering. uio wa.edu/ ~chemeng/

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I OWA STATE UNNERSITY OF SCIENCE AND TECHNOLOGY :-:... :B ro wn Robert: C D o r my LK. Fox, Rodny 0 G ie tz. CharlH E Oon u l u Ram o n Hebert. Kurt A Hlll JamH C Joll Kennath R M a ll ap rapda Surya K Na raN mha n B al JI Reilly PeurJ Aollfn 0..-ric lc K Sc hnder Glan n L Sa .. rave Richard C S hanb Brant H Sh a nb J .c qua U ne V Uh t ch.on Daan L V19U A Da n n ie Whaafoc k. ThomH D Youngqula t. Gordon R. Robert C. Brown Michiga n State L. K. Doraiswamy Wisconsin Charles E. Glatz Wisconsin Graduate Adm i ssions Comm i ttee Department of Chemical Engineering I owa State Un i v er s i ty Am e s I o wa 500 11 515-294-7643 Fax : 5 1 5 294 2689 chemengr@iastate.edu www .i as t at e. edu/ -c h _e Ramon Gonzalez Chile Kurt R Hebert Illinois JamesC Hill Wash i ngton Kenneth R Jolls Illinois Surya Mallapragada Purdue Balajl Narasimhan Pur d ue Peter J Reilly Pennsylvania Derrick K. Rollins Ohio State Richard C Seagrave Iowa State Jacqueline V Shanks Cal Tech Brent H Shanks Cal Tech Glenn L. Schrader Wisconsin Dean L. Ulrichson Iowa State R. Dennis Vigil Mich i gan Thomas D Wheelock Iow a Sta t e Gordon R. Youngquist Ill i nois

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Graduate Study and Research in Chemical Engineering at Johns Hopkins The Johns Hopkins University's Department of Chemical Engineering, established in 1936, features a low student-to-faculty ratio that fosters a highly collaborative research experience. The faculty are internationally known for their contributions in the traditional areas of chemical engineering re search, such as thermodynamic s, fluid dynamics, and rheology, and at the forefront of emerging technologies, such as membrane-based separation processes, recombinant DNA technology, tissue engineering, and molecular/cellular biomedical engineering. Insect Cell Culture Recombinant DNA Technology Protein Folding and Aggregation Michael J B ete nbau g h PhD University of Delaware Equations of State Statistical Thermodynamics Solvent Replacement Marc D Donohue PhD University of California B erke l ey Nanostructured Materials Colloid/Protein Adsorption Rheology of Suspensions Je ff re y J. Gray, PhD University of Texas at Austin Biomaterials Synthesis Controlled/Targeted Drug Delivery Tissue Engi neering Ju s tin S. Hane s, PhD Massachusetts Institute of Technology Biomaterials and Na nocomposite Materials Macromolecular Transport Rheolog y of Soft Materials Jame s L. Harden PhD University of California, Santa Barbara Nucleation Crystallization Flame Generation of Ceramic Powders Joseph L. Katz PhD University of Chicago Fluid Mechanics in Medical Applications Vascular and Cellular Biology Thrombosis, Inflammation, Cancer Metastasis Konstantino s Kon s tantopoulo s, PhD Rice University Th e John s H o pkin s Uni vcrs i1 y d oes not di sc riminat e on the basi s of ra ce. co l or. sex. r e ligi on, sex ual o rient atio n nali o n a l or e thni c origi n age. di s abilit y or ve t eran s tutu s in an y s tudent program or acti vi t y adminis t ered by th e Universi t y o r wi th rega rd to ad mi ss i o n or e mpl oyment. Defense Department d i sc rimin a t ion in ROTC program s o n the ba s i s of hom osex u11li1 y co nfli c t s w ith thi s unive rs ity policy. Th e uni vers it y i s co mm itte d t o e ncoura g in g a c h a n ge i n the Defense Depanm ent policy Questions regarding Tille VI. litle IX and Section 504 should be referred to Yconne M Theodore Affirmative Aclion Officer. 205 Garland Hall ( 410-516-8075) Fall 2002 Molecular Bioengineering Protein Engineering Molecular Evo lution Marc Ostermeier, PhD University of Texas at Austin Surfactant/Supercritical Fluid Phase Behavior Computational Molecu lar Thermodynamics Polymer/Protein Thermodynamics Michael E. Paulaitis, PhD U ni versity of Illinois Interfacial Phenomena Surfactant Transport Kinetics Maragoni Effects Kathleen J. Stebe PhD The City University of New York Phase Transitions and Critical Phenomena Polymer Systems Far from Equilibri um Particle-Tracking Microrheology Denis Wirtz, PhD Stanford University For further information contact: Johns Hopkins University Whiting School of Engineering Department of Chemical Engineering 3400 N Charles Street Baltimore MD 21218-2694 410-516-5455 I che@jhu.edu http://www.jhu.edu/~cheme OHNS HOPKINS 36 1

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Graduate Study in Chemical and Petroleum Engineering at the UNIVERSITY OF KANSAS The University of Kansas is the largest and most comp r ehensive university in Kansa s. It has an en rollm e nt of more than 28 000 and almost 2,000 faculty mem bers. KU offers more than JOO bachelors ', nearly 90 ma sters', and more than 50 d octora l pr ogra ms Th e main ca mpu s is in Lawrence, Kan sas, with ot h e r ca puses in Kansas City, Wi c hita, T o p e k a, and Ov e rland Park Kan sas. Graduate Programs [] M S. degree with a thesi s requirement in both chemical and petroleum engineering [] Ph D degree characterized by moderate and fleX_ible course requirement s and a s trong re searc h emphasis [] T y pical completion time s are 16-18 month s for a M S. degree a nd 4 1/2 years for a Ph.D. degree (fro m B.S .) Faculty Kenneth A. Bishop (Ph.D., Oklah o ma ) Kyle V. Camarda (Ph.D. Illin o i s) John C. Da v i s ( Ph.D. Wyoming) Don W. Green (Ph.D Oklahoma) Colin S Howat ( Ph.D ., Kansa s) Carl E Locke, Jr ., (Ph.D., T exas) Trung V. Nguyen ( Ph.D. T exas A&M) Karen J. Nordheden ( Ph.D ., Illinoi s) Russell D. O s terman (Ph.D ., Kan sas) Marylee Z Southard ( Ph D ., Kansa s) Susan M. William s (Ph.D., Oklah o ma) Bala Subramaniam Chair (Ph.D ., Notr e Dame ) Shapour Vossoughi (Ph.D ., Alb e rta Canada) G. Paul Willhite (Ph.D., Northwestern) Research Catalytic Kinetics and Reaction Engineering Catalytic Material s and Membrane Proce ss in g Controlled Drug Deli very Corro s ion Fuel Cells Batt eries Electrochemical Reactor s and Proce sses Electronic Materials Proce ssing Enhanced Oil Recovery Proce sses Fluid Ph ase Equilibria and Proce ss De s ign Molecular Product De s ign Proce ss Control and Optimization Supercomputer Applications Supercritical Fluid Application s Financial Aid Financial aid i s ava ilable in th e form of re sea rch and teaching assis tantship s at $16,000 a year (pl u s tuition ) and fellowships/scholar s hips s uch a s those noted below Madison & Lila Self Graduate Fellowship Mis s ion : identify recruit, and provide development opportunities for exceptional Ph.D s tudent s. Fourye ar award consistin g of an annual $20,600 s tipend plu s full tuition and fees. An additional bonu s of up to $ 10 000 per year is possible. For add itional information and app lic ation : http://www unkans.edu/~ se l fpro /home/index.html Kansas and Missouri High School Graduates Scholarship of $22,000 annually plu s full tuition and fee s Contacts Web s ite for information and application: http://www.cpe.engr.ku.edu/ Graduate Program Chemical and Petroleum Engineering University of Kan sas -Learned Hall 1530 W. 15 th Street Room 4006 Lawrence KS 660457609 phone: 785 -8 642 900 fax : 785-864-4967 email: cpeinfo@ku .edu

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Durl a nd H a ll H o m e o f Ch e mic a l E n g in ee rin g KANSAS STATE UNIVERSITY M.S. and Ph D. Programs Chemica l Engineering with Interdisciplinary Areas of: Systems Engineering Environmental Engineering Complex Fluid Flows Financial Aid Availabl e Up to $24 500 Per Year For More Information Write T o Professor J H Edgar Durland Hall Kansas State University Manhattan KS 66506 or v i s it ou r web s i te a t http : //www engg ksu edu / CHEDEPT / Fall 200 2 Areas of Study and Research Biopolymers Biotechnology Catalytic Hydrocarbon Conversion Chemical Reaction Engineering Crystal Growth of Semiconductors Environmental Pollution Control Hazardous Waste Treatment Membrane Separations Multiphase Flow Polymeric Materials Properties Process Systems Engineering and Artificial Intelligence Separative Reactors 363

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University of Kentucky Department of Chemical & Materials Engineering Catalysi s Environmental Engineering Biopharmaceutical & Biocellular Engineering The Chemical Engineering Faculty Material s Synthe s i s Advanced Separation & Supercritical Fluids Proce s s in g D o nn Hancher Interim Chair Purdu e U ni ve r s i ty K And e r so n Carn eg i eM e ll o n Univ e r s i ty D. Bhatta c haryya Illin o i s I nstitut e of T ec hn o l ogy A G ee rt se ma Uni ve r s i ty of Karl s ruh e Membranes & Polymer s Aeroso l s For more i11for111atio11: E. Grulk e Ohi o S tat e U ni ve r s i ty C. H a mrin ( Pro fesso r E m e ritu s) No rth wes t e rn U ni vers i ty D K a lik a U ni ve r s it y of Ca l ifo rni a B e r ke l e y M K ea n e Nat i o n a l U ni ve r s i ty of Ir e l a n d R. K e rm o d e No rth weste rn U ni ve r s i ty B Knut so n G eo r g i a In s titut e o f T ec hnol ogy S Rankin U niv e r s i ty of Minn eso ta A R a y C l a rk so n U ni ve r s i ty J.T S c hrodt U ni ve r s it y of Lo ui svi ll e T. T sa n g Uni ve r sity of T exas Paducah KY, Program P. Dunb a r U ni ve r s i ty of T e nn essee R. L ee -D esa ut e l s Ohi o S t a t e Un i ve r s i ty D Sil ve r s t e in V a nd erb ilt U ni v e r s i ty J. Smart Univers i ty of T exas W e b : http://www. e ngr.uky .e du/cme Email : cm e -admit @ en g r.uky. e du Addr ess: D e partment o f Chemical & M a t e rial s En g in ee rin g Dir ec t o r o f Graduat e Studi es, Chemical E n g in ee rin g 177 And e r s on Hall Uni ve r s it y of Kentu cky L exi n g ton KY 40506-0046 Ph o n e (8 59 ) 2 57 -8 0 28 F ax (8 5 9) 3231 929

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Polymer engineering P10eess modelling Rheology Polymer processing Fa/12002 F KUIM DES SC llNC ES ET D GE NIE UNIVERSJ'It !AV AL Research Areas Mosto M. Bousmina (Ph D Eco l e des Hauls Polymeres Strasbourg) bousmlna @gch ul ava l. ca (4 18 ) 656 2769 rhe o l ogy and modelling polymer blends and processing polymer physics and engineering Alain Garnie r (Ph.D Ecole Polytechnique de Montreal) alain garnier@gch ulaval ca (418) 656 3106 biochemical engineering anima l cell cu ltur e v iru s and protein production Suzanne Giasson (Ph.D University of Western O nt ario and IFP Paris) sgiasson@gch ulaval.ca (4 1 8) 656-3774 intermolecular and intersurtace forces comp l ex fluid systems, polymers biomaterials nanorheology nano tr lbology Bernard G r andjean (Ph.D Ecole Polytechnique de Montreal) grandjean@gch ulaval ca (418) 656-2859 catalytic membrane reactors neura l network, genetic algorithm pr ocess m ode llin g Serge Kaliagu i ne (D Ing I GC Toulouse) kaliagui@gch ulaval.ca (418) 656 2708 zeo l ites mesostructured materials perovskites catalytic merrt>ranes and fuel cells industrial catalysis Ren e Lacr o ix (Ph.D UnlverslM Laval) l acro i x@gch ulaval c a (4 18 ) 656 3564 finit e e l e m e nt m e th od numer ica l slmu l ation o f cooling processes therm o-electrk:a l s imulati on Fa "i,; al Larachi (Ph.D INPL Nancy) flarachi@gch.ulaval ca (4 18 ) 656-3566 multiphase reactors we t oxkiat i o n flow instrumentation Anh LeDuy ( Ph.D University o f Western Ontario ) leduy@gch ula v al.ca (418) 656 2634 biochemical and microbial processes biokinetics Jean C l aude Methot (P h.D Unlversile Laval) methot@gch ula val.ca (418) 656 2539 Denis Rodrigue (Ph.D Universite de Sherbrooke) denls rodrigue@gch ulaval ca (418) 656-2903 transport phenomena rheology polymeri c foams Christian Roy (Ph.D Universite de Sherbrooke) c r oy@gch.u la va l .ca (4 18 ) 656 7406 vacuum pyrolysis vapor phase m embranes industrial process engineering Additional information and A pplications may be obtained from : Head of Graduate Programs Al ain Garnier 08parteme n t de G8nle chimique Pavilion Adrien-Pouliot Universlte Laval Quebec (QC) Canada GlK 7P4 alain garnier@gch ulaval.ca www.gc h ulaval.ca Phone : (4 18 ) 656-3106 FAX : (418) 656 5993 365

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Synergistic, interdisciplinary research in .. Biochemical Engineering Catalytic Science & Reaction Engineering Environmental Engineering Interfacial Transport Materials Synthesis Characterization & Processing Microelectronics Processing Polymer Science & E n g in eering Process Modeling & Contro l Two-Phase Flow & Heat Transfer ... leading to M.S., M.E., and Ph.D. degrees in chemical engineering and polymer science and engineering Highly attractive financial aid packages "Which provide tuition and stipend, are available. Philip A. Bl y the (University of Manchester) fluid mechanics heat transfer applied mathematics Hugo S. Caram (Univers it y of Minnesota) gas-solid and gas-liquid systems optical technique s reaction engineering Marvin Charles (Po l ytechnic Institute of B rooklyn) bioprocess design cGMP R&D Manoj K. Chaudhury (SUNY-Buffalo) adhesion thin films surface chemistry John C. Chen (University of Michigan) two-phase vapor-liq u id flow fluidization radiative h eat transfer enviro nm e nt al technology Mohamed S. EI-Aasser (McGill University) polymer colloids and films emulsion copolymerization polymer synthesis and characterization James T. Hsu (Northwestern University) bioseparations applied recombinant DNA technology Andrew Klein (North Carolina State University) em ul s i on polymerization co lloid a l and surface effects in polymerization Mayuresh V. Kothare (Ca li fornia Institute of Technology) model predictive control constrained control microc h emical systems William L. Luyben (U ni versity of Delaware) process d es i gn and co ntr o l distillation William E. Schiesser ( Prin ceto n University) numerical algorithms a nd software in c h emical eng in eering Arup K. Sengupta (University of Houston) use of adsorbents ion exchange reactive polymers, membranes in environmental pollution Cesar A. Silebi (Le hi g h University) se p aration of co lloid a l particles e l ectrop h o r es i s m ass transfer Leslie H. Sperling (Duke University) mechanical and morphological properties of polymers interpenetrating polymer networks Fred P. Stein, Emeritus (University of Michigan) thermodynamic properties of mixtures Harvey G. Stenger, Jr. (Massachusetts In stitute of Technology) reactor engineering Israel E. Wachs (Stanford University) materials c h aracter i zation surface c h emistry heterogeneous catalysis environmen t a l catalysis Leonard A. Wenzel, E m er itu s (U ni versi t y of Michigan) thermodynamics cryoge ni cs and mixed-gas adsorp ti o n Living in B ethlehem, PA allows easy access to cu ltural and recreational opportunities in the New York-Philadelphia area. Additional information and applications may be obtained by writing to: Dr James T. Hsu, Chairman Graduate Committee Department of Chemical Engineering Lehigh University 11 Research Drive lacocca Hall Bethlehem, PA 18015 FAX: (610) 758-5057 E-MAIL: inchegs@lehigh.edu WEBSITE: www.lehigh.edu/~inchm/index.html

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UNIVERSITY -----OF----LQ UJSJAN A Lafayette MS in Engineering Chemical Engineering Faculty C.S. Fang PhD University of Hou sto n TX (1968) F F. Farshad, PhD, University of Oklahoma OK ( 1975) J.D. Garber (Head) PhD Georgia In s titute of Technology GA (197 1 ) A G. Hill PhD, Louisiana Technical University, LA ( 1 980) J.N Linsley PhD Rice University TX ( 1970 ) R.D K. Mi sra, PhD University of Cambridge, UK ( 1984 ) A.B. Ponter DSc Birmingham University, UK ( 1986 ) PhD, Manchester ( 1966 ) J.R. Reinhardt PhD Un i versity of Arkansas AR ( 1977 ) Research Centers Corrosion R esea r ch Center Dr. J D. Garber Director Center for Metals Pol yme r s and Composites R esearch Dr. R D K Misra Director Edith Garland Dupre Library For more information: Atomic Force Microscopy of Deformed High Density Polyethey/ene R esearch Areas Corrosion Gas and Oil Well Modeling Pipeline Steels Hydro ge n-Induced Cracking Materials: Structure/Processing/Performa n ce Irradiation of Polymers with UV /Ozone Deformation Behavior of Polymers and Composites Formability and Fracture Toughness of High-Strength Steels Co ld Work Embrittlement of Int e r stitia Free Steels Casting of Precious Metal s and Alloys Fluid Flow and Transport Phenomena Pha se in vers ion Drop Coalescence Liquid Spreading Multiphase Flow Surface Roughness Thermodynamics and Proces s Engi neerin g Pha se Equi l ibria in Multipha se System s Chemical Reactor Design Stability and Dynamics Pro cess Simulation and Design Department of Chemical E n gi ne ering U niv e r s ity of Louisiana at Lafayette PO Box 44130 Lafayette, LA 70504-4130 www.louisiana.engr.edu/chee/ or e-mail: dmisra@louisiana.edu (Graduate Coordinator) Fall 2002 367

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LOUISIANA STATE UNIVERSITY CHEMICAL ENGINEERING GRADUATE SCHOOL T H E CITY ------Baton Rou ge i s the s tat e capitol a nd home of th e m ajo r s t ate institution for hi g her education LSU. Situated in the Acadian region Baton Rouge blend s the Old South and Cajun Cultures. Baton Roug e i s one of th e nation 's bu s ie st port s a nd the city's economy rests heavil y on the chemical oil pla s ti cs, a nd agricul tural indu s tries. Th e great outdoors provide exce llent recre at ion a l activities year-round especially fishing hunting a nd water s port s The proximity of N ew Orleans provide s for s uperb nightlife especially during Mardi Gras. Th e c ity is a l so only two hour s away from the Mi ss i ss ippi Gulf Coast and four hour s from e ither Gulf Shores or Hou s ton. T H E D E P A R TMENT-----M.S and Ph D Program s Approximately 60 Graduate Students Average research fundin g mor e than $2 million per year DEPARTMENTAL FACILITIES Departmental computing-with mor e than 80 PC s Exten s ive laboratory facilities especially in reaction and environmental engineering transport phenomena and separations, polymer textile and materials proce s ing biochemical engineering thermodynamic s T O A P PLY, CONTACT DIRECTOR OF GRADUATE INSTRUCTION Gordon A. and Mar y Cain Dep a rtment of Chemical Engineering Louisiana St ate University Baton Rou ge, LA 70803 T e lephon e : 1 (8 00) 256 2084 FAX: (225) 578-1476 em a il : gradcoor @c h e. l s u .e du FACULTY T.J. CLEIJ ( Ph D ., Utrecht University) P o l y m er i c M ate ri a ls Science and Engineering A.B. CORRIPIO ( Ph.D. Loui sia na State University) Control, Simulation Computer-Aided D esign K.M. DOOLEY ( Ph D ., University of Delaw are) H ete r ogeneous Ca tal ys is, Hi g h Pr essu re Separations G.L. GRIFFIN (Ph.D., Prin ceto n University) Electronic Materials Surface Chemistry, CVD D.P. HARRISON ( Ph D. University of Texa s) Fluid-Solid R eactions, Ha zardous Waste Treatment M.A. HJORTS0 ( Ph.D. University of H o u sto n ) Bio c hemical R eactio n Engin ee ring Applied Math F.C. KNOPF (P h.D. Purdue University) Supercritical Fluid Extraction, U ltrafast Kinetics B.J. McCOY (P h D ., University of Minnesota) Separation Transport R eaction Engineering R.W. PIKE ( Ph D ., Georgia Institute of Technolo gy) Fluid D y namics R eaction Engineering, Optimi z ation E.J. PODLAHA ( Ph.D. Columbia University) Electrical Ph enomena, Allo y and Composite Materials D D. REIBLE ( Ph.D. Californi a In s titute of Technology ) Environmental Transport, Transport Mod e lin g A.M. STERLING ( Ph.D. University of W as hin gto n ) Transport Ph e n omena, Combustion J.J. SPIVEY ( Ph D ., Louisiana State University) Catalysis L.J. THIBODEAUX ( Ph.D ., Louisiana State University) Chemodynamics Ha za rdous W as t e Transport K.E. THOMPSON ( Ph.D. University of Michigan ) Tran s p or t and R eaction in P orous Media K.T. VALSARAJ ( Ph.D. Vanderbilt University) Environmental Transport, Separations D.M. WETZEL ( Ph D ., University of D e lawar e) H azardous Wast e Treatment Dr y in g M.J. WORNAT ( Ph D ., Ma ssac hu se tt s In st itut e of T ec hnol ogy) Co mbustion H eterogeneo u s R eactio ns FINANCIAL AID-------A ss istantship s at $ 17 ,5 00 $29,20 0 with waiver of out-of-state tuition

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MANHATTAN Offering a Practice-Oriented Master's Degree Program Fall 2002 COLLEGE This well-established graduate program emphasizes the app li ca ti o n of basic principles to the solution of mod e rn e n g in eeri n g problems with new features in e n gi n eering m a n agement, environmental management, and bi oc h emica l engineering Financial aid is available including industrial fellowships in a one-year program involving participation of the following companies: ABB Lummus Global Inc. Air Products and Chemicals, Inc. Consolidated Edison Co. Merck & Co., Inc. Pfizer Inc. Chevron Texaco Global Phillips 66 For information and application form, write to Graduate Program Director Chemical Engineering Department Manhattan College Riverdale, NY 10471 in Chemical Engineering ., ., ., Ill Manhattan College is lo cate d in Ri verdale, an attractive area in th e northwest section of New York City. chmldept@manhattan.edu http://www.en gineering.manha ttan .e du/ gra duate/application/ crea te _a ccount.aspx 369

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CHEMICAL ENGINEERING UNIVERSITY OF Faculty and Research Areas Raymond A. Adomaiti s (IIT) Systems modeling and simulation methodologies ; semiconductor manufacturing Mikhail A Anisimo v (Moscow) Critical phenomena and phase transitions in fluids and flu i d mixtures Timoth y A. Barbari ( T exas A u st in ) Mem b ra n e science, polymer science, biomaterials William E. Bentle y (Co l ora d o) B iochemica l/ meta b olic engineering, applications of molecular biolog y Richard V. Calabrese ( M assac hu se tt s) Mu l t ip hase flow, turb ul ence and mixing K y u Yong Choi (W i sco n sin) Polymer reaction engineering Panagiotis Dimitrakopoulos (Ill i nois-Ur b ana) B iofluid mechanics, bioph y sics and microrheology Sher y l H. Ehrman (UC L A) Aerosol and nanoparticle technology John P. Fisher ( Ri ce) Tissue engineering, biomaterials James W. Gentr y ( T exas A u st in ) Aerosol science and enginee r ing Sandra C. Greer ( Chi cago) P hysical chemistry, po l ymer science, biomacromolecules phase equilib r ia Maria I. Klapa (M IT ) Metabolic engineering, bioinformatics, modeling of biological networks Peter Kofinas ( MIT ) P olymer science and engineeri n g Thoma s J. McAvo y ( Prin ceto n ) P rocess control, fault detection Tracey R. Pulliam Holoman ( M ary l a nd ) B iochem i cal engi n eering and bioremediation Jan V. Sengers ( U Ams t erdam) Critical phenomena, the r moph y sical properties of fluids and fluid mixtures Srinivasa R. Raghavan (N C St a t e) P olymers, co ll oids, comp l ex fluids, self-assembl y Nam Sun Wang ( C a lt ec h ) B iochem i ca l enginee r i n g William A. Weigand ( II T) B iochemical engi n eering bioprocess control and opt i mi z ation Evanghelos Zafiriou ( C a lt ec h ) P r ocess cont r o l identification and optimi z ation Location : T h e U n iversity of Marylan d is l oca t ed in close p roxi mi ty to the natio n s ca p ital, W as h i n gton, D C., a nd a numb e r of government l a b ora t or i es, in clu d i n g NIST NI H N RL A RL U SD A, a nd F D A 37 0 For Applications and Further Information, Write Graduate Admissions Director Department of Chemical Engineering Room 2113 Building 090 University of Maryland College Park MD 20742-2111 http:/ /www.ench.umd.edu Ch e mi ca l E ngin eeri n g Edu c ati o n

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UMBC Universi t y of Maryland B a ltimore Count y EMPHASIS Th e D e p art m e nt of Ch e mi ca l an d Bi oc h e m c al En g in ee rin g at UMBC off e r s gra du a t e pro g ram s l ea din g t o M S a nd Ph D d egrees in Ch e mi ca l En g in ee rin g. Our r esearc h i s h ea vil y foc u se d in bi oc h e mi ca l bi o m e di ca l a nd bi o p rocess e n g in ee rin g a nd cove r s a w id e ra n ge o f areas in c ludin g ferme nt at i o n ce ll c ultur e d ow n s tr e am p rocess in g, dru g d e li ve r y, p ro t e in e n g in ee rin g an d b io op ti cs Uniqu e pro g ram s in th e r eg ul a t o r y-e n g in ee r in g int e rf ace o f bi o pro cess in g a r e offe r e d as we ll. FACILITIES Th e D e partm e nt off er s s t a t e ofth e-a rt fa ciliti es fo r fac ult y and gra du ate st ud e n t r esearc h Th ese m o d e m fac ili ties h ave been d eve l o p e d prim a ril y in th e l as t s i x years a n d co mpri se 6 000 s quar e fee t of l a b ora t o r y s p ace in th e T ec hn o lo gy R esearc h Ce nt er plu s 7 000 s quar e fe e t of d e p art m e nt a l l a b o rat o ri es in th e n ew En g in ee r i n g and C o mput e r S c i e n ce buildin g. LOCATION UMBC i s lo ca t e d in th e B a lt i m o r eW as hin g t o n co rridor a nd w ithin easy access to b o th metrop o litan ar eas. A numb e r of gove rnm e nt r es ear c h faciliti es s u c h as NIH F DA US DA NSA a nd a l arge numb e r of bi o t ec hn o l ogy co mpani es are l oca ted n ear b y a n d prov id e exce ll e nt o pportuniti es fo r r esea r c h int erac ti o n s. FOR FURTHER INFORMATION CONTACT: Graduate Program Coordinator Department of Chemical a nd Bi oc hemi ca l Engine e ring Univer s it y of Maryland B a ltimor e C o unt y IO00 Hilltop Cir c l e Baltim o r e, Mar y land 2 1 2 50 Phon e: ( 4IO ) 4 5534 00 FAX : (4 IO ) 4 55 104 9 F a ll 200 2 Graduate Study in BIOCHEMICAL ENGINEERING For Engineering and Science Majors FACULTY D. D. FREY Ph.D. California-Berkeley Separation and transport processes in biotechnology; protein purification; chromatography. T. GOOD, Ph.D. University of Wisconsin-Madison Cellular Engineering; Protein Aggregation: In Vitro Models of Disease M. R. MARTEN, Ph.D. Purdue Bioprocess engineering; Fermentation; Cell biology and protein secretion; Proteomics A. R. MOREIRA, Ph.D. Pennsylvania rDNA fermentation; Regulatory issues; Scale-up; Downstream processing G. F. PAYNE Ph.D.* Michigan Pl ant cell tissue culture; Streptomyces bioprocessing ; Adsorptive separation; Toxic waste treatment G. RAO Ph.D. Drexel Fluorescence-based sensors and instrumentation; Fermentation and cell culture J.M. ROSS Ph.D. R ice Cellular and biomedical engineering; Cell adhesion; Tissue engineering J oint appointment with t h e University of Maryland B iotechno l ogy In sti tut e 37 1

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372 Come to Chemical Engineering at the University of Massachusetts Amherst Amherst is a prett~ New England n1llege town in Western l\lassadrnsetts. Set amid farmland and rolling hills, the area offers pleasant living conditions and extensive recreational facilities, and urhan pleasures are easily accessihle. Faculty M.F. Malone (Massachusetts), Head S.R. Bhatia (Princeton) W C. Conner Jr. (Johns H op kin s) J.M Dougla s, Emeritu s ( D elaware) N S. Forbe s (Berkeley) V. Haen se l Emeritu s (Northwestern) M.A. Hen so n (U C Santa B a rb a ra) R.L. Laurence Emeritus (Northwestern) E. Kokkoli (Illinois-Urbana) D. Marouda s (MIT) P.A Monson (London) S C Robert s (Cornell) J D Sherman (MIT) M T s apat s is ( Calt ec h ) J.J. Watkin s (Massachusetts) P R. We s tmoreland (MIT) H.H Winter (Stuttgart) Current Areas of MS and PhD Research Process de s ign : Method s, di s till a tion proce ss control Material s: Polym e r s and inor ga ni cs, multi sca l e mod e lin g Kinetic s a nd reaction e n gi n ee rin g: Catalyti c, biolo gica l noncatal y ti c Molecul ar ly b ase d mod e lin g : Stati s tic a l mechani cs, quantum chemistry mole c ul ar s imulation s Fluid mechanic s and polymer rheolo gy Bioengineering and biomaterial s Supercritical fluid pro cess in g For application forms and further information on fellowships and assistantships academic and resea r c h programs, and st u dent h ousing, see: http : //www.ec s. uma ss.e du/che o r w rit e: Graduate Program Director Department of Chemical Engineering 159 Goessmann Laboratory 686 N Pleasant St. University of Ma ssa chusett s Amherst MA 01003-9303 Th e University of Ma ssac hu se tt s Am h e r st prohibits discrimination on th e basi s of race co l o r r e li g ion c r ee d sex, sex u a l o ri e nt a ti o n age, m ar ital s tatu s, n atio n a l o ri g in disability o r h a ndi ca p or vete r a n s t a tu s, in any aspec t of the a dmi ssion or tr eat m e nt of s tud e nt s o r in em plo yme nt. C h e mical Engineerin g Ed u cation

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Chemical Engineering at J -~-. ~ i 1 w i 1 ..-_ .. ,_ ~,:----MIT is located in Cambridge, just across the Charles River.from Boston, a.few minutes by subway from downtown Boston and Harvard Square. The area is world-renowned for its co/leges, hospitals, research .facilities, and high technology industries, and offers an unending variety <~l theaters, concerts, restaurants, museums, bookstores, sporting events, libraries, and recreational facilities. Fall 2002 Research in ... Biochemical Engineering Biomedical Engineering Biotechnology Catalysis and Chemical Kinetics Colloid Science and Separations Energy Engineering Environmental Engineering Materials Microchemical Systems, Microfluidics Nanotechnology Polymers Process Systems Engineering Thermodynamics Statistical Mechanics, and Molecular Simulation Transport Processes R.C. Armstrong, H ead P.I. Barton K.J. Beers D. Blankschtein H. Brenner R.A.Brown R.E. Cohen C.K. Colton C.L. Cooney W.M.Deen P.S. Doyle With the largest research faculty in the country, th e D epartment of Chemical Engineering at MIT offers programs of research and teaching which span the breadth of chemical engineering with unprecedented depth in fundamentals and applications. Th e D epart ment offers graduate programs leading to the master's and doctor s degrees. Graduate students may also earn a professional masters degree through the David H. Koch School of Chemical Engineering Practice a unique internship program that stresses defining and solving industrial problems by applying chemi ca l engineering fundamentals. In collaboration with the Sloan School of Management, the Department also offers a doctoral program in Chemical Engineering Practice, which integrates chemical engineering, re search, and management. A.P. Gast G.C. Rutledge K.K. Gleason H.H. Sawin W.H.Green K.A. Smith L.G. Griffith Ge. Stephanopoulos P.T. Hammond Gr. Stephanopoulos T.A. Hatton J.W. Tester J.B. Howard B.L. Trout K.F. Jensen P.S. Virk R.S. Langer D.I.C. Wang D.A. Lauffenburger K.D. Wittrup G.J.McRae J.Y. Ying For mor e information, c ontact Chemical Engineering Graduate Office 66-366 Ma ssac hu setts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307 Phone (617 ) 253-4579; FAX (617) 253-9695; E-Ma il c h emegrad@ mit. e du URL http ://web .mit .edu/cheme/index. html 373

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McMaster University Chemical Engineering Faculty M.H.I. Baird Emeritus PhD (Cambridge) Mass Transfer Solvent Extraction J.L. Brash Emeritus PhD (Glasgow) B i omedical Engineering Bio Material s Polymers J.M. Dickson PhD (Virginia) Membrane Transport Phenomena Reverse Osmosis C. Filipe PhD ( Clem s on) Environmental Biotechnology Environmental Engineering R. Ghosh DPhil (Oxford) Bioseparation Membrane Technology A.E. Hamielec Emeritus PhD (Toronto) Polymer Reaction Engineering A.N. Hrymak PhD (Carnegie Mellon) Computer Aided Design Polymer Proce ss ing J.F. MacGregor PhD (Wisconsin) Computer Process Control Polymer Reaction Engineering T.E. Marlin PhD (Massachusetts) Computer Proces s Control R.H. Pelton PhD ( Bristol ) Water Soluble Polymers Colloid Polymer Sy s tems Y. Samyudia PhD (Queensland) Computer Process Control C.L.E. Swartz PhD (Wiscon s in ) Computer Process Control Optimization H. Sheardown PhD (Toronto) Biomaterials Tissue Engineering L.W. Shemilt Emeritus PhD (Toronto) Radioactive Waste Management P.A. Taylor PhD (Wales) Computer Proces s Control M. Thompson PhD (Waterloo ) Polymer Processing Extrusion and Reactive Extru s ion J. Vlachopoulos DSc (Washington University) Polymer Processing Rheology Numerical Methods P.E. Wood PhD (Caltech) Experimental and Computational Fl uid Mechanic s Heat Transfer S. Zhu PhD (McMaster) Polymer Reaction Engineering Polymer Synthesis Polymerization Process Modeling Adjunct Faculty T. Kourti PhD ( McMaster) Computer Process Control K. Kostanski PhD (Tech U. Szczecin) Polymerization and Polymer Characterization S.L. Quinn PhD (Queens) Statistical Process Control J.D. Wright PhD (Cambridge) Pulp and Paper Computer Process Control Proces s Dynamics and Modeling 374 Graduate Study in Polymer Processing and Reaction Engineering, Computer Process Control, and much more! We offer a PhD program a nd t hr ee Master's optio n s ( The s i s, P roject Internship) R esearc h sc h o l ars hip s a nd teac h ing ass i s tant s hip s are ava il able Hamilton i s a city of 350,000 s ituated in so uth ern Ont a rio We are located about 100 km fro m b ot h Toronto a nd Niagara F a ll s. Excellent Facilities and R esearch Support through funding from Canadian govern m e nt and extensive interactions with industry Centre for Pulp and Paper Research Center for Advanced Polym er Processing and Design McMaster Advanced Control Consortium McMaster Institute for Polymer Production Technology For Further I nformation, Pl ease Contact Graduate Studies D epartme nt of Chemical Eng in eeri n g McMaster University Hamilton Ont ario Canada L 8S 4L 7 Ph o n e 905-525-9140 Ext 24292 Fax 905-521-1350 e-mail: c h eme n g@ m c m as t er.ca http://www .c h e m e n g. mcm as t er ca Che mi cal E n g in eering Ed u cation.

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I Chemical Engineering at The University of Michigan Faculty 1. Ronald Larson Chair Polymers, DNA complex fluids, fluid mechanic s 2. Stacy G. Bike Colloids, polymers, complex fluids 3 Mark A. Burns Microfabricated analytical systems biochemical separations 4. H. Scott Fogler Fused reactions colloids, gellation kinetic s 5 John L. Gland Surface science 6 Sharon Glotzer Soft materials and complex fluids 7 Erdogan Gulari Catalysis electronic materials, combinational chemistry 8 Jennifer J. Linderman Engineering approaches to cell biolog y 9 Susan Montgomery Undergraduate program advisor 10. David J. Mooney Cellular and tissue engineering 11. Chester Ni Bioinformatics, pharmaceutics 12 Phillip E. Savage Reactions in s up ercritical water, "green" chemistry 13 Johannes Schwank He t erogeneou s catalysis, surface science, gas sensors 14. Christina Smolke Biomolecular and metabolic engineering 15. Michael Solomon Light scattering and rheology of complex fluids 16. Levi T Thompson, Jr. Catal y sis electrocatalysis, materials processing 17 Henry Y. Wang Pharmaceutical engineering, bioproces s ing 18. Walter Weber En v ironmental processes and sustainability 19. Ralph T. Yang Separation s, adsorption, catalysis 20 Robert M. Ziff Percolation catalysis statistical thermodynamic s For More Information, Contact : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 5 16 17 1 8 19 20 Gradua t e Program Office, Department of Chemical Engineering / The University of Michigan / Ann Arbor MI 48109 2136 / 7 3 4 764-2383 Web : http: / / www engin umich edu / dept / cheme /

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Graduate Study in Chemical Engineering and Materials Science The Department of Chemical Engineering and Materials Science offers Graduate Pro grams leading to M S and Ph.D. degrees in Chemical Engineering and Materials Sci ence. The faculty conduct fundamental and applied r esearch in a variety of Chemica l En g in ee rin g and Materials Science disciplines. The Michigan Biotechnology In stitute, the Composite Materials and Structures Center, and the Bioprocessing Center provide a fo rum for interdisciplinary work in current hi gh technology areas ASSISTANTSHIPS Half-tim e grad u ate assistantships for incoming Ma ster's ca ndid a t es are ex p ecte d to p ay $ 18 ,852 per year plus a tuition and fee waiver of nin e credits for Fall and Spring Semesters, four credits for Sum mer Semester. University paid h ea lth insura n ce i s also provided. Theses are written o n th e project cov ered by the re searc h ass i sta nt s hip FELLOWSHIPS Available ap pointm e nt s pa y up to $19,500 p er year. FOR ADDITIONAL INFORMATIO N WRITE Chairperson Department of Chemical Engineering and Materials Science 2527 Engineering Building Michigan State University East Lansing, Michigan 48824-1226 e -mail : grad_rec@egr. m s u .ed u www: http : //www.chem s. m s u .e du / MSU is an Affirmativ e A c tio11/Eqt1al Opport1111ity I nstitution 376 M BAUMANN Ph.D 1 988, Case Western Reserve University Biomaterials, Ceramic Bone Substitutes Bone Tissue Engi n eering, Co ll oida l Processing of Ceramic s and Ceramic Composites K.A BERGLUND Ph.D. 1 98 1 lowa Stat e University Applied Spectroscopy Food and Biochemical Engineering, Crystallization from So l ut i on, New Uses of Agr i cu ltu ral Crops T.R. BIELER Ph.D 1989 University of California High Temperature Creep; Superplasticity ; Texture of Metals lntermetallics, and Composites ; Solder and Electronic H eat Sink Material s ; Metal Matrix Compos it e Fabrication ; High Strain Rate Deformation D.M. BRIEDIS Ph.D. 1 981 lowa State Un i versity Biochemical Engineering, Biobased Industrial Products, Biomass Conversion Life Cycle Ana l ysi s E .D. CASE Ph.D., 1 980 lowa State University M i crocrack.ing in Ceramics, Thermal Fatigue Ceramic/Ceramic Joining, Bioceramics Microwave Proce s sing of Ceramic s and Ceramic Composites C. CHAN Ph.D. 1990 University of Pennsylvania Metabolism and Diabetes Alzheimer and Parkinson's disease, Metabolic Engineering Tissue Engineering, Bioinformatics and Multivariate Analysis M A. CRIMP Ph.D 1987, Case Western Reserve University Transmission Electron Microscopy Diffraction and Channe lin g Studies u si n g Sca nnin g Electron Microscopy Deformation and Fracture lntermetallic Alloys, Magnetic Multilayer Structures L.T. DRZ AL Ph.D. 1974, Case Western Reserve University Surface and lnt erfacia l Phenomena Adhesion, Polymer Composite Materials, Surface Characterization Surface Modification of Polymers, Polymer Composite Processing Ad h esive Bonding D.S. G R UMM O N Ph D., 1 986 University of Michigan Superelasticity and Shape-Memory in Titanium-Nickel Thin Films Microactuators, Thermoelastic Martensite Transformations, Ion Beam Surface Modification of Materials, Surface Effects in Fa ti gue Crack Initi at i on, Mechanical Metallurgy M.C HAWLEY Ph D 196 4, Michigan State University Kinetics, Cata l ys i s, Reactions in Plasmas, P o l ymer i zatio n R eactio n s, Compos it e Processing, Biomass Conversion Reaction Eng in ee rin g K. J AYARAMAN Ph.D., 1975 Princeton University Polymer Rheo l ogy, P rocessing of Polymer Blends and Compos it es, Comp ut ationa l Methods A.LEE Ph.D., 1 987, University of Illinois at Urbana-Champaign Inorganic-Orangic Hybrid Polymers Physical and Mechanica l C har acterization Dynamics of Polymeric Glasses C.T LIRA Ph D 1985, University of Illinois at Urbana-Champaign Thermodynamics a nd Phase Eq uilibri a of Complex Systems Adsorption, Supercritical Fluid Studies J P. LUCAS Ph.D 1981, University of Minnesota Microstructure Evolu ti on/Character i zation of Pb-Free Solders Alloys and their Compos it es; Nanoindentation Characterization of Deformation in Small-Volumes and Thin Films; Moisture Effec t s in R esi n Matrix Composites; Metal Matrix Composite M.E MACKAY Ph.D., 1985, University of Illinois at Urbana-Champaign Polymer Rheology and Thermodynamics Nanotec hn o l ogy Dendrimer s H yperbranc h es P o l yme r s Surface Properties D.J. MILLER Ph.D. 1 982, University of Florida Kinetics and Catalysis, Reaction Engineering Catalytic Conversion of Biomass-Based Materi a l s R. NARAYAN Ph.D. 1 975, University of Bomba y P o l ymer Blends and A ll oys Biodegradable Plastics Biofiber Composites Extru s ion Polymer ization and Reactive Compounding, Biodegradation and Composting Studies J. NOGAMI Ph.D. 1986, Stanford University Electronic Materials Scanned Probe Microscopy, Surface Characterization, Growth of Nanos tru c tur ed Materials R .Y. OFOLI Ph.D., 1 994 Carnegie Mellon University Colloid and Interfacial Science: Colloid Stability, Adsorption of Proteins Receptor-Ligand Interactions at the Liquid-Liquid Interface Micellar So lubili zation C.A. PETTY Ph.D ., 1 970, University of Florida Fluid Mec h anics Turbulent Transport Phenomena Solid-F luid and Liquid-Liquid Separations Hydrocyclones K.N. SUBRAMANIAN Ph.D. 1966 Michigan Stat e Univ e rsity Mechanical Properties of Metals and Ceramics Crysta lli zation of Glasse s, Eros i on Composite Materials, Lead-Free Electronic So ld ers R. M. WORDEN Ph.D 1986, University of Tennessee Biochemical Enginee rin g Microbial Transport Proces s e s Synthesi s Gas Fermentat i ons Metabolic E n g in eer in g Microbial Eco l ogy Chemical Engineering Educat i on

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Leadership and Innovation in CHEMICAL ENGINEERING AND MATERIALS SCIENCE at the UNIVERSITY OF MINNESOTA FACULTY Rutherford Aris (E meritus ) Theoreti ca l s tudi es of chemica l reactors Arnold G. Fredrickson (Eme ritus ) Bi oc h em i cal eng in eer in g, microbial populations C. Daniel Frisbie Frank S. Bates Molecular materials and interfaces, molecular Th e rmod y nami cs and dynamics of polymers elect r onics and pol yme r mixtures Robert W. Carr Chemical kin et i cs, r eaction eng in eering C. Barry Carter Electron mi c r oscopy of semiconductors and ce rami cs, solid-state reactions and grow th of thin films James R. Chelikowsky Structural/electronic prop erties of complex systems Robert F. Cook William W. Gerberich Fracture micromechanics, interfacial defects Wei-Shou Hu B iochemical engineering Yianis Kaznessis Computer modeling of biological systems structura l bi oinfor mati cs, molecular r ecogni tion phenomena Satish Kumar Transport processes in comp l ex fluids stabil i ty, dynamics and manipulation of interfaces, transport processes in microscale s y stems M ec hani ca l behavior of mat e ri a l s, microChris Leighton elec tr o ni c device fabrication and packaging Magnetic and e l ectronic prope r ties of thin film magnetic materials and heterostructures Edward L. Cussler Ma ss transfer, novel separation processes John S. Dahler (E meritus) Nonequilibrium stat i stica l m ec hani cs Prodromos Daoutidis Nonlinear pr ocess co ntrol pr ocess analysis and design H. Ted Davis Colloid and interface science, stat i stical mechanics Jeffrey J. Derb y Materials pro cessing, hi g h p erfo rman ce co mputin g Lorraine Falter Francis Timothy P. Lodge P o l yme r structure an d dynamics, p olymer c hara cterization Christopher W. Macosko P olyme r processing, rh eology polymer networks and blends Richard B. McClurg Th ermodynamics and kinetics of phase c han ges Alon V. McCormick R eaction engineering of materials synthesis spec tr oscopy, molecular simu lati on David C. Morse Statistical mechanics, polymeric and complex fluids David J. Norris Ceramic pr ocessing, elec tri cal and mechaniNanomaterials, photonic crystals, molecular ca l propertie s of ce rami cs s pintroni cs Richard A. Oriani (E meritus ) Co rr osion, thermodynamics of so lids co ld fus i on Christopher Palmstr!'fm Epitaxial g r owt h processes and heterostructure formation, properties of thin film Lanny D. Schmidt Surface chem i stry, heterogeneous catal y sis reaction engineering L. E. Scriven Fluid mechanics and rh eo lo gy, transport reaction and stress ph e n ome na mat er ial s processing David A. Shores H igh temperature co rr osio n f u el ce ll s John M. Sivertsen (E meritus ) Magnetic microelectronic, and tribologi cal materials William H. Smyrl E l ectroc h e mical e n g in ee rin g mod e lin g electrochemical systems, microvisualization of reactive s urfa ces Friedrich Srienc Bi ochemical engineering, cell cycle and growth models, bi o pol ymers Robert T. Tranquillo Cell and tissue eng in eering Michael D. Ward Molecular materials crys tal g r owt h elect r ochemistry Renata M. M. Wentzcovitch Electronic and structura l properties of condensed matter systems; first prin c ipl es molecular dy nami cs For additional information. visit our web site at http://www.ccms.umn.edu Fall 2002 377

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Graduate Studies in Chemical Engineering Environmental R emediation, Electrokinetics, Chemical Extraction, Stabilization/Solidification, Waste Tr eatme nt Heav y Metal Soil s 378 W Todd French Assistant Research Professor Applied Mi cro biolog y, Biorem ed iation Industrial Mi cro biolog y, Mi cro bial Enhanced Oil Re covery Clifford E. George Professor I ndustrial Biot ec hnolog y Industrial Applications of Mi c rowa ve Power/H eating and Electrochemistry, Proc ess Control Chemical Plant /Oil R efinery Operations and Saf ety Priscilla J. Hill Assistant Professor Crystallization Process D es i gn, Solids Processing Irvin A. Jefcoat Professor and Henry Chair Pollution Pr eventio n/Waste Minimi za tion Rudy E Rogers Professor Natural Gas Stora ge and Tran sport, Formation Rat es in O cean S ed im e nts CO 2 S eq uesterin g Natural Gas Produ c tion from S ea b e d H ydrates Kirk H Schulz Director and Deavenport Chair Su,face Science, Catalysis Electronic Mat e rials Hossein Toghiani Associate Professor Composite Material s, Catalysis, Fuel Cells, Thermodynamics of Liquid Mixtur es Rebecca K Toghiani Associate Professor Thermodynamics Separations Mark E Zappi Professor Wa s te Tr eatment, Indust r ial Biote c hnolo gy, Chemical Oxidation, Biotreatment H y ph ena ted R emed iation T ec hniqu es Mississippi State University, located in the Golden Triangle region of Northeast Mississippi, is the largest of eight public institutions of higher learning in the state It is one of two land-grant institutions in Mississippi Area r es idents enjoy numerous university sporting and c ultural events, as well as scenic and recreational activities along the Natche z Trace Parkway and Tennessee Tombigbee Waterway. The Dave C. Swaim School of Chemical Engineering is poised for unprecedented growth in the next decade. A new $18 million facility recently was completed specifically for Chemical Engineering. The school offers both the M.S. and Ph.D. degrees in Chemical Engineering and an M.S. in Industrial Hazardous Waste Management. For more information, contact The Dave C. Swaim School of Chemical Engineering Mississippi State University P O. Box 9595 330 Swaim President's Circle Mississippi State, Mississippi 39762 Phone: (662) 325-2480 Fax: (662) 325-2482 Email: gradstudies@che.msstate.edu www.che.msstate.edu . . . . For a graduate application, contact The Office of Graduate Studies Phone (662) 325-7404 www.msstate.edu/dept/grad/application htm ,\li,,i,,i1111i .\{aft' { 'nil"tT\/{\' i, 1111 t 'l/11({/ /!/1/li!r/lll/ily i11,tit11tio11. Chemical Engineering Education

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University of Missouri-Columbia Rakesh K. Baipai Ph.D. (/IT, Kanpur) Bio c hemical En gi neering Hazardous Waste Paul C.H. Chan Ph.D. (CalTech) Reactor Anal ys is Fluid Mechanics Patricia A. Darcy Ph.D. ( Iowa) Prot e in Crystalli zatio n Biotechnology Eric Doskocil Ph.D (Virginia) Catal ys is Reaction Engineering William A .Tacob y Ph.D. (Colorado) Photo cata l ysis Transport Sunggyu Lee Ph.D. (Case Western) Pro cess Engine e rin g Polymer s Fuels Stephen .T. Lombardo Ph.D (California-Berkeley) Ceramic Composites Transport Kinetics Sudarshan K. Loyalka Ph.D. (Stanford) Aerosol Mechanics Kinetic Theory Ri ch ard H. Luecke Ph.D. (Oklahoma) Proc ess Control Modeling Thomas R. Ma rr e r o Ph.D. (Maryland) Coal Log Tran s port Conductin g Pol y mers David G. R etzloff Ph.D ( Pittsburgh) Rea c tor Anal ys is Materials Truman S. Storvick Ph.D. ( Purdu e) Nuclear Wast e R e processing Thermodynamics Galen .T. Suppes Ph.D (Johns Hopkins ) Biofuel Processin g Renewable Energy Thermodynamics Dabir S. Viswanath Ph.D ( Ro c hester) Appli e d Thermod y namics Chemical Kinetics Hirotsugu K. Yasuda Ph.D. (SUNY, Syracuse) Pol yme r s Sutfa ce Science The University is one of the most comprehensive institutions in the nation and is situated on a beautiful land grant campus halfway between St Louis and Kansas City, at the foothills of the O z ark Mountains and the recreational Lake of the O zarks. Th e Chemical Engineering D epartment offers M.S. and Ph.D. programs in a wide variety of research areas including surface science, nuclear waste, wastewater treatment, biodegradation, indoor air pollu tion, supercritical processes plasma pol y meri z ation, pol y mer pro cessi ng, coal transportation (hydraulic), fuels, chemical kinetics, protein crystallization, photocatalysis, ceramic composites, and polymer composites. For details contact: The Dir ector of Graduate Studies Department of Chemical Engineering University of Missouri Columbia, MO 65211 Tel: (573) 882-3563 Fax : (573) 884-4940 E-mail : preckshotr@missouri .e du Web s ite : www.mi ss ouri.edu/~chewww Fall 2002 Incentive scholarships available in the form of teaching/research assistantships and fellowships 379

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University of Missouri-Rolla Graduate Studies in Chemical Engineering Offering M.S. and Ph.D. De grees Established in 1870 as th e University of Miss o uri S c hool of Min es and M e t allurgy UMR ha s evo l ved into Miss our i 's t ec hnolo g i ca l uni ve sity. UMR is a medium-si ze d ca mpus of about 5 000 s tud ents lo ca t e d along In terstate 44 approximat e l y JOO mil es from St Louis and Spring field. it s pro x imi ty in th e Missouri Ozarks provid es plenty of scen i c and r ecreational opportunities. Th e University of Missouri-Ro/la's mission is t o e du c ate tomorrow 's l e ad e r s in eng ineerin g and science. UM R offers a full range of exper i e n ces that are v ital to the kind of c omprehensive e du c ation that t urn s yo un g men and wo m en into l e ad e rs UMR ha s a distinguished fa c ul ty dedi c at e d w holeh e art e dly to the teaching, r esea r c h and c r e ati ve a ct i v ties necessa r y for schola rl y l ea rning experiences and advan ce ments t o th e frontiers of kno w l e d ge T eac hin g and R esea r c h Apprenticeships available to M.S. and Ph D students. For additional information : Addr ess: W eb : E-mail: Online Application: 38 0 Graduate Studies Coo rdinat or D e partm e nt of C h e mi ca l En g in ee rin g Un i ve r s i ty of Missouri-Roi/a Rolla MO 65409/ 230 http ://www.umr.edu/~c h e m e n gr cheme 11 gr@umr.edu http://w1 v 1 v. umr. edu/~c i sappslgradap pd.ht111/ Neil L.Book Associate Profe ss or Ph.D. Colorado Computer-Aided Process Design Chemical Process Safety Engineering Data M a n agement Daniel Forciniti Assoc iate Profes so r Ph D North Carolina StaJe Bioseparation s, Thermodynamics Statistical Mech a ni cs A.I. Liapis Professor, Ph.D. ETH-Zurich Transport Phenomena, Adsorp ti on/Desorptio n. Fundamenta l s an d Processe s, Bioseparations C h ro m atogra phi c Separations Cap ill ary Electrochromatography Chemical R eaction E n gineer in g Lyophili zat ion Douglas K Ludlow Profe ssor and Chair, Ph.D Arizona StaJe Surface Characterization of Adsorbents and Cata l ysts Applications of Fractal Geometry to Surface Morpholo gy Nicholas C. Morosoff Professor Emeritus, Ph.D. Brooklyn Polytech Pl asma Polymerizati o n Membranes Parthasakha Neogi Profes s or, Ph.D. Carnegie-Mellon lnt erfacial Phenomena Drug Delivery XB Reed,Jr. Profes so r Ph D. Minnesota Fluid Mechani cs, Transport Phenomena and C h emical Reaction E n g in eeri n g, including those of Particl es, Drops and Bubble s, Lar ge-Sca l e Structure of Shear Turbul ence and Impact of Fine-Scale Structure on Chemical R eac tions Stephen L. Rosen Prof ess or Ph D Come/I Polymerization R eactio n s, Applied Rh eo l ogy, Polymeric Material s Y.T.Shah Professor and Pro vost, Ph.D. MIT Che mi ca l Reaction and React o r Engineering Oliver C. Sitton Associate Profe ss or, Ph D Missouri-Rolla Bi oe n gineering Jee-Ching Wang Assistant Professor, Ph.D Penn State Molecular Simulations of Transport in Co nfin ed Systems Molecular Simulations of Surfactant Sy s t ems, Molecular Propertie s of Material s Yangchuan Xing Assis tant Profe ssor, Ph.D. Yale Synthe s i s, Processing, an d Characterization of Nanomaterials Chemical Engineering Education

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University Nebraska of Graduate Studies in Chemical Engineering Jennifer Brand University of California, San D iego Supercritical Fluid Proce ss in g; Natural Product Proce ss in g; Environmental Remediation L. Davis Clements University of Oklahoma Computer-Aided Proce ss De sig n ; Proce ss S y nthe sis; Fuel s a nd Chemical s from Bioma ss James Eakman University of Minnesota Computer-Aided Proce ss Engineering; Solid s Propertie s & Proc ess in g; Reaction Engineering James Hendrix University of Nebraska Remediation of Mine T ailings Waste; Novel Analytical Chemi stry; Non-Ideal Re actors Gustavo Larsen Yale University Hetero ge neou s Catalysis: Spectroscopic Characterization of Catalysts Lee Lauderback Purd ue University Surface Analysis; Hetero ge neou s Catalysis Michael Meagher Iowa State University F e rment a tion and R eco mbin a nt Protein Expression in the P ichia pastoris; Cross-Flow Membrane Filtra tion; Do w n s tream P rocess, Pu rification, a nd Proc ess D evelop m e nt ; Butan ol Reco very b y Per va poration Chair Graduate Studies Hossein Noureddini University of Nebraska Production of Ch e mical s from Agricultural Product s; Mathematical Modeling of Polymerization Kinetic s Delmar Timm Io wa State University Polymer Compo sites; Step-W ise Polymerization Kinetic s; Kinetic Analysis Using GPC Hendrik Viljoen University of P retoria Fall 2002 Plasma -E nhanced CVD ; Detonation & Combu s tion ; Ceramics For further information, write Dr. Mi chae l Meagher Director of Graduat e Studie s D epa rtment of Ch emica l Engineering University of Nebraska Lin co ln NE 68588-0126 Al s o ple ase vis it u s at our web site at http : //www.unl.edu/chemengr/ Graduate admissions on-line applications and printable forms available at http://www.unl.edu/ gradstud/ gradadmission.html 38 1

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382 The department offers graduate programs leading to both the Master of Science and Doctor of Philosophy degrees. Exciting opportunities exist for interdisciplinary research. Faculty conduct research in a number of areas including: Polymer science/ engineering Membrane technology Hazardous waste treatment Particle technology Pharmaceutical engineering Nanotechnology America's Most Wired Public University Yahoo! Internet Life at New Jersey Institute of Technology The Faculty: P. Armenante; University of Virginia B. Baltzis; University of Minnesota R. Barat; Massachusetts Institute of Technology E. Bart; New York University C. Gogos; Princeton University T. Greenstein ; New York University D. Hahn; Agri Univ. of Wageningen (Netherlands) D. Hanesian; Cornell University M. Huang; University of Massachusetts K. Hyun; University of Missouri-Columbia H. Kimmel; City University of New York D. Knox; Rensselaer Polytechnic Institute G. Lewandowski; Columbia University N. Loney; New Jersey Institute of Technology A. Perna; University of Connecticut R. Pfeffer; New York University L. Simon; Colorado State University K. Sirkar; University of Illinois-Urbana S. Sofer; University of Texas R. Tomkins ; University of London (UK) J. Wu; University of Delaware M. Xanthos; University of Toronto (Canada) For further information contact: Dr Reginald P.T. Tomkins Department of Chemical Engineering New Jersey Institute of Technology University Heights Newark NJ 07102-1982 Phone: (973) 596-5656 Fax: (973) 596-8436 E-mail: tomkinsr@adm.njit.edu JI' A Public Research U niversity UNNERSITY HEIGHTS NEWARK, NJ 07102-1982 ~.njit.edu New Jersey Institute of Technology NJIT does not discriminate on the basis of gender sexua l orientation race handicap veteran s status national or ethnic origin or age In the administration of student programs Campus facilities are accessible to the disabled. Chemical Engineering Educa t ion

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Graduate Research at the Frontier __________________ THE UNIVERSITY OF NEW MEXICO Faculty Plamen Atanaso v Harold M. A nder son C. Jeffrey Brinker Joseph L. Cecchi John G. Curro Ab ha ya K. Datye Julia E. Fulghum SangM .Han David Kauffman Ronald E. Loehman Gabriel P. Lopez Richard W. Mead H. Eric Nuttall Jonathan Phillips Timothy L. Ward Ebtisam S. Wilkins Research Areas The future of chemical engineering is a bright one wit h rapidly developing technologies and exciting new opportunities. Pur s u e your graduate degree in a stim ul ating student ce nt ered intellec tual environment anchored by forward-looking research. We offer full tuition and competitive stipends The ChE faculty are leaders in explori n g phenomena o n th e meso-, micro-, and nanoscales. We offer grad u a te research projects in biotechnology and biomaterials; cata l ysis an d interfa c i a l phenomena; enviro nm enta l technologies and waste manage ment ; microengineered materials and self-asse mbl ed nanostructures; plasma processing and semico nduct or fa brica tion ; polymer theory and modeling The department enjoys extensive interactions and collaborations with New Mexico 's federal laboratories : Los Alamos National Laboratory, Sandia National La b oratories a nd the Air Force Research Laboratory, as well as hi g h technology industries b o th locally and nationally. Albuq ue rq u e is a unique combination of the ve r y old a nd the highly co nt emporary the natural world and the manmade environment the fro nti er town a nd the cosmopolitan city, a harmoniou s blend of diverse cultures and peoples. Join us! Be part of this future! Electroana l ytical Chemistry Biomedical Engineering Plasma Processing, Plasma Di ag n ostics Ceramic s, Sol-Gel Processing, Self-Assembled Nanostructures Semiconductor Manufacturing Technology Plasma Etching and Dep os ition Polymer Theory Computational Modeling Catalysis Interfaces, Adva n ced Materials Surface Characterization 3-D Materials Characterization Semiconductor Manufacturing Technology, Pl asma Etching and Deposition Plant D e s ign Enviro nm ental Engineering Glass-Metal and Ceramic -M eta l Bonding and Interfacial Reactions Chemical Sensors Hybrid Materials, Biotechnology, Interfacial Phenomena Unit Operations Resource Extraction Environmental Science Waste Transport Management Colloid Science Materials Science Catalysis, Plasma Physics and Chemistry Aeroso l Materials Synthesis, Inorganic Membranes Bi omedica l Sensors and Waste Treatment For more information, contact: Fall 200 2 J effrey Brinker Graduate Advisor Chemical an d Nuclear Engineering 209 Farris Engineering Center Albuquerque, NM 871311 341 505 277 5431 Phone 505 277 5433 Fax chne@u nm. ed u www ch n e.unm e du 383

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NEW MEXICO STATE UNIVERSITY Faculty and Research Areas ____________ 3 8 4 Paul K. Andersen, Associate Professor, University of California Berkeley Transport Phenomena Electrochemistry, Environmental Engineering Ron K. Bhada, Professor Emeritus, University of Michigan .Joe L. Creed, Assistant Dean New Mexico State University Engineering Design Francisco R. Del Valle, College Profe ssor, Massachusetts In stitute of Technology Food Engineering Charles L .Johnson, Profe ssor and Head Washington University-St. Louis Richard L. Long, Professor and Associate Head Ri ce University Transport Phenomena, Biomedical Engineering, Separations Martha C. Mitchell, Associate Professor, University of Minnesota Advanced Materials, Statistical Mechanics, Molecular Modeling Stuart H. Munson-McGee, Profe ssor, University of Delaware Advanced Materials, Separations .J oho T. Patton, Professor Emeritus, Oklahoma State University David A. Rockstraw, Associate Profe ssor, University of Oklahoma Separations Environmental Engineering Kinetics Rudi V. Roubicek, Profe sso r Emeritus, Technical University of Prague Edward F. Thode, Professor Emeritus, Massachusetts Institute of Technology D. Bruce Wilson, Professor Emeritus Princeton University LOCATION------~ Southern New Mexico 350 days of s un shine a year For Application and Additional Information Internet http : //chemeng.nmsu.edu/ E-mail chemeng@nmsu edu PO Bo x 30001 MSC 3805 Department of Chemical Engineering New Mexico State University Las Cruces, NM 88003 New Mexico St a te University i s an Equal Opportunity Affirmative Action Employer Chemical Engineering Edu c ation

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North Carolina State University 'Deyartment of Cliemica{ 'Engineering ~--.~ Fall 2002 385

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386 GRADUATE STUDY IN CHEMICAL ENGINEERING in the Heart of Boston Faculty: NurcanBac Gilda Barabino Carolyn Lee-Parsons Albert Sacco Jr. Ronald J. Willey Katherine S. Ziemer Northeastern University Chemical Engineering Department is the home of CAMMP (Center for Advanced Microgravity Materials Processing ) a NASA-sponsored Commercial Space Center. It is one of 16 NASA centers at major universities nationwide and the only one exclusively focused on materials. The Department offers full and part-time graduate programs leading to M.S. and Ph.D degrees. MS students may have the opportunity of co-op experience. The faculty of the chemical engineering program are committed to providing state of the art research areas. Research Areas: Biochemical Engineering Biomedical Engineering Catalysis Microgravity Advanced materials Nanocomposite Membranes Semiconductor Materials Selected Research Topics: Pharmaceutical compounds from plant cell cultures Carbon Nanotubes Mixed-Matrix Membrane Separation Sickle Cell Adhesion Surface Acidity of Ti-silicas Tissue Engineering Thin Film Heterostructures Biosensors For more information write: Chairman Dept of Chemical Eng. 342 SN 360 Huntington Ave. Boston, MA 02115 Visit our web site: http://www.coe.neu.edu/COE/grad _scboo C h e mi ca l Engineering Edu c ation

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Chemical Engineering at Luis A.N. A maral Ph D Bo s ton U ni ve r s it y 1 996 C o mpl ex sy stem s, c omputati o nal ph ys i cs, biol og i c al n etw orks Annelise E. Barron Ph D ., Berkel ey 1 99 5 Bi o s e p a rat io n s biop o l y m e r e n ginee rin g Linda J. Broadbelt PhD D e la wa r e, 1 994 R e a c ti o n e n g in ee rin g, kin e ti cs m ode li ng, p oly m e r r eso ur ce r ecove r y Wesley R. Burghardt Ph D St anford, 1 990 P o l y m e r sc ien ce, rh eo l ogy Buckle y Crist, Jr. Ph D ., Duke 1 966 P o l y m e r sc i e n ce, therm o d y nami cs, m ec h a ni cs Joshua S. Dranoff Ph D. Prin ce t o n 1 96 0 Ch e mi c al r eac ti o n e n g in ee rin g, c hr o m a t og raphi c se par a ti o n s Kimberly A. Gray Ph.D. J o hn s H o p k in s 1 988 Cat a l ys i s tr eat m e nt tec hn o l og i es environmen t a l c h e m istry Bartosz A. Grzybowski Ph D H arvard 2000 C o mpl ex c h e mi ca l sys t e m s Vassily Hatzimanikatis Ph.D ., C a lt ec h 1 996 C o mputational bi o te c hnol ogy, f un c ti o nal ge n o m i c s, bi o informati cs Harold H. Kung Ph D N o rth wes t e rn 1 974 Kin e ti cs, h e t e ro ge n e ou s c at a l ysis William M. Miller Ph.D B e r ke l e y, 1987 Ce ll c ultur e fo r bi o t ec hn o l og y a n d medici n e Lyle F. Mockros Ph.D B e r ke l ey, 1962 Bi o m e di ca l e n g in ee rin g, fluid m ec ha n i cs i n bi o lo g i c al syste m s Monica Olvera de la Cruz Ph D ., C a mbrid ge, 1 984 Stati s ti c al m ec hani cs in p o l y m e r sys t e m s Julio M. Ottino Ph D. Minn eso t a, 1 979 Fluid m ec hani cs, g ranular m a t e ri a l s c h aos mi x in g in m a t e rial s pr ocess in g E. Terr y Papout sa kis Ph D Pur d u e 1 980 Bi o t ec h no l ogy o f a nimal a n d mic r ob i al c e ll s m e tab o li c e n g in ee rin g ge n o mi cs Bruce E. Rittmann Ph.D. St anfo rd 1 979 I n s itu bi o r e m e diati o n biofilm s Gregory R ys kin Ph D C a lt ec h 1 983 Fluid m ec hani c s c omputati o n a l m e th o d s, p o l y m e ri c liquid s Lonnie D. Shea Ph.D ., Mi c hi ga n 1 997 Ti ss u e e n g in ee rin g, ge n e th e rap y Randall Q. Snurr Ph.D Berk e le y, 1 99 4 Ad so rpti o n and diffu s i o n in p o r o u s med ia m o l ec ular m o d e lin g Melody A. Swartz Ph D ., M.l.T 1 998 Bi o m e di c al tran s p o rt ph e n o m ena John M. Torkelson Ph.D. Minn e s t o t a 1 983 P o l y m e r sc i e n ce, m e mbran es Fa/l 20 0 2 Northwestern University For information and application to the graduate program, write Director of Graduate Admissions Department of Chemical Engineering McCormick School of Engineering and Applied Science Northwestern U ni ve r sity Evanston Illinois 60208-3120 Ph o n e: (8 4 7) 49 1 -73 9 8 o r (8 00 ) 8 4 8-5 / 3 5 (U. S. onl y) Em a il : ad mi ss i o 11 s c h e m e n g@ 11 o rtl11 v e s t e rn .e d u o r v i s it o ur we b s it e at www.c h e me n g .n o rthwe s tem .e du 387

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Graduate Studies in Chemical Engineering The University of Not r e Dame Faculty Joan F. Brennecke H.-Chia Chang Davide A. Hill Jeffrey C. Kantor David T. Leighton, Jr. Edward J. Maginn Mark J. McCready Paul J. McGinn Albert E. Miller Agnes E. Ostafin Andre F. Palmer Roger A. Schmitz Mark A. Stadtherr William C. Strieder Arvind Varma For more information and application materials, contact us at Dir ector of Grad u ate R ecruiting Department of Chemical Engineering University of Notre Dame Notre Dame, IN 46556 USA On-Line Application www.nd edu/~gradsch/app l ying/appintro.html 388 http : //www.nd edu/~chegdept chegdept.l@nd.edu Phone: l-800-528-9487 Fax: 1-219-631-8366 Research Areas Biomaterials Biological Photonic Devices Blood Rheology Inorganic Membranes Ionic Liquids Catalysis and Reaction Engineering Combinatorial Materials Synthesis Combustion Synthesis Molecular Modeling Multiphase Flows Nanostructured Materials Nonlinear Dynamics Parallel Computing Polymeric Materials Superconducting Materials Tissue Engineering Drug Delivery Electrochemical Processes Environmentally Conscious Design Enzyme Encapsulation Notre Dam e The University Notre Dame is an independent, national university ranked among the top twenty schools in the coun try. It is located adjacent to the city of South Bend, Indiana, approximately 90 miles southeast of Chi cago The scenic 1,250-acre campus is home to over 10,000 students. The Department The Department of Chemical Engineering is devel oping the next generation of research leaders. Our program is characterized by the close interaction between faculty and students and a focus on cut ting-edge interdisciplinary research that is both aca demically interesting and industrially relevant. Programs and Financial Assistance The Department offers MS and PhD degree pro grams Financially attractive fellowships and assis tantships, which include a full-tuition waiver, are available to student s pursuing either degree Ch e mi c al En g in ee rin g Edu c ati o n.

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FACULTY [I Bhavik Bakshi MIT Industrial Ecology Proce ss Engineering Analysi s of Complex Sy s tem s [I Robert S. Brodkey Wi sco nsin Experimental Mea s urement s for Validation of Computational Fluid Mechanic s and Applications to Mixing Proce ss Applications [I Jeffrey J. Chalmers lmmunumagnetic Cell Sep ara tion Effect of Hydrodynamic Forces on Cells Interfacial Phenomena a nd Cells Bioengine e ring Biotechnolog y, Cancer Detection [I L.S. Fan, W est Virginia Fluidi za tion, Particle Technology Parti c ulate s R eac tion Engineering [I Martin Feinberg, Princ eton Mathematic s of Complex Chemical S ys t e m s [I Winston Ho Illinois-Urbana Membrane Separations with Chemical Re act ion and Fuel-Cell Fuel Proce ss ing [I Kurt W. Koelling, Prin ceton Rheology Polymer Proces s ing Microtluidic s [I Isamu Kusaka, CalTech Nucleation [I L. James Lee, Minn eso ta Polymer and Compo s ite Proce ss ing Micro/-Nano-Fabrication BioMEMS [I Urnit S. Ozkan Io wa State Heterogeneou s Catalysis Kinetic s, Catalytic Materials [I James F. Rathman Oklahom a Colloids, Interfaces Surfactant s, Molecular Self-Assembly, Bioinformatics [I David L. Tomasko, lllin ois-Urbana Separations Molecular Thermodynamics a nd Materials Procesing in Supercritical Fluids [I Shang-Tian Yang, Purdu e Biochemical Engineering, Biotechnolo gy, and Tis s ue Engineering [I Jacques L. Zakin, New York Rheology Drag R eduction Surf ac tant Microstructure s, and Heat Transfer Enhancement Excellent facilities and a unique comb ination of research projects at the frontiers of science and technology. Outstanding faculty and student population who are dedicated and professional. Competitive financial support Close working relationships between graduate students and faculty. Attractive campus minutes away from downtown Columbus. For complete information, write, call, or catch us on the web at http://www.che.eng.ohio-state.edu or write Professor Shang-Tian Yang Department of Chemical Engineering The Ohio State University 140 West 19th Avenue Columbus, Ohio 43210-1180 Phone: ( 614) 292-9076 Fax: (614) 292-3769 E-mail address: che-grad@che.eng.ohio-state.edu The Ohio Stat e University is an equal opportunity/affirmative action institution Fall 2002 389

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390 Ohio University Chemical Engineering For More Information Contact: Graduate Programs Th e Departm e nt of Chemical E n gi n eeri n g offe r s programs l eadi n g to both the M.S a nd Ph.D. de grees. The department's activ iti es are e nh a n ce d b y th e Stocker e nd owme nt w hi c h was made possible by the ge n erosity of Dr C. P a ul a nd B e th K Sto cke r a nd which ha s no w grow n to ove r $ 14 milli o n The intere s t o n thi s e nd ow m e nt i s u se d t o h e lp s upport research effo rt s in s u c h ways as pro vi din g co mp e tit ive gra du a t e fe llow s hip s a nd assoc i a t es hip s, matchin g e quipm e nt funds a nd see d m o n ey for n ew project areas Research Areas Multiph ase Flow a nd Associated Corrosion Coal Conversion T ec hnolo gy a nd D es ulfuri zatio n Aerosol S cie n ce and Technology Pro cess Control Separ at ion s Energy a nd Envir o nm e ntal Engineering Thin Fi lm Materials Chemical R ea ction Engineering Bioreact or An a l ysis Down s tr ea m Proc ess in g of Prot e in s Bi o m e di ca l Engineering Financial Aid Financial s upport include s teaching and gran t-related assoc i ates hip s a nd fe llow s hip s ranging fro m $14,000 t o $ 1 8,000 per twelve months In addition s tudent s are gra nted a full tuiti o n sc holar s hip for both th e r eg ul ar and s ummer aca d e mi c term s. Stocker Fellowships are ava ilable to especially well -qu a lifi e d s tud e nt s. The Faculty Gerardine G Botte ( Ph.D. South Carolina 2000) W. J. Ru sse ll Ch e n ( Ph.D. S yrac us e, 1974) Nichola s Dino s, Emeritus ( Ph.D ., L ehig h 196 7) Dou g l as J Goet z ( Ph.D ., Cornell, 1 995) Tingyue Gu ( Ph.D ., Purdu e, 1990 ) Daniel A. Gulino (Ph .D ., Illin ois, 19 83) Srdjan Ne s ic (Ph.D., Saskat c h ewan, 1991) Michael E. Prudich Chair ( Ph.D ., W est Virginia 1979) D arin Rid gway, P .E. (Ph.D., Florida State, 1990) Kendree J. Samp so n ( Ph D. Purdu e, 19 8 1) Valerie L. Young ( Ph.D. Virginia T ech 199 2) Director of Gradu a t e Studie s Department of Chemical Engineering, 172 Stocker Center Ohio Uni ve r s ity Ath e n s OH 45701-2979 E-mail: c hedept @ bobcat.ent.ohiou.edu Visit our web s ite at: http://www. ent ohiou.edu/che Ohio U ni ve r s i ty i s an affirmativ e action instituti o n C h e mical E n gi n ee rin g Education

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The University of Oklahoma Graduate Studies in Chemical Engineering & Materials Science Join us in research on critical technological problems in the following areas: Environmental Energy Polymers Bioengineering Call, Fax, Write or E-mail: Chairman, Graduate Program Committe e School of Ch e mical Engineering and Material s Scien c e The University of Oklahoma 100 E. Boyd Room T-335 N orman OK 7 3019-1004 Phone: (405) 325-5811 Fax: (405) 325-5813 E-Mail: chegrad @o u.edu For more detailed information visit our World Wide Web site at: http: // www.cems.ou.edu Fall 2002 ""''""' "'""~ an Equal Opportunity Institution Fa c ul ty & R esea r c h Int erests M igu e l J Bagaje w ic z, Professor process plant simulation & data reconc i liation design of heat/mass exchange networks for waste minimization applications mathematica l background, algorithm development & process design applications of optimiza t ion t h eory high temperature fuel-gas clean i ng reactors modeling of fluid-solid diffusion-react i on problems Brian P. Grad y, Associa t e Professor multiphase & block copolymers ion-contain ing polyme r s polymer-matrix composites biodegradable and bioabsorbable po l ymers nanotechnology at inte1faces Roger G. Harrison Jr. Assoc i ate Professor production of proteins & peptides u s i ng recombinant DNA technology separation & purification of biochemica l s protein engi neering for biomedical and environmental application protein engineering Jeffre y H. Harwell Conoco/D u Po nt Professor, Exec u t i ve Assoc i a t e D ea n fo r t h e Co l l ege of Engineer in g te r tiary oil recovery unconventional low energy separat i o n pr cesses mass transfer dynamics of multicomponent mass transfer processes surface phenomena adsorption kinetics subsurface remediation Llo y d L. Lee C.M. S l iepcev i ch Professor thermodynamics molecu l ar liqu i d t h eory statistical mechanics interactions in nanostructures Monte Car l o & mo l ecu l ar dy namics studies conformal solution theory natural gas properties polar fluids, io n ic solutions & molten salts surface adsorption Lan c e L. Lobban Winn C h a i r & Director catalytic reaction rate mechan i s m s & modeling partial oxidation of hydrocarbons photocatalysis Richard G Mallinson P rofessor chemical r eact i on engineering energy pr oject va l uation synthetic and alternative fuels natural gas utilization me t hane conversion Peter S M cFetrid g e R esearc h Ass i sta n t P rofessor, D irecto r of Ce ll & Ti ss u e C ul t u re Fac il ity vascu l ar tissue engineering biomedical design, development and app li cat i on vascular perfusion reactor engineering Matthia s U N ollert Associate Professor biomedical enginee r ing ce ll u l ar metabo lism and transport platelet and leukocyte adhesion fluid mechanics Edgar A O Rear III Win n Professor drug delive y surface che m istry & p h ys i cs kinetics blood trauma associated with medical devices biorheology organic c h e m is try Dimitrio s Papa v a ss iliou Assista nt Professor integrated p r ocess simu l ations tr a n port phenomena in biological systems small scale transport at the i n terface between statistical mechanics and classical mechanics Daniel E. Re s asco S.A. Wi l so n Professor heterogeneous catalysis, react i on engi neering & kinetics design of catalysts for pollutant abatement carbon nanotu b es physical chemistry of surfaces Melissa M. Rieger Assistan t P rofessor e l ectrochemical pheno m ena and e l ectro c h emical engineering carbon nanotube electro-chem i stly materia l systems an d elec trochemical processes in mic r oelectronic processing electrochemica l be h av i or of p oly meric materials John F. Scamehorn Asahi G l ass Chair surface & colloid science tertiary oi l r ecov ery detergency membrane separations adsorption pollution co n tro l polyme r s paper & plastics deink i ng David W. Schmidtke Assista n t Professor design & deve l op m e nt of new ana l yt i ca l d evices & techno l og i es fo r m edical thera p y biosensors ce ll ad h esion h ig h s p eed/ hi gh reso lu t io n video m ic r osco p y of flu i d m ec h an i cs in th e b l ood s tr eam Robert L. Shambaugh P rofessor po l ymerization chemistry po l y m e r pr ocessing technology fiber spinning, texturing & extrusion wastewater eng in ee ri ng ph ysico c h emica l t r eat m e n t ozonat i on gas-liquid reactions Vassilio s I. Sikavitsas Ass i stant P rofessor tissue enginee r ing biosensors bio r eac t o r s proteomics 39 1

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Oklahoma State University "Where People Are Important" Faculty Gary L. Foutch (Ph.D., University of Missouri-Rolla) K.A.M. Gasem (Ph.D. Oklahoma State University) Karen A. High (Ph.D., Pennsylvania State University) Martin S. High (Ph.D., Pennsylvania State University) A.J. Johannes (Ph.D., University of Kentucky) OSU's School of Chemical Engineering offers programs leading to M.S. and Ph.D. degrees. Qualified students receive financial assistance at nationally competitive levels. Randy Lewis (Ph.D Massachusetts Institute of Technology) Sundarajan V. Madihally (Ph.D. Wayne State University) R. Russell Rhinehart (Ph.D., North Carolina State University) James E. Smay (Ph.D. University of Illinois) D. Alan Tree (Ph.D., University of Illinois) Jan Wagner (Ph.D., University of Kansas) James R. Whiteley (Ph.D. Ohio State University) Vi s i t o ur we b p age at Research Areas Adsorption Artificial Intelligence Biochemical Processes Biomaterials Colloids/Ceramics Environmental Engineering Fluid Flow/CFO Gas Processing Hazardous Wastes Ion Exchange Molecular Design Nanomaterials Phase Equilibria Polymers Process Control Process Simulation Solid Freeform Fabricat i on Tissue Engineering For more information contact Dr. Khaled A. M. Gasem http://www.cheng.ok s tate.edu School of Chem i cal Eng i neering Oklahoma State Un i versity Stillwater, OK 74078-5021 gasem@okstate edu 392 C h e mi ca l E n g in ee ri ng Ed u cat i on

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OREGON STATE Chemical Engineering M.S. and Ph.D. Programs Our programs reflect not onl y traditional chemical engineering.fields but also tech nologies important to the Northwest's industries, such as electronic material processing, forest products, food sci ence, and ocean products. Oregon State is located only a short drive from the Pacific Ocean, white water rivers, hiking I skiing and climbing zn the Cascade Mountains. Fall 200 2 FACULTY M.K. Bothwell Bi o int e rfa c ial Ph e n o m e na C.H. Chang S e mi co ndu c tor Ma te rials Int e grat e d Ch e mica l S y st e ms G. N Jovanovic Fin e P a r t i cle Pr ocess in g Tr a n s p o rt Ph e nom e n a S. Kimura R e a c ti on En g in ee rin g, Hi g h T e mp e r a tur e Materi a l s, Bio ce rami cs, El ec tro ce rami c s and Su1fa c e Modifi c ation. M. D. Koretsky El ec tr o ni c Mat e rial s Pr ocess in g K. L. Levien Pr ocess Optimi z ati o n. and C o ntr o l R e a c tion En g in ee ring C. McConica Ga s S o lid Kin e ti cs S e mi co ndu c tor Pro ces sing J. McGuire Bioint e rfa c ial Ph e nom e na Biomat e rial s R.A. Peattie Bi o m ec hani cs Ph y s i olog y, Fl u id and Bi o fluid D y nami c s W. E. Rochefort Rh eo l og i c al Th e rm a l and M o l ec ular Chara c t e ri z ation of P o l y m e r s; P o l y m e r Pro cess in g; Bi o mat e ria.l s ; En g ine e in g Edu c ation G. L. Rorrer Bio c h e mi c al R e a c ti o n En g in ee ring Competitive research and teaching assistantships are available. For further information write: Ch emica l Engineering Department Or ego n St a t e University 103 Gl eeso n Hall Corvallis, Or egon 97331-2702 Vi s it u s o n the web a t www/c h e/o r st/ed u or ema i l us at mai l @ che.or s t.edu 3 9 3

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394 University of Pe n nsylv an ia Department of Chemical and Biomolecular Engineering Eric T. Bo d er Biomolecular engi neering St u art W. C h urchil l Combustion, incineration crystal growth, rate processes Ru sse ll J Com p osto P olymeric materials sc i ence, surface and interface studies Jo h n C. Crocker Microrheology of biopolymers, recA sea r ching, 3D microscop y, device biophysics Scott L. Di a m o nd Endothelial cell mechano b io lo gy, drug and gene delivery, biotransport phenomena D enn i s E. Di sc h er Cell and molecu l ar mechanics biomembrane and biopol y m e r mesostructures and functions W illi a m C Forsman P o l yme r scie n ce and engineering E du a rdo D. G l a nd t Classical and stat i st i cal th ermodyna m ics, random media R aymo nd J. Gorte H eterogeneous cata l ysis, su pp o rt ed m eta l s, anodes for so lid -oxide fuel ce ll s ze olites D avid J G r aves Bi ochemical and biomedical engi n eer in g, biotechnolog y D a ni e l A. Hammer Cellular bioengineering, bi oi nt erfacia l ph e n omena, adhesion A l a n L. Mye r s Adsorption of gases and liquid s, mol ecu lar simulation D a ni e l D P e rlmutt er Chemical r eac tor design, gas-solid r eac tion s, ge l kin et i cs J o hn A Quinn M embrane transport biochemical/ biom e di ca l e n ginee rin g War r en D Sei d er Pro cess analysis, s imulati on, d esign, and con trol We n K S hi e h Bi oenvironmenta l enginee rin g, e nvironm e ntal syste m s mod e lin g T alid R. S inno Tran s port and r eactio n, sta tisti ca l m ec hani c al modelin g Ly l e H U ngar Artifi cia l intelligence in pro cess contro l n e ural networks John M Vo h s Surfa ce science, cata l ys i s, elec tr on i c mat e rial s pro cess in g Ka r e n I. W in ey Pol y m e r morphology pro cess in g, and prop e r ty int e rr e lation shi p s r "I Penn's graduate program in chemical engineering is designed to be flex i b l e wh i le emphasizing the fundamental nature of chemical and physical processes Students ma y focus their studies in any of the research areas of the department The full resources of this Iv y League university, i ncluding the Wharton S chool of B usiness and one of th i s country's foremost medical centers, are availab l e to students in the program. The cultural advantages, historical assets, and recreational facilities of a great city are with i n wa lki ng dis t ance of the University \. For additional information, write : Di rec t or of Graduate Admissions Chemical a n d Biomolec u lar Engi n eering University of Pennsylvania 220 So u th 33rd Street, Rm. 311A Philadelphia, PA 19104-6393 http: / /www seas up enn.e du / cbe / Chemica l E n g in eer in g Ed u c ation

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PENN STATE Pursue your Chemical Engineering Degree in a diverse Big-Ten University located in a vibrant college community. Individuals with a B.S. degree in related areas are encouraged to apply. For more information contact: C h airperson, Graduate Admissions Cornmjttee Department of Chemical Engineering T h e P ennsy l va n ia State University 158 Fe n ske Laboratory U n iversi t y P ark P A 16802-4400 h ttp://fe n ske che.psu.edu/ Fall 2002 Chemical Engineering Antonios Armaou ( Uni v of CA at Los An ge l es)Pro c e ss Control Sy s tem D y n am i cs Aziz Ben-Jebria (Univ of Pari s)Re s piratory Fluid Flow and Uptake, Inhalation Toxicology Ali Borhan (Stanford)-Fluid D y nam ics Tran s port Phenomena Alfred Carlson (Wisconsin)-Biotechnology Bio se paration s Lance Collins ( P enn)-Turbu l ent Flow Combu s tion Wayne A Curt i s ( Purdu e) -Plant Bi otec hnol ogy Ronald P. Danner (Lehigh)-Polymers Ph ase Equilibria, Diffu s ion J Larry Duda ( D e laware)-Pol y m e r s Diffu sio n Thermodynamic s Tribology Fluid Me c hanic s Rheolog y Kristen Fichthorn (Michigan)-Statistical Me c h a nic s, Fluid-Solid Interface s, Molecular Simulation Henry C. Foley (Penn Stat e) -Nan o porou s Materials Heterogeneous Catalysis Adsorption and Perme a tion Seong Han Kim (No rthwest e m)-Nano-tribology a nd nano-materials Costas D Maranas ( Prin ceton)-Co mput atio n al Chemi stry, Bioinformatic s, Suppl y Chain Optimization Janna Maranas ( Prin ce ton)-Mole c ular Simulation Polymers Thermodynamics Network Gla sses Themis Matsoukas (Michigan)-Aerosol Proce sses, Colloidal Particle s, Ceramic P owders A. Nagarajan ( SUNY at Buffalo )-Colloid and Pol y mer Science Joseph M. Perez ( P e nn Stat e) -Trib ology, Lubri ca tion Michael Pishko (Texas)-Bio -mat erials, Biose nsing, and Ti ss ue Engineering Jonathan Phillips (Wisconsin)-Heterogeneous Catalysi s, Surface Science John M Tarbell (Delaware)-Cardiovascu lar Fluid Mechanic s and Mas s Transfer, Artificial Heart James S. Ultman ( D e la ware)Ph ysio lo gica l Tran s port Pro cesses, Respirator y Mas s Tran sfe r M. Albert Vannice (Stanford)H eterogeneous Catalysis Darrell Velegol (Carnegie Mellon)-Colloidal Sy s tems Colloidal Particle Interaction s James S. Vrentas ( Dela w are )-Transport Phenomena Applied Mathematics Diffu s ion in Polym e r s Rh eology Penn State i s an a ffirmative action, equal opportunity uni ve r s it y 395

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Cheinical Engineering at the University of Pittsburgh RESEARCH AREAS Biotechnology FACULTY Artificial Organs Biocatalysis Biomaterials Metabolic Engineering Modeling & Control Catalysis Surface Chemistry Catalyst Deactivation Chemical Promotion Novel Materials Organometallic Chem i stry Energy and Environment Bioremediation Clean Fuels From Coal Contaminated Soil Cleanup Stack Gas Cleanup Materials Engineering Biocompatible Polymers CO 2 as a Solvent lnterfacial Behavior Polymer/Composite Modeling Polymer Processing Multi-Scale Modeling Molecular Modeling Polymer-Fluid Interactions Process Modeling & Control Particulate Systems Transport Mohammad M Ataai Will i am Federspiel John F. Patzer 11 Jerome S Schultz Julie L. d ltr i Vladimir Kovalchuk Geitz Veser Shiao-Hung Ch i ang Robert M En i ck Badie I Morsi Anna C Balazs Robert M. Enick J Thomas Lindt Sachin Velankar Anna C Balazs Joseph J McCarthy Degree Programs: PhD and MS in Chemical Engineering MS i n Petroleum Engineering Information on Fellowships and Applications: Graduate Coordinator Chemical and Petroleum Engineering 1249 Benedum Hall University of Pittsburgh Pittsburgh PA 15261 412-624-9630 che.pitt edu Eric J Beckman Robert S. Parker Alan J Russell William R Wagne r Dan Farcasiu John W. Tierney Irving Wende r James T. Cobb Jr Gerald D Holder Eric J Beckman George E Klinzing Joseph J. McCarthy J Karl Johnson Robert S. Parker Th e University of Pittsbur gh is an affirmative a c ti on e qu a l o pp o rtuni ty instituti o n. 396 Chemical Engineering Edu c ation

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GRADUATE STUDIES IN THE DEPARTMENT OF CHEMICAL ENGINEERING, CHEMISTRY AND MATERIALS SCIENCE AT POLYTECHNIC UNIVERSITY Come to Polytechnic University in New York City., the nation"s second oldest technological university A number of fellowships are available as a result of the completion of the $275-mill i on Campaign for Polytechnic Fulp/ling the American Dream Join our dynamic research oriented faculty and conduct research in our centers for biocatalysis and biotechnology, polymers and systems engineering. For more information contact Professor Christos Georpkls Head Department of Chemical Engineering, Chemistry and Materials Science Polytechnic Unlvenlty Six MetroTech Center Brooklyn NY I 120 I Phone : 7 1 8/260-3236 Or visits us at: www poly.edu and T op : The Josep h & Vi o l et J J acobs Bu il d in g http://cchems.poly.edu Bottom: Th e Donald F. & M il dred Topp Othmer Residence H all .-~w.w,,,~ Fa ll 2002 FACULTY M.Cowman Conformation and I nteractions I n blopolymers B Garetz Interaction s of lasers with molecules, polarization effects C.Georplds Modeling and contro l of chem i ca l processes s ystems engineering M.Green Ch i rality of macromolecules liquid crystals R.Grou Blosynthes l s blocatalys l s and biotechnology K.Levon Conductive polymers blosensors J Mljovlc Relaxation dynam i cs I n complex systems S.Motzkln Effect of m i crowave radiation on blosystems J Pinto Design scheduling and optimization of chemical processes Y.Shnldman Computational modeling of complex fluids LSdel Thermodynamics and transport properties of fluids I Teraoka Separation of polymers confined systems A.Ulman Surface science and enetneering. nanotechnology E.Zletlw Air polutlon control engineering J.Zlatancwa Chromatin structure and dynamics W. Zurawslcy Pluma polymerization polymer thin films 397

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Princeton University Ph.D. and M.Eng. Programs in Chemical Engineering F acu lty Ilhan A Aksay Jay B B enziger Jeffrey D Carbeck Pablo G. D ebe nedetti (C h air) Christodoulos A Floud as Yannis G Kevrekidis Morton D Kostin Athanassios Z. Pana gio t o poul os Robert K. Prud homm e Richard A. Register William B. Ru ssel Lynn M Ru sse ll Dudle y A Saville George W. Scherer Stanislav Y. Shvartsman Sankaran Sundaresan Sa l vatore T orq u ato Sandra M. Troian T. K y l e Vanderlick James Wei David W. Wood Write to: or call: or email: Dir ector of G ra du a t e Studies Chemical Engineering Princ e t on University Prin ce ton NJ 08544-5263 l-800-238-6169 chegrad@princeton .e du [I Applied and Computational Mathematics Computational Chemistry, Biology, and Materials Systems Modeling and Optimi zat ion [I Biotechnology Biomat er ials M etabolic Engineering Prot ein and Enzyme Engineering Math emat i ca l Biol ogy [I Environmental Science a nd Engineering Aerosol Ph ys i cs and Chemistry Atmospheric Chemistry Art and In frastructure Conservation [I Materials: Synthesis/Processing/Structure/Properties Adhesion and lnt erfac ial Ph eno m ena Ceramics Colloidal Di spersions Complex Fluids Nanoscien ce and Nanotechnolog y P o l y m e r s [I Process Engineering and Science Chemical R eacto r D esign, Stability, and D ynam i cs H ete r ogeneous Catalysis Pr ocess Control and Operations Pr ocess S ynthesis and Design [I Thermodynamic s and Statistical Mechanics Glasses Kin etic and Nucleation Theory liquid State Th eory Molecular Simulation Cl Fluid Mechani cs and Transport Phenomena Electrohydrodynamics Granular and Mul tip has e Flow Microfluidics and Bi o lo g i cal Flows Rh eology Please visit our website: http://www.princeton.edu/~chemical 398 Chemical Engineering Edu ca tion

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PURDUE UNIV E RS IT Y CHEM ICAL \_ E~N~G~I N~E~E=""""'R_I_N_G _________ FACULTY Ronald P Andres Osman A. Basaran Gary E. Blau James M. Caruthers David S. Corti W. Nicholas Delgass Roger E. Eckert Gil U Lee John A. Morgan Joseph F. fckny Nicholas \ Peppas Doraiswami'8amkr" Robert ~Hannemann Gintaras V. Reklaitis Michael T. Harris Jennifer L. Sinclair Hugh W Hillhouse R. N / Houze Financial Assstance Fell~ships / Resea~~ssrstantships Teaching Assistantships Kendall Thomson George T. Tsao Venkat Venkatasubram nian Nien-Hua L. Wang Phillip C. Wankat /\ Degrees O\~d Master of Science Doctor of Philosophy RESEAR(H AREAS e;om, J ";"'";" Engineeri h g Catal ~l { and Reaction Engineering Flu"d Mechanics and Transport Phenomena lnterfacial Engineering and Colloid Science Molecular Modeling and Statistical Mechanics Nanofabrication and Nanomaterials Particle Technology Polymer Materials Process Systems Engineering Separation Processes Surface Science O For More Information Graduate Studies Purdue University 1283 Chemical Engineering Bldg. West Lafayette, Indiana 47907-1283 Phone: (765) 494-4057 www.che.purdue.edu "i~ :::, -2 0 <>. <>. 0 "iii :::, .,. 1 u "' "iii :::, .,. a, C: "' .!!! a, :::, "E :::, Q.

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Chemical Engineering at Rensselaer Polytechnic Institute The Chemical Engineering D e parhnent at R ensselaer ha s long been r ecog ni ze d for its excellence in teaching and research. It s g radu ate pr ograms l ea d to r sea r ch-based M S and Ph.D. degrees and to a co urse-based M.E. degree Pro g rams are a l so offered in cooperation with the School of Mana ge m e nt and Te c nolog y which lead to an M.E. in Chemical Engineering and to an MBA o r the M.S. in Mana geme nt Owin g to funding, cons ulting and previous faculty ex p e ri e n ce, th e department maintains close ties with industry. Department web site: http : // www.eng.rp i .ed u/d ep t/ c hem -eng/ Located in Tro y, New York R e n sse l ae r i s a pri vate sc h oo l with a n e nrollm e nt of so me 6000 s tud e nt s. Situated o n the Hud so n Ri ve r ju st north of New York 's capi tal c ity of Albany, it i s a thr ee -h o ur drive from N ew York City Bo s t o n a nd Montreal. The Adirondack Mountain s of New York, th e Green Mountains of Ver m o nt and the B er k s hire s of Massachusetts are readily accessi bl e. Saratoga with it s battl efie ld racetrack, a nd Performing Arts Center (New York City B a ll et, Phil d e lphi a Orchestra, and j azz festival) i s n ea rb y. 400 Application m ater i a l s a nd informati o n from: Graduate Service s R ensse l aer Polytechnic In s titut e Troy NY 1 2180-3590 Telephone: 518-276-6789 e-ma i I : grad-admissio n s@rp i. ed u http :/ /w ww.rp i .ed u/d ept/grad-serv i ces/ Faculty and Research Interests Michael M. Abbott, abbotm2@rpi.edu Thermodynamic s; equat i ons of state; phase equilibria Elmar R. A lt wicker, altwie@rpi.edu Profe sso r Emeritu s Spouted-bed combustion; incineration; tra ce -pollutant kinetics Georges Belfort, belfo g@ rpi .e du Membran e se parati ons; a d so rption; biocatal ysis; MRI interfacial phenomena B. Wayne Bequette bequeb@rpi.edu Associate Departm en t Chair Process modeling control, de s ign and optimization Henry R. Bungay III, bungah@rpi.edu Professor Emeritus Wastewater treatment ; biochemical engineerin g Timothy S. Cale, ca let @ rpi.edu Semiconductor m ater i a l s proce ssi n g; transport and re ac tion analyses Steven M. Cramer, crames@rpi.edu Di s placement membrane and preparative chromatogra phy ; environmental research Jonathan S. Dordick, dordick @ rpi.edu D e partment Chair Biochemical engineering; biocatalysis polymer sc ience bio se parations Arthur Fontijn, fontia@rpi.edu Combustion; hi g h-temp era ture kinetic s; gas-phase reactions Shekbar Garde, gardes@rpi edu Macromolecular se lf-a sse mbly computer simulations s tatistical thermodynamic s of liquid s, hydration phenomen a William N. Gill, gillw@rpi.edu Microelectronic s; re ve r se osmosis; crystal growth; ceramic composites Ravi S. Kane, kaner @ rpi.edu Pol y mer s: bio s urface s; biomaterial s; nanomaterial s Sanat K. Kumar, kumar @ rpi.edu Polymer nano struct ure s, nanocomposite s, dynamics of glasses and gels thermodynamics of complex fl uid s Howard Littman, littmh @ rpi.edu Profes so r Emeritus Fluid/particle sys t e m s; fluidization spouting, pneumatic transport E. Bruce Nauman, nauman@rpi .e du Polymer blend s; nonlin ea r diffusion; devolatilization ; polymer s tructure and propertie s; plastics recycling Joel L.Plawsky,pl awsky@rp i. edu Electronic a nd photonic materials; interfacial phenom ena; transport phenomen a Susan Sharfstein, sharfs@rpi.edu Biochemic a l engineerig, mammalian cell culture, recombinant protein production Hendrick C. Van Ness, vanneh@rpi.edu In st itut e P rofessor Emeritus Peter C. Wayner, Jr., wayner@rpi.edu He at tr ansfer; inter fac i a l phenomena; porou s material s C h e mi ca l Enginee ri ng Education

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RICE Chemical Engineering at Rice University FACULTY William W. Ake r s t (Michigan 1950 ) THE UNIVERSITY C on sta ntin e D. Ar meniad es (Case W es t e rn R eserve, 1 969) Ri ce i s a leadin g r esearch university-small, private and hi g hly selective-dis tin guis hed b y a co llaborative highly int e rdisciplinary c ultur e Walter G. C hapman (Co rn e ll 19 88) Sam H. Davi s, Jr. t ( M I T, 195 7) Jacqueline L. Goveas ( Prin ce t o n 1 996) J. David Hellums t (Michigan, 1 96 1 ) Joe W. Hightower t ( J o hn s H o pkins 196 3) George J. Hira sak i ( Ri ce, /967 ) Riki Kobayashi t (Michigan, 195/) Paul E. Laibinis ( Harvard University, 1991) Nikolaos V Ma ntzari s (Minneso ta 2000) Clarence A. Miller ( Minn eso ta 1 966 ) Matteo Pa s quali (Minnesota, 2000) Mark A. Robert (Swiss F e d Inst T ec h., 19 8 0 ) Michael S. Wong (M IT, 2000) Kyriacos Zygourakis (Minnesota, 1 98 1 ) THE DEPARTMENT Offer s Ph D ., M.S ., and M.Ch .E degree s Currently ha s 50 g radu ate st udents ( predomin a ntl y Ph D .). L oca ted only a few mile s from downtown Hou s ton, it occupies an architecturally di s tinctive, 3 00ac re campus s h aded by nearly 4 000 tree s. State-of-th e -art facilities and laboratories internationally renowned ce nters and in s titutes, and o n e of the cou ntry 's l argest endowments s upport an ideal learnin g and li v in g e n v ironm e nt. Provide s st ipend s and tuition wa ivers to full-time Ph D s tudent s. Special fellowship s w ith high s tipends are availab l e for outsta ndin g ca didate s. Emphasizes interdi sc iplinary st udies in co llaboration with re searc her s from other Ri ce departm e nt s, NASA the Te xas Medical Center, and R&D centers of p e troch e mical companies. FACULTY RESEARCH AREAS Biochemi ca l Engineering Nanot ec hnolo gy Biomedical Engineering NMR Propertie s of Fluid s Comp l ex Fluid s Petroleum Engineering Computational Engineering Pol y mer Sci e nc e Control and Optimization Re actio n Engineering Environment a l Remediation Rheology Equilibrium Thermodynamic Propertie s Statistical Mechanics Fluid Mechanic s .Toint with Bioengineering Interfacial Phenomena Ti ss ue Engi n eering Transport Phenomena Lary V. McIntire ( Princeton 19 7 0) Antonios G. Mikos ( Purdu e, 19 88) KaY iu San (Caltech, 1984 ) Jennifer L. West (Texas, 1996) t Emeritus Faculty Fall 2002 Kinetic s and C a taly s i s For more information and graduate program applications write to: Or visit our website at: Chair Graduate Admissions Committee Chemical Engineering Department MS-362 Rice Unjversity P O Bo x 1892 Houston, TX 77251-1892 http://www rice edu/ceng 401

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Department of Chemical Engineering UniversitY. of Rochester Graduate Study and Research leading to M.S. and Ph.D. degrees Fellowships to $24,000 plus full tuition S H. CHEN, Ph.D. 1981 Minnesota Pol y m e r Science and Engineering Organic Materials for Optics and Phot o ni cs Molecular D y nami cs Simulation E. H. CHIMOWITZ Ph.D 1982 Connecticut Critical Ph eno m e na Statistical Mechanics of Fluids Computer-Aided D es i gn D.R. HARDING Ph.D. 1986 Cambridge (Eng land ) Chemical Vapor D epos ition Mechanical and Transport Prop e rti es Advanced Aerospace Materials S. D. JACOBS Ph.D 1975 Roche s ter Opti cs Ph o t o ni cs and Optoelectronics Magnetorheology Optics Manufacturing J. JORN E, Ph.D. 1 972, California ( Berkel ey) Electrochemical Engineering Microelectronics Pr ocessi n g Th eo r etical Biology R.H. NOTTER Ph.D 1 969, Washington ( Se a ttle ) M.D. 1980 Rochester Biom e di ca l Engineering Lun g Surfactant Molecular Bioph ysics L. J. ROTHBERG Ph D. 1 984, Harvard Or ga ni c Materials a nd D evice Sciences Li g ht -Em ittin g Diod es Thin Film Transit ors Y SHAPIR Ph.D. 1981 Tel Aviv (Israel) Critical Phenomena Transport in Disord e r ed Media Scaling B e havior of Growing Surfaces S. V SOTIRCHOS Ph D. 1 982, Hou s ton Rea c tion Engineering Transport and R eact i on in Porous M ed ia Pro cess in g of Ceramic Materials and Composites J. H. D. WU Ph.D 1987 M.l.T. Bio c hemical Engineering Fermentation Bi ocatalysis B o n e Marrow Tissue Eng in eer in g Genetic and Prot e in Engineering H. YANG Ph.D. 1998, Toronto Nanostructured Materials Ma g neti c Nanoparticles M esoporo us Solids Mi c roand Nanofabrication Material s and Structu r es for Photoni cs and Bioph o t o ni cs M. YATES, Ph D 1999 Texa s ( Austin ) Colloids and Interfa ces Mat er ials Synthesis in Microemulsions Nanoparticle/Polymer Composites Supercritical Fluids Mi croe ncapsulation 402 For further information and application, write Graduate Admissions Department of Chemical Engineering University of Rochester Rochester, New York 14627 Phone: (585) 275-4913 Fax: (585) 2 73-1348 e-mail: gradadm@che.rochester.edu Chemical Engineering Education

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,,. J: --c-~ J~ll tl!!!!!!!!!!!ll .,._ '.1f; ilt 1 __ l_ ~~ : J [l 1 \ ) _~ __ ROWA.N UNIVERSITY Master of Science Chemical Engineering State-of the Art Facilities Collaboration with Industry Individualized Mentoring Multidisciplinary Research Project Management Experi ence Part-time and Full-time Programs Da y and Evening Classes Assistantships Available The Ch emical Engineering Department at R owan University is housed in Henry M. Rowan Hall, a new $28 mi ll io n 95,000 sq. ft. m u ltidisciplinary teachi n g and research space. An emphasis on pro j ect man agement process research and development, and industrially relevant research prepares students for suc cessfu l careers in high-tech fields A recent award of $6 million as seed money for the South Jersey T ec hn o l ogy Center will provide further opportunities for student training in emerging techno l ogies Located in so u thern New Jersey, the nearby orchards and farms are a daily reminder that this is the G ar d e n St ate. C ul t u ra l a n d recreational o p portuni t ies are p l e n tiful in the area Philade l phia a n d th e sce n ic J ersey Sh ore are only a short drive away, and major metropolitan areas are within easy reach. Faculty---------------C. Ste w art Slater Chair Rutgers University Ke v in Dahm Massachusetts Institute of Technology Stephanie Farrell New Jerse y Institute of Technology Zenaida Gephardt University of Delaware Robert P. Hesketh University of Delaware Kathr y n Hollar Cornell Uni v ersity Jame s Newell Clemson University Mariano J. Sa v elski University of Oklahoma -----------------Re s earch A rea s Membrane Separations Reaction Engineering Mammalian & Insect Cell Culture Pharmaceutica l and Food Processing Technology Biochemical Engineering Green E n gineering Con t rolled Release Novel Separation P rocesses Hig hPerformance Polymer Processi n g Process D esign and O ptimization Particle Technology Supercr i tica l Fluids Environmental Engineering For A dditional information --------------------------D r. Maria n o J. Savelski Graduate Stude n t Advisor D epartment of Chemical Engineering R owan University. 201 Mullica Hill R oad. Glassboro NJ 08028 Ph o n e: (856) 256-5310 Fax : (856 ) 256-5242 E-mail: savelski@rowan.edu Web: http : //engineering eng.rowan.ed u Fall 200 2 403

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RUTGERS Graduate Program in Chemical & Biochemical Engineering Research Areas Biotechnolo gy Reaction Engineering Process Systems Engineering Pharmaceutical Engineering Polymer s Faculty Helen M. Bu e ttner Associate Pro fessor, Ph D. Univers it y of Penn srlXM i I\,.)987 Applied n e ur ob i ology, ce ll m o tili ty cell s ub s trat e int eractio n s, crys t a lli z ation of ph a rma ce uti c al s Yee C C hi ew, Profe sso r ; Ph.D. University of P. ~ ph e nom e na Alki~u~~ e ~~::t:,:;~; :~% ::: ~~;,~i~: ~~~,~,~~~aj~fts i ( ? 197~ : ; ~i~ l ;, ~#//;a l f~ in ~l~ i (jl, Of f.i f;;;!)/Jitl l~pffe rm e ntati o n processes applied Pet e~;;:~:;ap n ; 1 ;,~,:~: ~1,1l1l!::~ : Y~~y i )~~ 1 \76\ 1 Tli ;; ;n ~1J/ 'c~!!;'c ff; ; '. :; nsl ~ i oX JtJ~9pl,; i o 1 / ;)} tat e b;li4lffof single and multi co mp on e nt systems Burton z. Davidson, P~ i~ie 11.o. P.ij ., North~estem .u\l \~s it y f .i 96l $.YJ/,:lS sJ; J; 1 qifo i; t'iia.f pti;; ; i za ti o (e n v iro\wl e;/fi~il~[ rin g, h ea lth a nd safety e n g in ee rin g mana ge m~~~ i}? 1 Pano s G. Geo r g opoulo J1. ~~~s~ifi t ;Ptof1;sso r ; pj {Q., G.a1t{effi\i!OO~tiii;i:IJ!gf '.] i~f;,V/il&f ph~} J~y ;ro;;;;;~utafiJi;tilic;/~ifj1;1eering turbul e nt tran s p o rt Benj :: i : 1 ;~::~~ llt ~: ~; ; ~;;;: r ; ~~ '. ~:,ia1~:;1! 1 1 1 1~! 1 1 1 1;~l i; JlJI O ;; ~:tt ~~';;;~;;\\ ; s :'.::: at'ticulate !l we 11 s i o 1 1s; n o nlin ea r d y nami cs of@'f@jjjft P(
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Graduate Studies in Chemical & Environmental Engineering at National University of Singapore NUS National University of Singapore PROCESS & SYSTEMS ENGINEERING CHEMICAL ENGINEERING FUNDAMENTALS Al Applications Process Dynamics & Control Process Design & Development Pro c ess Model i ng & S i mulation Process Operations & Safety Process Optim i zation MATERIALS & DEVICES Advanced Catalyt i c & Crys t alline Materials Polymeric Electronic & Bio Materials Sensors & Electrochemical Dev i ces Surface Science & Engineering Biochem i cal & Biomedical Engineering lnterfacial Phenomena Reaction Engineering Separation & Purification Thermodynamics Transport Processes ENVIRONMENTAL SCIENCE & TECHNOLOGY A i r & Water Pollution Control Atmospheric & Aquatic Chemistry B i o remediation Environmental Assessment & Modeling Hazardous Waste Treatment National University of Singapore is internationally acknowledged as one of the best universities in the Asia Pacific region with a global outlook and focus on quality teaching research and entrepreneurship W i th more than 45 faculty members from diverse ethnic backgrounds and with excellent academic credentials from leading i nstitutions around the world the Depart ment of Chemical and Environmental Engineering offers graduate programs that provide a stimulating and challenging learn ing experience. The Department has comprehensive top-notch research facilities for carrying out cutting edge research. Close ties with the industry and overseas institutions provide infusion of new ideas and maintain a creative and dynamic atmosphere in the Department. GRADUATE PROGRAMS Coursework-based Master of Science (Chemical Engineering) (with specialization option in biopharmaceutical engineering) Master of Science (Environmental Engineering) Master of Science (Safety Health & Environmental Technology) NUS-UIUC Joint Master of Science (Chemical Engineering) Contact Us At: Department of Chemical & Environmental Engineering National University of Singapore 10 Kent Ridge Crescent Singapore 117576 Tel : (65) 6874-8076 Fax : (65) 6779-1936 E-mail : chegohsp@nus edu sg http :// www.chee.nus.edu sg Fa ll 2002 Research-based Master of Engineering Doctor of Philosophy NUS-UIUC Joint PhD Program Financial assistance is available for qualified applicants in the form of research scholarships. 405

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Z Department ~ U I V E RS I T Y OF ~Q\ROLINA. The Department of Chemical Engineering at USC is booming! Re s earch funding is at an all-time high-exceeding $4 million per year. Thi s progre s s ive department with its dynamic young faculty, i s already recognized a s one of the top teaching and re s earch program s in the South east. Chemical Engineering offer s MS, ME and PhD degrees and PhD candidate s are offered tuition reduction and highly competi tive, twelve month s ti pends ranging from $20,100 to $22,500 per y ear. For further i11Jormatio11 : 406 Th e G r a du a t e Dir ector D e p ar t me nt of C h e mi ca l E n g in eer in g, Swear in ge n E n g i n eer in g Ce n te r U ni versity of So uth Caro li na Co lum b i a SC 29208 P h o n e : 1800 763-05 2 7 Fax : 1 803 777 8265 W e b p ag e: www c h e .s c. e d u of Chemical Engineering Th e Uni ve r s it y of S o uth Car o lin a i s l oca t e d in Co lumbi a th e s t a t e ca pit a l. Co lumbi a i s co n ve ni e ntl y l oca t e d in th e ce nt e r o f th e s t a t e a nd co mbin e s t h e b e n efi t s of a bi g ci t y w ith th e c h ar m and h os pit a lity o f a s mall t ow n. T h e area's s unn y a nd mild clim a t e, co mbin e d w ith it s lak es a nd w oo ded park s pro v id e pl e nt y o f o pp o rtuniti es fo r year ro und o u t d oor r ec r ea ti o n In a dditi o n Co lumbi a i s o nl y h o ur s away fro m th e Blu e Rid ge Mo unt ai n s and th e A tl a nti c Coast. Ch ar l o tt e a nd A tlant a -ci ti es th a t se rv e as Co lumbia s int e rn a ti o n a l ga t e w ays -a r e n e arb y Research Pro,:rams Adsorption Technology Batteries and Fuel Cells Colloids and Interfaces Composite Materials Co"osion Engineering Crossflow Filtration Electrochemistry Heterogeneous Catalysis Molecular Simulations Nanotechnology Numerical Methods Pollution Prevention Process Control Rheology Separations Sol-Gel Processing Solvent Extraction Surface Science Supercritical Fluids Thermodynamics Waste Management Waste Processing C h e mi c a l E n g in ee r in g Edu c a t i o n

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University at Buffalo The State University of New York Faculty Integrative Research at the Frontiers of Chemical Engineering Nanosca le Science and Engineering B1 ochem1cal & Biom ed i cal Engine erin g Paschali s Alexandridis (MIT ) amphiphili c p o l y m e r s, se lf ass e mbl y, co mpl e x fluids nanomat e ri a ls int e rfa c ial ph e nom e na Stelios T. Andreadis ( Michi g an ) bi oe n g in ee rin g, ge n e th e rap y tissu e e n g in ee rin g o f ge n e ticall y modifi e d skin Jeffre y R. Errington ( C o rnell ) m o l ecu lar s imulati o n s tatisti c al th e nn o d y nami cs biopr e s e rvati o n Vladimir Hlavacek ( J CT -Pra g ue ) r e a c tion e n g in ee rin g, nanopo w d e rs, e xplosi ve s and d e tonation s, anal y sis of c h e mi cal plants Mattheo s Koffa s ( MIT ) m e taboli c e n g in ee rin g, bi o in fo nnati c s David A. Kofke ( Pennsylvani a) m o l ec ular m o d e lin g and s imulation s o lid phas e equilibria Carl R. F. Lund ( Wiscon s in ) h e t e r oge n e ous c atal y si s, c h e mi c al kin e ti c s r e a c tion e ngin ee rin g T. J. (Lakis) Mountziaris ( Princeton ) e l ec tr o ni c a nd pho to ni c mat e rials nan o particl e s bios e n s ors multiphas e flo w s Sriram Neelamegham (Rice ) biom e di c al e ngin ee rin g, ce ll biom ec hani c s v as c ular e n g in e erin g Johannes M. Nitsche ( MIT ) fluid m ec hani cs, tran s p o l1 ph e n o m e na bi o a c ti ve s urfa ce s bi o lo g i c al p o r es, transd e nnal transport Eli Rucken s tein ( Bu c h arest ) c atal ys is, surf ace ph e n o m e na c olloids and e mulsions bio c ompatibl e surfa ce s and materials Michael E R ya n ( McGill ) p oly m e r a n d ce r a mi c s pr ocess in g, rh eo l ogy, n o n N ew tonian fluid m ec hani c s Mark T. Swihart ( Minne so t a) c h e mi ca l kin e ti c s, m o d e lin g o f r e a c ti ve fl ow s, co mputational c h e mi s ll y, nanoparticl e Jonna/ion E. (Manolis) S. Tzanakaki s ( Minne so ta ) ce ll and ti s su e e n g in ee rin g, bio c h e mi c al e n g in ee rin g Adiunct Faculty V. James Hernandez ( Microbi o l ogy) r eg ulati o n o f ce llul a r r e spons es William M. Mihalko ( Scho o l o f Medi c in e) o rth o pa e di c s Bruce Nicholson ( Biological Science s) g ap jun c tions and co nn ex ins Athos Petrou (Phy s ic s) sp ec t ro s co p y, se mi co ndu c t o r nan o stru c tur es Carel Jan van Os s ( Microbi o l og y ) co ll o id and i n t e rfa ce s cie n ce Yaoqi Zhou ( Bioph ys ic s) pr ote in f o ldin g, s imul a ti o n o f bi o m o l e cul es Emeritus Faculty in Residence Robert J Good ( Michigan ) adh e sion and int e fa ce s c i e n ce, philosoph y of sci e n c e Thomas W. Weber ( Corne ll ) pro ce ss c ontro l Sol W. W e ller ( Chicag o) c atal y sis, c oal liqu e fa c tion histo ry of c h e mi c al engin ee r in g Chemical engineering faculty participate in m a ny interdisciplinary centers and initiatives, including The Center for Advanced Molecular Biolog y a nd Immunol ogy, The Center for Computational Research, The Center for Advanced Photonic and Electronic Material s, The In s titute for Laser s, Ph oto nic s, and Biophotonics The Institute for Bioinformatics, and The Center for Advanced Techn o l ogy for Bi o medi ca l Device s Fo r mor e in.Jonna/i o n and an appli ca tion writ e t o: Dir ec tor o f Graduat e Studi e s D e partment of Chemi c al Eng in eer in g Univ e rsi ty at Buffal o ( SUNY ), Buffal o, N ew York /4260-4200 o r go t o http: // www.cheme.buffalo.edu Fall 2002 All Ph D students are supported as research or teaching a ss i s tants. Additiona l fe ll o wships spo n sored by P raxai r In c., The Nat.i o nal Science Fo un dation JGERT program, and the State Univer s ity of New York are ava il ab l e to except i ona ll y well qualified app li ca nt s. 407

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Faculty------------R. Besser (PhD Stanford Un iversity) R. Blanks (PhD, University of California at Berkeley) G.8. Delancey ( PhD University of Pittsburgh) H. Du (PhD, Penn State University ) T.E. Fischer (ScD Federal Inst. of Technology Zurich) 8. Gallois (PhD, Carnegie Mellon University) D.M. Kalyon ( PhD McGill University) S. Kovenklioglu (PhD Stevens Institute of Technology) A. Lawal (PhD, McGill University) W.Y. Lee (PhD, Georgia Institute of Technology) M. Libera (ScD, Massachusetts Inst. of Technology ) G. Rothberg (PhD Columbia University) K. Sheppard (PhD University of Birmingham) Research in ____________ Micro-Chem ical Systems Polymer Rheology and Processing Processing of Electronic and Photonic Materials Processing of Highly Filled Materials Chem i cal Reaction Engineering Chemical Vapor Deposition Biomaterials and Thin Films Polymer Characterization and Morphology High Temperature Gas Solid and Solid-Solid Interactions Environmental and Thermal Barrier Coatings Tribochemistry and Tr i bology 408 STEVENS INSTITUTE OF TECHNOLOGY Multidisciplinary environment consisting of chemical and polymer engineering chemistry, and biology Site of a major engineering research center; Highly Filled Materials Institute Scenic campus overlooking the Hudson River and metropolitan New York City Close to the world's center of science and culture At the hub of major highways air, rail and bus lines At the center of the country's largest concentra tion of research laboratories and chemical, petroleum pharmaceutical and b i otechnology companies GRADUATE PROGRAMS IN CHEMICAL ENGINEERING Full and part-time Day and evening programs MASTER'S CHEMICAL ENGINEER PH.D. Fo r application, contact: Offi ce of Graduate Studies Ste ve ns I nstitute of Te c hnolo gy H oboken, NJ 07030 20 / -2 / 6-5234 For additional information co ntact: Ch e mi cal Bio c hem ica l and Materials Engineerin g D e partm e nt Stev e ns I nstitute a/ T ec hn o lo gy H oboken, N J 07030 20 /2 16-554 6 ( Financial Aid is Available to qualified students ) Steve n s Institute of Technology does not di sc riminate against any person because of race creed co lor n a tional ori g in sex, age marilal s tat us, handicap li ability for se rvice in the a rmed forces or sta tu s as a di sa bled or Vietnam era veteran. Chemi c al Engineering Education

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Graduate Studies in Chemical Engineering The University of Tennessee, Knoxville Piece together the elements of a great graduate experience ... I The Faculty ,-----------------------......., Graduate s tudents and faculty w orking together to reach The Research common goals that partnership is at the heart of the University ofTennessee-Knox v ille 's Department of Chemical Engineering. It s a partnership that w orks creating exciting and productive research in six major areas: ( I ) bio-process engineering (2) molecular science and engineering (3) separations and transport phenomena (4) computer aided process simulation and design (5) pol y mer and composite proce ss ing and ( 6) process control. These re sea rch program s reach out to other engineering and science departments to the nearb y Oak Ridge National Laboratory and to industry forming larger partnerships and creating an unsurpassed re sea rch environment. Found e d in 1794 as Blount College the first non The University sectarian college wes t of the Appa l achians The I Paul R Bienkowski (Ph.D ., Purdue 1975) Bioproc essing, Thermod y namics Duane D. Bruns (Ph.D ., Houston 1974) Pro cess Control Modeling John R Collier (Ph D. Case Institute 1966) Pol y m er Pro cessing and Properties Robert M. Counce (Ph.D ., Tennessee 1980) Separations and Transport, Environmental Peter T. Cummings (Ph.D. Melbourne 1980) Molecular Thermodynamics D esig n Environmental Brian J. Edwards (Ph.D ., Delaware 1991) Non Newton ian Fluid D y namic s Paul D. Frymier (Ph D ., Virginia 1995) Bio c h emical E ngin eer in g, Bios e n so rs Da v id J. Keffer (Ph D ., Minnesota 1996) Molecular Modeling of A dsorption Diffusion and R eac tion in Z e oli/es Charles F. Moore (Ph D. Louisiana State 1969) P rocess Control John W. Prados (Ph.D. Tennessee 1957) Safety and Ri sk Assessment Tsewei Wang (Ph.D. M.I.T ., 1977 ) Pro cess Control Biopro cess ing University ofTennessee toda y i s the s tate 's largest uni v ersity and Land-Grant in s titution w ith about 20 000 undergraduate s, 5 700 graduate and professional students and a faculty of 1 200 The University of Tennessee is located in Knox v ille near the headwaters of the Tennessee Ri ve r. Within an hour s drive are six Tenne ssee Valley Authority lakes and the Great Smoky Mountains National Park The Knoxville metropolitan area has a population of 600 000 but enjoys a pleasant generally uncrowded atmosphere and consistently ranks among the nation 's top ten metropolitan areas in surveys on quality of life East Tenne ssee ha s a four-season climate ranging from wa rm s umm er temperatures to winter temperatures cold enough for s now skii ng in nearb y mountain resorts Frederick E. Weber (Ph D ., Minnesota 1982) ~-----------------------.--,1 Computer-Aided D esig n Radiation Chemistry The Next Step For additional information contact : Department of Chemical Engineering University of Tennessee-Knox vi ll e 4 I 9 Dou gherty Hall Knoxville TN 37996 2200 Phone : (865) 974-2421 E-mail : chei nfo @ utk.edu World Wide Web: http :// www.che.utk edu Adjunct and Part-Time Faculty from Oak Ridge National Laboratory Hank D Cochran (Ph.D. M.I.T .): Th ermody nami cs, Statistical Mechanics Brian H Da v i s on (Ph D. Caltech): Bio c h e mical Engineering Jack S Watson (Ph D ., Tennessee ): Separations and Transport Nuclear Fusion

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The Univer at Austin Chemical Engineering at the U n iversity of Texas at A u s t in is an exciting, b roa d b ased an d i nter di sc i pli n ary program, wit h facu l ty of d iverse r esearc h interests. We are o n e of t h e l ea d i n g p rograms in che m ical e n gi n eer ing exce ll i n g i n al l aspects of scho l ars h i p researc h a nd education. Both M S C h E an d Ph.D ChE degrees are offered. Fellow sh i ps and research assistantships are pro vided, including tuition and fees. Faculty and their research David T. Allen Ph.D., Caltech, 1983 env i ronmental modeling reaction engineering Angela M. Belcher Ph D., U. ofC. Sama Barbara, 1997 organic/inorganic biomo l ecular & biological-e l ectronic hybrid materials Roger T. Bonn e caze Ph.D., Caltech, 1991 suspension rheology, transport phenomena electrical impedance tomography Thomas F E dgar Ph.D. Princeton U., 1971 process modeling control optimi z ation John G. Ekerdt Ph.D., U. of C. Berkeley electronic materials chemistry, surface science R. Bruce Eldridge Ph.D. U. ofTexas, 1986 separations r e search Benn y Freeman Ph.D., U. of C. Berkeley, 1988 pol y mer structures processing and properties Venkat Ganesan Ph.D. MIT 1999 statistical mechanics simulat i ons of self-assembly in comple x fluids George Georgiou Ph.D., Cornell U 198 7 microbial prote i n biotechnology Peter F. Green Ph D., Cornell U., 1985 materials science polymer melts Adam Heller Ph.D. Hebrew U., 1961 electrochemical biosensing, environmenta l photoelectrochemistty G y eong S. Hwang Ph.D., Caltech, 1999 multisca l e modeling & simulation semiconductors nanotechnology Keith P. Johnston Ph.D., U. of Illinois 1981 po l ymer and surface thennod y namics supercritical fluids Miguel J ose Yacaman Ph D., National University of Mexico 1973 materials science electron microscop y, nanoparticles Brian A. Korgel Ph.D., U. of C. Los Angeles, 199 7 complex fluids nanostructured materials Douglas R. Llo y d Ph D. U of Waterloo 1977 po l ymeric membrane formation liquid separation s Yueh-Lin Loo Ph D. Princeton U. 2001 polymer physics & chemistry micro& nanostructured materials C. Buddie Mullins Ph.D. Caltech, 1990 surface science, molecular beams semiconductor thin-film growth S. Joseph Qin Ph.D., U. of Maryland, 1992 process modeling and control Gary T Rochelle Ph.D. U. of C. Berkeley, 1977 air pollution control reactive mass transfer Peter J. Rossky Ph.D., Harvard U 1978 theoretical chemistry liquids condensed phase quantum d y namics Isaac C. Sanchez P h D., U of Delaware, 1969 statistical thermodynamics of pol y mer liquids and solutions Christine E. Schmidt Ph.D., University of Illinois, 1995 cell and tissue engineering Makul M. Sharma Ph.D ., U. of Southern California, 1985 surface and colloid chemistry T homas M. Truskett Ph.D., Pr i nceton U., 2001 statistica l mechanics molecular modeling J. Michael White Ph D., U. of Illinois 1966 chemical reactions on surfaces C. Grant Willson Ph D U. of C. Berkeley, 19 7 3 pol y mer s y nthesis photochemical processing Address inquires to: Graduat e Advisor D e p a rtm e nt of Chemic a l Engin ee rin g University of T ex as Austin TX 787 1 2106 2 Phon e: 51 2/47 1 6991 F ax: 51 2/47 1 -7824 ut g r a d @ ch e. ut ex as edu wwwch e .ut exas .edu 410 Ch e mi c al Engin e erin g Edu c ation

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TexasA&M University Large Graduate Program Approximately 120 Graduate Students Strong Ph.D. Program (75% PhD students) Diverse Research Areas Top 10 in Research Funding Quality Living I Work Environment Financial Aid to All Qualified Students Up to $24,000/yr plusTuition and Fees and Medical Insurance Benefits RESEARCH AREAS Biochemical Engineering/Bioprocessing Biomedical Engineering Composite Materials and Asphalts Environmental Remediation/Pollution Prevention Advanced Catalysts Interfacial Transport Kinetics, Catalysis and Reaction Engineering Microelectronic Materials Molecular Simulations Nanomaterials Polymers Computer-Aided Process Design and Modeling Separations Supercritical Phenomenal Technology Thermodynamics For More Information Graduate Admissions Office Department of Chemical Engineering Dwight Look College of Engineering Fall 2002 Texas A&I\I Uniwrsit~ College Station. Texas 778-IJ-Jl22 Phone (979) 8-15-.:U<,t \\!ebsik http://www-chen.tamu.edu Faculty R.G. A nthon y, H ea d Ph.D ., U ni versity ofTexas, 1 966 C. D H o ll a nd Prof essor Catalysis r eac tion e n g in ee rin g ion exc h ange A Akgerman Ph D ., U. of Vir gi ni a 19 7 1 Chevron Profes so r R eac ti on e n gi n ee rin g, was t e tr ea tm e nt J.T. Baldwin Ph D Texas A&M University 1968 P rocess design M A. B eva n Ph D Carne g i e Mellon Uni v ersity 1 999 Collo idal S c i e n ce D.B. Bukur A ssoc iat e H ea d Ph.D ., U.ofMin n esota, 1 974 R eac ti o n e n g in ee rin g, math m e thod s J.A. Bullin Ph D U. o fH o u s t o n 1 972, P rofesso r E meritu s Gas sweetening, asphalt c hara c t e ri za tions R. Darby Ph D Ri ce University 1 972, P rofesso r Emeritu s R heolog y, polymers R.R. Davi so n Ph .D. Texas A&M U. 1962 Pr ofessor Emeritus Asphalt c haracte ri za tion L.D. Durbin Ph D Ric e Univers i ty 1 961, Prof esso r Eme ritu s P rocess co nt rol M. E l-H a lwagi Ph D U ni ve r s it y of Califo rnia 19 90 M c F e rrin Profe sso r P rocess int eg ration P.T. E ubank Ph.D ort h weste m U ni vers it y 1 96 1 J oe M Nesbitt Prof esso r Th e rmod y n amics D.M. Ford Ph.D Univer s i ty of P ennsy l vania 1 996 Molecular m ode lin g/ transp ort G Frome nt Ph D. University of Gent B elg ium 1 957 R eaction e ngineerin g C. J Glover, Ph.D R ice U ni ve r s it y, 1 974 Director Cen t e r for Asphalt & Material s Chemistry P olymer so lwi ons asphah c hara c terizati o n K.R. Hall Ph D. Unive r s ity of Oklahoma 1 967 Jack E. a nd Frances B row n C h a ir Th e rm o d y nami cs D.T. Hanson, Ph D. Unive r s it y of Minnesota 1968 Bi oc h e mi c al engineering C.D. Holland Ph D T exasA&M Univ. 195 3 P ro f essor Emeritu s Separat i on processes distillation unst eady-s tat e processes J.C. Holst e, Ph D I owa State U ni vers it y, 1 973 Th e rmod y nami cs M .T. Holtzapple, Ph.D U niv e r s ity of Penn sy l va nia 1 981 Bi o c h e mi c al e n g in ee rin g Y Kuo Ph D. D ow Prof esso r Col umbi a Unive r si t y 19 79 Microe l ec troni c s S. Ma nnan Ph D U ni ve r s i ty ofOk l ahoma, 19 86 Dir ecto r, M ary K ay O Con n o r Pr ocess Saf e t y Ce nt e r E. Sevick-Muraca Ph.D. Carnegie Mellon Un i vers i ty, 1 989 Biom edica l/Bio c h e mi ca l D .F Shantz Ph D Un i v ersity of D e la ware, 2000 Struc/llre prop e rt y r e l a ti ons hip s of porous mat eria l s, sy n thesis of n ew porous solids V Ugaz, Ph D Northwestern Univer s it y, 19 99 Mi crofab ri ca ted Bi oseparation S y stems 4 11

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(.) ro u. 4 1 2 Chemical & Environment Martin A. Abraham, Professor Ph.D., University of Delaware Green Chemistry and Engineering, Supercritical Fluids Maria R. Coleman Associate Professor Ph.D., University of Texas at Austin Membrane Separations, Bioseparations Kenneth J. DeWitt, Distinguished Professor Ph.D. Northwestern Universit y Transport Phenomena, Mathematical Modeling & Numerical Methods John P. Dismukes, Professor Ph.D., University of Illinois Materials Processing Management of Technological Innovation Isabel C. Escobar, Assistant Professor Ph.D. University of Central Florida Membrane Fouling and Membrane Modifications Saleh Jabarin, Professor Ph D University of Massachusetts Physical Properties of Polymers, Polymer Orientation & Crystallization Dong-Shik Kim, Assistant Professor Ph.D. Universit y of Michigan Biomaterials, Metabolic Pathway Control Steven E. LeB l anc, Professor and Chair Ph.D. University of Michigan Chemical Process Control, Chemical Engineering Education G. G l enn Lipscomb, Professor Ph.D. University of California at Berkele y Membrane Separations, Bioseparations, Education Arunan Nadarajah, Professor Ph.D., University of Florida Transport in Biologi:al Systems, Nanotechnology Bruce E Poling, Professor Ph.D., University of Illinois Thermodynamics and Physical Properties Constance A. Schall, Associate Professor Ph D ., Rutgers University Enzyme Kinetics, Crystallization, Paraffin Deposition Sasidhar Varanasi, Professor Ph.D State Universit y of New York at Buffalo Colloidal & Interfacial Phenomena, Hydrogels The Department of Chemical & Environmental Engineering at the University of To l edo offers graduate programs lead in g to MS and Ph D degrees We are located in state of the art facilities in Nitschke Hall and our dynamic faculty offer a variety of research opportunities in contemporary areas of chemical engineering SEND INQUIRIES TO : Academic Coordinator Chemical & Environmental Enginee r ing 280 1 W Bancroft Street Mail Stop 305 University of Toledo Toledo, Ohio 43606-3390 Ph one : ( 41 9) 5 30 8080 F ax : ( 4 1 9) 5 30 8086 UR L : http : //www che ut o l e d o e d u E-m a il : c h ee d e pt @e n g ut o l e d o e du C h e mi c al En g in ee rin g Edu c ati o n

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Chemical and Biolo gical Engineering at Tufts University TUFTS OFFERS M.S., M.E. and Ph.D degrees in Chemical and Biotechnology Engineering University-wide Bioengineering Center involving Medical Dental and Veterinary Schools A friendly personalized small college" environment with all the advantages of a research university Opportunities to design and contribute to exciting university research at the state-of-the-art Science & Technology Center Small classes that ensure individualized attention from our superb faculty An active graduate student council working to en hance student social and academic life Located 5 miles north of Boston with easy access to the numerous educational and social resources of the local and New England area For further information contact: Graduate Studies Chair Tufts University Chemical and Biological Engineering Department 4 Colby Street Medford MA 02155 PHONE 617-627-3900 FAX 617-627-3991 Fall 2002 Email: chemstudent@infonet. tufts. edu Web: www.ase.tufts.edu/chemical Chemical Engineering at Tufts since 1901 Pr e paring for the next I 00 yea rs FACULTY AND RESEARCH AREAS FULL-TIME PROFESSORS Assoc. Prof. Eliana DeBernardez Clark Ph.D (U .N.L. Argentina) ( on lea ve) Bi oc h emica l engineering, protein folding, protein aggregation Prof. Gregory D. Botsari s Ph .D. (M .I.T. ) Crystalli z ation, nucleation, applied surface science Prof. Maria Flytzani-Stephanopoulos Ph D. (Univ. of Minnesota) Environmental catalysis pollution prevention, clean e n e r gy, and transportation technologies Prof. David L. Kaplan Ph.D. ( Syracuse University) Bi oengineered polymers related to self-assem bl y, biomaterials and tissue engineering Asst. Prof. Kyongbum Lee Ph.D (M .I.T. ) Bi otec hnol ogy, metabolic enginee ring bi o informati cs Assoc. Prof. Jerry H. Meldon Ph D ( M.I.T .) Membrane science and technolog y, mass transfer with c hemi ca l re action including mathematical modeling Assoc. Prof. Daniel F. Ryder Ph.D (Worcester Polytechnic Institute) Advanced pro cess co ntrol applications Prof. Nak-Ho Sung Ph.D (M. I.T. ) P olymers and composites interface science, polymer diffusion, sur face modification Prof. Kenneth A. Van Wormer Sc.D. (M.I.T.) Optimization nucleation, reaction kin etics, VLSI fabrication RESEARCH PROFESSORS Asst. Prof. Aurelie Edwards Ph D ( M.I T .) Transport across biological membranes role of microcirculation in the renal medulla Asst. Prof. Regina Valuzzi Ph.D (U ni v. of Massac hu setts, Amherst) Ordering of highl y structured patterned polymers into co mple x nanostru c tured materials Assoc. Prof. Vladimir Volloch Ph.D (Moscow University) Cellular and molecular biology ADJUNCT PROFESSORS Asst. Prof. Dale Gyure Ph.D (University of Colorado) Prof. Walter Juda Ph D (U niver si ty of Lyons) Electrochemistry and chemical reaction enginee ring Asst. Prof. Brian Kelley Ph D. ( M I.T. ) Novel methods for protein purification, large-scale purifications, high-density bacterial fermentation Prof. Gordana Vunjak-Novakovic Ph.D. (University of Belgrade) Transport phenomena, t issu e engi neerin g, bioreactors Asst. Prof. Stefan Winkler Ph.D (Tufts University) Prot ein assembly 413

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ulane ____ niversit Department of Ch emical Engineering Faculty and Research Areas Dan ie l C. R D eKee Rh eology of Natural and Synthetic Polymers Constitutive Equations Transport Phenomena and Applied Mathematics Richard D. Gonzalez S y nth es is and Characteri za tion of Supported Metal Catal ys ts Fundamental Studies in R eactor D esign Insitu Spectroscopi c Methods R eactions in Organi ze d Media Vijay T. J o hn Biomimetic a