Chemical engineering education ( Journal Site )

Material Information

Chemical engineering education
Alternate Title:
Abbreviated Title:
Chem. eng. educ.
Physical Description:
v. : ill. ; 22-28 cm.
American Society for Engineering Education -- Chemical Engineering Division
Chemical Engineering Division, American Society for Engineering Education
Place of Publication:
Storrs, Conn
Publication Date:
annual[ former 1960-1961]


Subjects / Keywords:
Chemical engineering -- Study and teaching -- Periodicals   ( lcsh )


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.
General Note:
Title from cover.
General Note:
Place of publication varies: Rochester, N.Y., 1965-1967; Gainesville, Fla., 1968-

Record Information

Source Institution:
University of Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
oclc - 01151209
lccn - 70013732
issn - 0009-2479
lcc - TP165 .C18
ddc - 660/.2/071
System ID:

Full Text

chemical engineering education



.t Featuring articles on graduate courses...

STeaching Coupled Transport and Rate Processes (p.254)
SA Computational Model for Teaching Free Convection (p.272)
Il Goldstein
A Graduate Course on Multi-Scale Modeling of Soft Matter ip.242)
Hung, Gubbins. Franzen
t Reflections on Project-Based Learning in Graduate Courses (p.262)
< Teaching and Mentoring Training Programs: A Doctoral Student's Perspective (p.250)
E ,Baber, Bfriedis. Worden
5 Relating Abstract Chemical Thermodynamic Concepts to Real-World Problems (p.268)
.0 CCastaldt, Dorao, Assaq-Anid

.4 ... and articles of general interest.

a Random Thoughts: An Educator for All Seasons (p.?280). .................. ... .. ................. Felder
S a JAVA-Based Heat Transfer Visualization Tools ip.282)................ .......... .......... ..... .. ... Zhen Keith
3 High-Performance Learning Environments ip.286) ............... .. .. .. .... ..... rce. Schreiber
5 a Deseloping Metacognitive Engineering Teams (p 316) ... .. ............... .......... Newell. Dahm. Harvey, Newell
S *' Pillars of Chemical Engineering: A Block-Scheduled Curriculum (p.292) ............. ...... ....... McCarth. Parker
SDevelopment of Cross-Disciplinary Projects in a ChE Undergraduate Cumculum (p.296' ................. Glennon
Deeloping Communication Skills in Engineering Students ip 302) .....Roeckel. Parra. Donoso, Mora, Garcia
Put Your Intuition to Rest: Write Mole Balances Systematically (p 308) ................ Gadewar. Doherty, Malone



Chemical Engineering Education
Department of Chemical Engineering
University of Florida Gainesville, FL 32611
PHONE and FAX: 352-392-0861

Tim Anderson

Phillip C. Wankat

Carole Yocum

James 0. Wilkes, U. Michigan

William J. Koros, Georgia Institute of Technology


E. Dendy Sloan, Jr.
Colorado School of Mines

Pablo Debenedetti
Princeton University
Dianne Dorland
Rowan University
Thomas F. Edgar
University of Texas at Austin
Richard M. Felder
North Carolina State University
Bruce A. Finlayson
University of Washington
H. Scott Fogler
University of Michigan
Carol K. Hall
North Carolina State University
William J. Koros
Georgia Institute of Technology
John P. O'Connell
University of Virginia
David F. Ollis
North Carolina State University
Ronald W. Rousseau
Georgia Institute of Technology
Stanley L Sandler
University of Delaware
Richard C. Seagrave
Iowa State University
C. Stewart Slater
Rowan University
Donald R. Woods
McMaster University

Chemical Engineering Education

Volume 38

Number 4

Fall 2004

242 A Graduate Course on Multi-Scale Modeling of Soft Matter,
Francisco R. Hung, Keith E. Gubbins, Stefan Franzen
250 Teaching and Mentoring Training Programs at Michigan State Univer-
sity: A Doctoral Student's Perspective,
Tylisha M. Baber Daina Briedis, R. Mark Worden
254 Teaching Coupled Transport and Rate Processes,
Yasar Demirel
262 Reflections on Project-Based Learning in Graduate Courses,
Satish J. Parulekar
268 Relating Abstract Chemical Thermodynamic Concepts to Real-World
Marco Castaldi, Lucas Dorazio, Nada Assaf-Anid
272 A Computational Model for Teaching Free Convection,
Aaron S. Goldstein

280 An Educator for All Seasons, Richard M. Felder

282 JAVA-Based Heat Transfer Visualization Tools,
Haishan Zheng, Jason M. Keith

286 High-Performance Learning Environments,
Pedro E. Arce, Loren B. Schreiber
316 Developing Metacognitive Engineering Teams,
James Newell, Kevin Dahm, Roberta Harvey, Heidi Newell

292 Pillars of Chemical Engineering: A Block-Scheduled Curriculum,
Joseph J. McCarthy, Robert S. Parker
296 Development of Cross-Disciplinary Projects in a ChE Undergraduate
Brian Glennon
302 An Innovative Method for Developing Communication Skills in
Engineering Students,
M. Roeckel, E. Parra, C. Donoso, O. Mora, X. Garcia
308 Put Your Intuition to Rest: Write Mole Balances Systematically,
Sagar B. Gadewar, Michael F Doherty, Michael F Malone

279 Positions Available

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 2004 by the Chemical Engineering Division, American
Society for Engineering Education. The statements and opinions expressed in this periodical are those of the writers and not
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
of Florida, Gainesville, FL 32611-6005. Periodicals Postage Paid at Gainesville, Florida and additional post offices.

Fall 2004

Graduate Education

A Graduate Course on



Chemical Engineering Department, North Carolina State University Raleigh, NC 27695-7095
Department of Chemistry, North Carolina State University Raleigh, NC 27695-8204

Theory and simulations play an important role in chemi-
cal engineering, chemistry, and physics. They provide
a link between the microscopic features of a system
and its macroscopic properties. Molecular simulations can
be used as "computational experiments" to get information
that would be very difficult or impossible to get in a labora-
tory, and they can also assist in the analysis of experimental
results. Moreover, simulations provide a way to test theories
and thus determine their range of validity.' 21
A number of problems of current interest require insight
on many length scales, ranging from angstroms (subatomic
and atomic scales: electron rearrangement, bond breaking and
formation) up to macroscopic scales (bulk material proper-
ties). For example, intermolecular interactions have length
scales on the order of nanometers, and the microscopic struc-
ture of some phases can involve length scales on the order of
tens of nanometers, microns, millimeters and even larger, de-
pending on the nature of the molecules present in the system.
In a similar way, the corresponding time scales of the dy-
namic processes associated with these phenomena can range
from femtoseconds to milliseconds, reaching seconds or even
hours in some cases. Examples of systems exhibiting such a
wide range of length and time scales are polymeric and col-
loidal systems, self-assembly of surfactants on surfaces, bio-
logical systems, and chemical reactions in nonideal or nano-
structured environments (liquid solutions, supercritical flu-
ids, porous media, micelles, composites, etc.).
Problems such as these require a multi-scale approach in
which ab initio, atomistic, and meso-scale methods are com-
bined. No single model or simulation algorithm can cover
this range of length and time scales. Figure 1 illustrates the
length and time scales presently accessible to the main groups
of computational methodologies. This figure was constructed

SCourse website:

assuming that calculations are performed for a maximum of
one week on the Blue Horizon (IBM SP3) supercomputer at
the San Diego Supercomputer Center (SDSC), which has a
maximum speed of 1.728 Tflops.31 More and more approxi-
mations are introduced as we move from ab initio algorithms
to the continuum level: larger length and time scales are
handled efficiently, but at the cost of reduced accuracy and
loss of fine structure. Electronic structure is lost in atomistic
simulations, and atomistic detail is lost in meso-scale simu-
lations. An important challenge and an active area of research
in modeling of soft matter is how to link the different meth-
ods available to cover the whole range of length and time

Francisco R. Hung received his BS (1996) and
MS (1999) in chemical engineering at Universidad
Simon Bolivar in Venezuela. He was Assistant
Professor in the Department of Thermodynamics
and Transport Phenomena at Universidad Sim6n
.Bolivar before coming to North Carolina State Uni-
versity, where he is currently a chemical engineer-
ing PhD candidate.

Keith E. Gubbins is the W H. Clark Distin-
guished University Professor at North Carolina
State University, where he has been since
1998. He obtained his PhD at the University of
London and has been a faculty member at the
University of Florida and Comell University prior
to joining North Carolina State University.

Stefan Franzen is Associate Professor of
Chemistry at North Carolina State University. He
has a BS degree from University of California,
Berkeley, and a PhD from Stanford University.
He was a Peace Corps Volunteer Teacher in
Kenya, EMBO Fellow at Ecole Polytechnique
in Palaiseau, France, and Director's Fellow at
Los Alamos National Laboratory, prior to joining
the faculty at NCSU.

Copyright ChE Division of ASEE 2004

Chemical Engineering Education

Graduate Education
: llm ^ TII --- T --- -- ^ ^

scales of interest (see, e.g., 1481 and references therein). In addition, a common criti-
cism from industry is that our PhD graduates are too specialized and have insuffi-
cient knowledge of modeling methods outside of their own specialization. In an
industrial setting, the researcher is expected to be able to choose the most appropri-
ate method for studying and solving a new problem and to apply it and get results
quickly. Therefore, students need to have familiarity with the full range of tools
available for theoretical and modeling work.
In the Spring Semester of 2004 we offered a new graduate course in multi-scale
modeling of soft matter. Most of the students taking the course were from chemistry
and chemical engineering, and the course was also offered via video transmission to
students from the University of North Carolina at Chapel Hill. The aim of this course
is to provide a background on the computational methods available at the different
scales, at a level suitable for graduate students whose primary research interests are
experimental as well as theoretical. Students in the class are asked to work problems
using web-based modules illustrating the different theoretical and simulation ap-
proaches for a variety of problems. This paper presents an overview of the most
important features of the course, including the basis, applicability, and strengths
and weaknesses of the methods. Examples of some of the computational exer-
cises are presented and discussed. The course material is posted on the course
website at and includes PowerPoint slides
and videos of the lectures, as well as links to the web modules containing the
computational exercises proposed.

The course syllabus is presented in Table 1. The course is suitable for students
who are already familiar with classical thermodynamics, and differential and inte-



(ms) 10-3

(s) 10-6

(ns) 10-9

(ps) 10-12

Based on SDSC Blue Horizon (SP3)
1.728 Tflops peak performance
CPU time = 1 week I processor


S method

(fs) 1015 -

10-10 10-9


J LTight-binding

10-8 10-7 10-6 10-5 10-4

Figure 1. Length and time scales accessible to different molecular simulation meth-
ods. We assume that the calculations are performed for a maximum of one week on
the Blue Horizon IBM SP3 supercomputer at the San Diego Supercomputer Center
(SDSC), which operates at a maximum speed of 1.728 Tflops.

Course Syllabus

Lecture Topic
1. Introduction; electronic, atomistic,
mesoscale modeling; examples
2. Ab initio methods; Schrodinger equation;
Born-Oppenheimer approximation
3. Ab initio methods; Hartree-Fock method;
density functional theory (DFT)
4. Ab initio methods; density functional
theory (DFT) and applications; semi-
empirical methods
5. Introduction to semiclassical statistical
6. Canonical ensemble; partition function,
7. Factoring the partition and distribution
8. Distribution functions and correlation
9. Uniqueness theorem; reverse Monte Carlo
10. Statistical mechanics in the grand
canonical ensemble
11. Intermolecular forces
12. Composite pair potentials and force fields
13. Force field parameterization
14. General features of molecular simulation;
Monte Carlo algorithm: Metropolis
15. Monte Carlo simulation: canonical
ensemble, isothermal-isobaric ensemble,
grand canonical ensemble
16. Monte Carlo simulation and phase
equilibria (I): Gibbs ensemble Monte
Carlo; determination of chemical
potentials: thermodynamic integration
17. Monte Carlo methods and phase equilibria
(II); Gibbs-Duhem integration method;
overview of other Monte Carlo methods
18. Molecular dynamics
19. Molecular dynamics; calculation of
dynamic properties: constraint dynamics
20. Mesoscale methods; lattice Monte Carlo
21. Mesoscale methods; Langevin dynamics,
coarse graining, and Brownian dynamics
22. Mesoscale methods; Brownian dynamics
and dissipative particle dynamics
23. Statistical mechanics of inhomogeneous
fluids: interface, surface tension.
24. Density functional theory (DFT) of
25. Adsorption, fluids in pores, phase
equilibria in confined systems
26. Colloids
27. Biological systems (I)
28. Biological systems (II)
29. Special topics; multiscale molecular
modeling of chemical reactivity (I)
30. Special topics; multiscale molecular
modeling of chemical reactivity (II)

Fall 2004


gral calculus. The course has no formal exams. The students
are asked to work on the proposed computational modules,
and to complete a term paper project on a free topic related to
the course. The course consisted of four groups of lectures:
1. Electronic (subatomic) scale: ab initio and semi-empirical
methods (lectures 1-4)
2. Atomistic scale: semiclassical statistical mechanics,
intermolecular forces, Monte Carlo and Molecular Dynam-
ics methods (lectures 5-19)
3. Meso-scale: Lattice Monte Carlo, Langevin Dynamics,
coarse graining, Brownian Dynamics and Dissipative
Particle Dynamics (lectures 20-22)
4. Applications: phase equilibria of bulk and confined
systems, adsorption and interfaces, colloids, biological
systems and chemical reactions (lectures 23-30)
Since there is presently no suitable text for such a course,


Main Idea

material was taken from several sources. The text by Leach19'
covers parts of the material in Section 1, much of Section 2,
and parts of Section 4. Additional references for Section 1
(ab initio and semi-empirical methods) include the books by
Jensen,l'o Szabo and Ostlund,1l" Parr and Yang, 12' and Koch
and Holthausen.'31 The section on semiclassical statistical me-
chanics is covered in Gray and Gubbins"14] and McQuarrie,"l5
and more advanced aspects of Monte Carlo and Molecular
Dynamics methods are covered in the monographs of Allen
and Tildesley1ll and Frenkel and Smit.121 Meso-scale meth-
ods (Section 3) are covered briefly in Frenkel and Smit,121
and in more detail in monographs by Zwanzig1161 and Mazo.1171

The first three groups of lectures covered the theoretical
basis of the methods, followed by a description of the most
relevant theoretical and modeling tools, their strengths and

Electronic scale: Calculate properties from first
ab initio principles solving the Schrrdinger
equation numerically

* Can handle processes that involve bond breaking/formation,
or electronic rearrangement (e.g., chemical reactions)
* Methods offer ways to systematically improve on the
results, making it easy to assess their quality
* Can (in principle) obtain essentially exact properties
without any input but the atoms conforming the system

* Can handle only small systems,
on the order of 102 atoms
* Can only study fast processes,
usually on the order of 10 ps.
* Approximations are usually
necessary to solve the equations

Electronic scale: Use simplified versions of equations Can also handle processes that involve bond breaking or *Difficult to assess the quality of the
semi-empirical from ab initio methods (e.g., treating formation, or electronic rearrangement results
explicitly only the valence electrons); Can handle larger and more complex systems than ab initio Need experimental input and large
include parameters fitted to methods, often of the order of 103 atoms parameter sets
experimental data Can be used to study processes on longer timescales than can
be studied with ab initio methods, on the order of 10 ns

Classical atomistic Use empirical or ab initio derived Can be used to determine the microscopic structure of more Results depend on the quality of the
scale: Molecular force fields, together with semi- complex systems, on the order of 105 106 atoms force field used to represent the system
Dynamics (MD), classical statistical mechanics to Can study dynamical processes on longer timescales, on the Many physical processes happen on
Monte Carlo (MC) determine thermodynamics (MC, order of 1 ps length- and timescales inaccessible by
MD) and transport (MD) properties these methods, e.g., diffusion in solids,
of systems. Semi-classical statistical many chemical reactions, protein
mechanics equations are solved folding, micellization

Meso-scale Introduce simplifications to atomistic Can be used to study structural features of complex systems Can often describe only qualitative

methods to remove the faster degrees
of freedom, and/or treat groups of
atoms ("blobs of matter") as
individual entities interacting
through effective potentials

on the order of 108 10' atoms
* Can study dynamical processes on timescales inaccessible
to classical methods, up to seconds

tendencies; the quality of quantitative
results may be difficult to ascertain
* In many cases, the approximations
introduced limit the ability to physically
interpret the results

(not covered in this

Assume that matter is continuous and
treat the properties of the system as
field quantities. Numerically solve
balance equations coupled with phe-
nomenological equations to predict
the properties of the systems

* Can in principle handle systems of any macroscopicc) size
and dynamic processes on longer time scales

* Require input (viscosities, diffusion
coefficients, parameters required in
equations of state, etc.) from experiment
or from a lower-scale method; they can
be difficult to obtain in some cases
* Cannot explain results that depend on
the electronic or molecular level of

Chemical Engineering Education

Pros and Cons of the Different Modeling Tools Described in this Course

Pros C(

~F~C-~C*I* --~;--- ---- ---- -

" -~~'I'~~' ~~'-'-`'i ~ '-"-"-' ~-~-- --- ~-L'~- -- -


i- :-

weaknesses, and the kind of problems studied with them. In
addition, ways to link results obtained using methods at dif-
ferent scales are described and discussed when appropriate.
The last section of the course covers the use of the different
methodologies in various applications. The main idea of each
one of the group of methods described in this course, as well
as their most important advantages and disadvantages, are
summarized in Table 2. We included continuum methods in
Table 2 for completeness, although these modeling tools are
not covered in this course. These methods are applied at the
macroscopic scale and usually involve solving the equations
of material and energy balance, coupled with phenomeno-
logical (constitutive) equations, to predict the properties of
the systems assuming that matter is continuous. The most
important continuum methods are usually studied in detail in
a number of courses in chemical engineering.
At the electronic (subatomic) scale, we discussed both ab
initio and semi-empirical methods. The purpose of the ab
initio methods (Table 2) is to calculate properties from first
principles using quantum mechanics.[5,9- l] The nature of the
atoms comprising the system is provided as input, and the
time-independent Schridinger equation is then solved numeri-
cally for a given many-atom system

H=O=E< (1)

where q$ represents the wave function of the many-atom sys-
tem, E is the total energy, and H is the Hamiltonian operator.
For the general case of a system of N electrons and M nuclei,
the Hamiltonian operator ists15

SN 1 M I N M N-1 N 1 M-l M ZZ
i H=-1 1 -I Yv I 1 X Z (2)
i=l 2 1=1 2M I i= ll= i i=1 ji+lrij I=1 J=I+1 R

where the indices i,j,... refer to the electrons and the indices
I,J,... refer to the nuclei. The symbol V2 is a Laplacian op-
erator with respect to the coordinates of electron i, and VI is
a Laplacian operator with respect to the coordinates of the
nucleus I. The first term in Eq. (2) represents the electronic
kinetic energy; the second term is the nuclear kinetic energy.
The remaining three terms are the Coulomb interactions be-
tween the nuclei and the electrons, between electron pairs
and between nucleus pairs, respectively. Equation (2) is writ-
ten in atomic units.[5,"1
Some of the basic elements common to most ab initio meth-
ods are introduced during the lectures. We also briefly ex-
plore some of the most common approaches to finding ap-
proximate solutions to Eq. (1), and discuss their advantages
and limitations (Table 2). Density functional theory (DFT) is
an alternative approach covered in class that permits one to
obtain reasonably accurate results for complex systems. The
main difference between DFT and the methods mentioned

before is that the fundamental variable is not the wave func-
tion ( but the electronic density p(r).[5,10,12,131 The energy
functional E[p] in DFT includes terms accounting for the ki-
netic and potential energy of both electron-nucleus and elec-
tron-electron interactions. The latter accounts for the poten-
tial energy of the Coulomb interaction and the so-called ex-
change-correlation potential energy.5, 10'12.131 Applications, ex-
amples, advantages and limitations of DFT are discussed.
An alternative and less rigorous approach to the electronic
problem is provided by the semi-empirical methods (Table
2), which are briefly discussed. In these algorithms,[5,9,"" the
many-electron problem is simplified in some way (e.g., by
treating explicitly only the valence electrons), and then some
parameters obtained from experiment or higher quality ab
initio calculations are included in order to get good results.
The main advantages and disadvantages of such methods
(Table 2) are discussed. Semi-empirical methods are particu-
larly useful for dealing with large systems (e.g., biomolecules,
chemical reactions in complex systems) where the more
computationally demanding ab initio methods are impos-
sible to apply due to limited computing power. On the
other hand, the semi-empirical methods are not truly ab
initio methods, since they make use of experimental in-
formation to obtain their results.
Before introducing the two main classical atomistic simu-
lation methods (Molecular Dynamics and Monte Carlo; see
Table 2), we include some lectures discussing semiclassical
statistical mechanics, intermolecular forces and commonly
used force fields and models to describe intra- and intermo-
lecular interactions. In atomistic simulation methods,'12'91
empirical or ab initio derived force fields are used, together
with semiclassical statistical mechanics, to determine ther-
modynamic and/or transport properties of the system of in-
terest. In general, the (mechanical) properties of a system,
such as internal energy, pressure and surface tension, depend
on the positions and moment of the N particles that form the
system. In Molecular Dynamics (MD), the molecules move
naturally under their own interaction forces; the positions and
velocities of each atom or molecule are followed in time by
numerically solving Newton's equations of motion

d2xi Fx
i (3)
dt2 mi
which describe the motion of a particle with mass mi along
one coordinate xi, Fxi being the force acting on the particle in
that direction (due to the presence of other molecules, or ex-
ternal forces). The average of a (mechanical) property A can
be determined as

average = lim 1 A[pN(t), r (t)]dt
"t-- 1:

Fall 2004

Ir ~ ii

Graduate Education

where pN(t) and rN(t) represent the N moment (p,, p,, .... pN)
and positions (r,, r,,..., r,), respectively, at time t. In Monte
Carlo, we determine average properties from an ensemble,
rather than a time, average

(A)= JfdpNdrNA(pN,rN)P(pN,rN) (5)

where P(pN,rN) is the probability density of finding a mo-
lecular configuration with moment pN and positions rN. The
probability density is given by the statistical mechanics of
the system.11,2'9 In MC, a random number generator is used to
stochastically perturb a system (by randomly moving the
molecules, changing the volume of the system, etc.). In order
to generate an ensemble of configurations distributed accord-
ing to P(pN,rN), only a fraction of these perturbations are ac-
cepted. According to the ergodic hypothesis,[1'2'9 the time
average and ensemble averages are equal, so Aaverage = (A).
The main features of MD and MC are introduced in this group
of lectures, together with their extension to different en-
sembles, derivation of the acceptance criteria, a description
of some of the more advanced MD and MC techniques, and
some typical applications of these methods.
Soft matter often consists of large, massive particles (e.g.,
polymers, colloids, surfactants, proteins, etc.) in a sea of small,
light particles (solvent). In such systems the time scales in-
volved for the large and small particles are very different. In
the meso-scale methods (Table 2), simplifications to classi-
cal atomistic methods are introduced (coarse-graining) to re-
move the faster degrees of freedom and/or treat groups of
atoms as individual entities interacting through effective po-
tentials. One example of such methods is lattice Monte Carlo
simulations,'2'9'18] in which molecules or groups of atoms oc-
cupy discrete positions on a lattice; the simulations are
computationally more efficient than those using atomistically
detailed continuum models. Other meso-scale methods cov-
ered are Brownian Dynamics and Dissipative Particle Dy-
namics,'1'21 which are based on the Langevin equation for the
dynamics of the large particles"1617'

mai(t)= Fi(t)= X Fi yvi(t)+ oGi(t) (6)

where m is the mass of the particles, ai(t), vi(t), and F (t) are
the acceleration, velocity and force, respectively, for particle
i, Fi is the conservative (solvent-mediated) force acting on
the large particle i due to another large particle j, and y is the
friction coefficient on the big particles due to the presence of
small particles. The last term in Eq. (6) is a "random force"
introduced to account for the Brownian motion due to colli-
sions between the small particles and the large particle, and
must be included to ensure that the equipartition theorem is
obeyed at long times.1'2,16,'71 The noise amplitude is oa, and

;i(t) is a Gaussian random variable. The noise amplitude (T
and the friction coefficient y are related via the fluctuation-
dissipation theorem.1'2'16'.171
The connection between results from modeling methods in
different scales via upscalingg" or downscalingg" approaches
are introduced. The upscalingg" approach is deductive: re-
sults from a lower-scale calculation are used to obtain poten-
tials and parameters for a higher-scale method. Examples
mentioned in class are the calculation of properties from ato-
mistic and/or meso-scale simulations, such as phenomeno-
logical coefficients (e.g., viscosities, diffusivities), for later
use in a continuum model; fitting of force-fields using ab
initio results, for later use in atomistic simulations; deriving
the potential energy surface for a chemical reaction, to be
used later in atomistic MD simulations; deriving coarse-
grained potentials for "blobs of matter" from atomistic simu-
lation, to be used in meso-scale simulations. In contrast, the
downscalingg" approach is inductive and involves using
higher-scale information (often experimental) to build param-
eters for lower-scale methods. This is more difficult, due to
the non-uniqueness problem. For example, atomistic simula-
tion results lack any electronic detail, and meso-scale simu-
lations do not contain atomistic detail; thus, there is no unique
way to reintroduce such detail and go to the lower level. Some
examples of downscaling are the fitting of two-electron inte-
grals in semi-empirical electronic structure methods to ex-
perimental data (ionization energies, electron affinities, etc.)
and fitting of empirical force fields to reproduce experimen-
tal thermodynamic properties, e.g., second virial coefficients,
saturated liquid density and vapor pressure.

A practical approach to the instruction of modern topics in
theory and simulation must rely on hands-on experience with
computers and software, illustrating the application of some
of the methods covered in class to a number of practical prob-
lems. We have used commercial software from Accelrys Inc.,
with a graphical user interface (Insight II) for the setup and
analysis of most of the simulations. This approach decreases
the amount of time required on software-specific issues. In
addition, some of the simulation exercises are based on home-
made programs done by members of our research groups.
We have defined two groups of applications, one based in
methods at the electronic scale and the other studying appli-
cations at classical atomistic scales. So far we have not in-
cluded any group of applications in meso-scale simulations,
but we are working in order to include some of them in fu-
ture offerings of this course. In each module, the students run
a small calculation that takes approximately 10 minutes (or
less) of CPU time, which helps them understand the process
of setting up a calculation and submitting the job on a com-

Chemical Engineering Education

Graduate Education

puter with a batch queue. The elementary (starting) modules
can be tackled in less than 2 hours, even for students who
lack basic computer skills. The advanced modules can take
three hours or longer. Since some students were not famil-
iar with UNIX operating systems (or even with command-
line computing), we provided a help menu on the course
website describing basic operations such as file transfer,
editing and storing data.

Quantum chemistry applications

We chose to focus on density functional theory (DFT) [-"10,2.13
since it is widely used and provides reasonably accurate re-
sults with fair computational costs for complex systems. Some
of the proposed DFT modules include
Water (starting module)
Surface adsorption
In the water module, the students are asked to determine
the values of properties such as bond lengths and angles,
charges and dipole moment, and force constants for stretch-
ing and bending. These are parameters required in typical
force field parameterizations and can also be compared to
experimental values. Moreover, these results can be compared

later with those obtained from simulations of water using
classical force fields, which is included in the atomistic
group of applications. The form of the molecular orbitals
for water can also be examined from the DFT calculations.
Beyond water, the students can progress to more advanced
calculations including geometry optimization for polymers
and surface adsorption.
In the polymer module, the elastic constants such as the
bulk and Young's modulus can be calculated for crystal-
line polymers such as polyethylene.[19'20 The surface ad-
sorption module involves calculation of the adsorption
energy of small molecules (diatomics, amines, thiols,
etc.), on a range of metals (Au, Ag, Pt, Pd, Ru, etc.)."211
The suggested example is the adsorption of carbon
monoxide on Ni( 111),122,231 which is depicted in Figure
2. In addition, the vibrational frequencies of adsorbed
CO can be compared to those exhibited by a free mol-
ecule of carbon monoxide.

Classical atomistic applications

Some of the proposed modules in this group of applica-
tions include
MD and MC simulation of Lennard-Jones argon (starting
MD simulation of liquid water
Reactive Monte Carlo (RxMC) study of the ammonia
synthesis reaction (bulk fluid phase)
Quench dynamics of cyclic peptide analog of Leu-
In the starting module, the students are asked to per-
form both MD and MC simulation of Lennard-Jones (LJ)
argon in the canonical ensemble. The students perform
simulations at different temperatures, calculate typical fluid
properties (e.g., internal energy, pressure, heat capacity,
radial distribution function), and compare the results from
both methods. The self-diffusion coefficient is also deter-
mined from the MD simulations using both the velocity
autocorrelation function and the mean square displacement,
and the results from both methods are compared.
In the water module, the students are asked first to equili-
brate two hydrogen-bonded water molecules, using the
transferable intermolecular potential TIP3P, in a very low-
density gas phase, and then to estimate the energy of for-
mation of the hydrogen bond and the angle between the
two associated water molecules. These two results can be
compared with experimental values. Following this, the
students perform MD simulations of liquid TIP3P water
at room temperature and determine properties such as in-
ternal energy, heat capacity, self-diffusion coefficient, di-

Fall 2004

Figure 2. DFT applied to surface adsorption. Depiction of
adsorption of carbon monoxide (CO) on Ni(1 11). (a) Lateral
view, and (b) top view. The distance between the adsorbate
layer of CO and the Ni surface is 1.2 A.

( GradufW Eucation

pole autocorrelation function, and the radial distribution
functions 0-0, O-H, and H-H, comparing to experimental
values when possible.
The constant pressure version of the Reactive MC
(RxMC) method1241 is used in one of the modules to deter-
mine the equilibrium state of a mixture of N2, H2 and NH3
in the bulk, reacting according to N2 + 3H2 <=> 2NH3, at
high temperatures and pressures. The typical trial moves
attempted in a RxMC simulation are summarized in Fig-
ure 3(a). The molecular models were the same as those
used recently by Turner, et al.,[251 and are depicted in Fig-
ure 3(b). The molecular constants used to calculate the par-
tition function for this reactive mixture (N2, H2 and NH3)
are all found in McQuarrie's text.""1 Close agreement with
the experimentally measured conversions of ammonia in
the bulk phase is expected to be achieved with RxMC.[251
The protein folding problem is one of the central issues
in bioscience and biotechnology today. While the problem
is enormously complicated, a simple demonstration of the
thermodynamic and statistical issues is exemplified by the
application of quench dynamics in the study of the folding
transition of a cyclic hexamer peptide, which is shown in
Figure 4. This molecule is hydrophobic and can penetrate
cell membranes; then, the cyclic peptide is hydrolyzed and
drugs such as Leu-enkephalin'26-281 can be released inside
the cell. Therefore, such a system can be used as a drug
delivery system model. To understand the peptide relative
membrane permeability, we must first search the thermo-
dynamically accessible states. To accomplish this, the pep-
tide is heated and equilibrated at high temperatures (e.g.,
500 K) to overcome any isomerization barriers. A number
of peptide conformations are selected, quenched (progres-
sively cooled to lower temperatures) and equilibrated. The
resulting structures represent local minimum conforma-
tions, and their relative energies map out the thermally ac-
cessible conformations of the peptide. Three families of
structures are expected to be found: folded, unfolded and
intermediate. The folded structure (Figure 4) has two hy-
drogen bonds, shown in Figure 4 as the N-H O=C dis-
tances of 2.07 A and 2.09 A. The folded structure has a 3-
sheet fold, as shown by the side view. The unfolded struc-
ture (Figure 4) has no hydrogen bonds (N-H O=C distances
of 4.35 A and 4.42 A), has a crowned morphology, and the
side view shows that the structure is more similar to an
extended chain, rather than to a 3-sheet. The intermediate
structure has only one hydrogen bond and combines fea-
tures of both of the previously described structures. This
particularly simple example illustrates how computational
modeling of folding transitions is possible using a relatively
simple model. The structures obtained from quench dynam-
ics simulations can then be used to compute vibrational

frequencies using density functional theory methods, obtaining
results in agreement with experimental trends.[29)

The main objective of this course is to make students familiar
with the most important computational methods available at dif-
ferent scales (electronic, atomistic and meso-scale), and to illus-
trate how they can be applied to some problems of current inter-
est. The course was offered for the first time in Spring 2004, and
we plan to further develop it for future offerings. We are cur-
rently working to include a group of computational exercises on
meso-scale simulations in the course, and thus provide practical
examples of modeling at all the different scales covered in this
course. In addition, we plan to include practical exercises linking
the results from methods at different scales, via upscaling or
downscaling. This is an active research area at present, and it is
important for a course of this kind to be up to date. Both the
course outline and computational modules need to be continu-
ally updated in order to cover the most recent and interesting
methodologies and applications.

We are grateful to the students from North Carolina State Uni-


*o 00 go 00

0 0 0 O
O 00* 0 000 Particle

SOo 0 Volume
0/000 000

o. sto volume
S* A o change

d () reaction ,repe reaction
e. Noe w tep a step
(a) (b)

Figure 3. Reactive Monte Carlo (RxMC) module: (a) illustration
of the RxMC typical moves for a reaction A <- B + C: particle
move, volume changes, forward and backward reaction steps;
and (b) schematic representation of the model molecules stud-
ied. Nitrogen was represented by a two-site LJ molecule, with
the addition of three point charges chosen to account for its
quadrupole. The hydrogen molecule was treated as a single LJ
sphere, and the model for NH, consisted of one LJplus negative
point charge site to represent the nitrogen, and three positive
point charges to represent the three hydrogens. No LJ sites were
used to account for the hydrogens on the ammonia molecules.

Chemical Engineering Education

7 :


- = L-- ~ ~ ~ 1-11- _.

Graduate Educatfion

versity and the University of North Carolina at Chapel Hill
who took this course, for their patience, support, feedback and
motivation. We gratefully acknowledge Professors David A.
Kofke (University at Buffalo, The State University of New
York) and Sharon C. Glotzer (University of Michigan), for
kindly providing us full access to their respective course ma-
terials. We thank Erik E. Santiso, who prepared the material
and lectured the classes about chemical reaction modeling. We
also would like to thank Henry Bock, Naresh Chennamsetty
and Supriyo Bhattacharya, for their help in the preparation of
the lectures about meso-scale simulations. It is a pleasure to
thank Professors Ken Thomson (Purdue University) and C.
Heath Turner (University of Alabama), for kindly providing
the data for Figures 1 and 3 of this paper.

1. Allen, M.P., and D. J. Tildesley, Computer Simulation of Liquids,
Clarendon Press, Oxford (1987)
2 Frenkel, D., and B. Smit, Understanding Molecular Simulation 2"d Ed.,
Academic Press, San Diego, CA (2002)
3. Information taken from 1 Tflop =
1012 floating point operations per second.
4. de Pablo, J.J., and F. A. Escobedo, "Molecular Simulations in Chemical
Engineering: Present and Future," AIChE J., 48, 2716 (2002)


Folded Unfolded

Figure 4. Quench Dynamics module. Depiction of the folded
and unfolded structures obtained from quench dynamics
simulations of a model cyclic hexamer peptide. Two hydro-
gen bonds are observed in the folded structure (N-H O=C
distances of 2.07 A and 2.09 A, top view), which has a 63-
sheet fold (side view). The unfolded structure exhibits a
crowned morphology with no hydrogen bonds (N-H O=C dis-
tances of 4.35 A and 4.42 A, top view), and an extended-
chain morphology (side view). The structures obtained from
quench dynamics simulations can be used to compute vibra-
tional frequencies using density functional theory methods.
The trends in the vibrational frequency of the folded and
unfolded structures agree with experimental results.

Fall 2004

5. Santiso, E.E., and K. E. Gubbins, "Multi-Scale Molecular Modeling of
Chemical Reactivity," Mol. Simul., in press (2004)
6. Maroudas, D., "Multiscale Modeling of Hard Materials: Challenges and
Opportunities for Chemical Engineers," AIChE J., 46, 878 (2000)
7. Glotzer, S.C., and W. Paul, "Molecular and Mesoscale Simulation Meth-
ods for Polymer Materials," Annu. Rev. Mater Res., 32, 401 (2002)
8. Abraham, F.F., J. Q. Broughton, N. Bernstein, and E. Kaxiras, "Span-
ning the Length Scales in Dynamic Simulation," Comp. Phys., 12, 538
9. Leach, A.R., Molecular Modelling. Principles andApplications 2nd Ed.,
Prentice-Hall, Harlow (2001)
10. Jensen, F., Introduction to Computational Chemistry, John Wiley and
Sons, Chichester (1999)
11. Szabo, A., and N. Ostlund, Modem Quantum Chemistry: Introduction
to Advanced Electronic Structure Theory, Dover, New York (1996)
12. Parr R.G., and W. Yang, Density-Functional Theory ofAtoms and Mol-
ecules, Oxford University Press, Oxford (1989)
13. Koch, W., and M.C. Holthausen, A Chemist's Guide to Density Func-
tional Theory, Wiley-VCH, Weinheim (2000)
14. Gray, C.G., and K.E. Gubbins, Theory of Molecular Liquids, Clarendon
Press, Oxford (1984)
15. McQuarrie, D.A., Statistical Mechanics, University Science Books,
Sausalito (2000)
16. Zwanzig, R., Nonequilibrium Statistical Mechanics, Oxford University
Press, New York (2001)
17. Mazo, R.M., Brownian Motion, Clarendon Press, Oxford (2002)
18. Panagiotopoulos, A.Z., "On the Equivalence of Continuum and Lattice
Models for Fluids," J. Chem. Phys., 112, 7132 (2000)
19. Miao, M.S., M.L. Zhang, V.-E. van Doren, C. van Alsenoy and J.L. Mar-
tins, "Density Functional Calculations on the Structure of Crystalline
Polyethylene at High Temperatures," J. Chem. Phys., 115, 11317 (2001)
20. Bruno, J.A.O., N.L. Allan, T.H.K. Barron, and A.D. Turner, "Thermal
Expansion of Polymers: Mechanisms in Orthorhombic Polyethylene,"
Phys. Rev. B, 58, 8416 (1998)
21. Franzen, S., "Density Functional Calculation of a Potential Energy Sur-
face for Alkane Thiols on Au( 111) as Function of Alkane Chain Length,"
Chem. Phys. Lett., 381, 315 (2003)
22. Doll, K., "Density Functional Study of Ni Bulk, Surfaces and the Adsor-
bate Systems Ni( 111)('3 x i3)R30o-C1, and Ni(111)(2x2)-K,", Surf. Sci.,
544, 103 (2003)
23. Shah, V., T. Li, K.L. Baumert, H.S. Cheng, and D.S. Sholl, "A Compara-
tive Study of CO Chemisorption on Flat and Stepped Ni Surfaces Using
Density Functional Theory," Surf Sci., 537, 217 (2003)
24. Johnson, J.K., A.Z. Panagiotopoulos, and K.E. Gubbins, "Reactive Ca-
nonical Monte Carlo: A New Simulation Technique for Reacting or As-
sociating Fluids," Mol. Phys., 81, 717 (1994)
25. Turner, C.H., J.K. Johnson, and K.E. Gubbins, "Effect of Confinement
on Chemical Reaction Equilibria: The Reactions 2NO tr>(NO)2 and
N2+3H, <2NH, in Carbon Micropores," J. Chem. Phys., 114, 1851
26. Wang, B., K. Nimkar, W. Wang, H. Zhang, D. Shan, O. Gudmundsson,
S. Gangwar, T. Siahaan, and R. T. Borchardt, "Synthesis and Evaluation
of the Physicochemical Properties of Esterase-Sensitive Cyclic Prodrugs
of Opioid Peptides Using Coumarinic Acid and Phenylpropionic Acid
Linkers," J. Pept. Res., 53, 370 (1999)
27. Dudowicz, J., K.F. Freed, and M. Y. Shen, "Hydration Structure of Met-
Enkephalin: A Molecular Dynamics Study," J. Chem. Phys., 118, 1989
28. van der Spoel, D., and H.J.C. Berendsen, "Molecular Dynamics Simula-
tions of Leu-Enkephalin in Water and DMSO," Biophys. J., 72, 2032
29. Maness, S.J., S. Franzen, A.C. Gibbs T.P. Causgrove and R.B. Dyer,
"Nanosecond Temperature Jump Relaxation Dynamics of Cyclic p-Hair-
pin Peptides," Biophys. J., 84, 3874 (2003) J


cGraduate Education




A Doctoral Student's Perspective

Michigan State University East Lansing, MI 48824-1226

any new faculty enter the academic world with
minimal teaching experience or training in peda-
gogy. In fact, a majority of engineering professors
have never had a formal course in education."' This defi-
ciency can easily be addressed through implementation of
teaching programs targeted at doctoral students who aspire
to an academic career. The rationale behind a formal teach-
ing program is that new professors who study educational
methods will likely be better prepared to teach and will be
more efficient during their first years in academia." 1 Benefits
of graduate training in teaching include
Helping confirm whether the student is well suited for and
would enjoy an academic career
Providing both conceptual knowledge and significant
experience in college-level teaching
Giving a significant advantage over other candidatesfor an
academic position.'12
A College Teaching Certificate (CTC) program was estab-
lished in the College of Engineering at Michigan State Uni-
versity (MSU) to help provide such training. It was initiated
in 1998 in response to a request from several graduate stu-
dents for training in college teaching methods. A planning
committee of faculty and graduate students was formed to
develop such a program. During the 1998-1999 academic year,
the committee submitted a proposal to establish the CTC pro-
gram, and it was successfully initiated in the spring of 2000.
A total of 23 engineering doctoral students have now suc-
cessfully completed the program and received certification
in college teaching.[3' The College of Natural Science had
previously established a similar program.'41

Theory and Practice of Teaching Engineering Students
The overall purpose of the CTC program is to provide
graduate students with valuable experience in college-level

teaching and to prepare them for careers in academia. To
achieve this goal, the program requires successful comple-
tion of two courses. The first course, "Theory and Practice of
Teaching Engineering Students," introduces students to peda-
gogical theories and effective methods used in teaching en-
gineering. The theory and practice component of the program
is similar to many courses at colleges of engineering around
the country.51 Learning objectives for the course include: 1)
applying fundamental theories of cognitive processes in the
practice of teaching engineering students, 2) designing ef-

Tylisha M. Baber is a doctoral candidate in
chemical engineering at Michigan State Univer-
sity. She graduated from North Carolina State
University with honors in December of 1998 with
a Bachelor of Science degree in chemical engi-
neering (bioscience option).

Daina Brledis is Associate Professor in the De-
partment of Chemical Engineering and Materi-
als Science at Michigan State University. She
has conducted research in bioadhesion and is
currently studying development of effective
learning tools for the multidisciplinary class-
room. She is active nationally and internation-
ally in engineering accreditation, and is a mem-
ber of the ABET Board. She leads the assess-
ment and evaluation efforts in her program.

R. Mark Worden, Professor of Chemical Engi-
neering, bridged to chemical engineering after
earning a Bachelors' degree with a double ma-
jor in chemistry and cell biology. His research is
in the area of biochemical engineering, and he
has been active in development of multidisci-
plinary training programs.
Copyright ChE Division of ASEE 2004

Chemical Engineering Education


Graduate Education
^ ^________ __ _____ __ ^_ -^ ^ --

Course Topics

* Attributes of a Professional
* Student Learning Styles and
* Starting the Academic Career
* Diversity and Gender Issues
* Teaching Assessment
* Mentoring
* Course Proposal
* Accreditation
* Dealing with Hostile Students
* Understanding "Class Personality" And
Student Perspectives
* Delivering Course Content: The Lecture

* Delivering Course Content: Active
Learning And Cooperative Learning
* Delivering Course Content: The Use
of Technology
* Designing Effective Laboratories
* Designing Effective Homework
* Incorporating Design Into Engineering
* Faculty And Student Rights And
* How To Be An Effective Junior

List of Projects
Statement of Teaching Philosophy
This entails a clear and concise, but personal statement of one's philosophy about
teaching. It is considered a living document, so as one's experience grows, it also
changes. This assignment is graded on the basis of the depth of thought presented.
Teaching Toolbox
This includes materials that can help and support the participant's teaching. The
teaching toolbox has two compartments. The first compartment deals with items
pertinent to the theory and practice of teaching and the second compartment includes
items that support the teaching of a specific topic in the student's discipline. Both
compartments are organized collections of papers, exams, projects, notes, physical
models, etc., that the student can use as a reference for future teaching assignments.
The Toolbox is graded for completeness with respect to the essential components
presented in the course, the richness of development the student added beyond the
course materials, and its overall organization.
This is the participant's reflection on the theory and practice of teaching engineering
students and an exploration of one's own philosophy of teaching. It is allowed to be in
the form of a diary, a collection of essays, a record of conversations, letters to
colleagues, or a mixture of these. The Journal is graded on the basis of the depth of
thought presented.
Mini- Lecture
This is a 15- to 20-minute lecture on a scientific subject area that is given by the
participant during the normal two-hour class period. The grade is based on a standard
oral presentation grading form that is given to the students in advance.
Course Web Page
Participants design and implement a web page based on the topics covered in the mini-
lecture. The web page must have at least one download and one link to another website.
This project is graded on the basis of its layout, utility, and overall organization.
Based on the topics covered in the mini-lecture, the participant prepares an assignment.
which could take the form of an examination, quiz, homework, or project. The grade is
based on the attributes of the Assignment.
Course Proposal
The participants submit a proposal for a course that includes all the administrative
details for a new course proposal at Michigan State University. A course description in
ABET format accompanies the proposal. The Course Proposal is graded for
completeness and innovative thought.

Fall 2004

fective lectures, laboratories, and assignments, 3) using ap-
propriate methods to deliver course content, 4) designing
and applying assessment tools, 5) writing a proposal for a
new course, and 6) developing a website as an engineering
educational tool."' A list of topics facilitated in the course
is shown in Table 1. In addition to the text'" used in this
course, supplemental reading is provided, including articles
from Prism, Journal of Engineering Education, and pro-
ceedings of the ASEE Annual Conference.
I (author TB) felt that the course provided an excellent
background on the theories and methods used to effectively
teach engineering students. The assigned projects, listed in
Table 2, taught me how to organize and present course ma-
terial and encouraged me to think critically about how to
reach engineering students through innovative teaching
strategies. The class, which met once a week for 2 hours,
was interactive and thus allowed participants to engage in
discussions about teaching and to exchange personal expe-
riences involving education and teaching styles.

Mentored Teaching of Engineering Students

In the second course, "Mentored Teaching of Engineer-
ing Students," participants gained experience in teaching
under the close guidance and supervision of an engineer-
ing faculty member of their choice. Typically, participants
chose their research advisors as the teaching mentor. I chose
my research advisor because of our well-developed rela-
tionship and his expertise in the subject matter. Faculty men-
tors participate in the program without special compensa-
tion and are largely motivated by their commitment to de-
veloping academicians of the future. The mentored teach-
ing experience allows participants to cultivate their own
teaching styles by taking full responsibility for developing
lecture presentations, delivering course materials, prepar-
ing assignments (homework and examination problems),
and conducting office hours, typically over 2-to-4 weeks.
In order to prepare for the mentored teaching experience, a
contract between the faculty mentor and the graduate stu-
dent is established that details the duties and responsibili-
ties of both parties. The mentor is mainly responsible for
attending all class sessions, for which the participant is the
instructor, and evaluating the participant's teaching. Fur-
thermore, CTC participants are required to compile a teach-
ing portfolio that includes all of their teaching aids and ma-
terials (e.g., lecture notes, homework assignments, exami-
nation problems), examples of student work, student and
faculty evaluations, and a statement describing their teach-
ing philosophy. Additional information contained in the
portfolio includes a listing of service contributions to Michi-
gan State University or to the profession, such as participa-
tion on teaching committees, work on curriculum revision,

( Graduate Education

attendance at professional meetings in education, evidence
of contribution to the larger community through Service
Learning Activities, and teaching honors or recognition. The
portfolio is evaluated on its organization, presentation, and
completeness. Grading for this course is based on the comple-
tion of both the graduate student's contractual duties and
evaluation of the teaching portfolio. Upon successful comple-
tion of this course, participants receive a College Teaching
Certificate notation on their transcripts.
In the second course described above, I had an opportunity
to teach a portion of an undergraduate thermodynamics
course. Through this experience, I gained an appreciation of
the challenges that faculty members face when balancing
teaching and research. Over a three-week period, I taught three
chapters from Introductory Chemical Engineering Thermo-
dynamics.'6 Content included Departure Functions (Chapter
7), Phase Equilibrium in a Pure Fluid (Chapter 8), and React-
ing Systems (Chapter 14). I chose these topics because of my
previous experience as a teaching assistant for this course
and my familiarity in these areas.
As a first-time instructor, I found that the time required to
prepare for lecture was much greater than expected. This is a
common issue for many faculty members and has been ad-
dressed by Reis,12' who suggests that the real time for class-
room preparation is three times the original estimation. Careful
preparation of handouts, meticulous attention to accuracy, and
thorough structuring of numerous example problems con-
sumed a significant amount of time. In the end, some of this
material wasn't covered in class due to time constraints. This
incident was an excellent lesson on the balancing of fervent
preparation with the pace of a typical classroom lecture.
As part of the requirements for the teaching portfolio, the
students provided feedback, suggesting strengths and weak-
nesses for my style of teaching. Most students expressed ap-
preciation for the detailed example problems worked in class
because it helped solidify concepts. Three chemical engineer-
ing faculty members (Carl Lira and coauthors DB and MW)
also provided valuable feedback.

I felt that the first course provided an excellent background
on the theories and methods used to effectively teach engi-
neering students, and the second course allowed me to imple-
ment these principles by having the same teaching responsi-
bilities as faculty members. My main recommendation for
enhancing the CTC program is to improve the recruitment of
participants. Recruitment efforts of both faculty and gradu-
ate students were originally done through email, but this
method was an informal and non-interactive way of promot-


ing the program. I recommend broader advertising to better
inform faculty and graduate students of this training oppor-
tunity, such as a general seminar or an informational session
scheduled each semester. During the session, the rationale
and benefits of the program could be explained, detailing the
success and areas of improvement of the program. This type
of session would allow both faculty and students to ask ques-
tions and to interact. It could also motivate graduate students
to pursue an academic career and encourage faculty mem-
bers to become mentors.
Currently, the CTC program is being jointly taught with
the College of Natural Science, specifically through the Di-
vision of Science and Mathematics Education (DSME). The
DSME is co-administered by the Colleges of Natural Sci-
ence and Education and its mission is to improve science and
mathematics education, from kindergarten through the un-
dergraduate years, through the professional development of
pre-service and in-service teachers and faculty members.'"
Academic specialists and faculty members with partial ap-
pointments in various departments and other colleges (includ-
ing the College of Engineering), graduate and undergraduate
students, and professional and clerical staff work together in
DSME to conduct a variety of courses, degree programs, and
other activities in support of its mission. In addition to con-
nections with the College of Natural Science, links to the NSF-
sponsored Center for the Integration of Research, Teaching
and Learning (CIRTL) are being established. The objective
of CIRTL is to create a model interdisciplinary professional
development program that will prepare graduate students,
post-doctoral researchers, and current faculty to meet the fu-
ture challenges of national Science, Technology, Engineer-
ing and Mathematics (STEM) higher education.18'

MSU offers numerous teaching and mentoring opportuni-
ties, through programs, seminars, and workshops that are di-
rected at faculty development, many of which are also open
to graduate students. As a doctoral student with a passion for
teaching, I tried to take advantage of all of them! I served as
a teaching assistant for the undergraduate introductory ther-
modynamics course during my first semester at MSU. In this
role, I was responsible for attending lectures, preparing and
facilitating recitation sessions, proctoring examinations, con-
ducting office hours, and preparing solutions to homework
problems. These tasks familiarized me with the essential tan-
gential responsibilities of a professor.
For the two consecutive summers of 2002 and 2003, I
served as a chemistry instructor for a summer enrichment
program conducted through the College of Human Medicine.

Chemical Engineering Education


Graduate Education)

The Pre-Health Professions Preparation Institute (PPPI) is a
six-week residential summer program designed to provide
students from under-represented minority and/or disadvan-
taged backgrounds with preliminary education to enhance
their preparation and probability for successful completion
of college-level course work. I taught the first four chapters
of the course material covered in the freshmen general chem-
istry course.'91 In addition to teaching and tutoring, I had the
opportunity to mentor these students. Mentoring included
leading discussions about the expectations of college-level
work and the importance of conducting research, even at the
undergraduate-level. I also assisted these students in week-
end community-based service learning activities.
MSU has developed a relatively new graduate program,
the Multidisciplinary Graduate Training Program on Tech-
nologies for a Biobased Economy (TBE), that promotes in-
terdisciplinary scholarly interactions between students and
faculty in various scientific disciplines. I am a participant of
this graduate program. It's purpose is to produce a diverse
group of PhD scientists and engineers who have broad train-
ing related to biobased industrial product formation, have
strong research skills, and are able to work effectively in mul-
tidisciplinary teams. The program addresses the increasing
need to conduct basic and applied research requiring the con-
tributions of two or more disciplines and yielding new areas
of inquiry and application.2' Furthermore, multidisciplinary
programs and centers allow graduate students to think "out-
side the box" through exposure to philosophies of other sci-
entists and engineers not in their immediate discipline. Work-
ing with a range of individuals who have differing perspec-
tives and skills is excellent training for the interdisciplinary
opportunities that await students as new professors.21
One requirement of the TBE program was participation in
the College Teaching Certificate Program described above.
As a TBE participant, I was also required to complete the
Multidisciplinary Bioprocessing Laboratory (MBL) course.
The goal of this course is to teach students how to work ef-
fectively in multidisciplinary teams in a research environ-
ment.1 01 The students, both undergraduate and graduate, are
divided into multidisciplinary teams that conduct a semes-
ter-long, mentored research project in a participating faculty
member's research lab. To prepare students to carry out their
projects efficiently, the MBL course also incorporates inno-
vative teaching practices to help students to develop commu-
nication and critical thinking skills; these include collabora-
tive and problem-based learning, project-management con-
cepts, peer assessment, and ethics. I also served as a research
mentor for this course, in which I was responsible for guid-
ing three students to complete a research project. This men-
toring role provided experience in another essential duty of a
professor-serving as a research advisor. The TBE program

Fall 2004

is an innovative training venue that allows graduate students
to participate in contemporary research problems and to de-
velop and enhance essential skills to effectively teach techni-
cal concepts. Detailed information on the institutionalization
of the CTC and TBE programs and the MBL course at MSU
is provided in several papers.E4,-1""0. They can assist colleges
or departments interested in developing similar programs and
courses at their institution.

I found the teaching and mentoring training programs of-
fered at MSU to be an effective and valuable program for
preparing future educators. As a result of participating in these
programs, I am a better-prepared, more competitive, and mar-
ketable engineer, researcher, and professor. My extensive
teaching and mentoring experiences have improved my or-
ganizational and communication skills. Furthermore, my ex-
perience explaining technical and abstract concepts has de-
veloped my critical-thinking skills. My experience suggests
that, although participation in these types of programs takes
time away from research, the time invested in graduate teach-
ing and mentoring experiences is worthwhile and has en-
hanced my preparation for a career in academia. Similar pro-
grams at other universities can provide the same benefits to
engineering graduate students.

Fellowship for author TB was provide by the Graduate
Assistance in Areas of National Need (GAANN) through the
United States Department of Education.

1. Wankat, P.C., and F.S. Oreovicz Teaching Engineering. Purdue Uni-
versity at Publications/teaching_engineering>
2. Reis, R. M., Tomorrow's Professor: Preparing for Academic Careers
in Science and Engineering, IEEE Press, New York, NY (1997)
3. Personal communication with Dr. C. W. Somerton
4. Somerton, C.W., Bohl, D., and M.J. Crimp, "Development of an En-
gineering Teaching Certificate Program," Proc. 2000 ASEE North
Central Section Ann. Meet., East Lansing, Michigan, April (2000)
5. Somerton, C.W., Davis, M., and R.Y. Ofoli, "A Teaching Certificate
Program at Michigan State University," Proc. 2001 ASEE Education
Ann. Conf., Albuquerque, NM (2001)
6. Elliot, J.R., and C.T. Lira, Introductory Chemical Engineering Ther-
modynamics, Prentice Hall PTR, Upper Saddle River, NJ (1999)
7. Division of Science and Mathematics Education at>
8. CIRTL Strategic Plan at
9. Kotz, J.C., and P. Treichel, Jr., Chemistry & Chemical Reactivity, 4*
ed., Saunders College Publishing, New York, NY (1999)
10. Worden, R.M., and D. Briedis, "Institutionalizing the Multidisciplinary
Lab Experience," Proc. 2003 ASEEAnn. Conf., Nashville, TN (2003)
11. Preston, C., Briedis, D., and R.M. Worden, "Training Chemical Engi-
neers in Bioprocessing," Proc. 2001 ASEE Ann. Conf, Albuquerque,
NM (2001) n

_._ ___






Virginia Polytechnic Institute and State University Blacksburg, VA 24061

Coupling refers to a flux occurring without its primary
thermodynamic driving force; for example, mass flux
without a concentration gradient called the thermal
diffusion is a well-known coupled process. Coupling also
refers to a flux occurring in a direction opposite to the direc-
tion imposed by its driving force; for example, a mass flux
can occur from a low to a high concentration region and is
called the active transport or uphill transport, such as potas-
sium and sodium pumps coupled to chemical energy released
by the hydrolysis of adenosine triphosphate (ATP) in bio-
logical systems. Although the coupled processes seem to be
in conflict with the principles of second law of thermody-
namics, interestingly, the second law allows the progress of a
process against its driving force and hence with a decrease in
entropy ASj< 0, but only if it is coupled with another process
with larger positive entropy change, i.e., ASk >> 0, thus pro-
ducing a positive total entropy change (AS + ASk) > 0. This is
consistent with the second-law statement that a finite amount
of organization may be obtained at the expense of a greater
amount of disorganization in a series of coupled spontaneous
processes. This can have important implications in describing
the coupled phenomena and organized structures in complex
systems, such as biological energy conversion cycles.( 7)
Some examples of coupled processes follow. Thermoelec-
tric phenomena have the Seebeck and the Peltier effects; in
the Seebeck effect, a temperature difference between two junc-
tions of dissimilar metals produces an electromotive force;
in the Peltier effect, the two junctions are maintained at the
same constant temperature, and a current applied through
the system causes a heat flux from one junction to another.
The uniform junction temperatures are maintained under a
steady heat flux."'
In heat and mass transfer, thermal diffusion (Soret effect)
and the Dufour effect are the coupled transport processes. In
the Soret effect, a mass flux occurs due to a temperature gra-
dient without a corresponding concentration gradient, while
in the Dufour effect, a heat flux occurs due to chemical po-
tential gradient, without temperature gradient. Thermal dif-
fusion is a critical separation process for isotope mixtures

and is of great interest in oceanographic problems. Another
well-known coupled process is the B6nard instability where
a critical temperature gradient in a fluid induces a structured
convection in the forms of cells or rotated flows (left and
right) and contributes to an effective coupling between hy-
drodynamic and thermal forces.'2 In living systems, the res-
piration system is coupled to the oxidative phosphorylation,
and ATP is produced.'45'7) The change from a simple to a com-
plex behavior is the order and coherence within a system that
leads to coupled processes and organized dissipative struc-
tures."'3 Such structures are not necessarily far from local
equilibrium and can only be maintained by a constant supply
of mass and/or energy fluxes. They have long been confined
only to biological systems, but this is changing and research-
ers from diverse disciplines are studying the occurrences and
implications of coupled processes."2,313)
Teaching of coupled processes in a first-year graduate class
should cover the approximate contents presented in Table 1,
which also lists some possible textbooks and their present
coverage. Textbooks for transport phenomena by Bird, et al.,
and Deen'9' describe some of the coupled phenomena with-
out the nonequilibrium thermodynamic (NET) theory, while
the texts for thermodynamics by Kondepudi and Prigogine,(1)
and Demirel(7' describe some of the coupled transport and
reaction processes with the postulates and formulations of
NET. The concept of nonequilibrium systems and the NET
theory would provide students with the basic fundamentals
of coupling (see Table 2). This study presents the use of NET
in teaching various coupled processes from physical and bio-
logical systems in the transport phenomena II graduate course
at Virginia Tech.

Yasar Demirel is a visiting professor in the Department of Chemical En-
gineering at Virginia Tech. He received his PhD from the University of
Birmingham, UK. He teaches senior design, thermodynamics, transport
phenomena, and simulation. His research focuses on coupled transport
and rate processes in physical and biological systems. He is the author of
a book titled Nonequilibrium Thermodynamics: Transport and Rate Pro-
cesses in Physical and Biological Systems, published by Elsevier, and
over 90 journal articles and conference proceedings.
Copyright ChE Division of ASEE 2004

Chemical Engineering Education


Transport and rate processes are open, nonequilibrium, and
irreversible systems with temperature, concentration, pres-
sure gradients, and affinities. Figure 1 shows a stationary-
state nonequilibrium system with coupled and uncoupled
fluxes. Although the system is not at global equilibrium, ther-
modynamic properties such as temperature, concentration,
pressure and internal energy are well-defined in an elemental
volume surrounding a given point. These volumes are small
enough that the substance in them can be treated as uniform,
and yet they contain a sufficient number of molecules so that
the principles of statistics and the methods of phenomeno-
logical thermodynamics are applicable. Therefore a local equi-
librium in any elemental volume exists, and the thermody-
namic properties are related to the state variables in the same
manner as in equilibrium.'"'1 Mostly, the internal relaxation
processes in the fluid or material are much faster than the
rate of change imposed upon the state variables, and the lo-
cal equilibrium concept is valid for a wide range of transport
and rate processes of usual fluid systems." l-9 For example,

Contents and Coverage for
Coupled Transport and Rate Phenomena

Bird, Stewart Kondepudi &
Contents & Lightfootl" Prigogine"' Deen'"
Nonequilibrium systems Ch 1.2
Local equilibrium Ch 24.1 Ch 3.4. 15.1
Dissipative structures Ch 19
Nonequilibrium thermodynamics Ch 15
Balance equations & entropy balance Ch 19.2, 24.1 Ch 15.3 Ch 11.8
Dissipation (entropy production) Ch 24.1, B7 Ch 15.2 Ch 11.8
Minimum entropy production Ch 17.2
Identification of fluxes and forces Ch 24.3 Ch 15.5
Phenomenological equations Ch 16.1 Ch 11.4
Phenomenological coefficients Ch 16.1 Ch 11.8
Onsager's reciprocal relations Ch 24.1 Ch 16.2 Ch 11.4
Curie-Prigogine principle Ch 24.1 Ch 16.2
Degree of coupling
Coupled systems
Multicomponent diffusion Ch 24.2. 22.9 Ch 11.8
Diffusion in electrolyte systems Ch 11.7
Heat and mass transfer Ch 24.2 Ch 16.8 Ch 11.4
Thermoelectric phenomena Ch 16.3
Chemical reactions Ch 16.5
Electrokinetic phenomena Ch 16.7
Membrane transport Ch 24.5
Biological systems Ch 19.3. 19.6
Second law analysis
Lost work, exergy loss
Extended nonequilibrium
Network thermodynamics
Mosaic in nonequilibrium
Rational thermodynamics
Example problems & questions Ch 24 Ch 15, 16 Ch 11.4

Fall 2004

Graduate Education)

the relaxation time for heat conduction for gases at normal
conditions is 10-2s, and for typical fluids 10-"-10-'3 s."" Lo-
cal equilibrium is not valid in highly rarefied gases where
collisions are too infrequent, however, and hence the relax-
ation times are much higher. The extension of equilibrium
thermodynamics to nonequilibrium systems with the local
equilibrium assumption is possible in terms of entropy s[T(x),
ni(x)] and energy u[T(x), ni(x)] densities, which are a func-
tion of the temperature and species mole number densities at
location x, when a well-defined local temperature T(x) ex-
ists. Consequently, the total entropy and energy can be ob-
tained from the integrals over the volume of system

S= s[T(x),ni(x)]dV U= fu[T(x),ni(x)]dV
v v
and using the s(x) and u(x), we obtain the local variables of2-6)
(as/au)ni =/T(x) and (as/ni)u =- i(x)/T(x)

The level of distance from the global equilibrium may be
treated as a parameter of a process, and is called the thermo-
dynamic branch as shown in Figure 2."' Near global equilib-
rium, there are linear relations between the driving forces in
the process and the fluxes that result; examples are Fourier's
and Fick's laws. Processes occurring far from global equilib-
rium, however, such as most chemical reactions, lead to non-
linear force-flux relations, and in some cases to the sponta-


Figure 1. Nonequilibrium distribution of components in a
stationary-state coupled system: Flux of species Uis coupled
with the flux of Y through an enzyme in a cell. Species of Y
do not take part in any chemical reaction. Neither the flux
of U nor the flux of Y is coupled to the flux of W, however.

General Procedure for Teaching Coupled Phenomena with
Nonequilibrium Thermodynamics Approach
Step Procedure
1. Start with the Gibbs relation in terms of the relevant thermodynamic
2. Establish the conservation laws for the variables
3. Establish an entropy balance equation and derive the rate of entropy
production or dissipation function to identify a set of conjugate fluxes
and thermodynamic forces
4. Use these fluxes and forces in linear phenomenological equations
5. Calculate total fluxes in terms of forces or forces in terms of driving forces
6. Calculate the transport coefficients using Onsager's reciprocal rules
7. Calculate the dissipations due to individual processes
8. Quantify the effects and degree of coupling on transport and rate processes

r -~*r j,
Cz 11m D

neous formation of self-organized dissipative structures.'4"6)

Change of total entropy of a system is
dS deS diS (1
+ (1)
dVdt dVdt dVdt
where the dS/dVdt is the rate change of total entropy, the
first term on the right is the entropy exchange through the
boundary that can be positive, zero, or negative, and the sec-
ond term is the rate of entropy production due to irreversible
processes within a system, and is always positive. We deter-
mine the volumetric rate of entropy production
S= (diS/dVdt) = YJkXk 0
or the rate of local dissipation of Gibbs free energy in terms
of a product of a flux, Jk and a thermodynamic force, Xk,
y = I JkXk = T 2> 0
For a multicomponent fluid system with n species and z
number of chemical reactions, the dissipation function can
be derived by incorporating the entropy balance into the gen-
eral balance equations of mass, momentum, and energy, and
the Gibbs relation7,10'


T IJu V i[TV( --]-Fi +- :(Vv)-- AjJrI L>0
T T i=1 T T T =1 'Jr

J and J


vectors of heat and mass fluxes respectively
chemical potential of species i
force per unit mass of component i
viscosity part of stress tensor
affinity (A=- vivi)
stoichiometric coefficients
reaction flux, which is a scalar.

In Eq. (2), the dissipation function consists of four separate
contributions of heat transfer, mass transfer, momentum trans-
fer, and chemical reactions (without electrical and magnetic
effects); their conjugate fluxes and forces are summarized in
Table 3. The relationship between the heat flux, J and the
conduction heat flux, J, is
Jq =Ju -hiJi
where hi is the partial specific enthalpy.
In the dissipation-phenomenological equation (DPE) ap-
proach,(12) Eq. (2) identifies a set of independent conjugate
fluxes and forces to be used in the following linear phenom-
enological equations in the form of a conductance formulation

J,= LikXk (3)
If the fluxes are easy to determine or relate to measurable
properties, then the following resistance formulation is pre-
Xi= 2 KikJk (4)
The phenomenological coefficients, Lik or Kk (i,k =
1,2,...,m) are related to the transport coefficients, such as ther-
mal conductivity, k, and mass diffusivity, D, and can be de-
termined experimentally; Kk = ILlk/ILI, ILI is the determinant
of the matrix of the coefficients Lik, and ILlik is the minor for
Lk. According to Onsager's reciprocal relations, the cross co-
efficients are symmetric Lik = Lki; (isk) for a set of indepen-
dent conjugate fluxes and forces identified by the dissipation
function or the rate entropy production. Onsager's relations
are based on microscopic reversibility, and are independent
of the state of a system or any other microscopic assump-
tions.",'0 The cross coefficients, Lik, describe the degree of
coupling, qik,,, of processes(4,12)

u Thermodynamic branch

Linear region Nonlinear region

Xc X
Figure 2. Thermodynamic branch indicating the linear and
nonlinear regions; X shows the force and Xc is the critical
force or distance from equilibrium state, where no force
exists. After a critical distance from global equilibrium the
system may move to an organized structure that needs con-
stant supply of matter and/or energy.

Conjugate Fluxes and Forces Identified by the Dissipation
Function (DPE) Approach"'"

Process Flux Force
Heat flux Ju=L Xq Xq =TV

Mass flux Ji=LXi X =Fi-TV 2

Viscous effect Jv=LvXv X,=(Vv)
Reaction velocity Jr =LrX, Xr =A-- ivij

VaI-v(tim +hiV( -

Chemical Engineering Education

Lik (5)
which can be determined using the transport coeffi-
From Eqs. (2) and (3), the dissipation is expressed by
'P=LikXiXk >0
and the matrix form of it shows that the dissipation function
is quadratic in form
for all forces and fluxes, where XT and JT are the transpose of
the respective vectors. Table 4 shows the four main postu-
lates in the linear NET approach.

Equation (2) consists of scalars of tensor rank zero YO,
vectors with tensor rank one ,', and a tensor of rank two TP2

0O =T(V.v)- Jr,jAj 0 (6)

Y;= JuTV I + il JiFi- TV(r ] 0 (7)

'2 = T':(Vv)'s >0 (8)
where T:(Vv)=T':(Vv)'+T(V-v) (the double dot product of a
symmetric and antisymmetric tensor is zero). According to
the Curie-Prigogine principle, in isotropic macroscopic sys-
tems, a scalar process cannot produce a vectorial change and
vice versa; for example chemical affinity cannot cause a di-
rected heat flux, and more generally, fluxes and forces whose
tensorial rank differ by an odd number cannot couple in an
isotropic medium. Such fluxes can be coupled at the system
boundaries (which are not isotropic) by the boundary condi-

Four Main Postulates of the Linear Nonequilibrium
Thermodynamics (NET) Approach
Global form of the flux-force relations is linear, and the propor-
tionality constants in these relations are the phenomenological
In an isotropic system, according to the Curie-Prigogine principle,
no coupling of fluxes and forces occurs if the tensorial order of the
fluxes and forces differs by an odd number
In an isotropic system, any flux is caused by all the forces that
satisfy the Curie-Prigogine principle, and any force is caused by
all the fluxes
Matrix of the phenomenological coefficients is symmetric
provided that the conjugate fluxes and forces are identified from a
dissipation function equation or an entropy production equation

tions, however.
The fluxes and forces in Eq. (2) can be defined in various
ways, for example, definitions of mass fluxes change with
the choice of reference velocity. The entropy production re-
mains invariant under certain transformations, however;1,14)
for example, for a system in mechanical equilibrium
S(ciFi ciV))= 0
(from the isothermal Gibbs-Duhem equation and mechani-
cal equilibrium equation), and for the transformation
Ji Ji + vci
where c is the concentration of component i, and v is an arbi-
trary average velocity. Equation (7) can be transformed fur-
ther by introducing the total potential

9i = i i+Wi
where is the specific potential energy, the isothermal gra-
dient of the total potential VwLi* and the heat flux J in the
following expression
T -Fi = Vg -gi +Vvi =VTi-h, (9)
where Vq, = F,. Using Eq. (9) in Eq. (7), we have for n-1
independent diffusion fluxes
't= -JqVlnT- I JiVT (g-I 0 (10)

This procedure eliminates an arbitrary choice of fluxes and
forces, and ensures that the cross phenomenological coeffi-
cients obey the Onsager's relations for linear phenomeno-
logical laws.
In the next section, some examples of transport and rate
processes from physical and biological systems are presented
to show the utility of NET in teaching coupled processes.

Heat and Mass Transfer For a fluid under mechanical
equilibrium with no chemical reaction, the dissipation func-
tion of heat and independent diffusion fluxes from Eq. (10)

n-l n-l1
T = -JqV In T i Jiaik n j V TP, (11)
i,k=l j=1 j TPwi

where ak = 8k + wk/w and 86k is the unit tensor, w is the
mass fraction of species j. In a binary liquid mixture, a set of
independent forces identified by the dissipation function of
Eq. (11) for heat and mass fluxes is X = -VlnT and X, =
-(1/w2)(l/fwl)TP Vw,, respectively. Then the linear phe-
nomenological equations are

-Jq = LVln T+L 1 Vw (12)
'4 W2 (aWi )T,P

Fall 2004

. ,, -- -- '?3

-q = VlnT+LVIn T + l Vwl (13)
w2 a )T,P

Here, by the Onsager reciprocal relations, L = Lql. From
Eq. (12), heat flux due to primary coefficient Lq is expressed
by Jq = -LqqVn T = -LqqTV( / T); after comparing with
Fourier's law Jq = -kVT the Lq is related to the thermal
conductivity k:L = kT. When no volume change occurs due
to the diffusion flows (no volume flow), the mass flux J, is:
J =-L11(1+ ViC /V2c2)(aVi1 /aci)Vc,
where c. and Vi are the concentration and partial molar vol-
ume of component i, respectively; comparing it with Fick's
law, J, =-DVc,, the L,, is related to the diffusion coefficient
of component 1 D,: LI = D[(1 +Vici /V2c2)(ag1 /3ac)] .
The heat of transport Q, of species 1 is defined by Q, = L q/
L,; it is the heat carried by a unit flux of species 1 when
there is no temperature gradient and no diffusion of other
species and can be measured experimentally.!5' Equations (12)
and (13) can be expressed in terms of heat of transport Q,
and the transport coefficients-7.121
-Jq = kVT +pD1QIVw, (14)
-J1 = pDT V ln T + pD1Vwl (15)
where DT is the thermal diffusion coefficient for species 1
and p is the density. The second term on the right side of Eq.
(14) shows the Soret effect, also known as thermal diffusion,
while the first term on the right side of Eq. (15) shows the
Dufour effect. Comparing Eqs. (13) and (15) with vanishing
concentration gradients yields Lq = pDT .The degree of cou-
pling can be expressed in terms of Q, and the other transport
coefficients from Eq. (5) (13)

qQkMaRT 2(1+r)


where M. and Mv show the molecular mass of species i
and mixture, respectively, R is the gas constant, and
F1 = (a In i /3 In xI )TP is called the thermodynamic factor,
and can be determined from experimental data or an activity
coefficient, y, model. As heat and mass fluxes are both vec-
tors, the sign of q indicates the direction of fluxes of a spe-
cies; if q > 0, the flow of a species may drag another species
in the same direction, while the flux may push the other spe-
cies in the opposite direction if q < 0.7,12) Using Eqs. (14) to
(16), effects of concentration and temperature on the coupled
heat and mass fluxes in liquid mixtures can be studied.(',13
Membrane Transport The dissipation equation for an
isothermal, nonelectrolyte transport in an ideal binary sys-
tem of solute (s) and water (w) through a membrane is(14'161

S= -JsA^s- JwA^w >0 (17)

With Onsager's relations, Lpd = Ldp, the transport through
the membrane can be described by the three coefficients in-
stead of four. The coefficient L is the mechanical coefficient
of filtration, the Ld has the characteristics of a diffusion coef-
ficient, the cross coefficient Ldp is the ultrafiltration coeffi-
cient, and Lpd is the coefficient of osmotic flux. The ratio -
L pd/L is called the reflection coefficient cr, which is always
smaller than unity. With these coefficients, the degree of cou-
pling is obtained from q = Ldp/(L Ld)112
Transport in Ion-Exchange Membrane For the diffu-
sion of a single electrolyte and water in an ion-exchange mem-
brane, the dissipation due to the fluxes of ions (1 and 2) from
a neutral salt and water across the boundary is(14,16
S= -JiAi J2A2 JwAug > 0 (24)
where pi is the electrochemical potential of ion i, and ex-
pressed by i-i=.i+ziFE; here z; is the charge and F is the
Faraday constant. For a pair of electrodes interacting revers-
ibly with one of the ions in the solution, the electromotive
force AE can be related to the electrochemical potential dif-
ference of the ith ion AE=Aji/ziF. By assuming that the ion
2 reacts reversibly with the electrode, and since ion 1 is not
produced or consumed, then the flux of ion 1 is the flux of
salt, and given by Js = J,/v,, where v, is the number of ions
decomposed per molecule of salt, which obeys the

Chemical Engineering Education

Equation (17) leads to the following general forms of the
Js = -Lss As LswAgw (18)
Jw = -Lws A[s LwwAgw (19)
where the forces Ais, ALiw are the differences of chemical
potentials, and J and J are the fluxes for the solute and wa-
ter across the membrane, respectively. It is customary to re-
place A~i with more easily measurable quantities, such as
AgLi = ViAP + RTA ln ci = ViAP + RTAc / c, and Eq. (17) be-
= -Js(VsAP+AI /cs)-Jw(VwAP- AHn/cw) O (20)
where Vs and Vw are the partial volumes, c and c are the
concentrations of solute and water, respectively, AH is the
osmotic pressure difference Al = RTAcs. Equation (20) is
further transformed by defining the total volume flux Jv across
the membrane as J, = JV, + JsVs, and the flux of the sol-
ute Jd relative to the water Jd =J /c J /c
'= -JAP- JdAnI 0 (21)
With the forces of AP and All identified by Eq. (21), the
commonly used phenomenological equations that describe
the transport through a membrane are
J = -LpAP LpdAH (22)
Jd = -LdpAP LdA (23)

electroneutrality condition v,z1 + vz, = 0. With the electric
current flux I = F(Jz, + Jz,), Eq. (24) becomes
T = -JsAps JwAp IAE > 0 (25)
It may be advantageous for certain cases to transform Eq.
(25) further by using the volume flux J instead of water flux
Jw, and by introducing the relationships Aps = VsAP + AH / cs
for a nonelectrolyte solute and A w = V (AP AHs) into
V = -J,(AP As)- JsAFs / cs IAE > 0 (26)
The related phenomenological equations are then

Jv =-Lv(AP- AHs)- LvsAHs/c, LveAE (27)
J, = -L,,(AP Anl)-LssAlls / cs LseAE (28)
I= -Lev (AP A )-LesAHsn/c LeeAE (29)
In Eqs. (27) to (20) six coefficients characterize the mem-
brane transport due to Onsager's relations. The coefficients
can be determined by measuring conductivity of the mem-
brane, transport numbers, and the fluxes due to electro-os-
motic, osmotic, diffusional, and pressure.
The thermodynamic efficiency of energy conversion q can
be defined as

s )+_Jw^ lw (30)
where IAE represents the driving process, and JALs and JwAw
are the driven processes. The degrees of coupling are the ion-
water qsw, ion-current qse, and water-current qw,, which are

q sw w )1/2
S(L ss ww

qse Lss e/2

qwe (LwwLee)l/2

Graduate Education )

the net oxygen consumption, and the out flux Jp is the net rate
of ATP production.
Based on Eq. (32), the linear phenomenological relations are

Jp =LpXo+LpXp (33)
Jo =LopX +LpXp (34)

Here, Lo is the influence of substrate availability on oxy-
gen consumption rate and Lp is the feedback of the phosphate
potential on ATP production rate. The cross-coupling coeffi-
cient Lop is the phosphate influence on oxygen flux, while Lpo
shows the substrate dependency of ATP production. Experi-
ments shows that Onsager's reciprocal relations hold for OP,
andL =L .L 5
op po
Thermodynamic efficiency of the coupled systems of res-
piration (driving, N >> 0) and OP (driven, P < 0) is defined
as the ratio of output power (Tp = JpXp) to the input power
(to = J0X0)(4.6)

n=- (35)
By dividing Eq. (33) by Eq. (34), and by further dividing the
numerator and denomerator by Xo(LoL )l2, we obtain the effi-
ciency in terms of the force ratio x and the degree coupling q

n = jx +q (36)
Jp XZ ( aL n )2 L
j= x= Z= Lo and q= Lop with 0 JZ' X" Lo (LoLp)1/2


Oxidative Phosphorylation (OP) Experiments and
empirical analyses of cellular processes show that linear re-
lations exist between the rate of respiration and growth rate
in many organisms, and for some of the steps in OP.'5.616' In
mitochondria, the respiration system is coupled to the OP,
and the electrochemical potential gradient of protons across
the inner membrane drives the synthesis of ATP from ad-
enosine diphosphate (ADP) and phosphate (Pi). The theory
of NET has been used to describe the thermodynamic cou-
pling, and how the mitochondria can control the efficiency
of OP by maximizing ATP production, the cellular phosphate
potential, or the cost of ATP production. ,6" For this coupled
system a representative dissipation expression is
Y = JoX, +JpXp > 0 (32)
where the input force X. is the redox potential of oxidizable
substrates, and X is the output force representing the phos-
phate potential, Xp = [AG p + RT ln(cATP / CADPCpi)], which
drives the ATP utilizing functions in the cell; the AGO is the
standard Gibbs free energy. The associated input flux Jo is
Fall 2004

The ratio J /J is the conventional phosphate-to-oxygen con-
sumption ratio P/O, the term Z is called the phenomenologi-
cal stoichiometry. For the biphasic function in Eq. (36), opti-
mal thermodynamic efficiency qopt is the function of q only,
as shown in Figure 3.
_opt + j (37)

The sequence of coupling is controlled at switch points
where the mobility, specificity, and the catalysis of the cou-
pling protein are altered in some specific ways, such as shifted
equilibrium. Equations (32) to (37) offer a phenomenologi-
cal description of respiration and oxidative phosphorylation,
and the NET approach does not require a detailed mecha-
nism of the coupling.
Chemical Reactions NET theory provides a linear re-
lation between the rate of reaction Jr and the affinity A of
reaction (A=- vili where the v, are the stoichiometic co-
efficients, which are positive for products and negative for

reactants) when IAI mon temperature interval of 200-1000 K this constraint is
very restrictive for chemical reactions. The use of internal
coordinate space in chemical reactions systems extends the
range of applicability of NET, however, and yields nonlinear
(generally with respect to its process probability density) and
linear Fokker-Planck equations to describe nonequilibrium
processes in internal coordinate space with NET theory's con-
ventional rules.(14) The multivariate Fokker-Planck equation has
a phenomenological parameter called the mobility matrix that
relates forces to fluxes and can be derived from kinetic trans-
port theory; the equations can describe the evolution of hydro-
dynamic fluctuations in irreversible systems, as well as the
Brownian motion of particles under nonuniform temperatures.(14'
For enzyme-catalyzed and some chemical reactions, under
certain boundary conditions, force-flux relationships can be
described by a simple hyperbolic-tangent function such as
the Michaelis-Menten kinetics, which can be approximated
as linear in some regions. Therefore, at very high positive
and negative values of the affinity, reaction flux is almost
independent of affinity, and there exists a quasilinear region
in between, which extends over an ~7 kJ/mol.(14'
For an elementary chemical reaction the flux Jr is"

Jr = rf(-e-A/RT) (38)

where the affinity A is expressed in terms of forward rf and
backward rb reaction rates A = RT ln(r/rb) as well as in terms
of chemical potential. Close to thermodynamic equilibrium,
where A/RT << 1, we can expand Eq. (38) as J = rfeq(A/RT),
and compare with the linear reaction flux
Ji = LijAj
to obtain the phenomenological coefficient as L j= rte,/(RT).
Consider a fluid film having a first order irreversible reac-
tion B k P; the evolution equations for heat conduction
and diffusion with reaction under nonisothermal conditions are

= VJB +B (39)

cp VJp-r (40)

pCp -t V.Jq+(-AHr)rB (41)

where r. = -kcB, and cB and c, are the concentrations of spe-
cies of B and P, with the linear phenomenological laws for J ,
Jp, and Jq, Eqs. (39) to (41) become(13)

=-V (pDBBVwB +pDBpVWp+pDTBVlnT)+rB (42)
ac _V (pDPBVWB+pDPPVw P+pDTPVInT)-rB (43)


-V.[p(DBBQB +DPBQ* )VwB+p(DBPQ +DppQ* )Vwp +kVT]

where AHr is the heat of reaction. Eqs. (42) to (44) are the
modeling equations that take into account the coupling be-
tween the two diffusion fluxes of the species B, P, and the
heat flux with a set of suitable boundary and initial condi-
tions. No couplings occur between the scalar reaction flux
and the vectorial transport fluxes assuming that the medium
is isotropic according to Curie-Prigogine principle.

ENET uses the evolution equations for the conserved vari-
ables and therefore it can describe a larger class of phenom-
ena. The resulting equations lead to nonlinear and non-Fickian
mass diffusion, and can describe diffusion in polymers, in
which the viscous stress and diffusion are coupled. The in-
troduction of the concept of internal degrees of freedom into
NET extends its range to describe a wider class of
nonequilibrium processes, and also leads to Fokker-Planck
equations; fluctuations of thermodynamic variables are con-
sidered as internal degrees of freedom, and therefore the fluc-
tuation theory is integrated into NET. This approach introduces
the distribution function in the space of fluctuating thermody-
namic variables and the Gibbs' entropy postulate, and deals with
very slow changes compared to the microscopic time scale.

1 -0.9 -0.8 -0.7 -06 -0.5 -0.4 -03 -0.2 -0.1 0
1 force ratio, x
Figure 3. Change of efficiencies in terms of flux ratios and
degree of couplings; for a maximal net rate of ATP flux at
optimal efficiency: q = 0.786, for an economic net ATPflux
(Jpr)op: qfc = 0.953, for a maximal output power (J X )o at
optimal efficiency: q = 0.910, and for an economic net
output power (JpX 7)op,: qpec = 0.972.14-'6

Chemical Engineering Education

_. -I. ~-. -

- -. --54


Coupled transport and rate processes are important part of
some natural and complex phenomena. Partial differential
equations obtained from the NET theory can provide a uni-
fied approach to describe coupled phenomena and organized
structures and other processes in physical, chemical, and bio-
logical systems. Therefore, the NET formulations within a
suitable graduate transport phenomena textbook may be use-
ful to teach coupled transport and rate processes.

A affinity (J mol-')
c concentration (mol m-3),
D diffusion coefficient (m2 s-')
D thermal diffusion coefficient
E electric potential (V)
F Faraday constant
F force per unit mass (kg m s2 kg')
G Gibbs' free energy (J)
h partial specific enthalpy (J mol-')
h enthalpy (J)
H heat of reaction (J mol-')
I current flux
j ratio of fluxes
J heat flux (J m-2 s-')
J mass flux for component i (kg m- s-')
J reaction velocity (flux)
k thermal conductivity (J m 's- K), reaction rate constant (s- ),
K phenomenological coefficient (resistance form), Eq. (4)
L, phenomenological coefficient (conductance form), Eq. (3)
m number of fluxes
M molar mass
n number of components
Nk number of moles
P pressure (Pa)
q degree of coupling, Eq. (5)
Q* heat of reaction
r reaction rate (mol s-')
R universal gas constant (J mol-'K-')
s entropy density (J K-'m-3)
S entropy (J mol-'K-')
t time (s)
T temperature (K)
u energy density (J m-3)
U internal energy (J)
v velocity (m s-')
V volume (m3)
V partial molar volume (m3)
w mass fraction
x ratio of forces, distance
X thermodynamic driving force
z charge (C)
Z phenomenological stoichiometry
Greek Letters
( entropy production rate (J K-' s-')

r thermodynamic factor
6 unit tensor
-y activity coefficient
,q efficiency
p. chemical potential (J mol')
v stoichiometric coefficients
H osmotic pressure (kPa)
p density (kg m 3)
T viscosity part of stress tensor (kg m' s2)
I4 potential energy (J)
P dissipation function (J s')
b,f backward and forward respectively
eq equilibrium
i,j,k components
o oxygen
opt optimum
p phosphate
q heat
s solvent
w water

1. Kondepudi, D., and I. Prigogine, Modern Thermodynamics; From Heat En-
gines to Dissipative Structures, Wiley, New York, NY; pp 351-366 (1999)
2. Glansdorff, P., and I. Prigogine, Thermodynamic Theory of Structure, Stabil-
ity and Fluctuations, Wiley, New York, NY; pp 157-160 (1971)
3. Nicolis, G., I. Prigogine, Exploring Complexity; W.H. Freeman and Company,
New York, NY; pp 10-13, (1989)
4. Caplan. S.R., and A. Essig, Bioenergetics and Linear Nonequilibrium Ther-
modynamics, The Steady State, Harvard University Press, Cambridge, MA pp
356-366 (1983)
5. Stucki, J.W., "The Optimal Efficiency and the Economic Degrees of Cou-
pling of Oxidative Phosphorylation," Euro. J. Biochem., 109, 269 (1980)
6. Demirel, Y, and S.I. Sandler, "Thermodynamics and Bioenergetics," Biophys.
Chem., 97, 87 (2002)
7. Demirel, Y., Noneequilibrium Thermodynamics: Transport and Rate Processes
in Physical and Biological Systems, Elsevier, Amsterdam; pp 234-257 (2002)
8. Bird, R.B., W.E. Stewart, and E.N. Lightfoot, Transport Phenomena, 2nd ed.,
Wiley, New York, NY; pp 764-793 (2002)
9. Deen, W.M., Analysis of Transport Phenomena, Oxford University Press,
Oxford; pp 442-453 (1998)
10. De Groot, S.R., and P. Mazur, Non-equilibrium Thermodynamics; Dover, Lon-
don (1985)
11. Advances in Thermodynamics: Volume 6. Flow, Diffusion, and Rate Processes,
edited by S. Sieniutycz and P. Salamon, Taylor & Francis, New York, NY; pp
148-149 (1992)
12. Demirel, Y., and S.I. Sandler, "Linear-Nonequilibrium Thermodynamics
Theory for Coupled Heat and Mass Transport," Int. J. Heat Mass Transfer, 44,
13. Demirel, Y., and S.I. Sandler, "Effects of Concentration and Temperature on
the Coupled Heat and Mass Transport in Liquid Mixtures," Int. J. Heat Mass
Transfer, 45, 75 (2002)
14. Demirel Y., and S.I. Sandler, "Irreversible Thermodynamics in Engineering
and Science," J. Phys. Chem. B, 108, 31 (2004)
15. Rowley, R.I., S.C. Yi, V. Gubler, and J.M. Stoker, "Mutual Diffusivities, Ther-
mal Conductivity, and Heat of Transport in Binary Liquid Mixtures of Al-
kanes in Chloroform," J. Chem. Eng. Data, 33, 362 (1988)
16. Katchalsky, A., and PE Curran, Nonequilibrium Thermodynamics in Biophys-
ics, Harvard University Press, Cambridge; MA, pp 85-91 (1967) 1

Fall 2004

_5 _






Illinois Institute of Technology Chicago, IL 60616

graduate school can be the foundation for a life-long
learning experience and a successful career.1" In
today's competitive world, development of effective
oral and written communication skills is an essential compo-
nent of an engineer's education."E81 The chemical engineer-
ing profession is about generating ideas and communicating
them to others, and throughout their careers, most engineers
spend considerable time writing memos, reports, or ar-
ticles.[3,6,71 Oral communication skills (speaking to groups, in-
terpersonal interaction, and questioning14'51) are just as im-
portant for a successful career. It is essential that the impor-
tance of writing and oral presentations be related to the core
engineering principles that the students value.131
Collaborative learning is well-established as an effective
method for teaching engineering students.141 In the past few
years, the author has been involved in teaching first-year
graduate courses in chemical reaction engineering and pro-
cess control. In both courses students have been required to
work on a short course-related theoretical project based on a
literature search and individual work. In addition to per-
formance on weekly homework assignments and midterm
and final tests, the grading has included performance on
individual projects.

The individual projects are geared toward promoting inter-
active learning among the class members and providing ex-
perience in communication skills and exposure to problem
solving in a research environment through use of process
modeling techniques in chemical and biological reaction pro-
cesses and control of chemical and biological processes. The
projects are intended to improve ability of students (i) to or-
ganize material, (ii) to give clear oral presentations, (iii) to
clearly convey ideas, and (iv) to provide practice in prepar-
ing brief and detailed reports (initial proposal and final re-

port). Each project brings depth in one topic in a course that
emphasizes, in a relative sense, breadth.191 The course projects
allow students to investigate specific topics of interest to them
in greater detail than the treatment given that topic in the
lectures.191 An added benefit of the project is that it forces
students to work continuously on a particular topic through-
out the semester.[4] The projects also foster faculty-student
and student-student interactions on course-related topics.
Guideline information on the project, such as motivation,
its scope, what is expected in the project, and important dead-
lines and milestones, is provided, along with the grading
policy and course outline, in the first class of the semester. A
list of archival journals (by no means exclusive) where the
students may be able to locate technical articles needed for
the project in the particular course (chemical reaction engi-
neering or process control) is also provided in the first class.
In the reaction engineering course, any topic related to re-
active processes, i.e., processes involving reactions with/with-
out other unit operations, is acceptable. The project may deal
Multiphase catalytic/non-catalytic reactors
Reactive separations (e.g., reactive distillation, membrane
Polymerization reactors
Biochemical reactors

Satish J. Parulekar is Professor of Chemical
Engineering at Illinois Institute of Technology,
where he served as Associate Department
Chair from 1999 to 2003. He obtained his de-
grees in chemical engineering from the Uni-
versity of Bombay (BChE), University of Pitts-
burgh (MS), and Purdue University (PhD). His
research interests are in biochemical engineer-
ing and chemical reaction engineering.

Copyright ChE Division of ASEE 2004

Chemical Engineering Education

Graduate Education

* Environmental applications of reaction engineering
* Electrochemical processes
* Catalysis
* Combustion processes
* Reactor stability and dynamics

The list of areas is included only as a guide
and is not exclusive. Projects can concentrate The
on different theoretical aspects of reaction en- projeI
gineering that include stud
Review of kinetic and reactor models in a inve
particular field (such as biochemical, specific
combustion, electrochemical, polymeriza-
tion, and petrochemical processes) interest
Models for a specific problem of interest in grec
Investigation of potential of a reaction the
process treat
Reactor optimization that to
Monitoring and control of reaction lectures
processes. bene
In the process control course, any topic re- proje
lated to control of biological, chemical, elec- p
trochemical, and environmental processes is forces
considered to be acceptable. Since multi-in- to
put (multiple manipulated inputs and distur- continue
bances) and multi-output (multiple output partic
variables) processes (MIMO processes) are th"lo
the mainstays in this course, each project has
to deal with a MIMO process. The project may sem
deal with
Interaction and structural analysis,
Interaction compensation and multiloop control
Model-based control including model predictive control
Nonlinear multivariable control
Adaptive control
Optimal control
System identification
Controller tuning
Control of unstable processes and processes with unusual
Survey of applications of adaptive, model predictive, and
multivariable nonlinear control

If it is appropriate, students pursuing thesis work may use
a topic that is related to their research, while professional
engineers (part-time students) may consider a topic related
to their technical work. Located in Chicago, the chemical en-
gineering graduate program at IIT attracts a significant num-
ber of industry professionals who are part-time students in-
terested in pursuing a Masters degree in chemical engineer-

Fall 2004

it ft


ing. For these students, the project can be based on the
student's ongoing technical work provided that the scope of
the work is compatible with the course outline and that the
student's employer consents to the use of relevant material
for the project. If consent from the industrial supervisor can-
not be obtained, the student must do an open
literature-based project.
ts to Whether a project is based on literature
gate search or on the student's current research/
pics of work activities, he/she selects a recent jour-
Sthem nal article (published in the previous five
detil years) dealing with the appropriate course (re-
detail action engineering or process control). This
he article serves as the core or source article for
given the project. Next, the student does a prelimi-
in the nary literature search by locating three or four
Added pertinent cross-references, and then meets
with the instructor to see if the source article
of the and its topic are appropriate for the course
that it project. If they are deemed not appropriate,
dents the student must expand or modify the litera-
rk ture search to locate another appropriate
sly on a source article.
r topic Upon receiving the go-ahead from the in-
ut he structor, the student prepares a project pro-
1 posal of up to two single-spaced pages. It
or.* should be a brief narrative, in the student's
own words, on the subject of the source ar-
ticle, its highlights, the objectives and scope
of the project, and appropriateness of the project to the course
outline. The student must attach the abstract of the source
article to his/her proposal.
The project proposal, which is similar to an extended ab-
stract required for presentations at professional society meet-
ings, is due by the end of week five of the semester. The
instructor reviews the proposal, evaluates it based on the qual-
ity of writing and the student's ability to state concisely the
highlights of the source article and the objectives and scope
of the project, and assigns a grade for the proposal. A written
feedback in the form of suggested changes in writing style
and technical content is given to each student. This feedback
is useful in conduct of the project and preparation of detailed
written report and oral presentation on the project.

Each student then proceeds with the project by studying
the source article and appropriate cross-references. Ongoing

dialogue between each student and the instructor (during of- effort when necessary-the instructor may want to inform
fice hours) regarding the progress of the project and any the student that more work needs to be done on the project,
glitches that may be encountered is encouraged. This dia- or if the project is getting overambitious, that the student
logue enables the instructor to provide timely feedback re- should scale back his/her effort.
garding the project's progress and to help redirect the student's Each student is expected to generate independent numeri-

Projects in Reaction Engineering Course

continuous and fed-batch bioreactors; structured models of bacterial growth and
product formation; autocatalytic reactions with Michaelis-Menten kinetics: steady
state multiplicity; airlift reactors; membrane bioreactors; impact of dispersion on
biological reactors; reactions in food systems: vitamin synthesis; sucrose inversion
in an immobilized enzyme reactor; gluconic acid production in airlift reactors with
immobilized glucose oxidase; beer fermentation using encapsulated acetolactate
decarboxylase; immobilized pectinase for fruit juice production; lactose hydrolysis
with immobilized P-galactosidase; oligosaccharide synthesis by enzymatic
hydrolysis of polysaccharides; enzymatic alchoholysis of palm kernel oil in
supercritical CO2; antibiotics (penicillin and Cephalosporin C) production;
delignification of Eucalyptus globules; urea reactors; photobioreactors for
hydrogen production; growth rate-related changes of the starvation response
regulators; energy metabolism of human liver; blood coagulation initiation by
tissue factor VIIa; drug-delivery systems using intravenous drug administration;
polyurethane-based hollow fiber hemodialyzers; ethylene oxide sterilization of
injectable drugs and devices; kinetics of denitrifying bacteria; bioremediation of
2,4-dinitro-toluene; degradation of xeno-biotics in a two-phase bioreactor; phenol
degradation in fluidized bed bioreactor
Catalysis and Catalytic Reactors
catalyst deactivation in fixed bed reactors; fluid catalytic cracking; temperature
and space time trajectories for fixed bed reactors with catalyst decay; aromatics
from liquefied petroleum gas using zeolites; NO, reduction by hydrocarbons over
zeolites; monolithic selective catalytic reduction reactors; oxidation of alkyl
aromatics over molecular sieves; homogeneous transition metal catalysts for
epoxidation reactions; self-methathesis of linear 1-alkenes; combinatorial
chemistry to test catalysts for water gas shift reaction; industrial scale low-
temperature water gas shift reactor; slurry reactors for catalytic hydrogenation of
2-butyne-1,4-diol to butenediol; mixed three-phase slurry reactor for methanol
synthesis; the UOP Isomar process; catalytic decomposition of hydrogen peroxide;
catalytic hydro-treating of unsaturated hydrocarbons; oxidation of n-butane to
maleic anhydride; partial oxidation of methane to formaldehyde; methanol and
dimethyl ether synthesis; adsorption and desorption of hydrogen by magnesium;
DeSO,/DeNO, reactions on a copper on alumina sorbent catalyst; novel
multiphase reactors employing supported glass fiber catalyst; photocatalytic
degradation of organic pollutants
Electrochemical Reaction Engineering
role of Nafion in methanol oxidation in direct methanol fuel cells (DMFCs); lead
acid batteries: accelerated life testing and grid corrosion; rotating disc electrode
study of DMFCs; electrocatalytic methanol oxidation in DMFCs; Pt-Ru anode cat-
alysts for DMFCs; lithium batteries: diffusive transport of lithium, lithium
insertion during galvanostatic discharge, and thermal runaway; anodic copper
deposition; inhibition of copper corrosion by organic agents; magnesium electrode
reactions in molten MgCl2-NaCI mixtures; methanol electrooxidation on platinum
(111); cathode electrocatalysts in polymer electrolyte membrane fuel cells and
DMFCs; electrocatalytic membrane reactor for synthesis of sorbitol; electroless
copper deposition; ion-selective electrodes for tracing metallic concentrations
High Temperature Reactions
methane reforming in thermal diffusion column and pyrolysis reactors; shock tube

research: roles in reaction engineering; shock tubes for study of gas phase
reactions; chemical and combustion kinetics in shock tubes: laser Schlieren
method; thermal decomposition of 2-butyne in shock waves; natural gas
combustion in fluidized beds; catastrophic thermal decomposition of toluene
Multiphase Chemical Reactions
fluidized bed reactors: development, four-phase model for catalytic reactions, and
removal of sulfur dioxide from flue gases using metal oxide sorbents; fluidized
bed combustion; high-pressure removal of hydrogen sulfide using calcium oxide;
a revisit to the Claus reaction; reduction of hexavalent chromium by sulfite; flue
gas desulphurization; SO2-limestone reaction under periodically changing
oxidizing/reducing conditions; liquid-liquid reaction in a semi-batch plant: nitric
acid oxidation of 2-octanol; calcium sulfide formation from calcium oxide upon
capture of hydrogen sulfide; mercury capture from flue gases by activated
carbon; selenium removal by dry sorbents; hydrofluoric acid etching of silicon
surface; oxidation of silicon germanium thin films
Polymerization Reaction Engineering
(co)polymerization: anionic, emulsion, extractive, free-radical, free-radical graft,
melt-phase condensation, mixed mode, and step growth; coupling of
functionalized chains at the immiscible polymer-polymer interface; attainable
regions for polymerization reaction systems; control of polymerization reactors;
nonlinear adaptive control of styrene polymerization; state multiplicity in
polymerization reactors with recycle; kinetics of reversible A-B propagation
polycondensation; (co)polymerization of ethylene, propylene, and styrene; high
pressure olefin polymerization; batch polymerization: methyl methacrylate -
benzoyl peroxide; production of nonionic surfactants; suspension polymerization
of vinyl chloride; vulcanization reaction: cross-linking and network changes;
cross-linking in thermosetting powder coatings; reactive polymer blends: nylon-
6-acrylonitrile-butadiene-styrene; polycondensation of hydroxyl-functional
polydimethyl siloxane; new processes for nylon production
Reactor Design and Operation. Mixing in Reactors. etc.
compartmental models for multi-chamber mixed reactors; time-dependent
turbulent mixing in stirred reactors; computational fluid mixing models for
reactors; energy management in reactors with flow reversal; nonideal reactors
with complex reactions; nonisothermal reactors with variable density; chaos in
tubular reactors with recycle; residence time distribution based on measured
velocity and turbulent fields; new design formulation for multiple reactions;
perturbations around steady-states: Monte Carlo method in reaction kinetics;
supercritical fluid reactions; fine chemicals manufacture; oxidation of
hydrocarbons to carboxylic acids; oxidation of cerous or manganese ions
dissolved in H2SO,; the environment and reaction engineering; water quality
Separation with Reactions
reactive distillation: ethyl acetate distillation and quantitative optimization;
dynamic interfacial tension variation in an acidic oil/alkali/surfactant system; H,S
removal by zinc oxide titanium oxide sorbents; chromatographic reactors;
liquid-phase membrane reactors; membrane reactors for hydrogen production

Chemical Engineering Education

r~rc~b~ ,-----l~--~.--r~~-n;----- -- -1

ii: 2

- i -= :

cal illustrations pertaining to key concepts that are devel-
oped and/or discussed in the source article. This reinforces
the key concepts in the source articles and also leads to
better understanding of the required analytical and compu-
tational techniques. The exercise involves moving beyond

Projects in Process Control Course

Adaptive Control
applications: distillation column, polymerization reactor, and tubular enzyme
reactor for extracorporeal leukemia treatment; strategies
Controller Tuning and Control of Open-Loop Unstable Processes
PID controller tuning: desired closed-loop responses, industrial applications,
integral mode control and direct synthesis, integrating processes, and non-
isothermal CSTR; external versus internal open-loop unstable processes;
analytical rules for model reduction and controller tuning; gain margin and
sensitivity-based PID controller design; chaotic behavior with control valve
saturation; feedback control of competing autocatalators
Model-Based Control
model predictive control: chemical vapor deposition, composites manufactur-
ing, exothermic reactions, inferential, input-output models, linear program-
ming-based, multi-stage flash desalination, neural network-based, on-line
tuning, periodically forced chemical reactors, polyethylene production, pulp
digestion, refinery debutanizer, and review of applications; improved
dynamic matrix control (DMC); neural nets-based dynamic optimization and
universal DMC; frequency-domain closed-loop identification; double filter
internal model controller for ill-conditioned distillation columns; modified
Smith predictor for unstable processes; control in the presence of strong
directionality and model errors; Tennessee Eastman challenge problem;
supervisory control based on off-line identification
Nonlinear Control
set-based control for dead-time compensation; dynamic simulation for
process identification and control; indirect feedforward control: feedback
control of distributed parameter systems; nonlinear dynamic analysis for
process identification and control; control of a polymerization reactor with
singular characterization matrix; control of anaerobic digestion, nutrient-
removing activated sludge systems, and mesophilic and thermophilic
anaerobic sludge digesters; control strategies for high temperature, short-time
Structural and Interaction Analysis
decentralized control: genetic algorithm for structure selection, pairing
criteria; multivariable decoupling and multi-loop controllers; relative
interaction array; structural analysis and output feedback control; online
closed loop identification; dynamic output feedback control of minimum
phase processes; closed loop identification and control loop reconfiguration;
inverted decoupling; Nyquist-based PID controllers; dynamic structural
transformations for distillation column control; selection of distillation
control configuration; control of continuous copolymerization reactors; robust
control: complex biological processes and distillation; decentralized control
of supercritical fluid extraction; partial control of fluidized catalytic crackers
Survey Papers and Miscellaneous
process plant control: automated safety assessment, interet-based, and role
of recycle; multiple oscillations in control loops; advanced process control;
distillation column control; plant automation with fieldbus; control of high
temperature polymerization; multi-path ultrasonic flow meters: control
implications; closed loop supersaturation control of batch crystallization;
control problems in materials processing; control of polymer electrolyte
membrane fuel cells

what is currently known and provides additional insight and
more calculations.[19 Further, the illustrations help in better
preparation of the written report and oral presentation near
the end of the semester.

In week seven, the instructor classifies the class projects
into multiple groups for oral presentations near the end of the
semester. The projects are arranged into sessions according
to the different areas, with presentations belonging to one area
being given in a consecutive manner. In organizing the ses-
sions, emphasis is placed on the common underlying ideas in
different area units to help tie the presentations together and
to provide better understanding of the material learned in the
class to various application areas."5l This organization is in-
tended to mimic the organization of technical sessions at pro-
fessional society meetings.

Since oral presentations are time-consuming, they are ar-
ranged outside the lecture periods so that they do not occur at
the expense of instruction time. A program of the presenta-
tions, containing information such as session name, day, time
period, student name, and project title, is then distributed to
all class members. The grouping of presentations described
above is useful for the instructor and class members in under-
standing the similar and distinct features of presentations in
the same area. The schedule for presentations is finalized af-
ter receiving feedback from students on the order they prefer
for presentations belonging to a particular session.

A condensed list of topics spanned by the projects in the
chemical reaction engineering course is provided in Table 1.
For the sake of brevity, recurring words such as analysis, de-
sign, kinetics, modeling, and mechanism have not been in-
cluded in the topics. The listing pertains to the course offer-
ings in the following spring semesters, with the class strength
being indicated in parentheses: 1997 (30), 1999 (33), 2001
(28), and 2002 (52).
A similar condensed list of topics for projects in the pro-
cess control course is provided in Table 2, also with recurring
words such as analysis, control, design, modeling, multivari-
able, and nonlinear being omitted for the sake of brevity. The
listing pertains to the course offerings in the following spring
semesters, with the class strength being indicated in paren-
theses: 1999 (34), 2003 (20), and 2004 (20).


A detailed written project report is due by the end of week
twelve of the semester. The suggested length of the report is
twelve pages or less, inclusive of abstract, bibliography, fig-
ures and tables. The technical journal article is the standard
for the written report.3' The report thus includes an abstract,
an introduction, a theoretical development section, the results

Fall 2004

li~a6~c~ _~

i r

Graduate Education

and discussion, the conclusions and recommendations, and
bibliography. The student can cut/scan and paste the key tables
and figures from the source article and the three or four per-
tinent cross-references. The report should focus on the high-
lights in these articles, the student's interpretation and cri-
tique of them, independent numerical illustrations developed
by the student, and any recommendations the student has
about additional applications. The report should be cohesive
and not compartmentalized with respect to different articles.
For example, the student should not report highlights of the
source article first and then those of the cross-references.
The instructor reviews each report prior to the start of oral
presentations and notes any questions he has about the report
and any clarifications that are needed. These matters are re-
solved in the discussion period following the student's oral
presentation and, if necessary, in a separate meeting between
the instructor and the student at a later time.

Each student gives a brief oral presentation pertaining to
the highlights of his/her written report to the class. The pre-
sentation, which is focused on the source and auxiliary ar-
ticles, is given at the end of the semester during weeks 14
and 15 of class. A student can use the multimedia presenta-
tion facilities at IIT or, if preferred, give a presentation based
on Power Point or transparencies. A laptop computer, an
overhead projector, and an Elmo projector (for projecting
hardcopy material) are available in the meeting room,
usually an auditorium.
Earlier in the semester the instructor emphasizes that use
of excessive equations should be avoided in oral presenta-
tions. Equations should be used only when needed to illus-
trate key concepts and milestones in the articles. The same
thing goes for figures and tables from the source and auxil-
iary articles and to illustrations generated by the student. The
visual aids should not be a word-to-word copy of the student's
written report. The students must make sure that their pre-
sentations will answer the three main questions
What was done?
How was it done?
0 What was the significance of the study?181
Preparation of effective visual aids is an important skill in
engineering, whether they are presented in a report, a poster,
or a transparency, and many of the same skills are required in
the preparation of each case.[4] Nirdosh'8 has provided sev-
eral useful tips on critical aspects associated with classroom
oral presentations, on preparation of visual aids, mental prepa-
ration before delivering the talk, delivery of the talk, actions
to avoid during presentation, and the instructor's checklist

for giving feedback to students.
Each oral presentation is kept under twenty minutes, inclu-
sive of questions and discussion by class members. Typically,
the student presentation is limited to about 15 minutes, leav-
ing at least five minutes for questions and discussion. Each
presentation begins with the instructor's brief introduction
of the presenter and the title of the presentation. Participation
by class members as audience and questioners in the presen-
tations of their peers is highly appreciated and encouraged.
Such participation is a good learning experience for the stu-
dents and allows them to see practical applications, some of
which rely on the material covered in the course and others
which complement the course material.
Each oral presentation is followed by a question-and-an-
swer period, which is highly effective for keeping the class
engaged in the presentations which in turn results in generat-
ing excellent questions. This peer questioning also helps de-
velop the communication skills of the students.i9' The way a
person asks and answers questions has a significant impact
in the working environment. The students learn about (and
practice) how to ask, as well as how to answer, open-ended
and closed-ended questions.'i'
Class participation picks up after two or three presenta-
tions on the first day of presentations. The instructor also pro-
vides constructive feedback to the presenter in front of the
entire class. This helps to improve the quality of subsequent
presentations since the students better understand the stan-
dards expected for their presentations.'9g
As a way to assess the effectiveness and utility of the project
presentations, the class members are asked to provide anony-
mous written feedback on both the strengths and the weak-
nesses of each presentation. This feedback assists the stu-
dents in maintaining the strengths and working on the weak-
nesses in order to improve their overall seminar presentation
skills. The written feedback consists of a duly filled-out oral-
presentation evaluation form that is distributed to the class
by the instructor. The form enables the students to provide
information on items such as organization (overview of pre-
sentation, summary, flow, targeting of audience, use of time),
poise and appearance, delivery of presentation (eye contact,
voice, rate of delivery), visual aids (neatness, font size, titles
and labels, use of space), content (level of information, ad-
equate discussion and analysis, summary), answering ques-
tions (conciseness, poise, interaction with audience, overall
answer), appropriateness of the project to the course content,
and overall effectiveness of the presentation. The presenta-
tion evaluation form is not shown here since similar forms
have been provided elsewhere.'15 The result is that at the end
of the presentation, each student has large amount of anony-
mous feedback from other students.'91

Chemical Engineering Education

Graduate Education

By requiring students to conduct a formal review of an-
other student's presentation, they are forced to consider what
elements comprise an effective presentation.'E3l The high
value of peer review has often been documented in the litera-
ture.[3.9- ll The students do an excellent job of identifying both
the strengths and the weaknesses in the presentations of their
fellow students. The peer review also aids the students in rec-
ognizing the strengths and weaknesses of their own oral and
written reports.'91
This peer review is not used to determine an individual's
grade on the project and hence it is nonbinding. For the in-
structor, its only use is to aid in assessing the effectiveness of
the course. The experience of the author has been that stu-
dents accept, appreciate, easily implement, and listen to the
feedback they receive from their peers vis-d-vis the feedback
received from instructor. Student feedback concerning the
peer reviews has been uniformly positive.
After each four presentations, the class takes a ten-minute
break. During each break and at the end of each presentation
session, the class members are encouraged to discuss the
key technical features of the presentations they just heard.
The presentation session concludes with brief comments
by the instructor on the relevance of each presentation to
the course content.
Requiring oral presentations helps students develop their
powers of communication and persuasion. It not only pre-
pares them for making effective presentations, but it also helps
them acquire confidence for their eventual entry into aca-
demia or industry.t81 Invariably, the students who present later
in the progression make fewer mechanical or lack-of-prepa-
ration errors. It is obvious they have learned from watching
and evaluating the previous speakers. This is taken into ac-
count while grading oral presentations in order to minimize
the disadvantage to students who presented earlier.'3
During each presentation, the instructor also completes the
presentation evaluation form mentioned earlier and assesses
how much the student learned from the project. The form is
useful in grading each oral presentation and project. Grade
for the project is based on the initial proposal, the written
report, and the oral presentation. In future course offerings,
following the suggestion of an anonymous referee, the au-
thor will consider using peer evaluation in arriving at the grade
for the oral presentation.

Unlike ordinary lecture courses, these courses require a
significant amount of student/instructor interaction outside
the scheduled class time. During the course of the semester,
the instructor has private review meetings, when necessary,

with each student. Considering the rewards of this exercise,
for both the instructor and students, it is well worth the ef-
fort. The course projects provide access to immediate and
extended applications of the material taught in the course for
the instructor and class members. The material in some of
the course projects can be incorporated as case studies,
short homework problems, and computer assignments in
future offerings of the course, which is a distinct benefit
for the instructor.

The instructor should extend and tune the course material
in subsequent offerings to keep pace with recent advances in
the course area. The instructor and the students have the plea-
sure of listening to oral presentations, which range from good
to excellent, and the students are able to pick up a few tips on
how to make their own presentations and visual aids more
effective and engaging. The project presentations also con-
tribute to preparing graduate students for future presentations
at other venues, such as professional society meetings.

Incorporation of a course project allows students to focus
on one particular topic in reaction engineering or process
control and to share with the class what they learned in the
process, and adds depth to the breadth of material covered in
these courses. The course project brings many positive fac-
tors to the learning environment of the class.'91 The projects
enable students to seek education beyond the classroom, to
observe and listen, to learn from their peers, to learn tech-
niques they can use to make their presentations engaging and
understandable,"l and to understand the broader context of
their and their peers' projects.1'

I. Rajagopalan, R. "Getting the Most Out of Graduate School," Chem.
Eng. Ed., 33(4), 258 (1999)
2. Kranzber, M., "Educating the Whole Engineer," ASEE Prism, 28 (Nov.
3. Newell, J.A., D.K. Ludlow, and S.P.K. Sternberg, "Development of
Oral and Written Communication Skills: Across an Integrated Labo-
ratory Sequence," Chem. Eng. Ed., 31(2), 62 (1997)
4. McConica, C., "A Course in Communication Skills: For the Corpo-
rate Environment of the 1990s," Chem. Eng. Ed., 29(3), 158 (1995)
5. Bendrich, G., "Just a Communications Course?: Or Training for Life
after the University," Chem. Eng. Ed., 32(1), 84 (1998)
6. Suraishkumar, G.K., "Improving Coherence in Technical Writing,"
Chem. Eng. Ed., 38(2), 116 (2004)
7. Friedly, J.C., "Top Ten Ways to Improve Technical Writing," Chem.
Eng. Ed., 38(1), 54 (2004)
8. Nirdosh, I., "Making Successful Oral Presentations: A Guide," Chem.
Eng. Ed., 31(1), 62 (1997)
9. Sinclair, J.L., "A Survey Course in Particle Technology," Chem. Eng.
Ed., 33(4), 266 (1999)
10. Newell, J.A., "Using Peer Review in the Undergraduate Laboratory,"
Chem. Eng. Ed.. 32(3), 194 (1998)
11. Holt, M., "The Value of Written Criticism," College Comp. and Comm.,
43, 384 (1992) O

Fall 2004

r M --




Columbia University New York, NY 10027

Thermodynamics is an exact theoretical and math-
ematics-based subject, normally presented in the con-
text of familiar mechanical systems such as heat en-
gines, dealing with vapor pressures and equilibrium. One good
way to teach a graduate thermodynamics course is to start
with an explanation of familiar concepts and experimental
observations and then transition to a rigorous mathematical
approach to complete and generalize the concepts. This, how-
ever, is where most students begin to have problems. More
specifically, while they do not have a problem with the con-
ceptual understanding of the basic laws of conservation of
energy, reversibility and entropy, and heat capacity, they do
encounter difficulties when the concepts or problem state-
ments are formalized into mathematical terms to enable pre-
cise solutions of the problems.
The overall objective of the graduate thermodynamics
course at Manhattan College is to help students become con-
fident enough about their understanding of the material and
familiar enough with mathematical manipulations to prop-
erly and accurately set up solutions to problems involving
thermodynamics. A full understanding of fugacity and its ap-
plication to pure fluids and multicomponent mixtures is
sought. The students are introduced to the tools used for ther-
modynamic property evaluation.
The course also covers analytical solutions and approxi-
mations for calculating vapor pressure from volumetric data
or equations of state. Other material not covered in the class
textbooks11 is introduced during lectures, with appropriate ref-
erences given for further information-for example, the group
additivity and group contributions methodsE[2 to calculate ther-
modynamic properties is introduced. If time permits during
the semester, a very brief introduction to statistical thermo-
dynamics is given to expose the students to the microscopic

* Engelhard Corporation, Peekskill, NY 10566
** ChE Department, Manhattan College, Riverdale, NY 10471

side of the subject. Finally, thermodynamic analysis of in-
dustrially significant issues is explored as a computer assign-
ment where students either conduct "what-if" scenarios or
propose solutions to a problem, such as the problem of Me-
thyl tert-butyl Ether (MTBE) contamination of groundwater.
A good way to accomplish proficiency in something is to
repeatedly work with it in different situations. With this end
in mind, students are given significant amounts of homework
to reinforce the mathematical aspect of the subject and to
allow them to do calculations on their own in evaluating vari-
ous systems and scenarios without losing the bigger picture

Marco J. Castaldi is an Assistant Professor in
the Earth & Environmental Engineering Depart-
mentat Columbia University. He received his BS
from Manhattan College and his MS and PhD
from the University of California, Los Angeles,
all in chemical engineering. His teaching inter-
ests lie in thermodynamics, combustion phenom-
ena, and reaction engineering.

Lucas Dorazio is currently working toward his
MSc in Chemical Engineering at Manhattan Col-
lege. He received his BSc in chemical engineer-
ing from West Virginia University He is also em-
ployed full-time by Engelhard Corporation as a
Product Development Engineer.

Nada M. Assaf-Anid is Associate Professor and
Chairperson of the Chemical Engineering Depart-
ment at Manhattan College. She earned her BS
and MS in Chemical Engineering from the Royal
Institute of Technology in Stockholm, Sweden, and
her PhD in Environmental Engineering from the
University of Michigan. Her research and teach-
ing interests are in biochemical engineering, haz-
ardous chemicals remediation, thermodynamics,
separations, and water purification.
Copyright ChE Division of ASEE 2004

Chemical Engineering Education

_ -- Gr~IdLwte5~fu4uJJ.')

of what those calculations are illustrating. Also, an attempt is
made to incorporate computer calculations, i.e., spreadsheet,
programming, etc., to allow students to conduct "what-if'
scenarios on multiple systems. The desired outcome is to
develop the students' engineering judgement and capabili-
ties along with their mathematical skills in solving compli-
cated equations with many inputs. A good example of this
technique is a fugacity comparison for various equations of
state for a number of fluids. The sheer number of calcula-
tions involved forces students to become efficient and thus to
program the problem into a calculator or computer. From that
point, a parameter such as pressure can be varied and the
student can see the effect for different fluids simultaneously.
All of this preparation during the semester sets the stage
for a major assignment where the students are introduced to
a practical and current problem that they can tackle intuitively
rather than by a direct application of formulas, as presented
by Cengal.'31 The only requirement for the problem's solu-
tion is the use of some sort of computer programming, a
spreadsheet or MathCad, and the thermodynamic principles
taught in class, such as phase equilibria, solubility, fugacity,
etc. Such an open-ended approach is common in engineering
education and is often used in thermodynamics courses141 be-
cause it resembles problem-solving situations encountered
in industry.

This open-ended problem was given as a final project to a
graduate process thermodynamics class. If presented in a less

The Problem Assignment
As indicated in the two articles handed out with this assignment, there is a
problem with MTBE contamination. Of particular concern is the contamina-
tion of ground water that is used for public consumption. As a newly
employed engineer with an environmental consulting firm, you are given the
task of suggesting a simple, economical way to treat ground water
contaminated with MTBE. While researching the problem, you find that
MTBE primarily gets into groundwater from gasoline contamination. Using
your knowledge of phase equilibria, suggest a simple process to remove
MTBE from an aqueous solution that has gasoline as well, and provide
calculations to show the feasibility of your solution.
The concentrations in the water are about 1% to 2% MTBE and 5% to 10%
gasoline. Since gasoline is a blend of many components, it is suggested that
you use n-heptane as a surrogate for gasoline. Use any of the phase equilibria
models presented in class (or in your text or elsewhere) and use the Internet
to find pertinent information, data, and/or solutions for the water/MTBE/n-
heptane mixture.
The feasibility calculations should show the behavior of the mixture over a
range of temperatures and pressures that are applicable to your suggested
process. For example, if distillation is suggested, show how the vapor and
liquid phase mole fractions change with temperature and pressure. Of course,
they must at least show feasibility for the concentrations given at ambient
temperature and pressure.

open-ended manner, however, it would also be suitable for
undergraduate thermodynamics, separations, or mass trans-
fer courses. The problem assignment, given to the students
with two references from Environmental Engineering, [5', is
presented in Table 1. In addition, the students were instructed
to use the Internet to find background information covering
issues associated with MTBE in ground water, physical prop-
erty data needed to perform calculations, and ideas for pos-
sible remediation solutions. Along with the problem state-
ment, a few Internet sites are given to the students (such as
About.comr17) to get them started.

The following section details the theory that we expected
the students to use solving the problem of stripping out MTBE
from gasoline-we also highlight the main principles that
were presented during the semester. The principal equations
needed to solve the problem will also be given and discussed.
It should be evident to the reader that the concepts used for
solving this problem are the classical concepts usually pre-
sented in class. The applications of the following relation-
ships then serve to strengthen the students' understanding of
the lecture material.
Beginning with the phase rule for multicomponent,
nonreacting systems, the variables of temperature, pressure,
liquid composition, and vapor composition for two phases
are determined. The standard form of the phase rule used for
the system is given by

F=2-7t+N (1)
This gives the number of variables that will fix the values of
the remaining variables. For a three-component, two-phase,
nonreacting system, the three variables that the students usu-
ally define are temperature and two of the three liquid mole
fractions. The intensive state of a system at equilibrium is
established when these variables are fixed. The phase rule
for nonreacting systems yields the number of variables that,
when arbitrarily set, will fix the values of the remaining vari-
ables. The remaining liquid mole fraction is determined us-
ing a mass balance. The pressure and vapor mole fractions
are then determined using three independent phase-equilib-
rium equations and a mass balance of the vapor system.
The starting point for the phase-equilibrium equations is
the phase equilibrium criterion, given as

fMi =fM,i (2)
To solve for the vapor-phase fugacity coefficient, students
use either an equation of state or the principle of correspond-
ing states with a simplifying assumption such as the Lewis-
Randall rule. Each method has the same essential basis, but

Fall 2004


Graduate Education

differs in the assumptions made. Similarly, the liquid-phase
fugacity can be solved using one of two methods-one based
on activity coefficients or one based on the equation of state
description of the liquid phase. Most students choose the former.
Since the pressure for the given problem is low (i.e., equal
to or less than atmospheric pressure), the Lewis-Randall rule
provides a reasonable estimate of gas-phase fugacity. This
relationship assumes that the gaseous mixture behaves as an
ideal gas. Obviously, this is an approximation-but it is suf-
ficient for the purpose of this assignment. The pure-phase
fugacity can then be determined using an equation of state
such as the van der Waals equation.
The van der Waals equation is the simplest nontrivial equa-
tion of state, yet it provides a reasonable estimation of volu-
metric behavior of the vapor phase. In addition, students now
gain some appreciation for more realistic, albeit familiar, equa-
tions of state and the data needed.
The liquid-phase fugacity is estimated using the activity

fMi = xiri(T,P, xi)fL(T, P)
An expression for the pure-component fugacity should be
developed by integration of the thermodynamic definition of
the fugacity coefficient, recognizing that a phase change oc-
curs within the integration range. The resulting equation in-
troduces the Poynting pressure correction factor to the stu-
dents and allows them to account for the change in fugacity
due to the system pressure being different from the vapor
pressure of the liquid. Vapor-pressure data for the pure liq-
uids can be calculated using the Antoine equation. The activ-
ity coefficient can be determined from experimental data or
from one of several liquid-mixture activity coefficient mod-
els. Due to the lack of documented experimental data, the
students' calculations usually use the UNIFAC group contri-
bution model,E8' which they programmed into their models.
Using expressions developed in the preceding paragraphs,
the phase equilibrium criterion can now be written as

P _
xiYi(T, P, xi)PivaP(T)(ate PvapP = yiP (3)

If the total pressure and vapor pressure are low, both the fugac-
ity coefficients in Eq. (3) may be neglected. Given these con-
ditions and recognizing that ly, = 1, the total pressure can be
estimated using

xxiTi(T,P, xi)pvaP(T)= P (4)

The composition of the vapor phase can now be estimated by
rearranging Eq. (3) to solve for the vapor mole fraction (y,).

The full details of the development of Eqs. (1) to (4) can be
found in standard thermodynamics texts.191g0] The derivation
presented here is to establish the initial set of equations used
with key intermediate steps that yield the final equations
needed to solve a problem similar in nature. The complete
solution to the model developed for this open-ended problem
can be obtained, in MathCad format, upon request.


While not all students followed the above development,
the results obtained were generally very good in that most of
them proposed a feasible solution and performed the mini-
mum calculations necessary to support their solution. Most
solutions using the above development led to the familiar
unit operations to achieve the necessary separations. A couple
of solutions, however, employed membrane technology and
incineration. This is encouraging as it demonstrates that the
students are beginning to use thermodynamic calculations to
initiate the design of an integrated system for practical prob-
lems such as MTBE remediation.
There were no instances where conflicting solutions were
found, as has been shown to be the case elsewhere."'I In ad-
dition, all the students found that using the Internet was valu-
able for finding data and background information on the prob-
lem as well as introducing them to new academic sites that
could be used in the future.
The discussion of the results will focus on one student's
report. The student's analysis was very similar to the devel-
opment described in the previous section, including incorpo-
ration of external research to help focus the analysis. For ex-
ample, the student's analysis included bounding the problem
(i.e., defining what percentage of MTBE needed to be re-
moved),"21 a literature search to find previously proposed so-
lutions,13l" and a high level of detail in the calculations to sup-
port the proposed solution.

Student's Solution
A computer program was created in MathCad to perform
the calculations described in the "Theory and Equations" sec-
tion. Given a user-defined temperature and liquid composi-
tion, the program calculates total and component vapor pres-
sures, component liquid activity coefficients, and vapor com-
position. The calculations were repeated at varying tempera-
tures and compositions, and the data were compiled and plot-
ted in Microsoft Excel to illustrate the relationship between
vapor-phase composition, temperature, and pressure (see Fig-
ure 1). The data, along with general design guidelines, were
used to propose potential separation methods.
Before performing extensive calculations, the relative vola-
tility of each component was determined to test the assump-
Chemical Engineering Education

Graduate Education

tion that a phase separation would occur, resulting in the treat-
ment of MTBE/water and gasoline/water phases. Due to the
low initial concentration and the extremely low target con-
centrations of MTBE (set to EPA guidelines of 2 x 10' mole
%), however, distillation would not be a practical method for
separating the MTBE from water. Potentially, some of the
gasoline could be removed by using some type of flash dis-
tillation. An initial reduction of the gasoline concentration in
the liquid may improve downstream operations that would
be required to remove the MTBE. Assuming that the concen-
tration of gasoline can be significantly reduced by distilla-
tion, air stripping could then
be used to remove MTBE
from water. MTBE & Water Vapor Mr
An initial analysis con- 0500
firmed the assumption that 0.450 350 K
MTBE can be separated from 0.350
the water and gasoline in two 0 o300
steps-distillation of the 0 250
gasoline and air stripping of0 0. so
the MTBE. Based on these > 00 300 K
initial results, more detailed 0050
equilibrium calculations o0 0.5 .0 i.5
were performed for two sys-
tems: MTBE/gasoline/water
and MTBE/water/air. The Figure 1. Representative ou
first case assumed that a signment. This stimulates an
phase dominated by gasoline if" scenarios on the calculate
would contain MTBE and
water. The second case as-
sumed that a resulting second phase would contain only
MTBE and water that could be separated by air stripping. In
both cases, the computer program developed to perform the
equilibrium calculations was used to estimate vapor compo-
sition for a given set of initial conditions.
Case 1: MTBE/gasoline/water To study this three-com-
ponent system, two sets of conditions were examined: one
where the liquid concentration is fixed and the temperature
is varied, and another where the temperature is fixed and the
liquid concentration of gasoline is incrementally reduced. For
this three-component two-phase system, temperature and liq-
uid composition were specified. The program then calculated
pressure, liquid-phase activity coefficients, and vapor-phase
composition. For the first set of conditions, the liquid-phase
composition was held constant at the initial condition (xMTBE
= 0.02 and water = 0.88) and the temperature was varied from
280 K to 350 K. At the initial condition, the liquid-activity
coefficient of gasoline was found to be significantly larger
than that of MTBE and water by nearly two orders of magni-
tude. As a result, gasoline tended to dominate the composi-
tion of the vapor phase regardless of system temperature.

ole Fract


d en

For the second set of conditions, the temperature was fixed
at 300 K and the liquid concentration of gasoline was incre-
mentally reduced from 0.10 to 0.01. As the liquid phase be-
came more concentrated with MTBE, i.e., gasoline was re-
moved, the tendency for MTBE to exist as a vapor increased.
This led to two limiting cases that helped set bounds on the
problem. In the first limiting case where the concentration of
gasoline was reduced to zero and the water and MTBE were
set to the initial concentration, the MTBE had a tendency to
concentrate in the vapor phase. In the second limiting case
where the concentration
of MTBE approached
zero, water became the
ton vs. Pressure and Temperature sole component of the liq-
uid phase and conse-
MTBE quently the predominant
.----. Waer component of the vapor
Case 2: MTBE/water/
air This second system
was examined to evaluate
the viability of air strip-
25 30 35 40 4.5 5.0 ping to remove the
ese (bar) MTBE from the water.
essure (bar)
Assuming the insolubility
from the computer program as- of air in liquid, only one
tables students to conduct "what- component mole fraction
they performed. specifies the composition
of the liquid. For these
calculations, pressure, temperature, and MTBE liquid mole
fraction were specified. The computer program was used to
calculate vapor pressure, MTBE and water activity coeffi-
cients, and vapor composition. The temperature, pressure, and
liquid concentrations were varied to evaluate the behavior of
the two-component liquid system in the presence of air. When
the liquid mole fraction of MTBE was 2 to 4%, the activity
and vapor pressure of MTBE were greater than that of water,
and as a result MTBE tended to exist in the vapor. On aver-
age, the concentration of water vapor was half the concentra-
tion of MTBE at 300 K. For a fixed-liquid composition, ad-
justing the temperature or pressure can control the concen-
tration of MTBE in the vapor phase. Figure 1 illustrates this
dependency of vapor composition on equilibrium tempera-
ture (300 K and 350 K) and pressure (0.5 to 4.5 bar).
Since the target concentration for MTBE nearly approaches
zero, distillation is not a viable method for removing the
MTBE from water. Distillation could be used to reduce or
remove the gasoline, however. Therefore, it remains that the
most likely method to remove MTBE from water is air strip-

cI.oInminuea on

page 279.

Fall 2004

Graduate Education

A Computational Model for


Virginia Polytechnic Institute Blacksburg, VA 24061-0211

Convective transport phenomena are fundamental prin-
ciples of chemical engineering and are covered in both
graduate- and undergraduate-level courses, but the
transport equations are frequently coupled and cannot be
solved analytically. Consequently, classroom teaching of this
topic is usually fragmented; the fundamental equations are
developed and the semi-empirical transport correlations
are applied, but methods to solve the fundamental equa-
tions and arrive at the Nusselt and Sherwood numbers are
usually neglected.
With the ubiquity of fast and inexpensive computers and
easy-to-implement software packages (e.g., Matlab, Visual
Basic), it has become tractable to fill this gap. Implementa-
tion of computational methods in engineering education re-
mains limited, however.'1 This deficit can be alleviated by
the incorporation of numerical methods into the curriculum
and increased availability of pertinent, ready-to-use code.
Free convection near a vertical wall is a classic example of
convective transport and involves simultaneously solving the
Navier-Stokes, heat, and continuity equations. Pohlhausen
showed that this problem could be approximated by coupled
second- and third-order ordinary differential equations
(ODEs) with respect to a similarity variable. Importantly,
this approximation can be readily solved using ODE solv-
ers built into common software packages (e.g., Matlab,
Mathematica, Polymath).
This example has been used in two separate courses taught
at Virginia Tech. In a graduate-level transport phenomena
course, students employed Euler's method to solve the
coupled ODEs and match the boundary conditions. The ob-
jective was for the students to reproduce Figure 12-5 and 12-
6 from Deen.'2' In an undergraduate numerical methods
course, students were provided with the complete and work-
ing code as part of an interactive laboratory exercise. The
goal was to introduce the students to the use of an ODE solver
and the shooting method to solve boundary value problems.
The example will be presented in four steps:

1. Brief derivations of the coupled second- and third-
order ODEsfrom the fundamental transport equations
are presented.
2. These two equations are solved as an initial value
problem consisting offive coupled first-order ODEs.
3. A shooting method algorithm is presented that
employs the Newton's method to iteratively find initial
conditions that satisfy conditions far from the wall.
4. The solution is used to predict the average Nusselt
number and temperature and velocity distributions
near the wall.
The solution is demonstrated using Matlab (version 6), but
other programming languages can be used. This step-wise
approach is intended to aid student learning by breaking the
full problem into discrete modules that implement different
numerical methods and programming structures. In addition,
the graphical display of predictions in dimensional form is
intended to aid visualization of fluid mechanics. The learn-
ing objectives that are illustrated in this example can be sub-
divided into categories of transport phenomena, numerical
methods, and Matlab implementation (see Table 1).

A fluid (e.g., air) of density p, viscosity L, heat capacity
Cp, thermal conductivity k, and coefficient of thermal
expansivity P, is in contact with a vertical wall (see Figure
1). If the surface temperature of the wall, Tw, is greater than

Aaron S. Goldstein is Assistant Professor in
the Department of Chemical Engineering at
Virginia Polytechnic Institute and State Uni-
versity and a faculty member of the Wake
Forest/Virginia Tech School of Biomedical
Engineering and Sciences. He received his
doctorate in chemical engineering and
bioengineering at Carnegie Mellon University
in 1997. His research interests include
biomaterials, interfacial phenomena, and
transport phenomena as they relate to tissue

Copyright ChE Division of ASEE 2004

Chemical Engineering Education

Graduate Education

the temperature of the fluid far from the wall, T then ther-
mal expansion of the fluid near the wall will lead to buoy-
ancy-driven flow (i.e., free convection) in response to grav-
ity, g. In this two-dimensional geometry the x-axis (defined
as parallel to the surface) is oriented vertically, and the y-axis
is oriented horizontally. For this two-dimensional system, the
relevant transport equations are continuity
avx av
+x dy
ax ay
the Navier-Stokes equation for flow in the x-direction (paral-
lel to the surface

( av. av, dP (a 2Vx v a2V
pVx-- +v y =-x-p T-T )pg- + -y 2
ax ay dx
and the heat equation

Learning Objectives
Relevant to the Free Convection Example

Learning Objectives Relevant to Transport Phenomena
Understand the transport equations that describe free convection
Understand the boundary conditions relevant to free convection
near a vertical wall
Visualize and evaluate the 2D temperature and velocity profiles
predicted from theory
Learning Objectives Relevant to Numerical Methods
Set up coupled ordinary differential equations and solve them as
an initial value problem
Employ secant method to iteratively solve multiple nonlinear
Employ secant method to solve coupled ordinary differential
equations as a boundary value problem
Learning Objectives Relevant to the Use of Matlab
Write and use an m-file as a function
Use ODE45 to solve a set of coupled first-order ordinary
differential equations
Implement matrix operations: inverse, transpose, multiplication
Graph data using "plot" and "contour" commands

Wall Fluid

Tw To

-- Vy

Figure 1. Coordinate system for free convection of a fluid
at T near a heated wall at T .

aT aT ( a2T a2 T
pCp vx-+vy r-=k + -J (3)
ax ay x- oy- W
These equations have four dependent variables (T, P, v and
v ) and two independent variables (x and y). From visual in-
spection, we see that ten boundary conditions are required to
completely solve them. Although they can be solved using
finite element or finite difference numerical approaches, an
approximation of the solution can be obtained by first sim-
plifying these equations to a pair of coupled ordinary dif-
ferential equations.
The simplification scheme (described in detail by Deen,
pp. 493-50112') is given here briefly. First, these mathemati-
cal expressions may be simplified by neglecting pressure drop,
dP/dx, and by recognizing that diffusive transport in the x
direction, a2v, / ax2 and a2T / ax2, is negligible relative to
convective transport. This eliminates the dependent variable
P and the need for three boundary conditions. Second, a stream
function, which automatically satisfies Eq. (1)

vx y Vy (4)
ay ax
is used to eliminate one equation and one dependent vari-
able. Third, a similarity variable originally proposed by
Pohlhausen, rl o y / x025, is used to convert the partial dif-
ferential equations into ordinary differential equations. Fourth,
the remaining dependent and independent variables are
nondimensionalized and scaled to achieve the follow two

d3F d2F dFr
+3F 2 +E
drl d dr)
d 2 dO
+ 3 PrF = 0
drp dr1

Here the variables are

^ 0.25

Tw To

I = (Grx / 4)0.25(y / x)

the dimensionless groups are

Grashoff number: Gr, = gv2x3p(T, T) (6d)
Prandtl number: Pr = [Cp / k (6e)

and kinematic viscosity is v = L/p. Equations (5a) and (5b)
must be solved subject to five boundary conditions

Fall 2004

GM~I~fEEMtIJfl .---

F( dF dF d
F( = 0) = =-- = 0
dilIo drn^
E(rl ->o)= 0 and O(ir = 0)= 1 (7)
Note that because 0 and dF/d'q must asymptotically approach
zero as 'q goes to infinity, their derivatives, d2F/d'r2 and dO/
d'r, also must go to zero.

To solve Eqs. (5a) and (5b) numerically, they are first re-
duced to sets of first-order equations by defining new depen-
dent variables F0, F', F2, 00, and 01 [3, p671]

dF F1
=- F
dF 2
d = F2

dF2 =-3FOF2+2(Fr)2 "-0 (8)

= -3 Pr Fo09

Here Fo and 00 are equivalent to F and 0 in Eq. (5), and F',
F2, and '0 are first and second derivatives of F and 0 with
respect to q. Note that Eq. (5a) is third order and is replaced
with three first-order equations, whereas Eq. (5b) is second
order and is replaced with two equations.
The five coupled first-order ODEs can be readily solved in
Matlab using the built-in ODE45 solver. The ODEs are de-
fined within a Matlab m-file "freeconvect.m" (see Table 2)
where the function "Y=freeconvect(eta,X,Pr)" calculates the
set of first derivatives

(dF /dn l
Y= dF2 /dr (9)
dO0 / dn

subject to three arguments: 1) the independent variable, eta,
2) the set of dependent variables, X (at eta)

X= F2 () (10)


and 3) the Prandtl number, Pr. For this example, Pr = 0.72 (for
air) is used. (Details regarding built-in ODE solvers for Matlab
version 6 can be found in Higham and Higham1[4, pp. 148-163.)
A driver program (see Table 3) is used to solve the five
coupled ODEs subject to a set of five initial conditions,
"Xinit," over a finite range of 9, "etaspan." In this case, the
finite range chosen is 0 < q5 < 10. From Eq. (7), only three
initial conditions (at -q = 0) are known: Fo=0, F'=0. and 00=1.
The remaining initial conditions must be guessed. Because
F2 and 0' are proportional to the velocity gradient, dvx/dy,
and temperature gradient, dT/dy, respectively, F2 should be
positive and '0 negative at q1 = 0. For the case of Pr = 0.72,
the initial values are F2 = 0.6761 and '0 = -0.5047 and pro-
duce the solid curves in Figure 2. In principle, a trial-and-
error method can be employed to determine these initial val-
ues, but it is tedious. Alternatively, an algorithm to solve two
coupled nonlinear equations, such as Newton's method, can
be devised to iteratively solve the boundary value problem.

Newton's method for solving nonlinear equations involves
an iterative process of iteratively refining x, by a correction, h

Subroutine for ODE45 Solver
function Y=freeconvect(eta,X,Pr)
% X=(FO; Fl; F2; Theta0; Thetal)
dF2deta=-3*X(1)*X(3)+2*(X(2) )2-X(4);
Y=[dFOdeta; dFldeta; dF2deta; dTheta0deta;

Solver Program
% ODE45 Solver for freeconvect.m
Pr=0.72; % Prandtl number for air
etaspan=[0 10] ;
options=odeset('AbsTol',le-7, 'RelTol',le-4);
% solver will call function 'freeconvect'
% plot results
figure(l), plot(Eta,X(:2), 'k-')
xlabel('\it{\eta}', 'Fontsize',16)
ylabel('{\itF)}^1', 'Fontsize',16)
figure(2), plot(Eta,X(:,4),'k-')
xlabel ('\it{\eta]','Fontsize',16)
label ('(\Theta)^0','Fontsize',16)

Chemical Engineering Education

Graduate Education

xi+1 = xi +h (11)
where h is calculated by linear extrapolation of the function,
f(x), to zero3' pp139-145]

0= f(xi)+(df / dx)xi h (12)

This approach can be scaled up readily to solve the roots of
coupled equations. For this particular example, the equations
in matrix form are

1'(0 = 0)i J, F'( 1 =0), h

where (h,h,) is the solution to the equation

0o FlF(l=10)i.
0) 00(l =10o)

Correct Solution
0.2 Incorrect Solution



-0.1 -


0 0.6



0.0- ----------------- -

0 2 4 6 8 10

Figure 2. Solution to dimensionless boundary value prob-
lem for Pr = 0.72. Solid line corresponds to the correct so-
lution, which satisfies F1 = dF'/dd- = 0' = dOP/dir = 0. Dashed
line is incorrect solution, which only satisfies F = 0 = 0.

Note that these equations are designed to find initial values
of F2 and 0' (at Tr = 0) that satisfy final values of F' = 0 and
0 = 0 (at rq = 10). Equations (13) and (14) can be expressed
using matrix and vector variables as

Xi+ =Xi +H (15)

0=F+KH (16)

respectively, and combined to achieve an iterative strategy

i+ =Xi-K- F (17)

where K is the Jacobian matrix.

Although the four derivatives that comprise K cannot be
expressed analytically, they can be estimated using two-term
forward finite divided differences. The first of the four is ap-
proximated by the equation

dFl(n=r10) Fl(l=10)F2+8p.e1 -FI(=10)IF2,'1
dF2(l=0) F (18)

To estimate the four derivatives, the ODEs in Eq. (8) have to
be solved for three pairs of initial values {F }, {(F + 8,F
O' }, {F2, 0i', + 8.}, where 8, and 8o are small numbers.
A Matlab code for solving this boundary-value problem is
given in Table 4 (next page). Within the iterative loop, three
steps are taken. First, the ODEs are solved over the interval 0
_< r < 10 for three pairs of initial values, { F, '}, { F2 + 8,,
0,'}, and {F2, 0' + 8)}, where "8F=dF2=0.001" and
"80=dTl=0.001". The resultant values of F and 0" at -q=10
for each pair of initial conditions is placed into an array (X1,
X2, and X3). Second, the four derivatives that comprise K
are calculated. Third, new estimates of initial values are cal-
culated using Eq. (17).
These three steps are repeated within a "while" loop that
continues until the magnitudes of both corrections fall below
10-6. Once the convergence tolerance is met, the algorithm
exits the iterative loop and plots the results. Because the ODE
solver evaluates { F2, i' } last, upon exiting the loop the matrix
X holds the solution for these initial values. The accuracy of
the solution can be improved by allowing 8F and 8, to de-
crease as the solution converges. In particular, a strategy of
setting "dF2=H(1)" and "dTl=H(2)" is analogous to the Se-
cant Method.[3 pp145-150]
When an initial guess of "initF2=l" and "initTl=-0.5" is
used, a solution is found in 7 iterations that matches the bound-
ary conditions (solid curves in Figure 2) and agrees closely
with the tabulated values reported by Ostrach.15 Interestingly,
when the code is run with initial conditions "initF2=0.5" and

Fall 2004

Graduate Education

"initT 1=-0.5", it converges in 15 iterations to yield the dashed
curves in Figure 2. Although this solution also matches the
specified boundary conditions at rq=10, the derivatives are
not asymptotically approaching zero.


Once the unknown initial condition 1'(rp=0) is determined,
it can be used to calculate local and average Nusselt num-
bers, Nux and Nu, respectively. Here, using the fundamental
equality between the bulk heat flux into the fluid and con-
duction near the wall

h(T, -To)=-kl (19)

the local Nusselt number can be derived in terms of dimen-
sionless groups151

hx -x 3T
k (T-To)ay Y=0

aO (cGrx .25r (20)
o --) C (20)
ln0 dy K4 al 1 =)

Likewise, the average Nusselt number can be derived

=Nu -~I dx=

( Grx 0.251 4(GrL 0.25 (21)
0 xr d)_x= 3K21)
f 4 T Ir =o 3a4 a4 B =0

where GrL corresponds to Grx at x = L. Using values of
O'(q=0) determined for a range of Pr values and Eq. (21),
the circles in Figure 3 can be obtained. These predictions show
very good agreement with the semi-empirical formula that
Le FevreE61 developed from numerical solutions to
Pohlhausen's approximation (solid curve).

( Gr Pr2 0.25
Nu= 2.435 + 4.884 Pr0.5+ 4.953 Pr (22)

In addition, Nu can be used to predict average heat flux, q


q= Lh(T,-T,)dx =L Nu T)
L L;(w T,

4k v 2 025( 1.25 -0
3 4L d _0


Iterative Solver
% Shooting Method (Non-Linear Solver) for
Boundary Value Problem
Pr=0.72; % Prandtl number for air
dF2=.001; % initial step size
dTl=.001; % initial step size
initF2-1.0; % initial guess
initTl=-0.5; % initial guess
K=zeros(2); % derivatives for Newton's method
etaspan=[0 10]; % solve ODEs over finite range
while max(abs(H))>le-6
% evaluate initial value ODEs
% estimate derivatives
K(1,1)=(X2(l)-X1(1))/dF2; % dFl/dF2
K(2,1)=(X2(2)-XI(2))/dF2; % dTO/dF2
K(1,2)=(X3(1)-Xl(1))/dT1; % dFl/dT2
K(2,2)=(X3(2)-XI(2))/dTl; % dTO/dT2
% calculate new initial conditions
if a>10^6, display('matrix becoming singular')
break, end
fprintf('initF2=%7.6f initTl=%7.6f
cond(K)=%3.2e\n',initF2, ...

% Plot Results
figure(l), plot(Eta,X(:,2),'k-')
figure(2), plot(Eta,X(:,4),'k-')

Chemical Engineering Education

Graduate Education

10-3 10-2 10- 100

10' 102

Figure 3. Average Nusselt number as a function
of Prandtl number. Values predicted using this
model (circles) are compared to semi-empirical
formula, Eq. 22 (solid line).

10 ,

8 5 20 5

S4 0 10 5

> 2 Vertical Velocity (cm/s)

S56 4 3 24 (b)

2 /

4 /32 1 2
E / 16
32 24 Temperature ('C)
o 16
0.0 0.5 1.0 1.5
Horizontal Position (cm)

Figure 4. Contour plots of a) vertical velocity and
b) temperature of air (T = 15 C) near a heated wall
(T =650C) as a function of vertical and horizon-
tal position. These plots were generated by
dimensionalizing the solution (Figures 2a,b) for
fluid properties of air at T,=40oC.

o computed
- Eqn 22

Fall 2004

Here we see that Pohlhausen's approximation predicts that q is pro-
portional to (T To)'25.
Although Figure 2 demonstrates that a solution can be found that
matches the boundary conditions, and Figure 3 validates the solution,
it may be difficult for undergraduate students to conceptualize this
abstract solution. Therefore, converting the solution into dimensional
form for a familiar fluid may be valuable for student learning. For this
example, air of T = 150C near a wall of T = 650C is used because
experimental data have been collected by Schmidt and Beckmann.171
T(x,y) and vx(x,y) can be determined using the definitions of the di-
mensionless groups (Eq. 6), the equation for vertical velocity'12 p.50]

x [4075 v(Gr )0.25( dF = (4 gATx)5 (24)
Jt( drl )ay ) d

and fluid properties at the film temperature [Tf=(Tw+To)/2], v = 0.1692
cm2/s, and 3 = 0.00319 K '. Under these conditions, flow is laminar
for vertical positions x < 6 m (GrxPr < 109). The resultant 2D contour
plots (see Figure 4) provide interesting insights into free convection.
First, the transport effects are localized to a region very close to the
wall; at x = 10 cm, the thermal boundary layer, 8T (defined where 0 =
0.01) is only 13 mm thick, and at x = 1 m, 8T = 23 mm. Second, the
velocity profile evolves in two ways: the velocity increases with vertical
position, x, and the point of maximal velocity shifts away from the wall.
Finally, the results can be compared with experimental measure-
ments of Schmidt and Beckmann[7 pp54-355] (see Figure 5, next page).
Here, the predicted temperatures agree well with measurements at two
different vertical positions (x = 2, 7 cm), but the predicted velocities
are 10% below experimental measurements. A similar deviation be-
tween model and experimental velocities was reported by Ostrach.E51


The example presented here is intended to lead the instructor through
stages of problem solving (formulation, calculation, and interpreta-
tion), but contains too much content for a standard 60-minute lecture.
To date, I have used portions of this example in two classes: transport
phenomena at the graduate level and numerical methods at the junior/
senior level. In both classes, students were already familiar with the
concept of free convection and the objective was to gain experience
numerically solving ODEs.
For the graduate course, I derived Eq. (8) and assigned as home-
work the task of generating Figure 2. I showed the students how to use
Euler's method, suggested they employ a trial-and-error approach, and
allowed them to use any software of their choosing (e.g., Matlab, Ex-
For the undergraduate course, I developed a computational exer-
cise to solve Eq. (5) and generate Figures 2 and 4 that the class could
work through in a computational laboratory. The exercise focused on
deriving Eq. (8) from Eq. (5) and implementing an ODE solver and a

0.0 0.2 0.4 0.6 0.8 1.0

Horizontal Position (cm)

Figure 5. Comparison of model predictions with experi-
mental measurements for a) vertical velocity and b) tem-
perature. Solid curves are profiles from Figure 4 at verti-
cal positions of 2 and 7 cm. Circles are experimental data
points. Dashed line is T .

shooting method algorithm.
The exercise was made available electronically on the course
website and the code was presented in a modular form (like
Tables 2-5) that could be readily cut and pasted into Matlab.
This minimized class time spent composing and debugging code.
Active learning techniques were incorporated to address learn-
ing objectives (Table 1) and involved short answer questions
such as identifying the relevant boundary conditions, testing
different conditions (e.g., "Xinit" in Table 3, "Pr" in Table 4),
explaining the role of particular commands (e.g.,
"Cond,max,axis"), and filling in missing fragments of code.
The value of this example as a computational exercise is
that it provides the students with working code that can be
adapted to solve related problems. Consequently, a useful ex-
tension of this in-class exercise would be to design a take-home
assignment that requires modification of the model (e.g., vary
Pr to generate Figure 3) or application of the model predictions
(e.g., apply Eq. 23 to estimate heat flux).

1. Jones, J.B., "The Non-Use of Computers in Undergraduate Engineer-
ing and Science Courses," J. Eng. Ed., 87, 11 (1998)
2. Deen. W.M., Analysis of Transport Phenomena, Oxford University
Press, New York, NY (1998)
3. Chapra, S.C., and R.P. Canale, Numerical Methodsfor Engineers, 4th
ed., McGraw-Hill, Boston, MA (2002)
4. Higham, D.J., and N.J. Higham, Matlab Guide, Society for Industrial
and Applied Mathematics, Philadelphia, PA (2000)
5. Ostrach, S., "An Analysis of Laminar Free-Convection Flow and Heat
Transfer About a Flat Plate Parallel to the Direction of the Generating
Body Force," NACA Rep., 1111 (1953)
6. Le Fevre, E.J., "Laminar Free Convection from a Vertical Plate," Ninth
International Cong. Appl. Mechanics, 4, 168 (1956)
7. Welty, J.R., C.E. Wicks, and R.E. Wilson, Fundamentals of Momen-
tum, Heat and Mass Transfer 3rd ed., John Wiley & Sons, New York,
NY (1984) 0

Conversion of Results to 2-Dimensional Solutions with Contour Plots

% Plot results in dimensional space
To=15;, Tw=65; % ambient and wall temp. in degC
g=980; % gravity constant in cm/s^2
b=l/(273+0.5*(Tw+To)); % average coefficient of expansivity
nu=0.1692; % kinematic viscosity at film temp. in cm2/s
% calculate x and y space
yy=(Eta*(4*nu^2*xx(:,l)'/g/b/(Tw-To)) .0.25)';

% calculate temperature in x and y space

[c,h]=contour(yy,xx,TT,cvals,'k-');, clabel(c,h,cvals)
xlabel('Horizontal position (cm)','Fontsize',16)
ylabel('Vertical position (cm)','Fontsize',16)
title('Temperature (\circC)','Fontsize',16)
axis([0 1.5 0 10])
% calculate velocity in x and y space
[c,h]=contour(yy,xx, ,V, cvals,'k-');, clabel(c,h,cvals)
xlabel('Horizontal position (cm)','Fontsize',16)
ylabel('Vertical position (cm)','Fontsize',16)
title('Vertical Velocity (cm/s)','Fontsize',16)
axis([0 1.5 0 10])

Chemical Engineering Education

Relating Abstract Concepts
Continued from page 271.

ping, either continuously or as a batch operation using an
agitated vessel equipped for air sparging. Since all calcula-
tions were performed at low pressures, the assumptions made
in the development of the equilibrium equations are valid.
Lastly, in order to meet regulatory requirements, waste va-
pors from the two steps that contain gasoline and MTBE could
be incinerated with little energy use. That method is not dis-
cussed here, however.


This paper has presented the results of one student's work
for a class-required computer project. The assignment required
that the students should use the thermodynamic concepts they
learned during the semester to analyze and propose a fea-
sible solution to a current environmental or industrially sig-
nificant problem. The outcome of such an exercise allows
the students to apply sometimes-abstract thermodynamic con-
cepts to an important problem, while it also trains them to
focus on the bigger picture. An additional benefit is that the
students obtain an appreciation for what commercially avail-
able thermodynamic packages involve and what they can do
since they find the need to obtain property information that
cannot be found in the literature. For example, many students
use UNIFAC or Pro/II for property data. Lastly, the exercise
gives students a sense of accomplishment in that they have
applied the principles of thermodynamics to analyze and pro-
pose feasible, realistic solutions to problems they may en-
counter during their careers.


fii fugacity of component i in the liquid mixture


fugacity of component i in the vapor mixture
liquid phase mole fraction of species i
activity coefficient of species i as a function of
temperature, pressure, and liquid phase mole

fiL(T, P) pure component fugacity of i in the liquid phase
pvap (T) vapor pressure of species i as a function of
0sat fugacity coefficient of the saturated vapor of
species i
V molar volume of the liquid (condensed) phase
y, gas phase mole fraction of species i
P total pressure of the system
0i fugacity coefficient of species i

1. Prausnitz, J.M., R.N. Lichtenthaler, and E.G. de Azevedo, Molecular
Thermodynamics of Fluid-Phase Equilibria, 3rd ed., Prentice Hall,
Englewood Cliffs, NJ (1999)
2. Benson, S.W., Thermochemical Kinetics, 2nd ed., Wiley, Interscience,

Use CEE's reasonable rates to advertise.
Minimum rate, 1/8 page, $100;
Each additional column inch or portion thereof, $40.

Johns Hopkins University
Faculty Position in
Chemical and Biomolecular Engineering

The Johns Hopkins University Department of Chemical and Biomo-
lecular Engineering seeks outstanding applicants for tenure-track fac-
ulty positions. Johns Hopkins has a tradition of excellence in bioengi-
neering and applicants in this field are especially encouraged to apply.
Applicants in other areas of strength including materials, nanotechnol-
ogy, and other areas of chemical and biomolecular engineering will also
receive consideration. Appointments at all academic levels are possible.
Candidates must hold a doctorate degree or be in the process of com-
pleting their degree.
Applications will be considered immediately. In order to ensure full
consideration, applications should be received no later than February
1st, 2005. Interested applicants should send a curriculum vitae, state-
ment of research plan, and names of three referees to:
Professor Michael J. Betenbaugh, Chair
Department of Chemical and Biomolecular Engineering
221 Maryland Hall
Johns Hopkins University
Baltimore, MD 21218
(410) 516-7170
Applications by mail are preferred, but e-mail applications sent to
mclancy2@ihu. edu will also receive consideration
The Johns Hopkins University is an Equal Opportunity/Affirmative Ac-
tion Employer. Women and minorities are especially encouraged to ap-

New York, NY (1976)
3. Cengel, Y.A., "Intuitive and Unified Approach to Teaching Thermo-
dynamics," Proc. ASMEAdvan. Energy Syst. Div., 36, 251 (1996)
4. Lombardo, S., "Open-Ended Estimation Design Project for Thermo-
dynamics Students," Chem. Eng. Ed., 34(2), 154 (2000)
5. Stuckey, H.T., "The Benefits and Problems Associated with MTBE,"
Environ. Protect., p. 49, May (2001)
6. Cataldo, R., and E. Moyer, "Overcoming MTBE Myths," Environ.
Protect., p. 24, May (2001)
7. , chemical engineering section
8. Fredenslund, Aa., R.L. Jones, and J.M. Prausnitz, "Group-Contribu-
tion Estimation of Activity Coefficients in Nonideal Liquid Mixtures,"
AIChE J., 21(6) (1975)
9. Smith, J.M., H.C. Van Ness, and M.M. Abbott, Introduction to Chemi-
cal Engineering Thermodynamics, 6th ed., McGraw Hill, New York,
NY (2001)
10. Sandler, S.I., Chemical and Engineering Thermodynamics, John Wiley
& Sons, New York, NY (1977)
11. Muller, E.A., "Thermodynamics Problem with Two Conflicting Solu-
tions," Chem. Eng. Ed., 34(4) (2000)
12. Perander, J., "Removal of MTBE from Groundwater: A Technical and
Economical Evaluation of Existing and Emerging Methods," MS The-
sis, Helsinki University of Technology, Department of Chemical Tech-
nology, Espoo, Finland (1999)
13. University of Wisconsin-Madison, College of Engineering, "Four
MTBE Treatment Technologies," Underground Tank Technology Up-
date (UTTU), 14(2) (2000) 0

Fall 2004


Random Thoughts...


North Carolina State University Raleigh, NC 27695

Most of this column will be guest-authored by the
individual responsible for these quotations.

No book or map is a substitute for personal experi-
ence; they cannot take the place of the actual journey.
The mathematical formula for a falling body does not
take the place of throwing stones or shaking apples from
a tree.

Science teaching has suffered because science has
been so frequently presented just as so much ready-
made knowledge, so much subject-matter of fact and
law, rather than as the effective method of inquiry into
any subject-matter

Where the school work consists in simply learning
lessons, mutual assistance, instead of being the most
natural form of cooperation and association, becomes a
clandestine effort to relieve one's neighbor of his proper
duties. Where active work is going on, all this is
changed. Helping others, instead of being a form of
charity which impoverishes the recipient, is simply an
aid in setting free the powers and furthering the impulse
of the one helped. A spirit of free communication, of
interchange of ideas, suggestions, results, both suc-
cesses and failures of previous experiences, becomes the
dominating note of the recitation.

Almost everyone has had occasion to look back upon
his school days and wonder what has become of the

knowledge he was supposed to have amassed during his
years of schooling, and why it is that the technical skills
he acquired have to be learned over again in changed
form in order to stand him in good stead. One trouble is
that the subject matter in question was learned in
isolation; it was put, as it were, in a water-tight com-
partment. When the question is asked, then, what has
become of it, where has it gone to, the right answer is
that it is still there in the special compartment in which
it was originally stored away. If exactly the same
conditions recurred as those under which it was
acquired, it would also recur and be available. It is
contrary to the laws of experience that learning of this
kind, no matter how thoroughly engrained at the time,
should give genuine preparation [for careers and lives].

In other words, to be effective, instruction should involve
experiential, inquiry-based, collaborative learning and mul-
tidisciplinary subject integration. To anyone who has not been
hibernating for the past decade, these are familiar calls that
could have come from any recent issue of the Journal of En-
gineering Education or Annual ASEE Conference Proceed-
ings. They didn't, though. They were stated by John Dewey
in 1915, 1910, 1899, and 1938, respectively.
In fact, you can hardly think of a research-based educa-

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
National Effective Teaching Institute.

Copyright ChE Division of ASEE 2004

Chemical Engineering Education

tional innovation or position in the past decade that Dewey
didn't anticipate and provide a solid theoretical rationale for
a century ago, be it problem-based or project-based learning,
constructivism (the need to correct misconceptions in exist-
ing knowledge before new knowledge can be acquired), the
importance of presenting technical information visually, the
need to take student differences into account when designing
instruction (addressing different learning styles), or even the
negative effects on true learning of standardized tests of the
"No Child Left Behind" variety. Education in Dewey's time
was clearly facing many of the same challenges it faces now;
those of us in the teaching business would do well to con-
sider what he had to say about addressing them. Two collec-
tions of his writings still in print,' from which all the quota-
tions in this column were taken, provide a convenient way to
do it.
I was particularly struck by a series of lessons Dewey de-
scribed in his 1899 monograph The School and Society, which
might be ideal to present to, say, freshman engineering stu-
dents today. Picture this:


The students begin by imagining present conditions
taken away until they are in contact with nature at first
hand. That takes them back to the hunting people, to a
people living in caves or trees and getting a precarious
subsistence by hunting and fishing. Then they go on in
imagination through the hunting to the semi-agricultural
stage, and through the nomadic to the settled agricul-
tural stage. The point I wish to make is that there is
abundant opportunity thus given for actual study, for
inquiry which results in gaining information. For
example, the students had some idea of primitive
weapons, of the stone arrowhead, etc. That provided
occasion for the testing of materials as regards their
friability, their shape, texture, etc., resulting in a lesson
in mineralogy, as they examined the different stones to
find which was best suited to the purpose. The discus-
sion of the iron age supplied a demand for the construc-
tion of a smelting oven made out of clay, and of consid-
erable size. As the students did not get their drafts right
at first, the mouth of the furnace not being in proper
relation to the vent, as to size and position, instruction
in the principles of combustion, the nature of drafts and
fuel, was required. Yet the instruction was not given
ready-made; it was first needed, and then arrived at
experimentally. Then the students took some material,

such as copper, and went through a series of experi-
ments, fusing it, working it into objects; and the same
experiments were made with lead and other metals. This
work has been also a continuous course in geography,
since the students have had to imagine and work out the
various physical conditions necessary to the different
forms of social life implied. What would be the physical
conditions appropriate to pastoral life? to the beginning
of agriculture? to fishing? What would be the natural
method of exchange between these peoples? Having
worked out such points in conversation, they have
afterward presented them in maps and sand molding.
Thus they have gained ideas of the various forms of the
configuration of the earth, and at the same time they
have seen them in their relation to human activity, so
that they are not simply external facts, but are fused and
welded with social conceptions regarding the life and
progress of humanity. The result, to my mind, justifies
completely the conviction that students, in a year of such
work (offive hours a week altogether), get indefinitely
more acquaintance with facts of science, geography, and
anthropology than they get where information is the
professed end and object, where they are simply set to
learning facts infixed lessons. As to discipline, they get
more training of attention, more power of interpretation,
of drawing inferences, of acute observation and continu-
ous reflection, than if they were put to working out
arbitrary problems simply for the sake of discipline.

The active, collaborative, problem-based approach embod-
ied in these lessons would probably lead to greater engineer-
ing skill development and more true learning about the his-
tory and nature of engineering than the students would nor-
mally get in two or three years of traditional classes. The
interesting thing is that the students who received this in-
struction were all seven years old. (I changed Dewey's "chil-
dren" to "students" in my transcription.) If second graders
could manage to negotiate all that engineering project work
(for that is what it was), surely our freshmen engineering stu-
dents ought to be able to handle it.
There's much more. Check it out.

1. (a) Dewey on Education: Selections, New York, Teachers College Press,
1959; (b) John Dewey on Education: Selected Writings, Chicago,
University of Chicago Press, 1974 O

Fall 2004

All of the Random Thoughts columns are now available on the World Wide Web at and at

r web-based tools



Michigan Technological University Houghton, MI 49931

here have been extensive technological advances in
computer technology applied to engineering educa-
tion, but one area that has yet to develop fully in this
capacity is use of the World Wide Web. In this manuscript,
we describe two particular JAVA applet programs that were
developed to simulate the classical problem of transient con-
duction/diffusion within a solid. A student who uses the tools
can capture the basic principles of heat or mass transport by
observing the dynamic phenomena and exploring the effect
of changing problem parameters on the solution. The applets
and sample problems are available at>.


One-Dimensional Unsteady-State
Heat Conduction Movie

Heat conduction is a key concept in transport phenomena
because of the large number of heating and cooling problems
occurring in industrial processes. It is also analogous to many
cases of mass diffusion. The classic example presented in
textbooks is unsteady-state heat conduction in various ge-
ometries, but to estimate a local temperature, a student must
either refer to a cumbersome analytical solution containing
an infinite series or interpolate data from charts. Usually, the
student fails to visualize the actual heat-transfer process.
Consider unidirectional unsteady-state heat transfer in an
object with constant material properties. The object is ini-
tially at a uniform temperature T0. At time t = 0, the object is
exposed to an environment at temperature T,. If the object is
a slab of thickness 2L, the thermal energy conservation model
balances the accumulation of thermal energy with heat con-
duction by
Copyright ChE Divisior

aT a2T
pC = k 2
P t a ax
subject to the following boundary and initial conditions:

at t = 0

at x 0

at x = L

h(T TI) = -k

Similar models can be developed for a long cylinder and for
a sphere.
As can be seen in Figure 1, this applet has an easy-to-follow
graphical user interface (GUI) design. To use the applet, the
student first chooses the geometry where heat conduction
occurs (slab, cylinder, or sphere) and enters a set of initial

Chemical Engineering Education

Haishan Zheng is a PhD candidate in chemi-
cal engineering at Michigan Technological Uni-
versity. He received his BS in 1993 and his MS
in 1996 from Beijing University of Technology,
China. Before studying at Michigan Technologi-
cal University he worked as a chemical engi-
neerat Guangdong Petrol-Chemical Research
Institute in China.

Jason Keith is Assistant Professor of Chemi-
cal Engineering at Michigan Technological Uni-
versity. He received his BS in chemical engi-
neering in 1995 from the University of Akron
and his PhD from the University of Notre Dame
in 2000. He teaches transport phenomena, re-
actor design, and a project-based elective de-
sign course in alternative fuels and fuel cells.

conditions and parameters. The software automati-
cally computes the following quantities:
Biot number (Bi = hL/k), the only dimensionless
parameter in this system. This allows the user to
determine if external convection or internal
conduction is the dominating heat-transfer
The characteristic time for thermal diffusion, t =
L2/(k/pCP). This allows the user to estimate the
time for the solid to reach thermal equilibrium
with its environment.

The simulation can be started, paused, or stopped
by pressing the appropriate button. The most unique feature of
applet is that a colored bar represents the cross section of the slab ar
colored circle represents the cross section of a cylinder or a sphere.
color is graduated to represent temperatures between To and T,. A
color (R = 255, G = 0, and B = 0) always represents the higher te
perature between To and T,, and blue (R = 0, G = 0, and B = 2
always represents the lower temperature. Therefore, there are two
stances for the color representing any temperature, T, between TO,
T,. (Although Figure 1 is shown in grayscale here, the online apl
shows the colors.)

TO>TI R=225x T-_T G=0 B=255x 1- T-
TIf-TO T,-To

G=0 B=255x T -T

To further strengthen the visual effect, the temperature profile is shc
below the color bar.
Another novel feature of this applet occurs when the simulation

Figure 1. Screenshot of the unsteady-state heat conduction apj

In this manuscript, we describe two particular
JAVA applet programs that were developed to simulate
the classical problem of transient conduction/diffusion
within a solid... [The students] seemed to enjoy using
the applets and to better understand the concept of
transient heat conduction. They also felt motivated
to simulate some different problems by
changing the parameters.

id a

paused-the cursor can then be placed within the
color bar or circle and the program outputs the di-
mensional distance (x), the temperature (T), and
the time elapsed (t) as well as the corresponding
dimensionless quantities (x* = x/L, T* = (T -T)/
(T,-T,), and t* = t/t).

in- The input parameter interface allows the student
and to input different heat transfer coefficients, ther-
plet mal conductivities, and heat capacities. The user
can also enter dimensionless parameters in one of
two ways: 1) enter values as length L = 1, thermal
capacity pC = 1, thermal conductivity k = 1, and
) heat transfer coefficient h = Bi. The final time cho-
sen would then be an increment of the characteris-
tic thermal diffusion time t.; 2) the user can di-
rectly enter the Biot number and characteristic ther-
mal diffusion time t In either case, the dimension-
(6) less initial temperature, To = 1, and fluid tempera-
ture, T, = 0, should be entered.
Students can learn how these parameters affect
iwn the heat conduction by comparing simulation re-
sults. For example, they can verify when the inter-
n is nal resistance is negligible (Bi < 0.1), which is the
S classical low Biot number approximation that stu-
dents encounter in most undergraduate heat-trans-
fer textbooks, sometimes without much justifica-
tion. The student can also show that the tempera-
ture profile of a slab of width w at time t is identi-
cal to that for a slab of width 2w at time 4t when
the Biot number is constant, which is the classical
result showing the power of dimensionless groups.

Heisler Charts
for Unsteady-State Heat Transferl
Infinite series solutions are available for the heat-
conduction problem given by Eqs. (1) through (4).
These solutions are presented graphically in Heisler
charts in Textbooks,0" but an online implementa-
tion is easier to use because of JAVA's power to
plet. determine the exact coordinates on the graph.

Fall 2004

One dimensional unsteady heat conduction
Parameter input area

Count rol

* i ai'aUij',-4B W ad.

Display area
\ Is-


Figure 2 shows the GUI of this applet. The user can choose
a slab, a cylinder, or a sphere and can also adjust the key
parameters m (m is the inverse of the Biot number, m = k/
hL) and n (a dimensionless distance, n = x/L). These param-
eters are also used in the charts in Geankoplis.'" A user can
directly enter the desired value of m or n in the text field or
increase/decrease the value by pressing the buttons. Unlike
the charts in the textbook that show many lines simulta-
neously, the JAVA chart shows only one line at a time. More
importantly, the user does not need to interpolate between
lines on the chart as is often the case in the textbook.

This applet also allows the student to move the graph by
pressing the "Up," "Down," "Left," or "Right" buttons. As
can be seen in Figure 3, double-clicking the chart calls up an
axes-properties dialog box. This allows the student to zoom
in and out on the graph. Another distinct advantage of this
applet is that the student can obtain the values of the dimen-
sionless time, X = t/tc, and the dimensionless temperature, Y
= (T, -T)/(T, To), from the X and Y text field simply by
moving the cursor to the desired position on the line. This
applet can also be used as a calculator. A relative value of X
can be obtained by entering a value in the Y text field and
pressing "Enter," and vice versa.






000 050 100 150 200 250?

300 3501


The web-based tools create possibilities for students to
learn effectively. FelderE[2 gives a good review of four learn-
ing-style models (Myers-Briggs Type Indicator, Kolb's
Learning Style Model, Hermann Brain Dominance Instru-
ment, and the Felder-Silverman Learning Style Model). The
Kolb Learning Style Model separates students into those tak-
ing in information by either concrete experience or abstract
conceptualization and internalizing information by active ex-
perimentation or reflective observation. 6] These two pref-
erences give way to four learning types:

Concrete and reflective (answers the question, "Why?")
Abstract and reflective (answers the question, "What?")
Abstract and active (answers the question, "How?")
Concrete and active (answers the question, "What if?")
Felder[2' states that traditional engineering instruction (lec-
turing on the basic information and methods associated with
a particular topic) almost exclusively teaches to the abstract
and reflective. Having access to web-based tools allows stu-
dents to work on a problem and learn by trial-and-error with-
out concern for failure (answering the question, "How does


Control panel :J

Move graph:

W> I


Figure 2. Screenshot of Heisler Chart apple.

Chemical Engineering Education

0 # -0(2 8OQ&DE

.] I]0 3 50


------ -- ~---- ----- ~----'


this work? ") and to discover things for themselves (answer-
ing the question, "What if I change these parameters?").
Thus, this tool expands instruction beyond that contained in
traditional material.
The web-based tools are currently being used by J. Keith
as auxiliary teaching tools in a transport/unit operations course
that is required of all junior students and which focuses on
the fundamentals of heat and mass transfer. An in-class dem-
onstration and homework assignment using these web-based
tools are applied during the course. The JAVA applet website
also contains example homework problems.

This paper describes two web-based instructional tools for
heat and mass transfer that are based on JAVA applets and
which can be accessed for free at -jmkeith/webtools>. Educators can use these tools in the
classroom to instruct students and enhance their understand-
ing. The tools were first used in the 2003 spring semester at
Michigan Technological University. Student feedback has
been extremely positive. They seemed to enjoy using the
applets and to better understand the concept of transient heat
conduction. They also felt motivated to simulate some dif-

S 1n.


0 I



.^ b

/ \
,' \ \
,/ \

000 OB06 100

-' -

1,50 \00 250 3

xi C 2

ferent problems by changing the parameters. Although the
treatment presented here is limited to symmetrical, steady,
boundary data, students who use the applets are now pre-
pared to solve more complex problems (perhaps by writing a
numerical finite difference method).


The authors want to acknowledge the financial support pro-
vided by the Michigan Space Grant Consortium.


1. Geankoplis, C.J., Transport Processes and Unit Operations, 3rd ed.,
Prentice Hall, New Jersey (1993)
2. Felder, R.M., "Matters of Style," ASEE Prism, 6(4), 18 (1996)
3. Kolb, D.A., Experiential Learning: Experience as the Source ofLearn-
ing and Development, Prentice Hall, New Jersey (1984)
4. McCarthy, B., "The 4MAT System: Teaching to Learning Styles with
Right/Left Mode Techniques," EXCEL, Inc., Barrington, IL (1987)
5. Stice, J.E., "Using Kolb's Learning Cycle to Improve Student Learn-
ing," J. Eng. Ed., 77, 291 (1987)
6. Harb, J.N., S.O. Durrant, and R.E. Terry, "Use of the Kolb Learning
Cycle in the 4MAT System in Engineering Education," J. Eng. Ed.,
82(2), 70(1993) 0


pro Axe

x o= F- p ro pert

:. a ft If ^

Figure 3. Interpolating data from the JAVA Heisler chart.

Fall 2004

4 -o --# a [ail a c3 S OfeH&______^ a

Prblem CoInllgunlon:

anOm rs-180 -3

- classroom



Tennessee Technological University Cookeville, TN 38505

Considerable research has been directed toward iden-
tifying educational methodologies that are effective
and efficient.[1-3 Growing evidence suggests that the
most successful approaches place the instructor in the role of
facilitator, rather than in the position of "chief eminence" in
front of the classroom.14-7' Moreover, optimal facilitation of
learning entails structuring the style, format, and day-to-day
activities of the course using a variety of proven practices,
such as those listed in Table 1 .[12,8,9] We collectively call these
strategies "modem" approaches to learning.
We have been developing a powerful methodology that
strongly and cohesively exploits these modern learning ap-
proaches. In particular, it places responsibility squarely on
students for their own learning. While most previous efforts
have shown one or more of the three "basic" levels of mod-
ernization (problem solving, communication, and teamwork),
we have added two more: experimental prototypes and in-
dustrial contacts. Together, they create active-collaborative
learning environments we call "High-Performance Learning
Environments" or "Hi-PeLE." This non-lecture based envi-
ronment encourages students to become efficient and inde-
pendent thinkers; it also promotes confidence in their knowl-
edge and ability to solve complex problems at a level that is
not observed in students where the environment is not used.
Further, we believe that using this methodology offers two
valuable by-products. First, it helps ease students' transitions
from the classroom setting in the early stages of the chemical
engineering curriculum to the laboratory environment of the
later stage. Second, it helps ease the transition for faculty to
adopt and retain modem learning approaches in the class-
room setting. This "dual" role of Hi-PeLE helps tremendously
to modernize many aspects of the chemical engineering cur-
riculum and, in addition, we believe it promotes an efficient
approach to developing a community of learners within a

'Address: FAMU-FSU College ofEngineering, 2525 Pottsdamer Street, Tal-
lahassee, FL 32310

The concept of unit operations was introduced at the on-
set of the evolution of the chemical engineering discipline
early in the twentieth century. Accompanying this idea was
the introduction of the unit operations laboratory, a traditional
core element of the chemical engineering curriculum. Indeed,
the course is so traditional that some faculty look upon it as a
quaint relic of the past that is out of place in our modem times.
To be sure, the physical facilities at some universities may
be old, with cluttered workspace, poor lighting, dirty floors,
smelly chemicals, and mercury manometers. Nonetheless, we
assert that a re-examination of the unit operations lab in the
context of educational methodologies has value."t' The rea-
son is that this learning environment inherently uses a stu-
dent-centered, hands-on approach; the activities are active,
collaborative, and sequential.
Further, communication is multidimensional in the sense
that students must communicate with peers as well as with
teaching assistants, lab technicians, and possibly other in-
structors. They also use a variety of formats to communicate,
such as written reports, oral presentations, calculations, pro-
cedures, data tables, diagrams, and graphs. They may also
have to deal with vendors to check or find equipment specifi-
cations for lab devices.

Pedro E. Arce is Professor of Chemical Engineering and Chair of the De-
partmentof Chemical Engineering. His ChE Diploma is from the Universidad
Nacional del Litoral (UNL), Santa Fe, Argentina, and his Master of Science
and PhD degrees are from Purdue University, both in ChE. He has devel-
oped a number of learning tools, all centered in active-collaborative ap-
proaches. His research focuses on nano-structured (soft) materials for
bioseparation and drug delivery as well as cold plasma high oxidation
methods, and electrokinetics.
Loren B. Schreiber is Professor of Chemical Engineering and Director of
the UOL in the Department of Chemical Engineering at the FAMU-FSU
College of Engineering. His degrees are from University of Illinois at Ur-
bana-Champaign, IL, and Caltech (PhD). Before joining FAMU-FSU, Dr.
Schreiber was involved extensively in research and development in pri-
vate industry. His teaching interest involves active and collaborative learn-
ing techniques and simulation approaches for distillation processes.
Copyright ChE Division of ASEE 2004
Chemical Engineering Education

In the unit operations laboratory, a student's success heavily
depends on the success of the team.1"I The fact that students
must learn how to program experimental activities to maxi-
mize time utilization, to understand equipment failure, and
to deal with experimental errors brings a dynamical, open-
ended component to the learning environment that is impos-
sible to reproduce in "dry" classroom environments.
In short, we claim that the traditional learning environ-
ment of the unit operations laboratory is a subset of modem
approaches to engineering education, at least as they are de-
fined in Table 1. In other words

"Traditional" Lab Work e "Moder" Learning Approaches (1)
and, therefore, the unit operations laboratory offers the most
appealing features that a modem engineering educational en-
vironment must display.
Since its inception, the technologies of the unit operations
laboratory have changed. But at its core, its preeminent sta-
tus as an educational paradigm for chemical engineering is
one of the most clearly defined invariants in chemical engi-
neering instruction.

In contrast, another well-defined invari- T
ant over the years in chemical engineering Character
education is the way that classroom instruc- Learni
tion has been conducted. It has been based
upon lectures, lectures, and more lec- > Active learn
tures. It is typified by a non-active, non-col- Sequential ta,
laborative approach that leads to low effi- Open-ended
ciency in student learning. We take the posi- Bloom's Tax
tion that lecture-based approaches are of Teamwork an
little help in developing the student as an
independent thinker and creative engineer utidie
Student invol
and, in general, that they are not consid-
ered among the modernization techniques > Instructor as
for learning approaches. Others have a
different view.
The traditional classroom is dominated TA
by one-way communication: information Moderni
flows from the high "wisdom" of the lec-
turer to the students-all without the possi- Short team e
ability of immediate, meaningful feedback. > Brief team r
The material is presented in a way (clean, > Team present
well-organized, no mistakes, closed ended) Journal artic
that is far removed from reality. Moreover, Facilitating
babying students by spoon feeding lectures On-line court
to them stymies their development in assum- Computer si
ing responsibility for their own learning.

In short, we believe that
"Traditional" Classroom 4
"Moder" Learning Approaches

Fall 2004

Table 2 summarizes some of the techniques that can be
found in the literature to modernize chemical engineering
classroom instruction. 1-391 While they have proven valuable,
we nonetheless assert that there is opportunity to do even
better by confronting nature as part of the learning process.
There is no substitute for an actual experiment. Conduct-
ing a hands-on experiment provides a different perspective
than lectures, textbook problems, or even computer virtual
experiments. Things go wrong with experiments and, when
they do, students have a chance to figure out why they went
wrong. But even more important, experiments bring an ele-
ment of excitement to the classroom."2'
A learning environment can be built on the features of
Table 2 and, in addition, take advantage of the learning quali-
ties found in the unit operations laboratory. The approaches
are complemented with other tools to create an environment
that is rich with a high level of active and collaborative ac-
tivities.'31 In fact, the mode of instruction is a multitask envi-
ronment centered on student learning;'"' we have named this
type of instruction mode "High-Performance Learning Envi-
ronment" or "Hi-PeLE." (The acronym
Swas selected because the soccer player

istics of "Modern"
ing Approaches

nong students
nal communication
vement with assessment

zing Classroom
class discussion
se materials
mulation -
books -
d enhance approaches

Pele epitomizes high performance in the
world's most popular sport.)
The Hi-PeLE construction is based on
five tools: problem solving, experimental
prototypes, industrial contacts, teamwork,
and communication. The idea behind the
methodology is that every activity is stu-
dent driven and that the instructor func-
tions as a facilitator or coach.'71 Further-
more, incorporating experimental proto-
types and industrial contacts into a "class-
room" course opens avenues for enhanc-
ing and reinforcing problem solving,"'3'14
teamwork, and communication.
One obvious benefit of Hi-PeLE is a high
level of student energy that occurs outside
of the traditional classroom routine. There
is hardly a moment when the students are
passively following a detailed recipe of in-
struction. Rather, they acquire indepen-
dence as they assume responsibility for
their own learning, manage their own team
project, and show off their skills.
A second advantage of Hi-PeLE is that
it directly addresses many of the ABET
criteria pertaining to Program Out-
comes."01 This benefit can be amplified by
establishing a sequence of courses in the

u- llUal .
0 Modem text
Assist an

curriculum with Hi-PeLE, thereby providing students with
successive opportunities to demonstrate and strengthen their
knowledge and abilities in the desired areas specified by the
Program Outcomes.
Hi-PeLE also helps instructors devise multifaceted, syn-
ergistic learning activities that span the entire range of
Bloom's taxonomy of thinking skills."1"' In particular, by in-
corporating an experimental project into the classroom envi-
ronment, as outlined in Table 3, students perform in a task
sequence that fosters creativity.'7'
Finally, we have found that the Hi-PeLE methodology has
been consistently appreciated by students in the courses where
we have been developing this approach.111

Environments based on Hi-PeLE have been designed, de-
veloped, and tested in several courses at the College of Engi-
neering jointly operated by Florida A&M and Florida State
University, at Rose-Hulman Institute of Technology, and at
Tennessee Technological University."6,171 A full-fledged Hi-
PeLE has been implemented for sophomore and junior courses
in momentum transfer and in heat transfer. A special com-
pressed version has been introduced in a two-week slice of
our freshman survey course called "First-Year Engineering
Laboratory," while a simpler environment has been adapted
for our senior and graduate courses in reactor design. At
Tennessee Tech, a senior-level transport phenomena elec-
tive and the required reactor design courses have been
taught recently in a Hi-PeLE.
Table 4 summarizes the course outline for an introductory
course in heat transfer. Four components of the Hi-PeLE are
included; the fifth component, "communication," permeates
all of the activities. Clearly, this outline departs considerably
from a traditional course outline where the entire course fo-
cuses almost exclusively on problem solving. In Hi-PeLE,
while an important portion of the course is centered on prob-
lem solving, four other components play a significant role in
the student learning process. A brief discussion of these as-
pects follows in the paragraphs below.

Problem Solving"'14] At the regular class meeting, stu-
dents learn to apply the fundamentals of the course topics to
solving problems. The instructor has the chance here to imple-
ment a variety of instruction modes-rather than just lectures,
we recommend that instructors move towards active and col-
laborative activities13 using the approaches in Table 2.
Using Hi-PeLE, student teams can bolster problem-solv-
ing skills by formulating problems related to their respective
experimental prototypes. Here, the instructor can encourage
student teams to develop open-ended problems in addition to
the closed-ended problems they typically conceive. These
student teams can then present their problems to the entire
class, using a variety of modes. This type of activity can bridge

all five learning tools. And, as students practice teaching one
another, they assume responsibility for their own learning.
Experimental Prototype This component anchors on-pa-
per application of the fundamental principles of the course
with hands-on equipment activity. The activity could be simple
inspection of an existing apparatus or construction of a scaled-
down, non-working model. We have observed, however, that
students gravitate towards building an experimental proto-
type to conduct measurements that enable calculation of val-
ues of, for example, the overall heat transfer coefficient. The
experimental prototype stimulates motivation (and, in turn,
high performance) because students can create their own ap-
proach to bring a fundamental concept into action.
We identify topics for the experimental prototypes prior
to the start of the course, gathering suggestions from other
faculty members such as the unit operations lab instructor
and from practicing engineers with substantial industrial ex-
perience. Example heat transfer topics for experimental pro-
totypes are listed in Table 4.
While small projects can be assigned to individual stu-
dents, we prefer to assign larger projects to teams of students.
We try to have sufficient topics so that only one team is as-
signed to a given topic. This approach creates useful negotia-
tion and discussion among the students-they must find a
procedure to assign the topics in a fair manner.
During the first week of the course, students are told that
the project will be for the entire semester, and that several
intermediate milestones must be reached to ensure comple-
tion of projects by the end of the semester.E[5 To further ener-

Incorporating Experiments into the Classroom

0 Introductory Courses
Fluid mechanics
Heat transfer
First-year engineering lab
> Paradigm
Unit operations lab
> Projects
Semester project involving experimental prototype
Student teams (3 members)
Each team assigned a different topic for project
25% of course grade for assigned project
25% of course grade for all class projects
Task Sequence
Theoretical foundation
Historical background
Experiment conception
Interim presentation
Design equipment and experiment
Build a working prototype
Final written report
Final presentation forum (panel of judges)
Final exam on all class projects

Chemical Engineering Education

gize student interest in the projects and their connection with
fundamental principles, we alert them that the final exam will
be based entirely on all the projects.
Industrial Contacts This component provides students
with a chance to engage directly in a "real" aspect of the
profession while still in an academic environment. We have
found that activities for this component increase student mo-
tivation and readiness for learning fundamental concepts. We
have explored several types of activities for this component.
One option is to ask vendor representatives to visit the
class for a workshop on selection criteria, price comparisons,
and troubleshooting. The vendor representatives can supply
extensive literature that illustrates the basic physics of the
equipment, as well as pertinent articles from engineering
magazines. So far, we have tried this approach for flow meters,
control valves, and process computers.
Another approach is to invite a seasoned engineer to re-
view a real case. We have had speakers cover condenser speci-
fication and bid review, distillation troubleshooting, and heat

Summary of Course Outline for Introductory Heat Transfer

A. Topics for Problem Solving
1. Conduction: fundamentals in ID. 2D with various geometries
2. Convection: temperature profile in ID flow
3. Thermal convection: open and closed systems
4. Radiation: Fundamentals and applications (solar heaters,
furnaces, combustion)
B. Team Projects for Experimental Prototypes
1. Condensers
2. Evaporators
3. CSTR heat transfer aspects
4. Tubular reactor heat-transfer aspects
5. Plate-and-frame heat exchangers
6. Heat-transfer coefficient measurement methods
7. Boilers
C. Suggested Activities for Industrial Contacts
1. Visit and inspect equipment in the unit operations laboratory
2. Visit local companies to inspect heat-transfer equipment
3. Contact vendors for equipment specifications and design
4. Discuss project issues with the instructor of the unit operations
D. Suggested Preliminary Activities for Teamwork
1. Pick team members
2. Discuss and assign among all teams the suggested projects
3. Formulate and write a Code of Cooperation for team
4. Develop a tentative meeting schedule, stating objectives for
each meeting
E. Assessment
1. 30% Mid-term exams
2. 10%- Homework
3. 10% Course folder
4. 15% Poster presentation on assigned project topic
5. 10% Written report on assigned project topic
6. 25% Final exam covering projects of all teams

Fall 2004

transfer in batch reactors. Students have been impressed when
they realized that the concepts and equations were the same
as the ones used in problem-solving exercises in the course.
We have found that the most effective mode is one in which
the students take the initiative to consult with sales personnel
and technical experts at equipment vendors, to tour chemical
plants, or to visit engineering construction firms to check
equipment functionality, specifications, and availability in
connection with their project for an experimental prototype.
We also encourage students to scour the library and the internet
for information (Perry's Handbook is not enough).
Teamwork The course may be the first significant oppor-
tunity for students to work in teams in the chemical engi-
neering curriculum, so we promote classroom discussion on
the roles and responsibilities of team members and on the
risks involved in the selection of team members. Further, in
order to help class members to become acquainted with one
another, each student prepares a written resume on his or her
educational objectives, work skills, and personal style and
preferences. Having laid this groundwork, students are well
positioned to select their team members early in the course.
In our pilot studies of Hi-PeLE, we found teams of three
members to be most effective. This size was sufficient to pro-
vide activities that enabled students to gain skills in project
management and teamwork in an engineering context. The
number of students participating in a team may affect signifi-
cantly the dynamics and efficiency of a team.E"l
Communication Hi-PeLE provides for a multidimension
environment (i.e., communications at various levels) for the
students to practice. For example, they work in teams where
information is exchanged. They also need to discuss guide-
lines for projects with the instructor and to inspect lab equip-
ment in the unit operations laboratory or industrial sites. Fur-
thermore, there is written communication in preparing progress
reports and the final report for the experimental prototype.
While different types of implementations are possible in a
Hi-PeLE, [6-181 we have frequently used a strategy based on
working the fundamental aspects of a subject in "classroom
activities" mode and placing the "applications" on the team-
based efforts. Most of the activities related to team-based ef-
forts are handled in a weekly optional recitation session for
which we have a regular TA for grading purposes. Therefore,
the manpower requirements are very similar to those in tra-
ditional approaches. In addition, all the experimental proto-
types were identified and developed by students with "home-
made" materials and devices.'7-'s Most of the projects can
be completed for $50 to $100. It is useful to note that the
training and the process of applying the ideas were more im-
portant than a "finished product"-this emphasis would
change if the applications were part of a design course. Stu-
dents were encouraged to consult other professors in the de-
partment, but these were "coached" beforehand to advise,
but not solve, the problems for the students. This is an impor-

tant aspect of the dual role of Hi-PeLE mentioned in the in-
troduction section. Finally, selection of the students for the
teams was conducted by using a functional-based approacht191
where the students are at the center of the selection process.


Among the objectives we set for the development of a Hi-
PeLE, we wanted to increase students' motivation to learn
fundamentals, to enhance their habit to learn (independently)
the necessary material to attack the solution of a given prob-
lem, and to augment their ability to apply fundamental prin-
ciples to practical applications, as well as to increase their
confidence and readiness to solve complex tasks. In addi-

tion, we hoped to observe these
characteristics as the students
worked in the UOL, i.e., before
they exited to the work force.
The learning environment was
assessed by using a multi-tool
approach'16-18) that included: a)
mid-terms and a final written
assessment, b) presentations (ei-
ther oral or poster) with external
judges, c) a debriefing session
with judges, d) the assessment
(by the instructor) of written re-
ports, and e) the randomly se-
lected interviews with students
during and at the end of the
course. In addition, at Rose-
Hulman, a "peer review" assess-
ment was used to monitor indi-
vidual contributions and overall
team progress and at FAMU-
FSU an intra-course observation

of students in the UOL was conducted.
As a general guideline for the breakdown of the course
grade, the experimental prototype must have substantial
weight in the overall grades for students in the course.110,161
Indeed, we based 25% of the course grade for the teams on
their respective experimental prototypes. This 25% was split
into two components: 10% for a written report on the project
and 15% for a poster competition.
Students found the poster competition to be exciting. It
served as a presentation and assessment forum, taking place
in the central atrium of the college where all students, fac-
ulty, and staff could look and interact. The judges were exter-
nal to the course, often from different departments or even
from different universities. They were given a general set of
guidelines, but the actual decision of using or modifying them
was left to the panel.1161 As an alternative to the poster presen-
tation, we have used a seminar presentation with faculty mem-
bers (other than the instructor) as the assessors.

The final two-hour written assessment for the course,
(weighted at 25% of the overall grade) covered all the projects
in the course. This approach broadens students' practical
knowledge of the course subject and reinforces their ability
to learn and apply fundamentals. To help students become
familiar with their peers' projects, teams periodically gave
oral presentations to underscore pertinent physical principles,
to communicate the progress, and to respond to questions.
The instructor, as coach, could stimulate further discussion
on aspects that may not be clear to everyone.6'71 Often, these
project sessions were scheduled during special recitation
meetings. They also served as rehearsals for the final poster
competition where the students had to convince the judges

Unit Operations Lab


_ ._ __| _^^^^^^





of their knowledge and under-
standing of basic concepts and
their application to the experi-
mental prototype.
While 50% of the over-
all grade was directly related to the
projects, as explained above, the
remaining 50% covered problem
solving related to fundamentals,
such as the four topics presented
in Section A of Table 4. These top-
ics prepared students to undertake
projects listed in Section B of
Table 4. Student learning was as-
sessed through homework, mid-
term exams (typically three), and
course folders. Pop quizzes and
informal group activities in class
proved useful in monitoring stu-
dent progress and also led to the
implementation of corrective mea-
sures, such as additional home-

work assignments, to address deficiencies.
The results of the assessment showed a dramatic increase
in student ability to solve problems of high complexity, a
level of student confidence not observed in previous courses,
and an independent and active student engaged in the pro-
cess of solving and/or implementing a task. In particular, stu-
dents exposed to a Hi-PeLE showed a degree of creativity
and readiness not found in those who had not been exposed
to this learning environment; these characteristics were al-
most totally absent in students not trained in a Hi-PeLE when
they were in the UOL activities. The reader interested in more
details is referred to Sauer and Arce.116-181


Since in Hi-PeLE the students work in teams and are ex-
posed to experiments, they acquire a solid preparation for the
unit operations laboratory (UOL) course. This benefit is il-
lustrated in Figure 1, which compares the impact of three
Chemical Engineering Education

Figure 1. Learning timeline for ChE undergraduates.


learning approaches in the curriculum from the freshman year
until students reach the senior year of their chemical engi-
neering major.
Students who follow the traditional classroom environ-
ment (see equation 2) experience few modern learning ap-
proaches during the freshman and sophomore years. Suddenly,
in their junior or senior year when they enter the UOL, these
students face a high step up to intensive teamwork, report
writing and presentations, and the application of fundamen-
tals to experiments. An improvement is observed at earlier
stages when the modern techniques, listed in Table 2, are
introduced into the classroom. The level, however, is
bounded because of the limited team structure and lack
of hands-on experimentation.
Finally, the Hi-PeLE exposes students to issues of team-
work, communication, experiments, and equipment while in
the classroom course and, therefore, offers the best possibil-
ity for helpful preparation towards the UOL.


For faculty involved in Hi-PeLE, the opportunities for pro-
fessional development are superb. They are exposed to a va-
riety of teaching techniques that will enrich their knowledge
and help them in becoming a modern engineering instruc-
tor. The Hi-PeLE methodology also encourages a close, on-
going interaction of classroom instructors with the UOL in-
structor and hence helps build a common language among
faculty.1i20 These personal relationships bridge aspects related
to the experimental component of the curriculum that too often
are segregated from the classroom. The Hi-PeLE thus tends
to stimulate faculty collaboration across the entire depart-
ment. Since the faculty is familiar with the educational meth-
odology of the UOL and because of Eq. (1), the interaction
offers an economical, gradual way to encourage "traditional"
classroom instructors to adopt modern learning approaches.


Hi-PeLE exposes students to an environment that works as
a mini-version of real-life engineering. There are no lectures.
Instead, students work together, program their activities, ini-
tiate industrial contacts, solve problems, and complete oral
and written reports in order to design, develop, demonstrate,
and document experimental prototypes. Thus, students ac-
quire a sense of what chemical engineering is all about
and the endless creative and practical possibilities that
our profession offers.

We are grateful to Dr. Mario O. Alfano (UNL, Santa Fe,
Argentina), Dr. Biernacki (TTU), Dr. Patricia Brackin (RHIT),
Mr. Richard Chrisler (FAMU-FSU), Dr. Sam Davis (Rice
University), Dr. Eric Kalu (FAMU-FSU), Dr. Cesar Luongo
Fall 2004

(FAMU-FSU), Dr. Clarence Miller (Rice University), Dr.
David Miller (RHIT), Dr. Atanas Serbezov (RHIT), Dr.
Sharon Sauer (RHIT), Dr. Venkat Subramanian (TTU), Dr.
Don Visco (TTU), and Dr. Dale Wesson (FAMU-FSU), who
have served as judges for student poster competitions and/or
presentations and have offered us excellent feedback for im-
provements. One of us (PA) has benefited extensively due
to the continued interaction with and encouragement from
Professor Sharon G. Sauer of the Department of Chemi-
cal Engineering at Rose-Hulman Institute of Technology,
Terre-Haute, Indiana.

1. Wankat, P.C., and F.S. Oreovicz, Teaching Engineering, McGraw Hill,
New York, NY(1993) [Out of print. Available free as pdf files at />]
2. Wankat, P.C., The Effective, Efficient Professor: Teaching, Scholar-
ship, and Service, Boston, Allyn & Bacon (2002)
3. Johnson, D.W., R.T. Johnson, and K.A. Smith, Active Learning: Co-
operation in the College Classroom, Interaction Books, Edina, MN
4. Arce, PE., "The Colloquial Approach: An Active Learning Technique,"
J. of Sci. Ed. and Tech., 3(3), 145 (1994)
5. Arce, P.E., "Group Projects-Based Final Exams," Proceedings ASEE
1999 Annual Conference & Exposition, CD ROM (1999)
6. Arce, PE. and P. Arce-Trigatti, "The Parallel between Active Learn-
ing and Sports Coaching Techniques: Analysis and Selected Examples,"
Proc. ASEE 2000 Ann. Conf. & Expo., CD ROM (2000)
7. Creighton, L., "Kicking Old Habits," ASEE Prism, p. 33, April (2001)
8. Felder, R.M., "Random Thoughts: It Goes without Saying," Chem.
Eng. Ed., 192 (1991)
9. Felder, R.M., "An Interview with Richard M. Felder," Journal of Sci-
ence Education, 3(2), 62 (2002)
10. Arce, P.E., and L.B. Schreiber, "Team-Based Final Exams as an Ef-
fective Integrating Approach between Classroom Environments and
Laboratory Work," Presented at Integration of ChE Knowledge ses-
sion, AIChE Ann. Meet., Indianapolis, IN (2002)
11. Smith, K., Project Management and Teamwork, McGraw Hill, New
York, NY (2000)
12. Feynman, R.P., The Pleasure of Finding Things Out, The Perseus
Books, Cambridge, MA (1999)
13. Woods, D.R., Problem-based Learning: How to Gain the Most from
PBL, D.R. Woods Publishing, Waterdown, Canada, distributed by
McMaster University Bookstore, Hamilton, Ontario, Canada (1994)
14. Woods, D.R., Problem-based Learning: Helping your students gain
the mostfrom PBL, D.R. Woods Publishing, Waterdown, Canada (1995)
Available free as pdf files at
15. Bloom, B.S., M.D. Engelhart, E.J. Furst, W.H. Hill, and D.R.
Krathwohl, Taxonomy of Educational Objectives: The Classification
of Educational Goals. Handbook I: Cognitive Domain, David McKay,
New York, NY (1956)
16. Sauer, S.G. and P.E. Arce, "Assessment Techniques in High Perfor-
mance Environments," NSF-Sponsored Workshop, April 2003, Rose-
Hulman Institute of Technology, Terre-Haute, IN (2003)
17. Sauer, S.G., and P.E. Arce, "Design, Implementation, and Assessment
of Hi-PeLE," ASEE Ann. Conf. & Expo., Nashville, TN (2003)
18. Sauer, S.G., and P. E. Arce, "Guidelines for the Design, Implementa-
tion, and Assessment of Hi-PeLE," Manuscript under preparation
19. Sauer, S.G., and P.E. Arce, "Team Member Selection: A Functional-
Based Approach," Proc. (CD-ROM) of the ASEE Southeastern Sec-
tion Ann. Meet., Auburn University, Auburn, AL, April (2004)
20. Livsey, R.C. and P.J. Palmer, The Courage to Teach: A Guide for Re-
flection and Renewal, Jossey-Bass, San Francisco, CA (1999) O




University of Pittsburgh Pittsburgh, PA 15261

Chemical engineering was once described by Lewis
Norton as, "a general training in mechanical engi-
neering ... [with] their time [devoted to] applica-
tions of chemistry.""' Within their short history, chemical en-
gineers have moved from focusing on petroleum refining into
such diverse fields as biotechnology, chip manufacture, spe-
cialty polymers, and nanotechnology.[2 51 It has long been com-
monplace for chemical engineers to ponder the future of the
discipline16-7] and most would agree that they have experi-
enced two distinct paradigms"5"1-a unit operations or pro-
cess paradigm[9' (during the discipline's infancy) and a sci-
entific fundamentals or continuum paradigm (some 40 years
ago).2-3' Today's engineering economy has shifted the focus
once again, this time to product design/engineering'25'81 where
we design/control macroscopic materials and processes
through manipulation of their most fundamental units. This
latest of shifts in research has yet to reach the undergraduate
curriculum. To put it simply, the reform of undergraduate
chemical engineering instruction is overdue and the curricu-
lum has lagged behind research and industry.
While any number of reasons can be cited as the current
cause of disconnect between chemical engineering research
and teaching, perhaps the two most significant are:
Including emerging technologies in undergraduate
education while at the same time increasing students'
exposure to experimentation and design has put sig-
nificant pressure on the number of credit hours taken
by students in the traditional four-year engineering
degree. A simple solution to the problem might be
to allow the curriculum to grow organically and add
courses wherever and whenever needed/desired, and
to add a fifth (or sixth!) year to the curriculum. Stu-
dents, in general, are opposed to this option, and
despite numerous recommendations,1['011 five year
approaches have not been widely accepted (and may
still fall short of the task)."12.31
The fear is very real that tailoring the curriculum to

specialize in any specific emerging fields, while
tempting, would likely cause chemical engineering
to lose exactly the versatility that has made it pos-
sible to move into these fields in the first place.'3,141

The challenge of developing a better chemical engineering
curriculum, or indeed any engineering curriculum, is to build
it in such a way that it prepares students for today's engineer-
ing economy, while enabling them (through a strong and well-
integrated core of engineering knowledge) to maintain ver-
satility through life-long learning and continuing education
for tomorrow.
Prevailing wisdom from engineering educators, both in the
US[I'12,15] and in Europe, is that the ideal engineering cur-
riculum focuses on the following three issues:
Giving students a strong fundamentalfoundation by
concentrating on the essential core of scientific and
chemical engineering basics, including biological
applications and molecular insight[2-51

Joseph J. McCarthy is associate professor in
the Department of Chemical and Petroleum En-
gineering at the University of Pittsburgh. His
educational interests focus on technology-en-
hanced teaching/leaming and integration of core
knowledge earlyin the curriculum. His disciplin-
ary research is focused on transport phenom-
ena in particulate and multiphase flow.

Robert S. Parker is assistant professor and
the Fulton C. Noss faculty fellow in the Depart-
ment of Chemical and Petroleum Engineering
at the University of Pittsburgh. His educational
interests focus on the area of dynamical sys-
tems analysis and control. He is currently in-
volved with the implementation of an integrated
curriculum and development of cross-cutting
problems to assist students with integrating ma-
terial across courses.

Copyright ChE Division of ASEE 2004

Chemical Engineering Education

Enhancing systems thinking"6' by helping students
integrate their knowledge across courses and disci-
plinest'71 so they are better prepared to address open-
ended problems
Preparing and providing for continuing education
and life-long learning"13
Specifically in chemical engineering, the shift in focus to
product design/engineering 12.5.81 has inspired some to rethink
the essence that sets the discipline apart and gives it the abil-
ity to accomplish these diverse tasks. It has been argued by
many within the community that what makes chemical engi-
neers unique is a focus on transformations (both chemical
and biological, and of material, energy, or both) and multi-
scale phenomena (from molecular to continuum to pro-
cess).1'61 Much of this is missing in the typical current cur-
riculum, however.
The time is ripe for dramatic change in chemical engineer-
ing education, and there is consensus on what is needed in a
revitalized future: molecular insight, systems/integrated think-
ing, and product as well as process design. What is needed,
therefore, is a means of accomplishing these ideals.

The strong focus throughout engineering on establishing
broad-based systems thinking within a discipline'7891 has lead
the National Science Foundation to fund a number of coali-
tions['91 that have championed the "integrated curriculum."
In integrated freshman programs,120-26] educators combine as-
pects of physics, chemistry, mathematics, etc., in order to more
clearly show the interconnectedness and interplay displayed
in basic engineering problems. Similarly, efforts have been
directed at implementing complementary, integrated sopho-
more-level courses,'27.281 including a notable effort in chemi-
cal engineering at WPI.1291 In both programs, educators find
that integration, while difficult to implement, can ultimately

Figure 1. Schematic of a traditional curriculum where
content is confined to individual courses.
Fall 2004

lead to better teaching and learning.'20"
In the upper levels of the curriculum, several integrated
single courses have been recently developed that cover such
specific subject matter as electromagnetic aspects of electri-
cal engineeringE30o and data acquisition and analysis,1'3 for
example. A few groups have even attempted to implement
these types of changes in a discipline-specific,'32' subject-wide
effort (examples include industrial,133 civil,'341 and computer
engineering'3"'). One example of an integrated chemical en-
gineering curriculum is a nontraditional, asynchronous, prac-
tice-oriented effort (PRIDE) attempted at West Virginia Uni-
versity in the 1970s'361 and continued in altered form (PRIDE
II) through the 1990s.'37' While the curriculum discussed in
the following sections shares many of the same goals as the
PRIDE and PRIDE II programs, its ability to fit more easily
into a traditional university structure, as well as its basis in
previously validated pedagogy, make it significantly easier
to implement in practice. It should be noted that the "blocks"
discussed in the PRIDE program differ significantly in for-
mat from the block scheduling researched in K-12 education
and discussed below. Ultimately, this scheduling plan was
abandoned in PRIDE II.'371
Building on these earlier efforts, a novel method to incor-
porate changes in topical material while at the same time fos-
tering integration is to reform the undergraduate curriculum
into a series of six "pillar" courses, using a successful peda-
gogical technique from K-12 education called "block sched-
uling."'38-40] In its simplest form, block scheduling involves
transforming multi-semester courses into a single-semester
course via extended, concentrated contact time. Among other
things, the flexibility afforded by extended and more frequent
contact time allows and encourages greater opportunity for
active and collaborative learning.'40' Whereas time is lost in
starting and ending both classes and courses in a traditional
schedule, a block-scheduling format actually increases the
total number of courses that can be offered'40' so that more
elective courses are possible. Block-scheduling teachers are
responsible for fewer classes; at the same time, the students
have fewer concurrent courses. Therefore, both can focus
more energy and effort on the course at hand so that, ulti-
mately, both report less overall stress.[421
Current engineering is often compartmentalized within a
traditional 3-4 credit-per-course schedule, so that knowledge
is disconnected and well-defined relationships are established
only during the senior year, if at all (see Figure 1).[7] By mov-
ing to a block-scheduled curriculum, one can integrate
complementary subject matter along with experiments and
open-ended problems, so that students see the connections
across the discipline during each course (see Figure 2). Also,
by moving to this system, the pillar courses can have greater
flexibility and therefore bridge to the length-scale that is (ironi-
cally) most often omitted in the undergraduate curriculum:
the molecular-scale,2'1 the microscale, or the nano-scale (de-

pending on the topic/application). Ultimately, implementing
block scheduling in a chemical engineering curriculum should
*Students to gain systems insight through integration
of their core knowledge

* The instructors to have the time to include truly
multi-scale descriptions (from molecular to macro-
scopic scales) of chemical engineering content
* The instructors to have the flexibility to accommo-
date diverse learning styles"39'
and incorporate active learn-
ing more effectively40o I

Chemical engineers (during the
unit operations paradigm) largely
used macro-scale balance equations
to perform analyses of interest. As
the discipline moved into the con-
tinuum paradigm, chemical engi-
neers shifted their focus somewhat
and began to study systems at two
distinct length-scales- the macro-
scale and the continuum-scale. As
continuum-level analysis in chemi-
cal engineering is of "higher order"
(i.e., requiring fewer assumptions
and/or less averaging) than macro-
balances of process units, the con-
nection between these two ap-
proaches is fairly clear. In fact, it is
a relatively simple matter to derive
the corresponding macro-balances
from a continuum analysis.
As chemical engineering moves
into a new phase in its history, it is
important to examine the inclusion
of yet another scale of analysisE21-

students a stronger sense of the connectedness of chemical
engineering knowledge. For example, in the transport phe-
nomena pillar one can discuss the molecular origins of the
thermal conductivity and calculate a theoretical value for a
new material, use that conductivity to derive a continuum
expression for the heat transfer coefficient into a flowing liq-
uid, and then use that heat transfer coefficient in a macro-
balance to establish design equations for a novel heat ex-
changer. This is but one example of intra-pillar synergy that
is possible in the new curriculum.

n enThmodynanics
(Fundamental Thermo., Chem. Equilibrium)

Transport Phenomena
(Heat, Mass, and Momentum Transfer)

Reactive Process Eng.
(Reaction Kinerics, Separationsi

Process Systems Eng. T: Dynamics/Modeling
(Process Control, Systems Analysis)

Process Systems Eng. II: Design "
(Process Design. Safety, Product Design)

Figure 2. Schematic of a block-scheduled chemi-
cal engineering curriculum. Here each individual
pillar course contains tightly integrated topical in-
formation and the pillars are tied together through
track-based examples/projects.

the sub-continuum scale (alternatively micro, nano, or mo-
lecular, depending on the problem at hand.) Sub-continuum
or "molecular" analysis relates to the continuum models in
much the same way that continuum models relate to macro-
balances. That is, molecular analysis can be used to derive
many of the continuum properties of materials studied in tra-
ditional courses such as thermodynamics, transport phenom-
ena, and kinetics.
Including the Sub-continuum approach, therefore, com-
pletes the picture in a way not previously possible in tradi-
tional chemical engineering courses.['6] The use of a block
schedule yields sufficient time for an instructor to make full
use of this multi-scale approach to ultimately convey to the

The most significant change in
moving to a block-scheduled cur-
riculum lies in the shift from
smaller, course-centered classes
(classes designed with credit hour
restrictions as the focus) to more
comprehensive, topically-cen-
tered classes. These topic-centered
pillar courses can range from five
to seven credits, where most
should include a one-credit experi-
mental laboratory and in some
cases a one-credit computational
laboratory as well. The typical
class might meet every day for one
to two hours. The pillar course
(plus labs) should be the only
chemical engineering core course
taken by students in a given se-
mester, thus relieving the distrac-
tion of coordinating multiple
chemical engineering workloads
as well as allowing them to im-
merse themselves in the current
topic. It should be noted that it is
expected that moving to pillar
courses will add few or no addi-
tional credit hours to a traditional
curriculum, instead using those

hours more effectively through restructuring.'4'421 In the re-
mainder of this section we outline six potential pillar courses
that cover, and expand on, the traditional content in chemical
E Foundations of Chemical Engineering
In many current chemical engineering curricula, students
face rudimentary thermodynamics first in the mass and en-
ergy balances class, then later (with more detailed material)
in the (first) thermodynamics course, and even later in sepa-
rations. While the repetition is undoubtedly helpful for stu-
dents who may have struggled the first time or two they were
exposed to rudimentary thermodynamics, remarkably few rec-

Chemical Engineering Education

The changing engineering landscape is quickly pushing chemical engineering into a third
paradigm-the product design (molecular manipulation) paradigm. Without a shift in
the curriculum, undergraduates will be wholly unprepared for what may
well be their job in the near future.

ognize that things are being repeated. Surprisingly, this is true
even when two thermodynamics courses are taught; in a
course-centered, disjointed curriculum students have trouble
seeing how the First and Second Laws of Thermodynamics
relate to chemical equilibria and fugacity, respectively.
By switching to a block-based scheduling system, one can
combine elements of mass and energy balances, thermody-
namics, separations, and product design to form a pillar course
on chemical engineering foundations. In this course, prob-
lem-solving techniques are introduced from both a (tradi-
tional) process-centric viewpoint and a product-centric view-
point. The course will span from theoretical (basic thermo-
dynamics) to applied (separations), allowing a simple route
to problem-based learning of difficult theoretical concepts.
The connections between balance equations, thermodynam-
ics, simple phase equilibria, and separations can be easily
conveyed as the material is interwoven throughout the course.
The flexibility afforded by the extended contact time will al-
low lecture, problem sessions, and group study to form a por-
tion of each class meeting so that students get constructive
hands-on experience at every stage, including the use of pro-
cess simulators (for process-centric problems) and molecu-
lar modeling tools (for product-centric problems). The ex-
perimental component (one credit) of the course will, as much
as possible, differ by student group and represent each of the
active elective "tracks" currently offered by the department
so that students are continually exposed to varying fields of
chemical engineering (polymers, process engineering, biotech-
nology, biomedical applications, etc.) and their relationship to
the currently examined material. This course will be similar in
many ways to the spiral curriculum used at WPI.'29'43'441
* Thermodynamics
This pillar course combines ideas from pure component
thermodynamics (typically the first course) with multicom-
ponent thermodynamics (typically in the second course). Ad-
ditionally, it introduces molecular insight and use of com-
mercial software (process and molecular simulators, such as
Aspen, HySys, ChemCAD, Pro/II, Batch Plus, Superpro
Designer, Accelrys, etc.) for solving complex problems. The
main goal in this pillar is to provide students with the tools
needed to solve realistic problems in phase and chemical equi-
libria. The course will have a strong focus on multi-scale
analysis, for example, covering intermolecular potentials (mo-
lecular-scale) to aid students in choosing equations of state
for novel materials (macro-scale.) The course will add a mo-
lecular description of entropy as well as vapor-liquid equi-
librium (i.e., gaining molecular insight into nonideal phase

behavior). Extensive use of computational tools will allow
time for the course to explore interfacial behavior, adsorp-
tion, and osmotic equilibrium.
* Transport Phenomena
Combining the transport courses into a single pillar will
greatly facilitate the study of analogies between the three
modes of transport phenomena typically covered in chemi-
cal engineering curricula. Integration will allow coverage of
the Reynolds and Colburn analogies in boundary-layer flow
as well as direct comparison of linear transport relations, such
as fluid drag and mass/heat convection. Removing the over-
lapping materials will allow the time to explore coupled heat,
mass, and momentum transfer as might be important in prob-
lems ranging from traditional packed-bed reactors to micro-
fluidics or microelectromechanical systems. Extensive use
of commercial computational tools for equation solutions will
also be included.
* Reactive Processes Pillar
This pillar course will integrate reactor design, reaction
kinetics, and advanced separation processes to allow com-
prehensive study of systems ranging from polymerization re-
actors to enzyme-catalzyed metabolism to (bio-)artificial or-
gans. The course comprises topics from both the traditional
kinetic and reactor design course as well as a small portion
from the separations course. The material will integrate con-
cepts from chemistry (kinetics, catalyst manufacturing), phys-
ics (transport, fluid flow), biochemistry/medicine (enzyme
reactions, biomedical devices), and reactor engineering. Also,
problems will bridge all length scales from the molecular level
to the reactor level to the full-systems level (fuel cell with
fuel reformer, gas separation, and heat-integration or micro-
reactors.) As with the other pillars, both theory and experiment
will be highlighted and detailed simulations will be included.
N Systems Engineering Sequence
Traditionally, process control and process design are taught
independently. It has been recognized, however, that within
chemical engineering there is a significant interplay between
process/product design, dynamics analysis, and control, as
evidenced by a series of conference sessions (AIChE 1999-
2003, FOCAPO/D meetings, etc.).[45-46 An integrated systems
and design sequence will help students learn the fundamen-
tals of dynamical modeling and analysis, control system de-
sign, optimization, and design engineering. Furthermore, the
block-scheduling and laboratory time will allow for the in-
corporation of molecular insight and dynamic process simu-
Continued on page 300.

Fall 2004




In a ChE Undergraduate Curriculum

University College Dublin Belfield, Dublin 4, Ireland

he Batchelor of Engineering degree in Chemical En-
gineering at the University College Dublin, established
in 1952, is awarded after a 4-year program and is ac-
credited by the Institution of Engineers of Ireland and the
UK Institution of Chemical Engineers. As with most chemi-
cal engineering degree programs, design education plays a
central role. During the third (junior) year, students tradition-
ally work on a variety of design assignments, with particular
emphasis on the design of process equipment. In the final
(senior) year, the capstone design project forms a major part
of the design education, with students working in groups of
5-6 on chemical and biochemical plant design. The scope of
the project ranges from process identification and equipment
design and specification to safety and loss prevention and
economic feasibility.
In March 2001, a workshop was held at University College
Dublin with the objective of reviewing and assessing current
practices in the area of chemical engineering design educa-
tion in Ireland and Northern Ireland. It was jointly sponsored
by the three third-level institutions offering degree programs
in chemical engineering-Cork Institute of Technology (CIT),
Queen's University of Belfast (QUB), and University Col-
lege Dublin (UCD). Together with 13 academics involved in
design education, there were 13 representatives of employers
of chemical engineering graduates who attended the event.
The attendees were also addressed by Professor Warren Seider
of the University of Pennsylvania. A central recommenda-
tion of the workshop was that the increased need for chemi-
cal engineering graduates to function in cross-disciplinary en-
vironments should be reflected in the undergraduate curricu-
lum. The facility to function in a multidisciplinary team-based
environment is widely recognized as an increasingly impor-
tant skill for engineering graduates.111
Subsequently, there were two steps necessary for imple-
menting this recommendation in the undergraduate curricu-
lum at UCD, both realized within the design course offered

in the third year of the undergraduate curriculum. This course
consists of both a formal "lecture-and-examination" element
and a practical, project-based component-the changes to the
course related to the project element. First, the author initi-
ated an interdisciplinary design project involving third-year
chemical engineering and third-year chemistry undergradu-
ates. This development was facilitated by the fact that the
Chemical Engineering Department has an option (titled
"Chemical Engineering for Chemists") in the chemistry BSc
program. Second, a cross-disciplinary project was established
in conjunction with the Mechanical Engineering Department
at UCD, with the assistance of Dr. Donal Finn, a lecturer in
that department with responsibility for supervision of a heat
transfer-related design project during a third-year mechani-
cal engineering design class.
For both projects, the primary challenge was, and contin-
ues to be, identification of an appropriate problem statement
that presents a suitable learning vehicle for each set of under-
graduate students. This paper discusses the development of
such projects and assesses the success of efforts to date.

Several examples of multidisciplinary engineering design
projects have been presented that have either focused on se-
nior-level design projects2"31 or have concentrated on engi-
neering students.[14 The objective of this particular curricu-

Copyright ChE Division of ASEE 2004

Chemical Engineering Education

Brian Glennon is a lecturer in the Department
of Chemical Engineering at the University Col-
lege Dublin, where he received a BE and PhD
in Chemical Engineering. Prior to joining the staff
of UCD in 1995, he worked for four years in a
variety of positions with Merck Sharp and Dohme
in Ireland. He teaches principally in the areas of
design and separation processes. His research
activities focus on crystallization, biochemical
engineering, and distillation.

lum development was to establish suitable assignments that
could beneficially introduce, to third-year/junior-level stu-
dents, the practice of design both in terms of the implemen-
tation of chemical engineering theory and the organizational
interactions that characterize team-based activities.
Joint Chemical Engineering/Chemistry Project
Central to the development of this project was a process
description supplied by a local pharmaceutical company for
use in undergraduate design teaching. The information that
was supplied is shown in Figure 1, and the first Project State-
ment developed is given in Table 1.
Cross-disciplinary groups of 5-to-6 students worked on this
project for three 2-hour sessions. While the project was gen-
erally successful (about 75% of all students, based on anony-
mous questionnaires with a 90% response, rated it 'better than
average'), it proved less satisfactory for the chemistry stu-
dents, who felt excluded from much of the decision-making
within the groups, despite the fact that the problem was for-
mulated to facilitate active learning of process analysis tech-
niques. This situation may be attributable to the relative im-
balance in numbers between the two sets of students: approxi-
mately 10 chemistry students took the process engineering
option, compared to 34 chemical engineering students. In-
evitably, the chemists felt outnumbered in the group work.
The majority of all students expressed satisfaction with
the principle of cross-disciplinary interaction on such an

p-hydroxyphenethylamine HCI




Methanol Distillation
Dissolve in water
EtOAc Dilution
H20 Dilution
NaC Crystallization
Filtration and drying


Dobractamine Hydrochloride

Figure 1. Dobractamine HCI process description and
reaction scheme.

assignment, however.
Other criticisms were of the generic sort, typically leveled
at group undergraduate projects ("Some people in the group
didn't do any work"; "The lecturer should assign specific tasks
to each member of the group"; "Problem specification was
too general, and it took us a long time to figure out what we
should be doing"; "It was very difficult to find all the neces-
sary data"). This experience is reported elsewhere during the
development of multidisciplinary projects.11
In the following academic year, the project description was
modified in an attempt to more effectively integrate the two
groups of students. The effort was also aided by an improved
student balance, with 21 chemistry and 31 chemical engi-
neering students. The process was divided into two parts-
Part A and Part B. Part A focused on the first reaction step in
Figure 1, while Part B focused on the second step (hydroge-
nation reaction). The instructions issued to students complet-
ing Part A are reproduced in Figure 2 (next page). A similar
set of instructions was issued for Part B.
This modified project proved much more successful, from
a number of aspects. First, with the scope of the project re-
duced, all students could contribute more fully to the overall
task. A single, three-hour session was allocated for comple-
tion of the assignment to ensure that the students used all
members of the group as effectively as possible. Rather than
one or two individuals within the group completing all the
work, with the other students passively looking on (or not),
this assignment required full participation if it was to be com-
pleted on time. The instruction sheet produced at the end of
the session formed the basis for group evaluation. This as-
sessment was then largely completed by the students them-
selves. During a succeeding session, the groups had been par-
tially reassigned, so that only some of the original group
members remained. These new groups then completed a pre-
liminary safety assessment of the previously prepared instruc-

Initial Joint Engineering/Chemistry Project Statement

The object of this assignment is to complete a "process fit" based on
the process description providedfor the production of a pharmaceuti-
cal product, Dobractamine HCI. In particular, it is required to specify
the equipment train necessary for timely completion of a single
production campaign of 50 tons of product. The following conditions
Available equipment includes a 2400-litre hydrogenation reactor
Assume the first reaction takes 2 hours
Assume an average hydrogenation time of 12 hours

The finished report should include the following information:
Process flow diagram
Complete mass balance, indicating composition and flow rates
for all process streams
Cycle times for each vessel
Calculation of overall batch time
Determination of length of production campaign

Fall 2004

tion sheets and highlighted any potential safety or ope
issues that might arise during implementation of the batc
In addition to the improved student participation in
ercise, the problem assignments presented a number
portunities to better illustrate various important safety
of chemical processing. Nitrogen inertion, leak testi
ids and liquids handling, control system operation
alarms and interlocks, and instrumentation reliabili
some of the topics that arose during completion of
signments. The principal modification to the project p
for future years is to introduce the assign-
ment as early as possible during the course
to provide more time for discussion of these
and other various topics during the accom-
panying lectures.

jnint IChemical En ineeprin/

Mechanical Engineering Project

As a first effort in the development of this
project for a Chemical-Mechanical Engi-
neering audience, the pharmaceutical pro-
cess described in Figure 1 was reduced to a
generic process flow diagram (Figure 3).
The first assignment was attempted by 16
chemical engineering and 16 mechanical
engineering students. Split into 4 groups of
8, they were asked to specify heating and
cooling utilities to meet the process require-
ments. The actual project description given
to the students is reproduced in Figure 4.
Each group was given a slightly different
process description (varying heat loads, pro-
cess times, etc.), but otherwise was asked
to complete identical assignments. As with
the projects described above, the assign-
ment was constructed with the expectation
that many of the important design issues
raised during the course of the project could
be further discussed both within the design
sessions (of which there were three 3-hour
periods, followed by a presentation) and
within the formal lecture classes which ac-
companied the project work.
Student feedback from this project indi-
cated that the mechanical engineering stu-
dents felt that the groups were dominated
by the chemical engineers, who were far
more familiar with the processing aspects
of the problem. Additionally, the chemical
engineering students had covered more heat
transfer theory than their mechanical en-
gineering peers.
Based on experiences with this project, a

:h sheet.
the ex-
r of op-
ng, sol-
ty were
the as-

completely revised project statement was developed for the
following year. Rather than basing the problem on a batch
process, a simple distillation system was chosen, and the stu-
dents were asked to specify the associated process compo-
nents (pumps, heat exchangers, pipework, etc.). The prob-
lem statement is reproduced in Figure 5. As the mechanical
engineering students are almost entirely unfamiliar with the
principles behind the operation of a distillation column, a basic
introduction was provided. Of particular use was a CD, de-
veloped in conjunction with the text Process Design Prin-
ciples, 51 which contains still- and video-images of a distilla-

Figure 2. Project assignment (Part A) for reaction scheme (step 1).

Figure 3. Process flow sheet for production of bulk pharmaceutical product.

Chemical Engineering Education

A proposed Piping and Instrumentation Diagram (P&ID) for the vessel used in this
process step is given.

Establish a batch instruction sheet to complete this process step. The instruction sheet
should address all activities required to ensure that the process operates appropriately.
Thefollowing assump-
103 SV) 04 AG105 tions can be made:
S /Tv to scrubber All solids (KCI, p-HCI)
CV102 102 Vi103 CVi SV10 are available in the
e c ,correct amounts for
SV102 1c charging through the
101 106 F to reactor solids valve (SV104),
CV101 0 acket
n Methanol and butanone
SV lo are charged from
supply tanks.
Nitrogen is used to
R100 ensure that an inert
atmosphere is
maintained in the
The reactor jacket
system will maintain
PU12 the reactor temperature
v at the appropriate level,
if required.

Project Outline
For the production of a pharmaceutical product, it is proposed to meet the plant utility requirements using a single heat-transfer fluid. The fluid is to be
provided to the plant vessels via a closed circulation loop. As required, connections to individual vessels can be made at various points along this loop.
It is necessary to provide the fluid at both a high (1200C) and low (-50C) temperature. The design pressure of the system is 13 barg. The proposed site
lay-out is shown below.
Project Scope
The designed system should provide a hot and cold service to the plant under the conditions described above. The capacity of the system should be
sufficient to meet all anticipated heat transfer demands over the course of a typical
production year. The project should address the specification and design of the heating
and refrigeration plants and the heating and cooling circulation circuits.

The plant should be designed for year-round operation. The circulation systems should be
designed for a maximum heat transfer fluid temperature change of 10C. Details of a
range of possible heat transfer fluids will be provided.
The following items should be considered:

* Materials of construction
* Circulation loop layout and components, including
Pipework and connections
Recirculation pumps
Insulation requirements
Refrigeration requirements, including
T-S and P-H diagrams
COP performance rating
Heating requirements, including
Heat exchanger requirements
Any additional requirements to address the following issues:
Oxidation of the heat transfer fluid in the presence of air
Density fluctuations in the heat transfer fluid

( R-201 )


Tank Solvent Util
Farm Recovery i

R-301 ( R-401 (R-501

S) C-301


( R-701) (R-601

Proposed plant site layout (part of). Process vessels shown
are located on second floor, which is 8m above ground
level. (Assume the process area shown is 30-m long.)

Design Project
You have been asked to specify the process components (pumps, heat exchangers, pipework, etc.) associated with the distillation of methanol as part
of an overall methanol production plant. In the final stages of the methanol manufacture, an aqueous methanol stream (F), containing 15,000 kg/h of
methanol and 10,000 kg/h of water, is produced at 250C. To purify the mixture, it is necessary to remove the water using a continuous distillation
column (height = 13.0 m; diameter = 1.0 m). The column produces a distillate (D) of essentially pure methanol (b.p. = 650C), and a bottoms product
(B) of essentially pure water b.p. = 1000C).

The distillation column configuration is shown schematically below.


--R 0D

The following table summarizes the streams and their states. All
streams are saturated at the point of entry to and exit from the col-
umn (i.e., all liquid streams are at their bubble point and all vapor
streams are at their dew point). The column is operated at atmo-
spheric pressure.

Stream Phase

L, L


Flowrate (kg/h) wt% methanol


The distillate and bottoms products are required at 250C for
storage purposes.

Figure 5. Revised joint chemical/mechanical engineering design project.

Fall 2004

Figure 4. Joint chemical/mechanical engineering design project.


tion unit, showing typical condenser and reboiler arrangements
and associated piping. Completion of the project placed little
emphasis on the process aspects of the column, other than de-
termination of the various stream temperatures. Students again
worked in groups of 8.
Overall, this project has proved more successful in terms
of achieving better student integration between the two co-
horts. Neither group felt excluded from the project activities,
and the completed reports exhibited clear signs of good co-
operation. As is often the case with student assessment of
group project work, some students were critical of the rela-
tively loose nature of the problem specification and did not
appreciate the fact that they had to struggle for some time to
come to terms with what exactly was required of them. This
criticism formed the basis of a subsequent lecture on project
management and quality assurance aspects of design!

Working in a cross-disciplinary environment is an impor-
tant part of the chemical engineering profession. Recogni-
tion of this fact has led to the development of a number of
projects in the chemical engineering curriculum at the Uni-
versity College Dublin, which brings chemical engineering
undergraduates (at third year/Junior level) together with chem-
istry and mechanical engineering students. Based on the ex-
periences of a number of years, sample projects are presented
that appear to offer good learning opportunities for each stu-
dent group. We hope to further develop these projects in com-
ing years to better integrate the project work with formal lec-
ture classes, with the potential for joint lecture classes be-
tween each set of students.
Successful implementation of this type of endeavor inevi-
tably depends on scheduling constraints and on the willing-
ness and flexibility of the home departments of the students.
Equally problematic are the differences in learning objectives
for students in various departments, which clearly influence
the choice of project. In the case of the chemical engineering
undergraduate course discussed here, development of team-
work skills, along with a capacity to tackle loosely specified
project assignments, are regarded as key learning outcomes.
Based on the experience to date, these cross-disciplinary
projects are regarded as a successful addition to the chemical
engineering curriculum at the University College Dublin. We
anticipate that they will continue to be a part of the under-
graduate program for several years to come.

The contribution of Dr. Donal Finn of the Department of
Mechanical Engineering has made this curriculum possible.
The support of the Heads of both the Chemistry and Me-
chanical Engineering Departments is also noted. Eli Lilly is
also thanked for its support in the development of the pro-
cess description in Figure 1.

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Block-Scheduled Curriculum
Continued from page 295.
lator software into the systems and dynamics pillar for the
inclusion of industrial-style examples, as well as molecular
effects on processes through changes in thermodynamic equa-
tions of state, etc. Also, optimization (a topic not generally
covered in chemical engineering curricula), can be added to
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between (feedback) control and design will be explored to
demonstrate how changes in plant-operating state alter the
difficulty of the controller design problem, thereby leading
to design for control. Finally, product design will be intro-
duced alongside of process design to highlight the similari-
ties and differences that exist.


The focus of chemical engineering, and indeed all of engi-
neering, is changing. One needs only to scan the literature to
find numerous references to "self-assembly," "nano-struc-
tured," "biomimetic," etc. All these topics are as foreign to
the traditional chemical engineering curriculum as Beowulf,
Jung, or (literally) Greek. The changing engineering land-
scape is quickly pushing chemical engineering into a third
paradigm-the product design (molecular manipulation) para-
digm. Without a shift in the curriculum, undergraduates will
be wholly unprepared for what may well be their job in the
near future. At the same time, even biomimetic or nano-struc-
tured materials need to be manufactured, likely in a plant;
therefore, we clearly still need chemical engineers to fulfill
traditional roles. The ideal new curriculum will balance
these needs such that chemical engineering students main-
tain the versatility that they have enjoyed for years, while
at the same time becoming more prepared for today's (and
tomorrow's) marketplace. By integrating the core subject
matter of the discipline into topic-centered pillar courses
arranged in the curriculum according to block-schedul-

Chemical Engineering Education

ing principles, we can gain
The time to connect theory to application and to integrate
intradisciplinary ideas into rational constructions
The flexibility to accommodate diverse learning styles in
extended classroom experiences using well-integrated, active
learning components and other modem teaching methods
The activation energy to address emerging technologies and
incorporate truly multi-scale analysis into the undergraduate

By integrating successful pedagogical techniques from K-12
education (block scheduling138-40]) into the university environ-
ment and developing a block-scheduled curriculum in an en-
gineering department, chemical engineers may build a model
for all engineering disciplines.

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41. Irmsher, K., "Block Scheduling," bases/ERIC_Digests/ed393156.html>, ERIC Digest, 104, (2002)
42. Center for Innovative School Scheduling, "What We've Learned,"
comparison.html> (2002)
43. DiBiasio, D., W.M. Clark, and A.G. Dixon, "A Project-Based, Spiral
Curriculum for Chemical Engineering: Part II. Implementation," Chem.
Eng. Ed., 35(2), 140 (2001)
44. DiBiasio, D., L. Comparini, W.M. Clark, and A.G. Dixon, "A Project-
Based Spiral Curriculum for Chemical Engineering: Part III. Evalua-
tion," Chem. Eng. Ed., 35(2), 140 (2001)
45. Georgakis, C., D. Uzturk, S. Subramanian, and D.R. Vinson, "On the
Operability of Continuous Processes," Ctrl. Eng. Prac., 11, 859 (2003)
46. Perea-Lopez, E., B.E. Ydstie, and I.E. Grossman, "A Model Predic-
tive Control Strategy for Supply Chain Optimization," Computer Chem.
Eng., 27, 1201 (2003) O

Fall 2004

I curriculum

An Innovative Method for



Universidad de Concepci6n Concepcidn, Chile

Although a student's knowledge of chemical engineer
ing remains the most important part of his or her
professional training, the ability to communicate is also
pivotal and has a decisive role in a chemical engineer's profes-
sional life. In Chile, there is a growing recognition of the im-
portance of developing communication skills for engineering
students. Ayarza, et al.,[11 have identified skills that graduating
engineering students should have, based on a profile that con-
siders international standards. They emphasize that a future en-
gineer should be able to work well on multidisciplinary teams
and should be able to communicate effectively. Furthermore,
another Chilean academic, Schewember,'21 has noted that every
engineer who reaches a certain professional level is required to
make complex, high-quality presentations. He adds that an en-
gineer not only needs to be able to speak well in the rhetorical
sense, but he must also dominate active communication, which
implies an efficient and effective use of language.
The Chilean National Undergraduate Accreditation Commis-
sion (CNAP), together with the engineering deans of the princi-
pal Chilean universities, have elaborated an evaluation criteria
for undergraduate engineering programs, where the standards
of the U.S. Accreditation Board for Engineering and Technol-
ogy (ABET) have been especially influential. Included in the
CNAP's engineering student profile are the abilities to be cre-
ative and innovative, to communicate effectively with third per-
sons, and to solve problems with a holistic, systemic approach.
Although academic leaders emphasize the importance of these
abilities, there are only a few cases where activities that permit
engineering students to develop these communication skills have
been incorporated into the curriculum.
During the evaluation of student performance, chemical en-
gineering professors at the Universidad de Concepci6n noted a
deficit in students' communication ability-a number of them
had difficulty with oral and written expression when com-
municating results to their peers and professors. This inability
to communicate effectively could negatively influence their

future job possibilities.
These deficiencies could be due to any one of several causes.
First, chemical engineering students have few opportunities
to develop communication abilities as part of their university
education. Second, a significant number of engineering stu-
dents at the University of Concepci6n come from a low socio-
economic and cultural background, further limiting develop-
ment of communication abilities on their own and requiring
support programs to overcome the deficiency. For example,
in 2002, 72% of the students came from subsidized or public

Marlene Roeckel von Bennewitz is Professor of Chemical Engineering at
the Universidad de Concepcidn. She obtained her MS degree (1983) in
Engineering Sciences and her BS degree (1977) in Chemical Engineering
at the Universidad de Concepci6n. Her research interests include environ-
mental and food biotechnology, and clean technology in the food industry
with emphasis on organic matter removal, biological treatment, and modi-
fied atmosphere packaging.
Elizabeth Parra Ortiz is Assistant Professor of Social Sciences at the
Universidad de Concepcidn. She received her MA in Hispanic Literature
(1986) from the Universidad de Concepcidn, her MSc in Communication
Sciences (1999) from the Universidad de la Frontera, and her BA is a Phi-
losophy Professor (1978) from the Pontificia Universidad Catdlica de Chile.
Her research interests are in university teaching innovations, skill develop-
ment and society of knowledge, professional planning, entrepreneur cul-
ture, and media discourse analysis.
Carmen Gloria Donoso is Assistant Professor of Social Communication
at the University de Concepcidn. She received her MA in Journalism from
Syracuse University (1969) and her BA from the University of Chile (1966).
She is presently a doctoral candidate in Communication and Information at
the Universidad Pontificia de Salamanca. Her research interests include
organizational communication, new information technologies (NIT), and
NIT's impact on organizations, mass communications uses, and gender
Olga Mora Mardones is Associate Professor of Social Sciences at the
University of Concepcidn. She received her MA in Social Communication
from the Universidad de Chile, Santiago, in 1989 and her BA in Social
Work from the University of Concepcidn in 1973. She is presently a doc-
toral candidate at the Universidad Pontificia de Salamanca, Spain, and the
theme of her dissertation is communication and interpersonal trust. Her
research interests are families, youth, and values.
Ximena Garcia Carmona is Associate Professor of Chemical Engineering
at the University of Concepcion. She obtained her PhD (1991), her MSc
(1989), and her BSc (1984) in chemical engineering from the University of
Concepci6n. Her research areas are in conversion processes (pyrolisis,
combustion, and/or gasification) of coal, wood, and related materials, het-
erogeneous catalysis, and chemical reactions engineering (kinetics, mecha-
Copyright ChE Division of ASEE 2004

Chemical Engineering Education

* Social Sciences Faculty

education (public education in Chile has deteriorated greatly),
and 47% of them received financial aid scholarships because
of their family's low incomes. This socio-economic distribu-
tion could be indicative of fewer opportunities to develop lan-
guage performance and interpersonal communication skills.
Third, engineering student in general have better mathemati-
cal than verbal skills, which can be observed in their average
scores for university admission tests. The average math score
in 2003 was 739 points, while the language score was 625
out of 845 points. These scores can be compared with the
national results. The 80'h percentile nationally was 615 point
and 593 points for mathematical and verbal admission tests,
respectively. For the verbal skills, the national average was
500 points, with standard deviation of 122.2; in mathematics,
the average was 500 points, with standard deviation of 140.
The introduction of new activities designed to strengthen
communication abilities in a strongly scientific discipline
breaks with the traditional teaching curriculum and raises a
series of challenges for professors. Delors131 asserts that people
possess attributes that they can use creatively and positively
to communicate with others. He also states that to better
communicate, students need to understand their role in
social life as part of their professional success. Thus, a
program can be developed where students can acquire
communication skills that will permit them to control how
to present themselves and to relate with others in an aca-
demic, as well as social, environment.
The Chemical Engineering Department at the University
of Concepci6n recognizes that one of its weaknesses is the
"significant preponderance of subjects and methodologies that

do not contemplate nor promote team work, effective technical
communication (either written or oral) and other issues that per-
mit a more integral, functional student formation". As a result,
one of the objectives of the Department is to "promote an inte-
gral, highly competent professional formation" and specifically
to "improve oral and written communication abilities, social par-
ticipation, and ethical and moral values".
Consequently, the Department developed a "Communication
Skills Development Workshop" as part of the Chemical Pro-
cesses Laboratory class for fourth-year students. The principal
objectives of this class are to strengthen the students' under-
standing of fundamental chemical engineering principles and
to introduce them to laboratory work. In this class, the students
carry out experiments in areas such as reactor design, fluid
mechanics, and heat transfer. They work in groups of five and
present oral and written reports. This paper presents and ana-
lyzes the results of the workshop.

A pilot experience was held with a representative sample of
thirty chemical engineering students in the Chemical Pro-
cesses Laboratory. The students attended workshops for two
hours a week during two 14-week semesters. Table 1 pre-
sents the workshop themes.
Each session progressed inductively from personal experi-
ence to conceptualization, emphasizing the capacity of each stu-
dent to internalize concepts based on his or her own experi-
ence. The didactic intervention model has three levels:
SPersonal development to recognize the strengths and weak-
nesses as a person who interacts maturely, proactively, and

Fall 2004

Communication Skills Development Workshop

Module Name Activities
Satisfaction Survey Presentation of workshop objectives
Personal Development
Proactive attitude and process of change Development of a proactive and positive attitude with respect to change
Commitment Development of an attitude to participate with others in the achievement of goals
Maturity Development of an attitude to make a decision and stick with it
Negotiation Development of an attitude of collaboration and flexibility in dealing with differences (leadership and
Verbal/Nonverbal Communication
Credibility Development of the ability to channel audience intentions by orator behavior
Good orator qualities
The art of listening Development of the ability to retain participants' attention to what is said
Recognition of why it is important to know how to listen
Audience analysis Ability to detect audience characteristics
Use of nonverbal elements Development of the ability to control one's voice and body to emphasize the message's meaning
Planning Oral and Written Discourses
Discourse purpose Ability to identify the objective of the discourse
Outline design of an oral or written presentation Outline design of an oral or written discourse
Verbal and visual support Ability to use support materials to emphasize and make explicit one's ideas before a group
Satisfaction survey Workshop evaluation

committed with others
Development of body skills and spatial movement to support
public presentations
Development of communication skills to elaborate oral and
written presentations, enabling the students to communicate
effectively and efficiently
The students were evaluated before and after the didactic in-
tervention for their
Personal satisfaction
Ability to present a theme orally and in a precise time
Use of audiovisual material as support for the oral presenta-
The instruments used to evaluate these issues were a per-
sonal satisfaction survey (self-evaluation), video tapes of the
presentations (performance evaluation), and team evaluations

To measure student satisfaction with respect to the
semester's work, self-evaluations were incorporated to stimu-
late student feedback and student reflection of their class per-
formance. The self-evaluations took place twice during the
first semester of 2002-halfway through the course and at
the end of the semester. The first evaluation permitted us to
correct certain issues of class work and to program the sec-
ond-semester academic activities.
The evaluated issues were the
Degree of participation
Degree of responsibility in assignment completion
Level of comprehension
General participation
The second self-evaluation also included the students' per-
ception of their performance in verbal and nonverbal com-
munication. On both occasions, open questions were in-
cluded to gather student opinions and suggestions, which
allowed us to generate modifications when necessary to
plan future activity.
The value scales (categories) used to evaluate the opinion
with respect to the distinct affirmations contained in the evalu-
ation tool were
1. I believe that the statement is highly exact.
2. I believe that the statement is exact in general.
3. 1 believe that the statement is minimally exact.
Oral Performance Evaluation
To evaluate the initial student conduct, during the first ses-
sion each student was asked to do a three-minute video, an
unprepared presentation, on any theme. The evaluation guide
considered the following performance evaluation scale:
Above average: Fully satisfied all the requirements
Satisfactory: Partially satisfied the requirements
Minimally Satisfactory: Satisfied the minimal requirements

Deficient: Did not satisfy minimum requirements
The issues that were evaluated were
Actions to catch the audience's attention (induction)
Clear andprecise presentation on a theme, in an orderly man-
Use of vivid language
Voice use
Space administration
At the end of the first semester's workshops, the students
were asked to prepare (within a week) a three-minute pre-
sentation on a technical theme related to chemical engineer-
ing laboratories. The presentations were individually video-
taped and were evaluated using the first evaluation's criteria,
with an additional evaluation on audiovisual use.
Co-Evaluation: Feedback on the Experience
During the second semester of 2002, previously defined
work teams held meetings with supervision by both the com-
munications and the chemical engineering professor. The fol-
lowing co-evaluation was carried out in each session:
Two video presentations of each group member were presented
Each student received an evaluation guide noting the area that
was effectively achieved (effective area) and the area where
improvement was required (opportunity area)
Each student indicated the effective and opportunity areas for
each member of their group
Each student recognized his/her strengths and weaknesses in
front of the team
The professorfacilitated constructive dialogue between the stu-
This stage permitted recognition, both at an individual level
and before the group, of the students' strengths and weak-
nesses, which allowed the teaching team to plan the topics
that should be reinforced in short workshops.

A summary of survey results with respect to attendance
and participation from the students' self-tests can be seen in
Figure 1. In the first survey, the majority of the students per-
ceived that the best-achieved activities were related to their
classroom performance and with their care and dedication to
complete the required activities in each workshop (82% and
64%, respectively). The less-achieved activities were associ-
ated with the performance of out-of-class activities in the
assigned time, which was associated with the lack of time
available for workshop activities with respect to other classes.
The comprehension results are presented in Figure 3. It is
interesting to note that a considerable percentage of students
did not perceive the positive value their contributions had in
stimulating others in the learning process (57%). This result
could be related to their prior experiences in teamwork. As
can be seen, for attendance and participation, a high percent-
age (82%) did not have a clear perception of the value their
Chemical Engineering Education

own work had for others. The students perceived that the im-
portance of the workshop and the self-test were highly
achieved activities (75% and 71%, respectively). A conclu-
sion that can be drawn from this first survey is that the stu-
dents were unaware of the importance of teamwork and con-
sequently did not value it.
When students were asked to evaluate their participation
with a general grade for their participation in all the work-
shop activities at this time (on a scale from 1 to 7), the largest
percentage, 82%, perceived that they had good participation
(between 6 and 7), with only 18% classifying their participa-
tion between 4 and 5.
In relation to the general usefulness of the workshop and
suggestions, the students were satisfied with the activities.
With respect to its personal usefulness for professional life,
the students mentioned that it provided an opportunity to com-
municate and to learn more about themselves and their class-
mates. The suggestions were oriented toward increasing the
workshop time in order to deepen some of the topics and
incorporating it as a required activity in the engineering
program in order to ensure continuity and the ability to
dedicate the required time.

1st survey
2nd survey

u Time responsibility
20 Activity completion
Participation in discussions
0 Participation in groups
1 2 3 1 Optimal attendance
1 2 3 123
Figure 1 Results of self-evaluations: attendance
and participation.

1st survey
2nd survey


a r Value of the self test
2 Value of my work
3 Understand workshop
Category 1 2 importance

Figure 2. Results of self-evaluation: comprehension.

Fall 2004

Additionally, the results of the second self-test are presented
in Figures 1 and 2 for comparison. It can be noted that the
students changed their perception with respect to certain items
and to their dispersion. The percentage of highly achieved
increased (from 28% to 50%) with respect to the first self-
test. The perception of active participation in front of others
increased 11%, which could be a product of the participatory
methodology used in activity development. This result is con-
sistent with the increase of the students' perception that their
contributions could stimulate others' participation.
Between the first and second self-tests, there is a drop (from
82% to 43%) in the percentage of students who thought their
attendance and optimal participation were highly achieved.
This result could be due to the growing consciousness of
the commitment and individual responsibility required to
fulfill the requirements and that they prioritized their other
curricular demands.
With respect to the value to others that the students place
on their own work, those who felt they highly achieved this
value increased from 14% to 27%. The percentage of stu-
dents who felt that the self-evaluation process was highly
achieved increased from 71% to 83%.
Table 2 presents the responses to the questions on verbal
and nonverbal communication that were included only on
the second self-test.
It is interesting to note that the issues perceived by the stu-
dents as less achieved are all related to nonverbal communi-
cation (body movement, voice use, and physical space con-
trol). The students' self-evaluation on nonverbal commu-
nication is in agreement with the professor's evaluation
of the students' video performance, which is discussed in
the following section.

The professors evaluated student performance considering
the areas that were effectively achieved and the areas where
improvement was needed, using the categories of above
average, satisfactory, and minimally acceptable. In gen-
eral terms, students performances before and after the
pedagogical intervention, evaluated according to the pre-
determined categories, improved substantially, as can be
seen in Figure 3.
The video registration of the presentations permitted iden-
tification of the principal weaknesses in both verbal and non-
verbal communication during the students' oral presentations.
The evaluation results are presented in Table 3. The highest
frequencies are concentrated in the opportunity area-in those
issues that students need to improve in order to equilibrate
their personal capacities and skills, especially with respect to
their ability to structure their presentation from a communi-
cational point-of-view. There are accumulated frequencies of
71% in each of the indicated issues.

In the effective area there is a greater frequency in the non-
verbal communication items, fundamentally in the following
issues: maintaining visual contact with the audience (48%),
voice use (38%), and stage control (36%).
Based on the students' self-tests and the professors' evalu-
ations, the following weaknesses needed to be strengthened
during the feedback sessions:
Voice use with respect to volume and modulation
Use of common language vices and crutches
Presentation structure from a communicational point-of-view
The continuous evaluation of the video presentations per-
mitted identification of each student's achievements. In the
following section, we describe in detail the changes for three
students. Initially, these students presented similar perfor-
mance levels with significant communication difficulties, and
after the pedagogical intervention they presented distinct
achievement levels.

First Student: Radical Change Table 4 presents the radi-
cal changes in this student's performance before and after
the communication workshops. In the surveys, this student
I have learned to have more confidence when speaking in
public and to recognize the defects of
others as well as my own when working
in a group .... In my case, I learned
that there is an attitude of my personal-
ity, idealism, that bothers others who
work with me.

Second Student: Incorporation ofEle-

ments Taught in Class The second stu-
dent made use of all the elements taught
in the workshop, incorporating them into
the oral presentation, as can be seen in
Table 5. The student commented
Among all the things that I learned in
class, I would like to identify the one that
is the most importantfor me .... I
learned to recognize that I am afraid that
others think that I am "stupid" and I do
my best to demonstrate the opposite.

Third Student: Continued with Diffi-
culties Despite Efforts to Improve The
third student had difficulties in effectively
communicating. The greatest difficulties
were observed in voice control and dic-
tion (see Table 6). Despite the difficulties,
the student demonstrated perseverance and
enthusiasm during the workshops. This
student commented
...this workshop has helped me under-
stand my capacities in other fields. ... it
has enabled me to better communicate
with others. I have learned to

recognize the potentials of each person and to respect their

The feedback activity was considered highly valuable by
the students, who highlighted and recognized the importance
of peer evaluation, the constructive environment generated
during team work, and the added value for personal learning
once they could visualize their performances, recognize their
weaknesses and strengths, and accept the opinions of others.
In response to students' comments, during the second se-




Above average Satisfactory acceptable
SBefore intention 13,3 40 46,7
o After intention 23,3 56,7 20

Figure 3. Effect of intervention on student performance.

Results of the Second Self-Test
n = number of students; f% = percentage response frequency)

1 2 3
verbal expression and communication n f% n f% n fj%
s clearly and precisely 16 (53) 14 (47) 0 (0)
sin acoberetlogical order t (63). 11 (37) 0 (0)-
idience is listening to and understanding me 18 (60) 11 (37) 1 (3)
iovtsual materials 19 (63) 11 137- 0 (0)
movements and gestures 7 (23) 17 (57) 6 (6)
paralanguage 7 (23) 14 (47) 9 (9)
space 6 (20) 21 (70) 3 (3)

Results of the Professors' Evaluation of the Students' Filmed Performances
(n = number of students; f% = percentage response frequency)


Exposiuon of ideas
Logical Order
Vivid Language
Visual Contact
Paralanguage use
Special use

Effective Area
Above Average (7)
n J%

Opportunity Area
Satisfactory (5) Minimally Acceptable (3)
n f% n fj

14 (45)
16 (52)
15 (48)
14 (45)
14 (45)
16 (52)
14 (45)

Chemical Engineering Education

Perception ofnon
I present my ideas
I develop my idea
I make sure the at
I can develop aud
I control my body
I pay attention to
I control physical

Performance Evaluations

Items Initial Performance Final Performance
Before Intervention After Workshops
Induction: generate No, did not Yes, well achieved
audience's attention?
Clear and organized No Yes, a logical order and sequence of
presentation ideas observed
Use of vivid Did not use vivid language Used greater lexical richness, could ex-
language Told an experience flatly plain technical themes in simple manner
Maintained visual Yes Yes-the student generated greater
contact with the connection with audience and greater
audience interaction with movement
Used paralanguage Flat voice, without variation, Controlled voice, made less use of lan-
intonation, or pauses; spoke guage crutches, used pauses, empha-
quickly sized with intonation changes, gesti-
culated to reinforce theme
Controlled the Was static; fallen arms Made coordinated movement toward
space the blackboard and toward public
General attitude Withdrawn, nervous, difficulty Secure, calm, relaxed, scene dominance
controlling respiration and believable attitude
Appreciation of future Staff member Manager
role in a company

Induction: generate No, did not Yes, well achieved
audience's attention?
Presentation Order and sequence of ideas Logical order of presentation was ob-
not clearly observed served. Student introduced theme,
developed, and closed presentation
Maintained visual Yes Yes-also coordinated it with the use
contact with audience of audiovisual materials
Used paralanguage Flat voice without pauses in Control of voice improved with respect
intonation; reiterated use of to intonation, with more pauses, better
crutches; gestures uncoordina- control of gestures with the hands
ted, especially the hands
Controlled physical Did not make use of physical This aspect still requires
space space; made swinging body improvement
movements; crossed hands/legs
General attitude Nervous, timid, low self-esteem More relaxed, more vivid, elaborated
reflected in the position of fallen language; conquered the audience; even
shoulders, faltering voice introduced a certain amount of humor

Induction: generate No Yes
audience's attention?
Presentation Central message needs greater There was concern to do a better job;
precision; told story but did not greater coherence was observed; there
communicate it; no transfer of was an attempt to open, develop, and
ideas to audience and close the presentation
Used paralanguage Fallen arms, brusque move- Partially controlled gestures and move-
ments with arms and spacial ments/ use of velocity and volume of
movement; lack of variety in voice did not improve, although the
intonation and velocity of the diction did
voice; spoke quickly
Controlled the Without spatial movement and Improved use and control of physical
physical space with swinging body movement space; partially controlled gestures
General attitude Flat Proactive attitude, tenacious, conscious
of limits and open to improvements

mester we scheduled strengthening workshops for
those students who continued to present weakness
in certain areas. Fifteen of them attended the work-
shops, and the topics developed in four sessions were:
voice control, body expression, and spatial move-
ments. The workshop results were also analyzed ac-
cording to the students' educational backgrounds,
socio-economic levels, and the College Admittance
Exams. Students who came from private
(unsubsidized) schools, without government finan-
cial scholarships, and with high scores on the Col-
lege Admittance Exam performed better and did not
need the strengthening workshops, which was noted
when the performance of the best-evaluated students
were compared with those who had more difficulties.


The students' opinions and professors' evaluations
indicated that the workshops were useful experiences
in the students' education.
This pilot experience should be formalized in the
student academic process for two reasons: there is an
observed need to generate spaces for personal and
social development, and the workshop had positive
effects on student attitude and performance.
This intervention was shown to have the greatest
effects on those students who exhibited the greatest
difficulties in expressing themselves, partially
associated with the educational background and with
socio-economic factors.
To achieve a greater impact of the described activity,
a larger number of academics need to participate and
the activities need to be incorporated as a habitual
practice in the engineering curriculum.


This work was supported by the project 02-61 of
the Quality in Teaching Program (Direcci6n de
Docencia) of the Universidad de Concepci6n, Chile.


1. Ayarza, H., P. Backhouse, A. Canales, E. Crovetto, I.
Gutierrez, J. Herrara, M. Letelierr, C. Perez, and A. Poblete,
"Desarrollo y Aplicaci6n de Instrumentos Para Evaluaci6n
de Competencias Profesionales en Carreras de Ingenierfa,"
in Evaluacidn de Aprendizajes Relevantes al Egreso de la
Educaciin Superior Centro Interuniversitario de Desarrollo
(CINDA), Alfabeta Artes Graficas, Santiago de Chile, p. 8
2. Schwember, "Fundamentos Para un Intento de Adivinar
Algunas Competencias Principales de los Ingenieros del
Future," in Evaluaci6n de Aprendizajes Relevantes al Egreso
de la Educacion Superior Centro Interuniversitario de
Desarrollo (CINDA), Alfabeta Artes Grificas, Santiago de
Chile, p. 8 (2001)
3. Delors, Jackes, "La Educati6n es un Tesoro," Santillana
Ediciones UNESCO Mexico (1996) 0

Fall 2004

e O I curriculum

Put Your Intuition to Rest



University of California Santa Barbara CA 93106

Conservation laws describe processes in which some
quantity of a system is conserved under a particular
set of conditions. Material balances are important
conservation equations for chemical processes. They consist
of two subsets-one that depends on reaction kinetics as well
as parameters like the reactor size and type, temperature, pres-
sure, etc., which we call material balance design equations,
and the other subset that depends only on the stoichiometry
of the reaction chemistry, which we call stoichiometric mole
balances, or simply mole balances. Mole balances have been
an integral part of the chemical engineering curriculum since
its inception. One important use involves estimation of the
raw materials consumption and production rates of products
in a chemical process. In experiments, mole balances can be
used to check the consistency of measured data. They also
augment rate equations used in equipment sizing and se-
lectivity modeling.
We develop the analysis for reactions in a continuous pro-
cess; it is equally applicable to batch processes, however. Con-
sider a system with components A, B, and C. If there are no
reactions, the material balances at a steady state are simply
Moles of component i per unit time in the outlets =
Moles of component i per unit time in the inlets
i=A,B,C, (1)
Here, the material balances are identical to the mole bal-
ances. If reaction A -> B +C occurs in the system, at a steady
state, the material balances are
Moles of component i per unit time in the outlets =
Moles of component i per unit time in the inlets +
Moles of component i generated per unit time
i = A, B, C, (2)
In this case, however, the material balances involve a gen-
eration term that depends on rate expressions and reactor

* PDC, Process Design Center, Inc., Santa Barbara CA 93111
SDepartment of Chemical Engineering, University of Massachusetts,
Amherst MA 01003-3110

parameters. A subset of Eq. (2) that depends only on the re-
action chemistry can be identified from the reaction stoichi-
ometry. The moles of B and C that are produced are equal to
the moles of A reacted. Therefore, the mole balances can be
written as

(nB n) = (n nA) (3a)

(nc-n)= (n nA) (3b)
where ni and n; are the moles of component i per unit time
in the inlet and outlet, respectively. This three-component

Sagar B. Gadewar is Consultant at PDC Pro-
cess Design Center Inc., in Santa Barbara, CA.
He received his BS in Chemical Engineering from
the Department of Chemical Technology, Uni-
versity of Mumbai (UDCT), and his PhD in
Chemical Engineering from the University of
Massachusetts at Amherst. His research inter-
ests include reaction combined with distillation,
conceptual design for complex chemistries, and
modeling crystal shape evolution.

Mike Doherty is Professor of Chemical Engi-
neering at the University of California, Santa
Barbara. He received his BSc in Chemical Engi-
neering from Imperial College, London, and his
PhD in Chemical Engineering from Trinity Col-
lege, Cambridge. His research and teaching in-
terests include chemical process design and
synthesis, separation systems design with spe-
cial interests in simultaneous reaction and sepa-
ration, and crystallization of organic materials.
Mike Malone is the Ronnie and Eugene
Isenberg Distinguished Professor at UMass,
Amherst. He studied chemical engineering at
Penn State and UMass where he joined the fac-
ulty in chemical engineering. He has also been
Visiting Scientist at the DuPont Company
twice. He has won the UMass Distinguished
Teaching Award and the "Computing in Chemi-
cal Engineering" Award from CAST Division of
the AIChE for joint work with M. F Doherty de-
scribed in the text Conceptual Design of Distil-
lation Systems.
Copyright ChE Division of ASEE 2004

Chemical Engineering Education

reaction mixture has two independent mole balances, unlike
the case without chemical reactions, which has three inde-
pendent mole balances. This happens due to the addition of
a new variable- the 'extent of reaction' that determines the
amount of reaction per unit time in the system. The extent of
reaction sets the generation term in
the conservation equations and is
defined as the ratio of the moles of This method n
a component generated in a reac- mole bala
tion divided by its stoichiometric are i
coefficient (stoichiometric coeffi-
cient is -ve for reactants and +ve
for products). The extent of reac-
tion for each component in a given
reaction is identical and can either
be determined from kinetic models
or from experimentally measured data. This paper focuses
on the formulation of the mole balances, which are indepen-
dent of the reaction rates and equipment specifications. If
the reaction occurs in a CSTR, Eqs. (2) can be written as

nA = n (4a)
nB = n +V (4b)
nc= n +V (4c)

where Vis the volumetric holdup of the CSTR, and 9Y is the
reaction rate in moles reacted per unit volume per unit time
(e.g., 9 = k,cA). Each equation in (4) is a material balance
design equation, and Eqs. (3a,3b) are the stoichiometric mole
balances. Equations (3a,3b), along with any one of Eqs. (4a-
4c) are equivalent to the three conservation equations shown
in Eqs. (4a-4c).
The task of writing mole balances becomes difficult for
complex chemistries with multiple reactions where the ex-
tent of each reaction cannot generally be represented in terms
of a single component. The extents of reaction depend on
variables such as reactor type and size, temperature, reac-
tion kinetics, etc. These variables, however, need not be
known in order to formulate mole balances, as shown in the
above example. Standard textbooks provide a good collec-
tion of examples for developing intuition and skills that are
useful in setting mole balances.!-31 No systematic procedure
to formulate mole balances is available to deal with com-
plex chemistries with many reactions. Our aim is to present
an algorithmic procedure to eliminate the extents of reac-
tion from the conservation equations to give stoichiometric
mole balances. For reaction systems with known components
but unknown or incompletely known reaction chemistry, a
mathematical procedure to formulate candidate chemistries was
published by Aris and Mah[41 and later simplified by Smith and
Missen.151 Aris and Mah's method by itself cannot be used to
formulate the mole balances, but can be used in conjunction
with the mole balance methodology described here.

Spreadsheets have become tools of choice among under-
graduates and many practitioners. Misovich and Biasca161
describe the use of spreadsheets in teaching mass and en-
ergy balances. We will demonstrate the application of the
procedure using a commercially available computation pro-

rot only greatly simplifies the task of deriving
nces, but also ensures that the mole balances
dependent and allows easy determination of
the degrees of freedom. The procedure has
been applied to other complex
reaction chemistries

gram called Mathcad.171 A Microsoft Excelsl'-based software
to formulate the mole balances using the methodology de-
scribed in the paper is available. The mole balance method-
ology is presented with useful rules and is illustrated with a
few examples.

Systematic treatment of mole balances must provide for
an easy and foolproof procedure. The underlying theme of
the method is to determine the mole balances expressing the
extents in terms of molar flows. Let us consider, as an ex-
ample, the hydrodealkylation (HDA) of toluene to benzene
given by the following simplified scheme of reactions:

C7H8(T)+ H2(H) C6H6(B)+ CH4(M)
2 C6H6(B) C12Ho1(D)+ H2(H)

The species present in the reaction system are toluene (CH,),
hydrogen (H,), benzene (C6H6), methane (CH4), and diphe-
nyl (C,,H0,). There are two extents of reaction (one for each
reaction), which set the generation or consumption terms.
We pick two reference components to express the extents of
reaction. Once the extents of reaction are written in terms of
the reference components, we formulate a mole balance for
each of the remaining components. For example, if we choose
toluene and diphenyl as the reference components, the ex-
tents of reaction are given by

J = -(nT-nO)

E2 =(D-nO)

Writing material balances for the remaining components
(benzene, hydrogen, and methane) using Eq. (2), we get

= +E1 2 E2
nH =n -1 +E2
nM = nM +El

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Substituting for the extents of reaction using Eq. (6), the
mole balances are

nB =n-(nT-n)-2(nD- n) (8a)

nH =nH+(nT-")+("D n) (8b)

nM =no-(nT- n) (8c)

Equations (8a-8c) are independent of the reactor type and
size, reaction kinetics, etc., and depend only on the reaction
An immediate question is, "How can one decide which
components to pick as the reference components?" The aim
of choosing the reference components is to completely de-
fine each extent of reaction. For example, if we choose hy-
drogen and methane as the reference components, the ex-
tents of reaction are

E =(nM- nM ) 2 M=(nM- n)+(nH- n) (9)
The stoichiometric mole balances can then be written for
components toluene, benzene, and diphenyl. There are, how-
ever, certain combinations of reference components that will
not work. For instance, if we pick toluene and methane as
the reference components, the extent for the second reaction
cannot be expressed in terms of the mole numbers of tolu-
ene and methane since neither of the reference components
appears in the second reaction. One can, therefore, choose
any combination of reference components so long as the
extents of reaction can be explicitly expressed in terms of
only those reference components. The number of extents of
reaction is equal to the number of independent reactions (R),
which is the same as the number of reference components.
For a system with c components, one can write c R mole
balances (one for each nonreference component).


For systems with a few reactions, it is possible to write the
extents of reaction in terms of the reference components by
inspection. For systems with many reactions, however, the
task is not trivial-we use matrix algebra to determine these
relationships and a general procedure for any number of re-
actions and components.
Consider a reaction system consisting of c components
undergoing R independent chemical reactions (a block dia-
gram is shown in Figure 1). This process block can contain
any complex combination of unit operations or processes,
or cells, etc. The inlet to the process is represented by a c-
dimensional column vector of inlet molar flow rates for each
species, no; the outlet of the process is represented by a vec-
tor of outlet flow rates for each species, n. The R indepen-
dent chemical reactions are written as

VlrAI + V2rA2 +"'.+ Vc,rAc <> 0

r = 1,2,...,R (10)

where A, are the reacting species and v,r is the stoichiomet-
ric coefficient of component i in reaction r. The convention
used is vir > 0 if component i is a product, and vi.r < 0 if it
is a reactant.
We can write c conservation equations (Eq. 2). The gen-
eration term can be expressed in terms of the 'extent of reac-
tion' that relates molar quantities of components. The extent
of reaction for reaction r is defined as

Er =-(n (11)

where Er is the extent of reaction r, (n n,) is the number of
moles of component i reacted in reaction r, and vi,r is the
stoichiometric coefficient of component i in reaction r. The
numerical value of the extent of reaction r is the same for
each species that participates in reaction r.
Since a component can be present in more than one reac-
tion, we express the overall consumption (production) of
reactants (products) in terms of the extents of reaction as

ni-n = ni-n) = virer i=,...,c (12)
r=l r=l

ni -no = i=l ...,c (13)
where n,o is the inlet molar flow-rate of component i, n. is
the outlet molar flow-rate of component i, v T is a row vector
of dimension R containing the stoichiometric coefficients of
component i in each of the R reactions

VT =(Vi,"...,Vi,R) (14)

and e is the column vector of the extent of reaction for each
of the R reactions

.=(El....E R)T (15)
Equation (13) can be written as

n=nO+ V (16)


n VI,IAi+V2,1A2+'"-+Vc,iAc -- 0
V1,2AI+V2,2A2+-"+Vc,2Ac <=> 0

ViRAI+v2,RA2+- +Vc,RAc + 0

Figure 1. Schematic of a reaction process.

Chemical Engineering Education


(V,I VI.R)
vC,l ... c.R

is a non-square matrix of dimension (c,R) of the stoichio-
metric coefficients for the c components in the R reactions,
and n = (n ,,..., nc)T is the column vector of dimension c of
mole numbers and no = (n,o,...,nc0) is the column vector of
dimension c of the initial mole numbers.
Equations (16) are c conservation equations and have the
same structure as those in Eqs. (2). They can be solved once
we have specified a value for each of the R extents of reac-
tion. Alternatively, R material balance design equations in-
volving rate expressions, equipment size and type, etc., can
be written to determine the extents of reaction. We can, how-
ever, eliminate the R extents of reaction from the c conser-
vation equations, giving a set of stoichiometric mole bal-
ances that must be obeyed regardless of the constitutive re-
lations or equipment configuration. In this way, we can find
a set of balances whose solutions are independent of some
of the details arising in rate expressions, etc. This can be
accomplished by choosing a subsystem of R equations from
among the c equations (16). These define the reference com-
ponents and the reference equations as

nRef =nRef +RefE (17)

((c-R+1),l V(c-R+I),R
VRef Vi,r (18)
v c,1 ... Vc,R

and nR,f = (n(cR+I),... ,nc)T is the column vector of dimension R
of mole numbers for the reference components; nRef =
(nOc-R+1).. ncO)T is the column vector of dimension R of the
inlet molar flow-rates for the reference components. For con-
venience, the components are numbered so that the refer-
ence components are at the end of the column vector of mole
numbers. The reference components must be chosen such
that the square matrix v Ref is invertible (this means that the
extents of reaction can be explicitly expressed in terms of
only the reference components). Any set of reference com-
ponents that gives an invertible reference matrix is called a
"feasible set," and at least one feasible set always exists.
Using Eqs. (17) and (18), the extents of reaction can be
expressed as

E =(VRef)1 (nRef-- Ref) (19)

Substituting relation (19) in Eq. (13) we get

ni n +vT Ref (nRef-nRef) i=l,...,c-R
ni =n9 +vy (Y Ref) R

Equation (20) is rearranged to write the mole balances in
terms of reaction invariants, N,, which take the same values
before, during, and after the reaction.'9 10 These are

NP=n -v( v Ref) Ref i=,...,c-R

Ni=n,-v (VRef nRef i=l.....c-R

where vf is defined in Eq. (14). N are the reaction invari-
ants based on the inlet molar flow rates, and N. are the reac-
tion invariants based on the outlet molar flow rates. When
we equate these reaction invariants, the stoichiometric mole
balances for the reacting system are simply

N = Ni i = ,...,c R (23)
We note that the number of stoichiometric mole balances
for R independent reactions is c R and these balances are
always linear when written in terms of flow rates. The mole
balances in Eq. (23), augmented by R material balance de-
sign equations (17), are equivalent to the conservation equa-
tions (16). The extents of reaction e, are a function of pa-
rameters such as temperature, pressure, molar feed ratios,
reactor type, and volume, etc. The mole balances in Eq. (23)
are also applicable for reactions in a batch process; the only
difference is that n, corresponds to the initial number of
moles of component i, and n corresponds to the number of
moles of component i at any given time t.
The reaction invariants are a linear transformation of the
number of moles of species to give conservation relation-
ships, which depend only on the reaction chemistry, and these
linear transformations correspond exactly to the mole balances.

Rules for Mole Balances
1. Mole balances are equivalentfor any set of the
reference components. The formulation of the mole
balances using the reaction invariants methodology
requires a choice of a feasible set of reference compo-
nents that gives an invertible reference matrix. The
mole balances for one feasible set of reference
components can be obtained by linear combinations of
the mole balances for any other feasible set of
reference components. Therefore, the mole balances
resulting from any feasible set of reference compo-
nents are equivalent (i.e., they will give the same
2. Mole balances are unchanged by linear transforma-
tions in the reaction chemistry. For instance, the mole
balances for the reaction chemistry (A+B C+D;
2A -+ E+D) are equivalent to the mole balances for the
chemistry obtained by linear combinations of the
reactions (e.g., 2B + E 2C + D; B + E A+ C).
3. Mole balances are unchanged by addition of depen-
dent reactions to the reaction chemistry. For example,

Fall 2004

the mole balances for the reaction chemistry (A + B
-> C + D; 2A E + D) are equivalent to the mole
balances for the reaction chemistry after adding a
dependent reaction (2B + E -- 2C + D) to the set.
4. Mole balances for a set of maximum number of
independent reactions are equivalent to the element
balances. Any reaction consisting of the given species
can be obtained by linear combinations from the set of
maximum number of independent reactions for those
species. The maximum number of independent
chemical reactions can be determined using linear
algebra and the chemical formulae of the reaction
species.'3 For this case, one can write the element
balances (e.g., carbon balance, hydrogen balance, etc.)
instead of writing the mole balances.
5. For any set with less than the maximum number of
independent reactions, the element balances are
incomplete and form a subset of the mole balances. In
practice, the reaction chemistry is often represented by
less than the maximum number of independent
The mathematical proofs for these rules can be found in
Gadewar, et al.o101

Categories of Mole Balances

Case 1: R = 0
For a non-reactive system with c components, the stoichio-
metric mole balances are given as

NO = Ni

i= 1,...,c

From the definitions of the reaction invariants, the mole bal-
ances given in Eq. (24) are equivalent to

n = ni

i = ,...,c

Equations (25) are the standard mass balances for non-reac-
tive systems with c components.

Case 2: R=R
The mole balances for a system with the maximum num-
ber of independent reactions are given as

NO = Ni

i=,..c c-Rm

where Rmax is the maximum number of independent reac-
tions.131 These mole balances are equivalent to the element

Case 3: R In the case when the number of reactions is less than the maxi-
mum number of independent reactions, the mole balances

can be written as

No =Ni

i=l ...(c-Rmax)+r

where, r = Rx- R > 0. Therefore, there are more mole bal-
ances than independent element balances. For such chemis-
tries, element balances give an incomplete set of stoichio-
metric constraints imposed due to reactions. For example,
the hydrodealkylation of toluene to benzene, discussed ear-
lier in the paper, is represented by two reactions instead of
Rmax = 3, and a production process to make the pharmaceuti-
cal intermediate, 2-methyl pyrazine (described by Gadewar,
et al."01), is represented by six reactions instead of Rmax = 7.

Unsaturated nitriles such as acrylonitrile are important in-
termediates in the polymer industry. A process for the manu-
facture of a, p -unsaturated nitriles from methanol over a metal
oxide catalyst was published by Kurokawa, et al. "I The spe-
cies present in the reaction system are: acetonitrile (CHCN),
methanol (CH3OH), acrylonitrile (CH2CHCN or AN), hydro-
gen (H2), water (H20), propionitrile (CH3CH2CN or PN), and
methacrylonitrile (C3HCN or MAN). The reactions occur-
ring in the system are

vCH3CN := vCH30H := vAN := vH2 :=

vH20:= vPN:= vMAN:=

We choose I reference components o be H20, PN and MAN

vH20T I 1
Vref:= vPNT Vref:= 0 1 -1
WAT 0 0 1
1 vCH3CNT.Vref =(-1 0 1)

vCH3I0TVrefl =(-1 0 0)
(aUoHo- 0n mosiy .(n
3 vANT-ref =(1 -1 -2)
nfl- nAy= (nom- ifmo)l l n- ,r 2 i B N--MlM)

4 vH2T Vref =(-I 1 1)
(n- I?2Oi1t2O^ +OWPsa -iWp- njiOi

Figure 2. Mathcad worksheet for setting mole balances in
Example 1. (Shaded areas are text regions.)

Chemical Engineering Education

CH3CN + CH30H -- CH2CHCN + H2 + H20
CH3CH2CN + CH30H C3H5CN + H2 + H20

From Eqs. (21) (23), the mole balances for the system are N= N

n TRef -TRef)-lnRef i=l,...,c-R

Degrees of Freedom (c = 7, R = Rmax= 3)
There are 2c = 14 variables and c R = 4 mole balances. There
there are 10 degrees of freedom for solving the mole balances.
Choosing water, propionitrile, and methacrylonitrile as the
ence components (any three components can be chosen as the
ence components as long as the reference matrix is invertible
reference matrix is

(1 1 1
VRef =0 1 -1J
0 0 1

Using Eqs. (28a 30), the stoichiometric mole balances after rearr,
ments are the following system of linear equations:

(nCH3CN -nCH3CN)+(nH20 H20)-nMAN -MAN)=0
o o
(nCH3H nCH3H)+(nH20 -H20)=0

(nAN -AN)-(nH20 -n20)+(nPN ON)+Z2nMAN -MAN)=

(nH2 -n2 ) H20 -n20 )+(npN-n )+nMAN MAN)=0

Our methodology systematically identifies the number of indepen-
dent mole balances, the degrees of freedom, and allows the procedure
of writing down the mole balances to be automated. A Mathcadt71
worksheet for determining the above mole balances is shown in Fig-
ure 2.

Acetone, Fi F
Alant 1 BPA, F4 BPA, Fl-
Plant 1 --->
Phenol, F2 TPA, Fs

i-BPA, F6


Plant2 I PPlant3 PA_
di-PIPH, Fg BPA, Fl I

Phenol, F2 Phenol Phenol, F Phenol, Flo
el Phenol, Fic f t

Figure 3. Process schematic for the manufacture of bisphenol-A.

(28a) We solve the mole balances in Eqs. (31 34) us-
(28b) ing the experimental data from Kurokawa, et al.1101
The reaction system feed consists of acetonitrile and
(28c) methanol. Using a basis of 1 mol/hr of acetonitrile
0, or and a molar feed ratio of 10 (methanol to acetoni-
trile), we have
(29) o
nCH3CN 1 CH30H 10
0 0 0 0 0
nAN= H2 = nH20 = "PN = MAN = 0

before, From Table 1 of Kurokawa, et al.,110' for a Fe-MgO
catalyst, the observed conversion of acetonitrile is
11.2%, and the selectivities to acrylonitrile and
refer- propionitrile are 73.2% and 11.6%, respectively.
ee- Therefore, nc,,,N = 0.888 mol/hr. Since selectivity is
), the
defined as the ratio of the moles of product formed to
the moles of limiting reactant reacted, we get

nAN AN = 0.732
pN -nPN =0.116


Therefore, nAN= 0.08198 mol/hr and npN= 0.013 mol/
(31) hr. Using these values for the 10 degrees of freedom
) in the mole balances, we solve for the remaining flows
(in mol/hr) giving

nCH30H = 9.87
nH20 = 0.129

nH2 = 0.099

nMAN = 0.017

The minimum number of measurements needed from
experiments to determine all flows is equal to the
degrees of freedom for the mole balances. In case
extra measurements are made, they can be used to
cross-check the consistency of the experimental data
and the proposed chemistry.
The mole balances expressed only in terms of flow
rates are linear. Based on the specifications, some-
times the mole balances become nonlinear (e.g., when
ratios of flow rates are specified). Westerberg, et
al.,1[2 describe analytical and numerical approaches
to solving linear and nonlinear equations. Both the
linear equations and nonlinear equations can be
solved using standard mathematical computation pro-
grams such as Mathcad, Matlab,'131 etc.

The purpose of this example is to write the mole
balances for a system consisting of multiple plants,
to identify the degrees of freedom, and to solve the
balances by satisfying the necessary degrees of free-

Fall 2004

dom. This example is more involved than the first one since
it consists of interconnected reaction systems. The process
for the manufacture of bisphenol-A consists of multiple
plants-each plant either produces the desired product or an
intermediate that leads to the desired product. A schematic
for the system is given in Figure 3. The flows in and out for
each plant are identified in the figure. Each plant can be a
complex combination of unit operations, with one or more
reaction systems; we will determine the overall input-output
balances for the plants.
The species in the reactions are
bisphenol-A (BPA)
isomer of bisphenol-A (i-BPA)
trisphenol-A (TPA)
phenyl isopropyl hydroxide (PIPH)
dimer of PIPH (di-PIPH)
The reactions are

Plant 1 (750C, P = 15 psia)

2 Phenol + Acetone -> BPA + H20
2 Phenol + Acetone -> i BPA + H20
3 Phenol + 2 Acetone -- TPA + 2 H20


products are water and TPA; i-BPA is an intermediate that is
sent to Plant 2, and the desired product is BPA. Three (c R =
3) independent mole balances can be written for Plant 1. We
choose BPA, TPA, and i-BPA as the reference components,
and from Eqs. (21-23) the stoichiometric mole balances are

F F4 2F5 F6 = 0
F2 -2F4 -3 F5 -2F6 = 0
F3- F4-2F5 -F6=0


Note that Eqs (41 a,b,c) are obtained by substituting n0Acetone
F,, nAcetone = 0, etc. (see Figure 3 for the meaning of each

> Balances around Plant 2
There are four components (c = 4) and two independent
reactions (R = 2) in this plant. Therefore, two (c R = 2)
independent mole balances can be written. In this plant, i-
BPA is a reactant, di-PIPH is an unwanted by-product, and
PIPH and phenol are intermediates. The flow rates of PIPH,
di-PIPH, and phenol in the inlet and the flow rate of i-BPA in
the outlet of Plant 2 are zero, since no products, by-products,
or intermediates enter the plant and no reactants leave. We
choose phenol and di-PIPH as the reference components, and
from Eqs. (21-23) the stoichiometric mole balances are

F6 F7 = 0
F7 -2F8 -F9 =0


Plant 2 (250'C, P = 4 psia)

i- BPA = Phenol + PIPH


Plant 3 (700C, P = 15 psia)

Phenol + PIPH BPA

The aim is to write the overall stoichiometric mole balances
for Figure 3. The complete set of mole balances consists of
(c R)p balances for each plant, p, and species balances for
each mixer and splitter.

- Balances Around Plant 1
There are six components (c = 6) in this plant and three
independent reactions (R = 3). The process block represents
a complete plant, and if we assume perfect separation of re-
actants and products within the plant, only the reactants enter
the plant and only products leave. Unreacted reactants are
recycled within the process block. Therefore, the flow rates
of the products, by-products, and intermediates in the inlet,
and the reactants in the outlet, of Plant 1 are set to zero. The
reactants for this plant are acetone and phenol, while the by-

Equations (42a,b) are obtained by substituting ni-BpA = F6,
ni-BPA = 0, etc.

1 Balances around Plant 3
There are three components (c = 3) and one independent
reaction (R = 1) in this plant. Therefore, two (c R = 2) inde-
pendent mole balances can be written. PIPH and phenol are
reactants for the plant and BPA is the desired product. The
flow rate of BPA in the inlet, and the flow rates of PIPH and
phenol in the outlet of Plant 3 are zero. We choose BPA as the
reference component, and from Eqs. (21-23), the stoichio-
metric mole balances are

F9 -Fl =0
F10 -F1 =0


Equations (43a,b) are obtained by substituting noPIPH = F9,
nPIPH = 0, etc.

- Mixer-Splitter Balances
To complete the mole balances, we must write balances for
flows that are mixed or split. In Figure 3, F12 and F13 are mixed
to get F2, F4 and F,, are mixed to get F 4, and F7 is split into
F10 and Fl3. Therefore, we write the following balances for

Chemical Engineering Education

mixers and splitters:

F12 + F3 = F2 (44a)
F4 + F1 = F14 (44b)
F7 = Flo +F13 (44c)

> Solutions
Equations (41 a) to (44c) are the mole balances for the plant
complex and must be solved simultaneously. Since they are
all linear equations, this is not a difficult task. There are 14
variables and 10 mole balances leaving 4 degrees of free-
dom. We choose to specify the overall production rate of BPA,
F14 = 100 mol/hr. Also, from experiments, the selectivity to
i-BPA in Plant 1 is F6/F4 = 0.8, the selectivity to TPA in Plant
1 is F,/F4 = 0.05, and the selectivity to PIPH in Plant 2 is F/
F6 = 0.95. The solution of the mole balances yields

Fl = 107.95 F2 = 213.06 F3 = 107.95 F4 = 56.81
Fs = 2.84 F6 = 45.45 F7 = 45.45 Fg = 1.13
F9= 43.18 Flo = 43.18 F,, = 43.18
F12 = 210.79 F13 = 2.27 (45)

Note that all flow rates have units of mol/hr. To produce
100 mol/hr of BPA, we need 107.95 mol/hr of acetone fresh
feed and 210.79 mol/hr of phenol fresh feed. The plant com-
plex produces 107.95 mol/hr of water, 2.84 mol/hr of TPA,
and 1.13 mol/hr of di-PIPH as unwanted by-products.

We have described a systematic method for writing mole
balances for complex reaction systems. The procedure is com-
pletely general and straightforward to implement. We use a
mathematical program to demonstrate the implementation of
the procedure. The procedure is applied to the process for
manufacturing bisphenol-A, which involves interconnected
reaction systems. This method not only greatly simplifies the
task of deriving mole balances, but also ensures that the mole
balances are independent and allows easy determination of
the degrees of freedom. The procedure has been applied to
other complex reaction chemistries-for example, manufac-
ture of an anticonvulsant drug CI-1008 consisting of 11 reac-
tions and 27 components.114]

Mole Balances in Biological Systems
Metabolism in living organisms is a chemical phenomenon and is
usually represented by sets of chemical equations called metabolic
networks. Varma and Palsson"'' published a method of metabolic
flux analysis that combines the stoichiometric mole balances to-
gether with an optimization algorithm to predict the flow rates of
the species in a metabolic network under specified process condi-
tions. The reaction invariants methodology can be used to simplify
the formulation of the metabolic flux analysis equations for bio-
logical systems. A good review on the basics and applications of

Fall 2004

metabolic flux analysis can be found in Edwards, etal.,['61 and Vallino
and Stephanopoulos.[~7'

Mole Balances in Conceptual Process Design
Conceptual design generates potentially profitable alternatives
based on the laboratory analysis of chemical routes to produce a
desired product from available raw materials. During the process of
generation and evaluation of alternatives, estimation of the raw
materials requirement is essential. The reaction invariants method-
ology can be used to formulate the overall mole balances for pro-
cess flowsheets with single or multiple reaction systems. Details on
a method used to automate the formulation of mole balances for
interconnected reaction systems with application to petrochemical
and pharmaceutical production can be found in Gadewar, et al.[14]


We are grateful to the sponsors of the Process Design and
Control Center at the University of Massachusetts at Amherst.
We thank Professor J.M. Douglas for suggesting the
bisphenol-A example.

1. Himmelblau, D., Basic Principles and Calculations in Chemical En-
gineering, 6th ed., Prentice Hall, New Jersey (1996)
2. Schmidt, A.X., and H.L. List, Material and Energy Balances, Prentice
Hall, New Jersey (1962)
3. Felder, R.M., and R.W. Rousseau, Elementary Principles of Chemical
Processes, 3rd ed., John Wiley & Sons, New York, NY (2000)
4. Aris, R., and R.H.S., Mah, "Independence of Chemical Reactions,"
Ind. Eng. Chem. Fundam., 2, 90 (1963)
5. Smith, W.R., and R.W. Missen, "What is Chemical Stoichiometry?"
Chem. Eng. Ed., 13, 26 (1979)
6. Misovich, M., and K. Biasi, "The Power of Spreadsheets in a Mass
and Energy Balance Course," Chem. Eng. Ed., 25, 46 (1991)
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8. Microsoft Excel, Version 9.0; Microsoft Corporation, Seattle, WA
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Systems," Chem. Eng. Sci., 50, 3201 (1995)
10. Gadewar, S.B., M.F. Doherty, and M.F Malone, "A Systematic Method
for Reaction Invariants and Mole Balances for Complex Chemistries,"
Comp. Chem. Eng., 25, 1199 (2001)
11. Kurokawa, H., T. Kato, W. Ueda, Y. Morikawa, Y. Moro-Oka, and T.
Ikawa, "Solid Base-Catalyzed Reaction of Nitriles with Methanol to
form t., P -Unsaturated Nitriles," J. Catal., 126, 199 (1990)
12. Westerberg, A.W., H.P. Hutchison, R.L. Motard, and P. Winter, Pro-
cess Flowsheeting, Chapter 3, Cambridge University Press, Cambridge,
United Kingdon (1979)
13. Matlab, Version 6.5; The Mathworks Inc. Natick. MA
14. Gadewar, S.B., M.F Doherty, and M.F. Malone, "Reaction Invariants
and Mole Balances for Plant Complexes," Ind. Eng. Chem. Res., 41,
3771 (2002)
15. Varma, A., and B.O. Palsson, "Metabolic Flux Balancing: Basic Con-
cepts, Scientific and Practical Use," Bio. Technology, 12, 994 (1994)
16. Edwards, J.S., R. Ramakrishna, C.H. Schilling, and B.O. Palsson,
"Metabolic Flux Balance Analysis," in S.Y. Lee and E.T. Papoutsakis,
Metabolic Engineering, pp. 13-27, Marcel Dekker (1999)
17. Vallino, J.J., and G. Stephanopoulos, "Metabolic Flux Distibutions in
Corynebacterium Glutamicum During Growth and Lysine Overpro-
duction," Biorechnol. Bioeng., 41, 633 (1993) 0


MM, -classroom



Rowan University Glassboro, NJ 08028

Behavioral scientists classify thought processes into
cognitive and affective domains."1 The cognitive
domain includes higher-order thought processes such
as logic and reasoning and is the primary (and in many cases,
the only) target of engineering curricula. The affective do-
main includes attitudes, values, and self-concept. These at-
tributes typically cannot be measured directly through ex-
ams and other classroom instruments, yet they are essential
components of the overall developmental process.
ABET itself recognizes the importance of the affective
domain by including criteria in their assessment of engineer-
ing programs such as "engages in lifelong learning," "under-
stands the impact that engineering has on society," and "com-
municates effectively."'21 Besterfield-Sacre, et al., observed
that students' attitudes about engineering and their abilities
change throughout their education and influence motivation,
self-confidence, perception of engineering, performance, and
retention.l3 The same group also found that attitudes toward
engineering directly related to retention during the freshman
year.141 Seymour and Hewitt[5' examined students who left
engineering programs and found that according to measures
external to the engineering curriculum (high school GPA, SAT
scores, IQ, etc.), they were not academically different from
their peers who continued in the program. Retention did, how-
ever, correlate closely with student attitude. For many stu-
dents, college challenges their level of motivation and the
academic aptitude for the first time, but too often provides
them with little or no help in identifying and overcoming the
barriers to their learning.
The Study Group on the Conditions of Excellence in Ameri-
can Higher Education stated "there is now a good deal of
research evidence to suggest that the more time and effort
students invest in the learning process and the more intensely
they engage in their own education, the greater will be their
satisfaction with their educational experiences, their persis-
tence in college, and the more likely they are to continue their
learning."'[6 Thus, it is reasonable to conclude that an effec-
tive student must be both self-aware and self-directed, yet these
* College of Communications, Rowan University


issues are often completely ignored by engineering faculty.
Student awareness and understanding of their learning
skills, performance, preferences, and barriers is referred to
as metacognition. Although different research groups empha-
size different aspects of metacognition,7'l it clearly refers to
two distinct, but related issues: 8E
Awareness and knowledge of self as learner
Conscious self-control and self-regulation of cognition
In essence, metacognitive learners must understand their
strengths and weaknesses in learning and control how they
will approach a problem. Engineering professors tend to per-
ceive barriers to student learning as lack of intelligence or
motivation, when in reality, students may simply lack aware-
ness of the causes of the barriers they are facing.
Barriers to student learning also arise in connection with
what has become a basic component of engineering educa-
tion-working in teams. Experts agree on the importance of
involving undergraduates in teamwork.[9-ll] Seat and Lord[12]
observed that while industry seldom complains about the tech-
nical skills of engineering graduates, industrial employers and

Jim Newell is a Professor of Chemical Engineering at Rowan University.
He currently serves as Secretary/Treasurer of the Chemical Engineering
Division of ASEE and has won both the Ray Fahien award from ASEE for
contributions to engineering education and a Dow Outstanding New Fac-
ulty Award. His research interests include high-performance polymers, ru-
bric development and forming metacognitive engineering teams.
Kevin Dahm in an Associate Professor of Chemical Engineering at Rowan
University. He received his BS from Worcester Polytechnic Institute in 1992
and his PhD from Massachusetts Institute of Technology in 1998. His cur-
rent primary teaching interests are assessment of student learning and
integrating process simulation throughout the chemical engineering cur-
Roberta Harvey is an Assistant Professor in the Composition and Rhetoric
Department at Rowan University. She holds a PhD from the University of
Wisconsin-Milwaukee and team-teaches the innovative multidisciplinary
design and composition Sophomore Clinic course at Rowan. She is certi-
fied to administer LCI surveys.
Heidi Newell is currently the assessment coordinator for the College of
Engineering at Rowan University. She previously served as the assess-
ment consultant for the ChE department at the University of North Dakota.
She holds a PhD in Educational Leadership from the University of North
Dakota, a MS in Industrial and Organizational Psychology from Clemson,
and a BA in Sociology from Bloomsburg University.
Copyright ChE Division of ASEE 2004

Chemical Engineering Education

educators are often concerned with performance skills (i.e.,
interpersonal, communication, and teaming). Lewis, et al.,31'
correctly observed that if students are to develop effective
teaming skills, then teaming must be an explicit focus of the
project. A metacognitive approach would encourage students
to become conscious of their team skills. Thus, metacognition
may be valuable for improving an individual's relation-
ship not only to their own learning processes, but also to
the learning processes of others and to the collaborative
learning process in general.
Weinstein and Meyer[141 described the
importance of students understanding their learn
own learning preferences, abilities, and is referred
cognitive styles, and discussed how "learn- learners m
ing how to learn" helps students develop learnii
knowledge of strategies required to
achieve specific tasks. To provide this
metacognitive awareness to our students, we used the Learn-
ing Combination Inventory (LCI), a survey instrument de-
veloped by Johnston and Dainton to profile an individual's
learning patterns.['5] The theoretical basis for the LCI is the
Interactive Learning Model, which posits that learning pro-
cesses occur through four distinct learning patterns: sequen-
tial, precise, technical, and confluent. The patterns are used
by all learners to varying degrees; a given individual's LCI
profile is determined by the strengths of his/her preferences
and avoidances, scored as "avoid," "use as needed," and "use
first." Some learners lead with one or two patterns, some
avoid certain patterns, some are able to use a number of pat-
terns on an as-needed basis, and still others exhibit strong
preferences for a number of patterns. Each pattern is distin-
guished by a number of features. A few hallmarks are listed
Sequential learners prefer order and consistency. They want
step-by-step instructions, and time to plan, organize, and
complete tasks.
Precise learners thrive on detailed and accurate information.
They take copious notes and seek specific answers.
Technical learners like to work alone on hands-on projects.
They enjoy figuring out how something works and insist on
practical objectives for assignments.
Confluent learners have a strong desire for creativity and
innovation. They are not afraid of risks or failure and prefer
unique, unconventional approaches.
Depending on the interaction of an individual's patterns,
strong preferences associated with one pattern may coincide
with strong avoidances of another pattern. For example, the
sequential learner's preference for order and consistency may
be evidenced as a desire for predictability, and, therefore, as
a corresponding avoidance of the risk and openness to chaos
that is a characteristic of the confluent learner. In each case,
knowledge of this profile provides extremely useful insights
into the conditions that promote learning. The LCI is based
on three assumptions about these conditions:"1'
Fall 2004

1) Learners learn most efficiently and successfully when
allowed to use their stable-over-time patterns of cognition
(intelligence, aptitude, experiences, levels of abstraction),
conation (pace, autonomy, natural skills), and affectation
(sense of self, values, and range of feelings) to engage in a
learning task.
2) Learners learn best when given the opportunity to know their
learning process, allowed to negotiate their learning environ-
ment, and provided the tools to strategize to meet the rigors
of standardized and alternative methods of assessment and

Student awareness and understanding of their
ing skills, performance, preferences, and barriers
to as metacognition.... In essence, metacognitive
lust understand their strengths and weaknesses in
ng and control how they will approach a problem.

3) Learners receive the most effective instruction when their
teachers have an appreciation for their diverse learning
Other attempts to gain a better understanding of engineer-
ing students as learners have employed the concept of learn-
ing styles, using instruments such as the Myers-Briggs in-
ventory.[16-171 The developers of the LCI explain the differ-
ence between their approach and that of learning styles in
this way:E.18
Unlike learning styles, [the Interactive Learning Model]
is an advanced learning system that provides an inward
look at a learner's internalized metalearning behaviors, an
outward analysis of a learner's actions, and a vocabulary
for communicating the specific learning processes that
yield externalized performance. Other measures of
personality, multiple intelligence, or learning styles
provide information about the learner and then leave the
learner informed but unequipped to use the information...
[The LCI] not only provides the learner with the means to
articulate who s/he is as a learner but then provides the
strategies (metawareness) for the learner to use these
learning tactics with intention.
The LCI survey is composed of 28 Likert scale items-
descriptive statements followed by a five-point set of re-
sponses-and three questions requesting written responses.
The 28 questions are scored according to the patterns they
illustrate, and from these scores the LCI profile is generated.
The three written responses are used to validate the prefer-
ences and avoidances exhibited by the scores. Over the past
9 years, teachers and administrators in 11 national and inter-
national sites, along with faculty at Rowan University, have
tested the reliability and validity of the LCI.I181 Studies con-
ducted to verify the reliability and validity of the LCI are de-
scribed in the LCI Users Manual. [I Professor Newell partici-
pated in a three-hour workshop on learning preferences and
consulted regularly with personnel for Let Me Learn. Professor
Harvey has subsequently begun a 10-week intensive course


involving 28 hours of instruction on all aspects of the LCI.
The LCI has been used in the engineering program at Rowan
University to enhance the performance of student teams.1191
In Sophomore Clinic I, a multidisciplinary sophomore de-
sign and composition course that is taught collaboratively by
faculty from engineering and composition and rhetoric, fac-
ulty used the results of the LCI to form teams with balanced
components of each learning pattern, based on research sug-
gesting that successful learning in team environments occurs
if team members have complementary learning patterns.
Our hypothesis was that this particular combination of
avoidances and preferences leads to barriers that specifically
impact performance of student teams in the upper-level de-
sign courses, such as the Junior/Senior Clinics.[20] In these
courses, students work independently in teams on semester-
long and sometimes multi-year projects. Many of the projects
involve external funding, real clients and sponsors, and ac-
tual product development. For example, student teams under
the supervision of chemical engineering faculty have worked
on emerging topics including enhancing the compressive
properties of Kevlar, examining the performance of polymer
fiber-wrapped concrete systems, advanced vegetable process-
ing technology, metals purification, combustion, membrane
separation processes and other areas of interest. Every engi-
neering student participates in these projects and benefits from
hands-on learning, exposure to emerging technologies, in-
dustrial contact, teamwork experience and technical commu-
nication practice.t21-221
These conditions make the Junior/Senior Clinics meaning-
ful and exciting learning experiences, but the pressure de-
rived from the intense and often unpredictable environment
exacerbates the students' barriers to learning. Preferences for
sequence and avoidance of chaos and risk leave students frus-
trated by what they see as the lack of structure of a real-world
project. They are unsure how to cope in situations where clear
instructions and step-by-step procedures have been replaced
by multi-tasking, frequent shifts in direction, uncertain
timelines, and inconsistent expectations. They may become
impatient with learning patterns exhibited by team members
that conflict with their own. The situation is further com-
pounded by the high technical preference that many of them
have, which in addition to the hands-on, problem-solving
aptitudes listed above, has other significant hallmarks. Al-
though technical learners are distinguished by a love of chal-
lenges, which serves the Junior/Senior Clinic student well,
they are also known for preferences that are not so compat-
ible with this situation: working alone, keeping knowledge
and/or feelings inside, and resisting changes to familiar or
preferred patterns. These students are not likely to naturally
communicate regularly with team members, nor to reflect on
or seek guidance about obstacles they are experiencing. Of
particular interest to us is the technical learner's resistance to
writing. Because technical learners keep information in their

heads and do not readily volunteer it to others, they tend to
write minimally, not seeing a need for a great deal of detail to
be committed to paper.
This situation is addressed by using writing to harness the
metacognitive awareness yielded by the LCI. In large part
because of what we know about technical learners and their
particular barriers, we believe that focusing on writing will
be a productive approach on multiple levels:1231
To see that students get increased opportunities to write in
their classes, both in order to communicate and in order to
aid learning
To develop further the leadership skills faculty need to sustain
long-term writing across the curriculum projects and the
evaluation and assessment skills they need to determine these
The perspective available from the LCI is used to target
the specific barriers to student learning that have been

All chemical engineering students had completed the Learn-
ing Combination Inventory (LCI) prior to beginning the Jun-
ior/Senior Clinic. They met with Dr. Dahm and Dr. James
Newell during the first weeks of clinic to discuss their LCIs
and those of their team members. These discussions included
strengths and weaknesses of each preference, possible sources
of conflict, consideration of how different people process
information and approach problems, and ways to bridge dif-
ferences in learning preferences. As a specific example, when
most members of a team have strong preferences for sequence
(like most participants in this study) but one member avoids
sequences, the high-sequence team members would likely
view the other learner as lazy or a procrastinator. At the same
time, the sequence-avoidance learner would view the rest of
his team as up-tight and bossy. Recognizing the potential for
this conflict in advance and understanding its cause can help
teams deal with it more effectively when it happens.
Because of the likelihood that team profiles are not bal-
anced, students were counseled on the barriers presented by
strong preferences for the technical learning pattern, so that
team members would begin to fill the gaps created by lack of
diversity. Technical learners prefer to immediately begin
hands-on work and are less likely to read directions or per-
form a comprehensive literature review first. While having
technical learners is beneficial in the lab, someone needs to
do the background work first. Groups with all technical learn-
ers were encouraged to appoint a member to start the litera-
ture review first, even though this meant working against their
preferred pattern.
Two activities that further enhanced this effort were bi-
weekly status reports and team charters. Most faculty mem-
bers, in supervising a clinic project, require some sort of pe-
riodic progress report or update. Historically, however, there

Chemical Engineering Education

has been little coordination between faculty concerning the
scope and format of these status reports. In the Fall 2003,
every faculty member in the chemical engineering depart-
ment required each member of each clinic team to answer
the following questions, in the form of a written status re-
port, every two weeks:
1. What issues are you having with the technical aspects of the
2. What logistical issues (ordering problems, scheduling,
software issues, etc.) are you facing?
3. What issues in team dynamics have arisen since our last
meeting and how are you dealing with them?
4. What do you think the highest priority task is during the next
two weeks?
5. What is the largest barrier to accomplishing that task?
These questions resemble the journaling activities used at
Clemson University[241 and the University of Texas at Aus-
tinE251 in which students write reflective pieces summarizing
key concepts, discuss concerns, and (at UT Austin) create an
analogy for the presented material. Unlike these journals,
however, the questions posed in the proposed status reports
have the student focus on barriers to completing the project,
team dynamic issues, and prioritization. They represented an
effort to have the student evaluate not only whether they have
made suitable progress, but also what issues are creating prob-
lems. Standardizing the status report across the department
made it a more valid assessment instrument, as well as a
useful aid to the supervisor for project management. An
additional goal is to help students avoid hierarchical judg-
ments and focus instead on what made their teams effec-
tive or ineffective.
Also during the first week of the semester, each team was
asked to develop and sign a team charter that dealt with spe-
cific issues in team dynamics including the role of each indi-
vidual, the responsibility of each individual to the team, the
responsibility of the team to each individual, and an algo-
rithm for dealing with potential future conflicts. Note that
only chemical engineering faculty members participated in
this preliminary test, so chemical engineering students who
were working on projects supervised by faculty members
in other engineering disciplines were not required to form
a team charter or participate in any of the other activities
described in this section.
There can be little doubt that writing within the engineer-

ing curriculum has intrinsic benefits of its own. Kranzber'26'
reported that, for engineers who had been out of school for
ten years, the most common answer to the question, "What
courses do you wish you had taken?" was English or writing
courses. Both ABET and the Canadian Accreditation Board[271
now require the development of communication skills for en-
gineering students. As a result, many engineering programs
incorporate writing-to-learn in their curricula.128-291 The abil-
ity to formulate a coherent written report requires that the
student think clearly about the technical engineering prob-
lem.'29-321 In much the same way, requiring students to con-
template, in writing, their approach to problem solving
and the barriers that they are facing will compel the same
clarity of thought. This clarity is an essential component
of metacognition.

One issue noted during this study was that there was rela-
tively less diversity among the profiles of engineering stu-
dents compared to other students, which hindered the cre-
ation of balanced teams. Though not universal, there is a strong
tendency for engineering students to lead with the technical
pattern. We have also observed among our Rowan engineer-
ing students a tendency to exhibit relatively low scores (that
is, in the "avoid" or low "use as needed" range) in precision
and confluence, and relatively high scores ("use first" or high
"use as needed" range) in sequence.
Only one engineering student showed an avoidance for the
technical pattern. When this was discussed with her team-
mates, they immediately decided to send her to the library to
start the initial literature review while the other members of
the team went directly to the lab to figure out the equipment.
Although not a specific goal of this project, an overall in-
crease in student use of writing as a tool for engineering work
was observed. This was partially a result of increased prac-
tice. Students were surveyed about the effectiveness of the
various aspects of the team charters, status reports, and LCI
interpretations. The survey used a four-point Likert scale.
Table 1 summarizes the key findings.
From these results, it is clear that the students felt the team
charters helped them focus on teaming issues and that the
LCI discussion helped them understand the differences in
problem-solving approaches among their teammates. One

Fall 2004

Results of Metacognitive Student Survey
Mean Response Percentage of
4=strongly agree respondents who agree
Survey Question I=strongly disagree or strongly agree
The team charter helped my team define expectations. 3.4 85
The LCI discussions helped me understand differences in how my teammates approach problems. 3.4 80
The biweekly status reports helped our team identify priorities. 3.2 77
The biweekly status reports helped with team dynamics. 2.8 62

team specifically referred to the value of the team charter
when one member stopped showing up and eventually left
the major. Many teams actually developed formal grievance
procedures for their teams, even though they were not asked
to do so as part of the assignment.

The teams seemed to value the status reports as a mecha-
nism to identify barriers, but were less convinced of its role
in aiding team dynamics. Several students suggested that the
biweekly status reports should be shared with their teammates
instead of just being submitted to the faculty team leader.
Although the reports were submitted individually, the con-
sistency within groups of identifying barriers and priorities
showed that the teams were communicating effectively.

In support of the student responses, the faculty observed
that not a single clinic team supervised by a chemical engi-
neering faculty member in the fall of 2003 experienced crip-
pling team dynamic issues. While anecdotal, this observa-
tion is compelling. In a typical semester, there are several
teams that struggle, and there is at least one team that fails to
meet its semester goals for reasons directly attributable to
team dynamics. In the fall of 2003, one chemical engineer-
ing senior who worked on a project supervised by a civil en-
gineering faculty member (and consequently was not partici-
pating in the teaming exercises described here) was on a team
in which communication between members was so poor the
team failed to turn in a final report.

From these data, it appears that combining an awareness of
their own learning styles and those of their teammates with
a continual written dialogue focused on identifying barri-
ers to success and identifying priorities resulted in in-
creased student success measured in terms of both indi-
vidual and team performance.

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Chemical Engineering Education

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A rizona, University of ........................................ ....... ..... 326
Arizona State University......................... ...................327
A uburn U university ........................................ .................. 328
A rkansas, U university of .............................. .......................... 329
Brigham Young University ..................... ....................425
British Columbia, University of......................................425
Brown University......................... ... .....................436
Bucknell University ........................................ .......... ...... 426
Calgary, University of............................. ....................330
California, Berkeley; University of ......................................... 331
California, Davis; University of......................................332
California, Irvine; University of .................... ......................333
California, Los Angeles; University of .....................................334
California, Riverside; University of.......................................... 335
California Institute of Technology .......................................... 336
Carnegie-Mellon University ........................................337
Case Western Reserve University...........................................338
Cincinnati, University of.................. ........................339
City College of New York........................... ...................340
Cleveland State University ................... ....................341
Colorado, U university of ............................. ..........................342
Colorado School of Mines ..........................................343
Colorado State University......................... ..................344
Columbia University......................... ....................426
Connecticut, University of ..................... .....................345
Cornell University......................... ... .. .................... 346
Dartmouth College............................ .....................347
Delaware, University of ................... .........................348
Denmark, Technical University of............................................349
Drexel University......................... .... .....................350
Florida, University of....................................................351
Florida A&M/Florida State University...................................... 352
Florida Institute of Technology.......................................353
Georgia Institute of Technology ........................... .......... 354
Houston, University of............................ ................... 355
Howard University......................... .....................427
Illinois, University of............................ ..................... 356
Illinois, Chicago; University of ..............................................357
Illinois Institute of Technology.....................................358
Iowa, University of ............................ .....................359
Iowa State University................... ... ...........................360
Johns Hopkins University......................... ...........................361
Kansas, University of............................ .. ...................362
Kansas State University .................... ......................363
Kentucky, University of.............................. ......................364
Lam ar University ........................................ ....................427
Laval U university ...................................... ....................... 428
Lehigh University .................................. ..................365
Louisiana, Lafayette; University of .......................................... 366
Louisiana State University......................... .........................367
Louisville, University of ...................... ......................428
M aine, University of ............................. .....................368
M anhattan College........................................... ................... 369
Maryland, University of.............................. .......................370
Maryland, Baltimore County; University of.............................371
M assachusetts, University of .....................................................372
Massachusetts Institute of Technology .....................................373
McGill University .......................... ...........................429
M cM aster U university ................................. ........................... 374
Michigan, University of ....................... ...........................375

Fall 2004 3
Fall2004 3,

Michigan Technological University............................... ............ 429
Minnesota, University of .................... ...................376
M ississippi State U university .......................................................377
M issouri, Columbia; University of .......................................... 378
Missouri, Rolla; University of ............................ ........... 379
M onash University......................... ... .. ...................430
Montana, University of ....................... .......................430
Nevada, University of ....................... ...... .........................431
New Jersey Institute of Technology.....................................380
New M exico, University of .....................................................381
New Mexico State University.................... ........................382
North Carolina, University of ..................... ..........................383
North Dakota, University of .......................... ..................... 431
Northeastern University......................... ...................384
Northwestern University....................... ..................... 385
Notre Dame, University of..................... ....................386
Ohio State University.................................... ..................... 387
Oklahoma, University of..................... ......................388
Oklahoma State University....................... .. ..................... 389
Oregon State University......................... ..................... 432
Pennsylvania, University of ..........................................390
Pennsylvania State University .................... ..........................391
Pittsburgh, University of..................... .....................392
Polytechnic University........................................... 393
Princeton University ........................................ ......... ......394
Purdue University .................................. ..................... 395
Rensselaer Polytechnic Institute ..............................................396
Rice University .................................. ..................... 397
Rochester, U university of ............................. ........................... 398
Rose-Hulman Institute of Technology ......................................432
Rowan University ....................... ...........................399
Rutgers University .............................. .....................400
Singapore, National University of ........................................... 401
South Carolina, University of ............................. ............ 402
South Florida, University of ...........................................433
Southern California, University of...................... ...................... 403
State University of New York.................... ....................404
Stevens Institute.......................... .... ......................... 405
Sydney, University of ......................... ...... .................433
Syracuse, University of.......................... ..... .................433
Tennessee, University of.................... ..........................406
Tennessee Technological University..........................................407
Texas, University of ........................ ...........................408
Texas A&M University........................... .. ..................409
Texas A&M Kingsville ...................... ...........................434
Toledo, University of ............................ ... ........................410
Tufts U university ........................................ .............. ........411
Tulane University......................... ... ... ..................412
Tulsa, University of ................... .............. .................... 413
Vanderbilt University................................. .............. 414
Villanova University .......................... ..... ...................435
Virginia, University of ............................... ........................ 415
Virginia Tech.................... ..............................416
Washington, University of ....................... ...... ........... 417
W ashington State University..................... ....... .............418
Washington University......................... ....................... 419
W aterloo, University of....................... .............. .................. 435
Wayne State University........................ ....................... 420
W est Virginia University...................... ... ........... ...............421
Wisconsin, University of ............................................422
Worcester Polytechnic Institute ............................................... 423
Wyoming, University of ....................... ......................436
Y ale U university .......................................... ..................... 424


Graduate Education in Chemical Engineering

Teaching and
research assistantships
as well as
industrially sponsored

In addition to
tuition and fees
are waived.

PhD students
may get
some incentive

The deadline for
April 15th.

Multiphase Processes,
Fluid Flow, Interfacial
Phenomena, Filtration,

Nanocomposite Materials,
Sonochemical Processing,
Polymerization in
Nanostructured Fluids,
Supercritical Fluid

Catalysis, Reaction
Engineering, Environ-
mentally Benign
Fuel Cell

Molecular Simulation,
Phase Behavior, Physical
Properties, Process

Materials Processing and
CVD Modeling
Plasma Enhanced Deposition
and Crystal Growth

L. K. JU
Biochemical Engineering,

BioMaterial Engineering
and Polymer Engineering

Surface Modification,
Polymer Thin film

Nonlinear Control,
Chaotic Processes
Product Development

Biocatalysis and

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

Chemical Engineering Education




& Biological


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

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, Molecular
Simulations, Nanoscale Modeling, Polymer
Processing and Rheology, Self-Assembled
Materials, Suspension Rheology

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


An equal employment / equal
educational opportunity institution

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)
A. Gupta, Ph.D. (Stanford)
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)
C. H. Turner, Ph.D. (NC State)
J. M. Wiest, Ph.D. (Wisconsin)
M. L. Weaver, Ph.D. (Florida)

Fall 2004

Chemical &

Materials Engineering


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
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

Michael R. Banish- Ph.D. (University of Utah)
Thermo physical property measurements
(256) 824-6969,
Ram6n L. Cero Ph.D. (UC-Davis)
Professor and Chair
Capillary hydrodynamics, multiphase flows, enhanced heat transfer
(256) 824-7313,
Chien P. Chen Ph.D. (Michigan State)
Multiphase flows, spray combustion, turbulence modeling,
numerical methods in fluids and heat transfer.
(256) 824-6194,
Krishnan K. Chittur Ph.D. (Rice)
Protein adsorption to biomaterials, FTR/ATR at solid-liquid
interfaces, biosensing.
(256) 824-6850,
James E. Smith Jr. Ph.D. (South Carolina)
Kinetics and catalysis, powdered materials processing, combustion
diagnostics and fluids visualization using optical methods.
(256) 824-6439,
Katherine Taconi Ph.D. (Mississippi State)
Assistant Professor
Methanogenic generation of biogas from synthesis gas fermenta-
tion on waste waters
(256) 824-6874,
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,

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

Chemical Engineering Education

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

38 professors and over 145 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

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 Thennodynamics of Polymer Solutions and Blends
K. T. CHUANG, Ph.D. (University of Alberta)
Fuel Cell Catalysis Separation Processes Pollution Control
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
Optimization High Performance Control
M. R. GRAY, Ph.D. (California Inst. of Tech.)
Bioreactors Chemical Kinetics Bitumen Processing
R. E. HAYES, Ph.D. (University of Bath)
Computational Fluid Dynamics Chemical Reaction Engineering
B. HUANG, Ph.D. (University of Alberta)
Controller Perfonnance Assessment Multivariable Control Statistics
S. M. KRESTA, Ph.D. (McMaster University)
CFD Turbulent Mixing and Reactor Design
S. M. KUZNICKI, Ph.D. (University of Utah)
Molecular Sieve Synthesis Separations Catalysis
S. LIU, Ph.D. (University of Alberta)
Fluid-Particle Dynamics Transport Phenomena Kinetics Pulp and Paper
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) EMERITUS
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 Surface Science Computational Chemistry
M. RAO, Ph.D. (Rutgers University)
Al m Intelligent Control Process Control
S. L. SHAH, Ph.D. (University of Alberta)
Computer Process Control System Identification Process and Peronrmance 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 e Coal Cleaning
and Combustion
T. YEUNG, Ph.D. (University of British Columbia)
Emulsions Interfacial Phenomena Micromechanics

Fall 2004


ROBERT G. ARNOLD, Professor (CalTech)
Microbiological Hazardous Waste Treatment, Metals Speciation and
PAUL BLOWERS, Assistant Professor (Illinois, Urbana-Cham]
Chemical Kinetics, Catalysis, Surface Phenomena
JAMES C. BAYGENTS, Associate Professor (Princeton)
Fluid Mechanics, Transport and Colloidal Phenomena, Bioseparation
WENDELL ELA, Associate Professor (Stanford)
Particle-Particle Interactions, Environmental Chemistry
JAMES FARRELL, Associate Professor (Stanford)
Sorption/desorption of Organics in Soils
JAMES A. FIELD, Professor (Wagenigen University)
Bioremediation, Microbiology, White Rot Fungi, Hazardous Waste
ROBERTO GUZMAN, Associate Professor (North Carolina St
Affinity Protein Separations, Polymeric Surface Science
ANTHONY MUSCAT, Associate Professor (Stanford)
Kinetics, Surface Chemistry, Surface Engineering, Semiconductor
Processing, Microcontamination
KIMBERLY OGDEN, 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
EDUARDO SAEZ, Associate Professor (UC, Davis)
Polymer Flows, Multiphase Reactors, Colloids
FARHANG SHADMAN, Professor (Berkeley)
Reaction Engineering, Kinetics, Catalysis, Reactive Membranes,
REYES SIERRA, Associate Professor (Wageningen University
Environmental Biotechnology, Biotransformation of Metals, Green
JOST 0. L. WENDT, Professor and Head (Johns Hopkins)
Combustion-Generated Air Pollution, Incineration, Waste

For further information, write to

Chemical and Environmental





The Department of Chemical and Enrivonmental Engineering
at the University of Arizona offers a wide range of research
opportunities in all major areas of chemical engineering and
environmental engineering. The department offers a fully accredited
undergraduate degree in chemical engineering, as well as MS and PhD
degrees in both chemical and environmental engineering. A signifi-
cant portion of research efforts is devoted to areas at the boundary
between chemical and environmental engineering, including environ-
mentally benign semiconductor manufacturing, environmental
remediation, environmental biotechnology, and novel
water treatment technologies.
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.


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 Engineering Education



Department of Chemical and Materials Engineering

A Distinguished and Diverse Faculty
Chemical Engineering A multi-disciplinary research
Jonathan Alien, Ph.D., MIT. Atmospheric aerosol chemistry, single-particle measurement environment with opportunities
techniques, environmental fate of organic pollutants in electronic materials
in electronic materials
James Beckman, Ph.D., Arizona. Unit operations, applied mathematics, energy-efficient water pro
purification, fractionation, CMP reclamation processing biotechnology
Veronica Burrows, Ph.D., Princeton. Surface science, environmental sensors, semiconductor processing, characterization,
processing, interfacial chemical and physical processes in sensor processing and simulation of materials *
Ann Dillner, Ph.D., Illinois, Urbana-Champaign. Atmospheric particulate matter (aerosols) ceramics air and water
chemistry and physics, ultra fine aerosols, light scattering, climate and health effects of purification atmospheric
aerosols chemistry process control
Jeffrey Heyes, Ph.D., Colorado, Boulder. Modeling of biofluid-tisue interaction, tissue and
biofilm mechanics, parallel multigrid solvers
Jerry Y.S. Lin, Ph.D., Worcester Polytechnic Institute. Advanced materials (inorganic mem-
branes, adsorbents and catalysts) for applications in novel chemical separation and reaction
Chan Beum Park, Ph.D., POSTTECH, South Korea. Bioprocess in extremist, 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)
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
Joe Wang, Ph.D., Israel Institute of Technology. Nanomaterial-based bioelectronics, biosensors
and biochips, electrochemistry
Materials Science and Engineering
James Adams, Ph.D., Wisconsin. Atomistic stimulation of metallic surfaces, adhesion, wear, and
automotive catalysts, heavy metal toxicity
Terry Alford, Ph.D., Comell. 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
Cody Friesen, Ph.D., MIT. Surface/Interface physics, nanomechanics, nanostructured materials, thin
film growth, novel approaches to catalysis and sensing, electrochemical processes
Ghassan E. Jabbour, Ph.D., Arizona. Development of materials for optical and electronic applications
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
Nathan 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 at ASU, 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, or visit us at

Fall 2004

Graduate Program in the Department of Chemical Engineering

University of Arkansas

,1 The Department of Chemical Engineering at the University of Arkansas
Offers graduate programs leading to M.S. and Ph.D. Degrees.
Qualified applicants are eligible for financial aid. Annual Departmental
'a stipends provide $20,000, Doctoral Academy Fellowships provide
$25,000, and Distinguished Doctoral Fellowships provide $30,000. For
stipend and fellowship recipients, all tuition is waived. Applications re-
rs zool ceived before April 1st will be given first consideration.
Areas of Research
El Biochemical engineering
E Biological and food systems
1E Biomaterials
E Chemical process safety
E Consequence analysis of hazardous chemical releases
E Electronic materials processing
1E Fate of pollutants in the environment
E Fluid phase equilibria and process
E Integrated passive electronic Faculty
E Membrane separations M.D. Ackerson
E Mixing in chemical processes R.E. Babcock
R.R. Beitle
E.C. Clausen
R.A. Cross
J.A. Havens
W.A. Myers
W.R. Penney
T.O. Spicer
G.J. Thoma
J.L. Turpin
R.K. Ulrich

For more information contact
Dr. Richard Ulrich or 479-575-5645
Chemical Engineering Graduate Program Information:

Chemical Engineering Education


Chemical Engineering

Fatu1W U
William R. Ashurst, Jr. University of California, Berkeley
Mark E. Byrne Purdue University
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
Mark R. Eden Technical University of Denmark
Said S.E.H. Elnashaie University of Edinburgh
James A. Guin University of Texas at Austin
Ram B. Gupta University of Texas at Austin
Thomas R. Hanley Virginia Tech Institute
Gopal A. Krishnagopalan University of Maine
Yoon Y. Lee Iowa State University
Glennon Maples Oklahoma State University
Ronald D. Neuman The Institute of Paper Chemistr
Timothy D. Placek University of Kentucky
Christopher B. Roberts University of Notre Dame
Arthur R. Tarrer Purdue University
Bruce J. Tatarchuk University of Wisconsin t l11

Research Areas
m Fuel Cells Hydrogen
* Biochemical Engineering Drug Delivery
* Pulp and Paper Microfibrous Materials
* Process Systems Engineering
* Integrated Process Design
* Environmental Chemical Engineering
. Catalysis and Reaction Engineering
* Materials Polymers Nanotechnology
* Surface and Interfacial Science
" Thermodynamics Supercritical Fluids
" Electrochemical Engineering
" Transport Phenomena

-Inquiries4o: '
lector of Graduate Recruitulg
Department of Chemical Engineering
auburn ,lniversit),-AL 36849-5127 t7
Phone 334.844.4827
Fax 334 844.2063

Financial assistance i avaflable'sotualified applicants.

Fall 2004


T. G. Harding, Head (Alberta)
J. Azaiez (Stanford)
L. A. Behie (Western Ontario)
C. Bellehumeur (McMaster)
P. R. Bishnoi (Alberta)
I. D. Gates (Minnesota)
J.M. Hill (Wisconsin)
M. Husein (McGill)
A. A. Jeje (MIT)
M. S. Kallos (Calgary)
A. Kantzas (Waterloo)
D. Keith (MIT)
B. B. Maini (Univ. Washington)
A. K. Mehrotra (Calgary)
S. A. Mehta (Calgary)
R. G. Moore (Alberta)
P. Pereira (France)
M. Pooladi-Darvish (Alberta)
A. Sen (Calgary)
A. Settari (Calgary)
W. Y. Svrcek (Alberta)
M. A. Trebble (Calgary)
H. W. Yarranton (Alberta)
B. Young (Canterbury, NZ)
L. Zanzotto (Slovak Tech. Univ., Czechoslova



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
Upgrading, Catalysis and Fuel Cells
Environmental Engineering
Modeling, Simulation & Control
Petroleum Recovery & Reservoir Engineering
Polymer Processing & Rheology
Process Development
Reaction Engineering/Kinetics
Transport Phenomena
Fellowships and Research Assistantships are available to all qualified applicants.

For Additional Information Contact *
Dr. J. Azaiez Associate Head, Graduate Studies
Department of Chemical and Petroleum Engineering
University of Calgary Calgary, Alberta, Canada T2N 1N4


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.



Chemical Engineering Education

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.

Nitash P. Balsara Alexis T. Bell
Harvey W. Blanch Elton J. Cairns
Arup K. Chakraborty Douglas S. Clark
Jean M.J. Frechet Enrique Iglesia
David B. Graves Jay D. Keasling
Alexander Katz Roya Maboudlan
Susan J. Muller John S. Newman
John M. Prausnitz Clayton J. Radke
Jeffrey A. Reimer David V. Schaffer
Rachel A. Segalman

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


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

Chairman: Arup K. Chakraborty I



Fall 2004


Bell, Graves, Iglesia,


Graves, Maboudian,
Reimer & Segalman

Univrsit of alifrnia Berele

Darlil -EBloek, Asoic Professor Ph.D..aJivemrys ofMineso 1992 Industrial jenriaion, bioproces
Ssi"miam~r andaroi iaaemtllgencew mthmod
Roer-B.'oulloain, Pfe~or dEnidoed Cair* Ph.D..Universiy of Malboums 19764 -Tne wf lcmllo. ;.rmennLo
-N-iofD.nBrlmIgfmfes~n PfrhD. University otCamfnrdge.U.K. 1992* M farimalt ur atre.opopr relation.
shjs at ammirMcalt. aomric nsokaina d sensirivirvy unagg. tit -a microsmco
Stepanie &. Plmgr Pro h.JD .Massiaciset Insmmre of Iecholog, 1992 Thrmodwamics and tans.
pmr in muieIlar aid miirTocN atsinviis, slilfacrant iracntmir wie h biological ad fi'od mI n~a'leculei
Nad El.rra, Assmant Pirfeosor Ph.D, Umiensy of Califomia. Los Angeles 2 1) Pr.xJ nj sems n gnmrinng.
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rry. Lcainc moatrils ira. nowmarios, kn eics. ~ ie rMsnoimneng mer
jJiffqiitdft wtnPh fortlii ,ltlfyr.91W9D Ddom linmfrarare aadfadgmoqmeai, y9.

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StephliaWbiPmhr nhsorEmeriftisP .D Uniatliksy-eloware. 1959* Matdphae rasrupon phtenwmna

Department of

Chemical Engineering

& Materials Science


The multifaceted graduate study experience in the Depart-
ment of Chemical Engineering and Materials Science allows
students to choose research projects and thesis advisers from
any of our faculty with expertise in chemical engineering, bio-
chemical engineering, and/or materials science and engineer-
Our goal is to provide the financial and academic support
for students to complete a substantive research project within
2 years for the M.S. and 4 years for the Ph.D.

Davis is a small, bike-friendly
University town located 17
miles west of Sacramento
and 72 miles northeast
uMEO of San Francisco,
within driving distance
LAKE of a multitude of
recreational activities.
SWe also enjoy close
s \ collaborations
SA with
1 Snational

Sacramento: 17 miles
San Francisco: 72 miles
Lake Tahoe: 90 miles

?im zyau'f lm~ -~

-- laboratories,
including LBL, LLNL,
and Sandia.

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

Chemical Engineering Education


'~~' 'e~U '~'~~"~~~''~"~I"''~'~' '~ '~~~'~~"

~it_P;~~anusi~a~i~_~a4~u~i~~iuly~l~rB nl~P~ rrvr_ ~nvl


Graduate Studies in
Chemical Engineering IR VIN
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.
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)
Henry C. Lim (Northwestern University)
Jia Grace Lu (Harvard University)
Martha L. Mecartney (Stanford University)
Farghalli A. Mohamed (University of California, Berkeley)
Daniel R. Mumm (Northwestern University)
Andrew J. Putnam (University of Michigan)
Regina Ragan (California Institute of Technology)
Frank G. Shi (California Institute of Technology)
Vasan Venugopalan (Massachusetts Institute of Technology)
Szu-Wen Wang (Stanford University)
Albert F. Yee (University of California, Berkeley)
Joint Appointments:
G. Wesley Hatfield (Purdue University)
Noo Li Jeon (University of Illinois)
Guan Pyng Li (University of California, Los Angeles)
Roger H. Rangel (University of California, Berkeley)
William A. Sirignano (Princeton University)
Adjunct Professors
Andrew Shapiro (University of Califoria, Irvine)
Victoria Tellkamp (University of Califoria, Irvine)

:--iore daiation-


I .

I wa -
r -

I- -

-----~-~r~a ---

-i WIN fWp-

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
or contact
Department of Chemical Engineering and Materials Science
School of Engineering University of California Irvine, CA 92697-2575

Fall 2004



0 Molecular and Cellular

0 Process Systems Engi-
neering (Design,
Optimization, Dynam-
ics, and Control)



1 Energy and the' '
Environment i

1 Nanoengineering


UCLA's Chemical
Engineering Department
offers a program of teaching
and research linking
fundamental engineering
science and industrial practice. Our Department has strong graduate research programs in Bioengineer-
ing, Energy and Environment, Semiconductor Manufacturing, Engineering of Materials, and Process
and Control Systems Engineering.
Fellowships are available for outstanding applicants interested in Ph.D. degree programs. A
fellowship includes 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 pro-
grams and to a variety of experiences in theatre, music, art, and sports on campus.


J. P. Chang
(William F. Seyer Chair in
Materials Electrochemistry)
P. D. Christofides
Y. Cohen
J. Davis
(Vice Chancellor for
Information Technology)
S. K. Friedlander
(Parsons Professor of
Chemical Engineering)
R. F. Hicks
(Nobel Laureate)
E. L. Knuth
(Professor Emeritus)
J. C. Liao
V. Manousiouthakis
H. G. Monbouquette
K. Nobe
(Professor Emeritus)
G. Orkoulas
L. B. Robinson
(Professor Emeritus)
S. M. Senkan
Y. Tang
W. D. Van Vorst
(Professor Emeritus)
V. L. Vilker
(Professor Emeritus)
A.R. Wazzan
(Dean Emeritus)

334 Chemical Engineering Education



Offering degrees at the M.S. and Ph.D. levels in frontier areas of Chemical, Biochemical and
Biomedical, Advanced Materials, and Environmental Engineering. We welcome your interest and
would be delighted to discuss with you the details of our graduate program and your admission
into our graduate program. We have outstanding laboratory research facilities and well supported
infrastructure, and offer competitive fellowship packages to qualified applicants.

Bio- and Chemical Sensors
Structural Bioinformatics
Biomolecular Engineering
Environmental Biotechnology
Catalysis and Biocatalysis
Nanostructured Materials
Carbon Nanotubes
Complex Fluids & Colloids
Zeolites & Fuel Cells
Membrane Processes
Aerosol Physics
Atmospheric Chemistry
Renewable Fuels
Advanced Vehicle Technology
Water/Wastewater Treatment
Advanced Water Reclamation
Site Remediation Processes

* Wilfred Chen, Caltech
* David R. Cocker, Caltech
* Marc A. Deshusses, ETH Zurich
* Robert C. Haddon, Penn State
* Eric M.V. Hoek, Yale
* Kenneth J. Kauffman, Delaware
* Mark R. Matsumoto, UC Davis
* Dimitrios Morikis, Northeastern
* Ashok Mulchandani, McGill
* Nosang V. Myung, UCLA
* Joseph M. Norbeck, Nebraska
* Mihri Ozkan, UC San Diego
* Jerome S. Schultz, Wisconsin
* Sharon L. Walker, Yale
* Jianzhong Wu, UC Berkeley
* Yushan Yan, Caltech

The University of California, Riverside (UCR) is the fastest growing and most ethnically diverse of the 10
campuses of the University of California. UCR is located on over 1,100 acres at the foot of the Box Springs
Mountains, about 50 miles east of Los Angeles. Our picturesque campus provides convenient access to the
vibrant and growing Inland Empire, and is 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. This is an ideal setting for students, faculty and staff seeking to study, work, and live in a
community steeped in rich heritage, offering a dynamic mix of arts and entertainment and an opportunity for
affordable living.

For application materials and information contact
the Graduate Student Secretary at
or you can write to the Graduate Advisor
Department of Chemical and Environmental
Engineering, University of California
Riverside, CA 92521

Fall 2004

Chemical Engineering at the





"At the Leading Edge"

George R. Gavalas (Emeritus)
Konstantinos P Giapis
Sossina M. Haile
Julia A. Kornfield

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

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:

Chemical Engineering Education

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

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

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John Angus

Harihara Baskaran

Robert Edwards

Donald Feke

Daniel Lacks

Uziel Landau

Chung-Chiun Liu

J. Adin Mann

Heidi Martin

Peter Pintauro

Syed Qutubuddin
Robert Savinell

Thomas Zawodzinski

Research Opportunities

Advanced Energy Systems
Fuel Cells and Batteries
Micro Fuel Cells
Hydrogen Infrastructure
Energy Storage
Membrane Transport
Membrane Fabrication

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 and Sensors
Fine Particle Science and Processing
Polymer Nanocomposites
Electrochemical Microfabrication
Self Assembly Chemistry

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

Graduate Coordinator
Department of Chemical Engineering

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

Chemical Engineering Education

CaeW ite Reiev U inl er sitycliT i =ai

Full Text