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85 Chemical Engineering Education Volume 45 Number 2 Spring 2011 CHEMICAL ENGINEERING EDUCATION (ISSN 0009-2479) is published quarterly by the Chemical Engi neering Division, American Society for Engineering Education, and is edited at the University of Florida. Cor respondence regarding editorial matter, circulation, and changes of address should be sent to CEE, Chemical Engineering Department, University of Florida, Gainesville, FL 32611-6005. Copyright 2011 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 120 days of pub lication. Write for information on subscription costs and for back copy costs and availability. POSTMAS TER: Send address changes to business address: Chemical Engineering Education, PO Box 142097, Gainesville, FL 32614-2097. PUBLICATIONS BOARD EDITORIAL ADDRESS: Chemical Engineering Education c/o Department of Chemical Engineering 723 Museum Road PHONE and FAX: 352-392-0861 EDITOR Tim Anderson ASSOCIATE EDITOR Phillip C. Wankat Lynn Heasley PROBLEM EDITOR Daina Briedis, Michigan State William J. Koros, Georgia Institute of Technology C. Stewart Slater Rowan University Jennifer Curtis University of Florida John OConnell University of Virginia Pedro Arce Tennessee Tech University Lisa Bullard North Carolina State Stephanie Farrell Rowan University Richard Felder North Carolina State Jim Henry University of Tennessee, Chattanooga Jason Keith Michigan Technological University Milo Koretsky Oregon State University Suzanne Kresta University of Alberta Steve LeBlanc University of Toledo Marcel Liauw Aachen Technical University David Silverstein University of Kentucky Margot Vigeant Bucknell University DEPARTMENT Chemical Engineering at The University of Houston Michael P. Harold and Ramanan Krishnamoorti CURRICULUM A Freshman Design Course Using Lego NXT Robotics Bill B. Elmore Two-Compartment Pharmacokinetic Models for Chemical Engineers Kumud Kanneganti and Laurent Simon Conservation of Life as a Unifying Theme for Process Safety in Chemical Engineering Education James A. Klein and Richard A. Davis LABORATORY 93 to Determine Polymer Molecular Weight Using a Microviscometer Stephen J. Pety, Hang Lu, and Yonathan S. Thio Continuous and Batch Distillation in an Oldershaw Tray Column Carlos M. Silva, Raquel V. Vaz, Ana S. Santiago, and Patrcia F. Lito A Semi-Batch Reactor Experiment for the Undergraduate Laboratory Mario Derevjanik, Solmaz Badri, and Robert Barat Combining Experiments and Simulation of Gas Absorption for Teaching 2 from Air Using Water and NAOH William M. Clark, Yaminah Z. Jackson, Michael T. Morin, and Giacomo P. Ferraro CLASSROOM Fluids Questions Christine M. Hrenya RANDOM THOUGHTS Hang in There! Dealing with Student Resistance to Learner-Centered Teaching Richard M. Felder CLASS AND HOME PROBLEMS Brian J. Anderson, Robin S. Hissam, Joseph A. Shaeiwitz, and Richard Turton OTHER CONTENTS inside front cover Teaching Tip, Justin Nijdam and Patrick Jordan Book Reviews by Joseph Holles, Kimberly Henthorn
86 C ivil engineering majors have their concrete canoes and steel bridges and the mechanical engineers have their solar cars. Certainly, the discipline of chemical engi neering is no less visualwe just cannot haul a skid-mounted process unit into the classroom (without raising administrative eyebrows and inviting an immediate visit from the campus giving our students a clear picture of chemical engineering? Pursing K outreach and teaching freshmen for a substantial for trying to help students understand what chemical engineers do in daily practice. Most attempts coalesced into a series of chemistry demonstrations accompanied by pictures of chemical processing equipmentleaving my audience with a conceptual gap between the two. In the Swalm School of Chemical Engineering at Mis sissippi State University, the ideal opportunity to tackle this problem came with the revision of a three-credit-hour, junior-level courseChemical Engineering Analysis and designed to address the application of numerical methods to fundamental topics in chemical engineering, the course has pre-requisites that, over time, allowed a shift in class com position to a mixture of underclassmen taking the course on in the course among other requisite courses. This led to an unsatisfactory pressure on the course content ( i.e. review revealed an opportunity to strengthen our curriculum by moving Analysis to the freshman yearusing it as a ve hicle to incorporate teamwork, experimentation, and project design into the early stages of our curriculum. LEGO ROBOTICSFOR CHEMICAL ENGINEERS? The incorporation of problem-based or project-based learn ing strategies into the classroom has swept the educational scene from K [1-4] across multiple disciplines in higher education. [5-7] LEGO robotics kits have proven to be widely adaptable to a variety of disciplines and learning styles in engineering education. Building on the work of chemical engineering educators such as Levien and Rochefort,  Moor and Piergiovanni, [9,10] and Jason Keith,  my students and I began a journey in the Fall semester of 2006 to incorporate this relatively inexpensive technology into the Analysis course. At under $300 per base set, the LEGO NXT robotics kit offers tremendous versatility for designing model engineer ing apparatus and processes in the classroom. With modest additional cost for accessories ( e.g. a number of units can be built to allow an entire class to be A FRESHMAN DESIGN COURSE USING LEGO NXT ROBOTICS BILL B. ELMORE Bill Elmore is an associate professor of chemical engineering and the Interim Direc tor for the School of Chemical Engineering at Mississippi State University. Now in his 22nd year of higher education, his focus is primarily on engineering education and the integration of problem-based learning across the curriculum. Copyright ChE Division of ASEE 2011 ChE curriculum
actively involved in the same design project simultaneously (in contrast to the traditional Unit Operations laboratory ap proach relying on the rotation of student groups through a LEGO NXT ( e.g. the robotics kits for monitoring processes and performing LEGO NXT robotics kits is the use of an intuitive graphical interface for programming (based on National Instruments Labview terface removes the focus from programming and places it on the broader objectives of problem analysis and design of engineering processes. CHE 2213 Chemical Engineering Analysis is a required, three-credit-hour course, offered once per year in the second semester of the freshman year (after a one-hour orientation and before the sophomore-level Mass & Energy Balances are community/junior college transfers from an extensive two-year college system throughout the state. Analysis is rollment lies typically between 55-70 students. The course is conducted in a 160-seat auditorium, the adjacent Unit Operations laboratory, and, with some design competitions, in the connecting hallway for maximum exposure to passing students from other classes. Through loads of laughter and enthusiasm, discovery and creativity, and precautions to avoid spending an inordinate amount of time on their robotics projects, teams of students have consistently pushed the course content forward in subse quent semestersdemonstrating the value of a highly visual, project-based approach to learning engineering fundamentals. Through several iterations we have constructed projects more directly oriented to chemical engineering for illustrating the importance of fundamental concepts including basic units and measures, materials balances, and the fundamentals of process control. LEARNING OBJECTIVES AND OUTCOMES Table 1 describes the learning objectives and outcomes and a learning outcome as a broader response to particular situations requiring use of that acquired knowledge or skill, time in coordination with our overall chemical engineering program objectives. THE LEARNING ENVIRONMENT AND COURSE STRUCTURE Offered Tuesdays and Thursdays for two 2-hour-and-20minute sessions, Analysis comprises one credit hour of labora tory and two credit hours of lecture. The learning environment is patterned after a studio setting. I provide instruction on environment that allows students to immediately put that knowledge or skill to practice on the current project. Projects are structured to require use of accumulated knowledge over the course of the semester. Class discussions center around knowledge and skills needed for use on a timely basis. Home work problems are assigned to allow practice of key tools. exam (evaluating their understanding of skills and concepts is derived from team participation in oral and written reports (in varying percentages over the semesters since the courses performance of designs. ate junctures, evaluating students comprehension and use of the concepts, skills, and tools learned to date. Beginning with Team Challenge #2, all designs require quantitative data acquisition and analysis and are accompanied by team written reports, team self-evaluations, and oral reports. Over the eight semesters we have offered Analysis in its cur rent format, a surprising number of students have expressed little past experience playing with LEGOs To put every one at ease at the course outset, student teams construct the LEGO NXT robotics kits and build a mobile robot of their Learning Objectives & Outcomes Learning Objectives: At the end of this course, you should be able to ideas steps/tools involved in engineering design a complete, functioning prototype using the LEGO NXT robotics system and accessories engineering designs rial balance for and maintaining control of a chemical process. Learning Outcomes: Upon completion of this course, you should be able to design and for making performance improvements in an existing design cal engineering is all aboutgiving some very practical examples.
88 engineering design principles. Introduction of the Design Cycle optimal solution for the problem they are tasked with solving. TABLE 2 Course Structure ChE 2213 Analysis comprises approximately 28 studio sessions over 14 weeks. a. Brainstorming b. Using the Engineering Design Cycle c. Data acquisition and analysis using Microsoft Excel d. Exploration of LEGO NXT robotics kits a. Learning to use the LEGO NXT system a. Teams design an experiment of their choosing using one or more of the sensors provided in the LEGO NXT kit ( i.e. rotational, pressure, light, d. Data acquisition and analysis using Microsofts Excel a. Interfacing the robotics kits with a tank/submersible pump/valve system assembled in-house by the student teams b. Level control experiment c. Explanation of fundamental control concepts a. Case 1Two feed tanks supply two separate components for mixing in a third tank ( e.g. e.g. pH, coloration, dissolved Figure 1. Students becoming familiar with the LEGO NXT kit. choice, using as a guide the Taskbot design included with LEGO structural elements and the various sensors included in the kit to quickly learn something about the capabilities and limits of both the building components and the available sensor technology. Key aspects of the course content are shown in Fig ure 2. The Analysis course was placed in the second semester of the freshman year to engage our chemical engineering students in team-oriented, real engineer ing projects at a critical stage of their collegiate (and ing their communication and working relationships among one another, while giving them insight into the importance of their preparatory mathematics and science courses. Students have commented on the timeliness of design projects requiring use of topics just covered in math and chemistry. Through the introduction of increasingly complex team challenges students are engaged in an integra tion of communication skills, engineering topics, and
Figure 2. CHE 2213 AnalysisCourse content. Figure 3. Design Cycle. TEAM DYNAMICS On the opening day, students self-assemble into teams of robotics kits. In some semesters, I have allowed groups to remain constant over the course of the semester; in others, group members were reassigned approximately at mid-term. Through frequent, informal interviews and anonymous surveys, the feedback has been roughly constant for both approaches ( i.e. most class members favoring staying in their self-selected teams with one or two teams wishing for anyone other than throughout the class periods, coaching and exchanging ideas, and watching for problems that crop up with group dynamics. Additionally, this interaction is an excellent opportunity for get ting an idea of the broader issues that arise among our chemical cover key tools they will be expected to put to use early in the course including brainstorming for initial problem solving, us ing the Engineering Design Cycle, and use of Microsoft Excel for data acquisition and analysis. Team Challenge #1: Taskbots and Sumo Wars The team challenge announced to the class is a Sumo war requiring teams to build a robot capable of staying within a motivates a high-energy response. I have used this team chal lenge to bring in upperclassmen and, with loud music and the AIChE chapter providing food, the result was a memorable social event. Team Challenge #2: Free-Format Design After the dust settles and emotions subside, a second team challenge opens the door to a more fundamental, and me thodical, approach to engineering problem solving. Teams are tasked with designing an experiment and constructing a of one or more LEGO NXT sensors of their choiceac quiring data from a set of independent/dependent variables. Using available computational tools and the course text,  teams report raw and processed data in graphical form with
Team Challenge #4: Mixing Tank/Continuously Stirred Tank Reactor (CSTR) Design In the latest course iteration, we have strengthened emphasis on chemical engineering process variables ( e.g. concentra dent teams conduct team challenges using these measures as indicators of product quality. For example, one challenge two separate reservoirs to a mixing tankmaintaining a pre scribed salt concentration in the outlet stream (as indicated by feed dilute acid and base solutions (typically vinegar/sodium as an indicator of the product quality. Students are required to conduct calculations using basic stoichiometry and mass balances to predict their system behavior and to assess actual performance. In some semesters, we have engaged in free-form chal lengeseach team deciding on a design depicting some process of their own choosing with certain guidelines/goals. Creative design projects have included building a robotic device for titration and assembling a multi-step station for simulating the application of photo-resist to a silicon wafer, Figure 5. Elements of level-control system. Figure 4. Sumo Wars using LEGO Taskbots. appropriate oral and written reports. Student designs have included measuring the volume of liquid dispensed from a soft drink can as a function of robot tipping velocity; the angle of projection by a ball hit in a robotic batting machine; and colorimetric sensitivity of the light sensor as a function of varying shades. Team Challenge #3: Level Control The importance of process control in chemical engineering adapt the LEGO to a tank through a small needle valve operated by a LEGO motor which in turn is controlled by programming the NXT robotics Intelligent Brick ( i.e. Teams must design the system to maintain a prescribed of level-control technology used in industry, detects the controlling motor. Small adjustments in the liquid level are applied to the valve/motor coupling. Students record, as a function of time, +/ displacements from an established set plot for qualitatively evaluating system control performance. allows teams to investigate the capacity of their system ( i.e. varying dynamic conditions. While relatively simple in construction, this team challenge allows students to gain an intuitive sense of the importance of controls. Class discussions focus on the importance of automatic control for safety and operability of systems and on basic controls concepts. Ad ditionally, this challenge touches, to some degree, on each of the course objectives.
OUTCOMES AND ASSESSMENT Mechanisms for teaching and learning and the effects on student motivation have received wide attention in higher education. [13,14] Students in a project-based, studio environment face both challenges to their social and learning cen ters of security and opportunities for growth beyond their level of comfort. When conducted in a sup portive/collaborative environment, this approach to student learning  and prepara tion for advanced learning. Using a Service Quality ap proach,  a multi-semester study of Analysis was conducted to assess variances between desired expecta a resulting gap score. The gap score is the difference between what a customer expects from a service and what the cus tomer perceives as being delivered. A negative quality gap score indicates the service is not meeting expectations, while a positive score indicates the service exceeds expectations. Scores are weighted according to students relative expecta tions from certain characteristics of the course. The study was structured to examine whether or not an individual students of the course, preparation, and team experiences. Multiple surveys were given over the course of each semes terin weeks 3, 8, and 15. Surveys were structured to measure [17,18] were structured to evaluate student expectations, their ratings of the importance of various factors, and their perceptions of various service quality dimensions as related to the course. Responses, using a 7-point Likert scale, were then correlated to respondents academic preparation in high school and per sonal goals and expectations. Examples of survey questions their students, and In ChE 2213, instructors listen carefully to their students. As anticipated, students with positive gap scores ( i.e. the  an indication of ingly challenging chemical engineering curriculum. A close match between student perceptions and expectations served as a primary hypothesis for the study. This hypothesis was decreased slightly. While this requires more study, a contribut at the end of the semester when multiple exams and projects satisfaction, perceived quality, and behavioral intention ( i.e. how well a student believes he/she can perform in this chosen A perhaps intuitive but valuable and statistically valid implication of the study is that making changes to the course quality, and behavioral intention. Changes made to the course over its multiple offerings provides opportunities for frequent, informal discussions across far-ranging questions about the curriculum, co-opera tive education, and general academic issues. among some students that chemical engineering isnt for Figure 6. Silicon-wafer treating station.
a change of major may best serve their interests, the better for all concerned. A distinct advantage I have as the instructor for this course is that I also serve as the undergraduate coordinator for our chemical engineering program. As a result, I can also maintain ongoing academic/career advisementregularly discussing with individual students their academic progress, interest, and preparation for participating in cooperative ing communication that allows students to readily express concerns or doubts about their majorsorting out critical decisions before too much time on task has elapsed before Additional improvements include informal team surveys and individual interviews to assess the impact of projects. Through this process, and with enthusiastic inventiveness of many students, the team challenges have continuously improved. In several instances, students returning from their co-op experience have reported that the work with spread on their job preparation and performance. Additional feedback from co-op students has been re-invested into the course for making continual improvements. SUMMARY The placement of CHE 2213 Chemical Engineering Analy sis in the second semester of the freshman year has enabled our program to maintain a steady, continuous contact with our ing immersed in teamwork and engineering design, thereby solidifying their working relationships with others in their class and adapting to engineering problem solving. Projectbased learning proves to be a worthy vehicle for integrating seemingly disjointed concepts studied in calculus, chemistry, and physics into practical problem solving and it is much more fun than merely lecturing! ACKNOWLEDGMENTS Sincere thanks go to Dr. Lesley Strawderman, assistant professor in Mississippi States Department of Industrial Engineering, and her doctoral student, Arash Salehi, for their Service Quality experimental design and data analysis. REFERENCES 1. Lund, H.H., O. Miglino, L. Pagliarini, A. Billard, and A. Ijspeert, Evolutionary RoboticsA Childrens Game, In Proceedings of IEEE 5th Intl. Conf. on Evolutionary Computation; IEEE Press, NJ, 2. Chambers, J., M. Carbonaro, and M. Rex, Scaffolding Knowledge Study, Electronic J. for the Integration of Technology in Education 3. Carbonaro, M., M. Rex, and J. Chambers, Using LEGO Robotics edu/articles/2004/1/02/index.asp> 4. Kolodner, J., P. Camp, D. Crismond, B. Fasse, J. Gray, J. Holbrook, S. Puntambekar, and M. Ryan, Problem-Based Learning Meets CaseLearning by Design TM into Practice, J. of Learning Sciences Students Learn?, Edu. Psych. Rev. 6. Thomas, J.W., A Review of Project-Based Learning, 1-45, found ment, Rev. Edu. Rsrch. 8. Levien, K., and W.E. Rochefort, Lessons with LEGO Engaging Students in Chemical Engineering Courses, Proceedings of the ASEE leges/engineering/clinics/asee/papers/2002/1672> Control with LEGOs Proceedings of the 2004 ASEE Annual Conf. cfm?id=19879> Flexible Process Control Kits for the Classroom, Proceedings of the 11. Keith, J.M., Learning Outside the Toy Box, Proceedings of the 12. Larsen, R.W., Engineering with Excel 3rd ed., Pearson Prentice Hall 13. Fink, L.D., Jossey-Bass, How Students Learn: History, Mathematics and Science in the Classroom The National 15. Strawderman, L., B.B. Elmore, and A. Aslehi, Exploring the Impact AC2009-62; Second PlaceASEE First-year Programs Division; presented at the 2009 ASEE Annual Meeting J. of Bus. Rsrch. ; 949-959 17. Strawderman, L., and R. Koubek, Quality and Usability in a Student Health Clinic, Intl. J. of Health Care Quality Assurance 225-236 18. Strawderman, L., and R. Koubek, Human Factors and Usability in Service Quality Measurement, Human Factors and Ergonomics in Manufacturing
F in industry, research, and medicine. The diverse ap  stud ies of protein dynamics,   and the clinical detection of diseases such as paraproteinemia  and ischemic heart disease  through the study of blood. An additional use of viscometry is in the determination of the hydrodynamic volume and molecular weight of macro molecules. Using the data analysis seen later in this paper, a polymers molecular weight can be estimated. It is important to be able to measure a polymers molecular weightbecause of its impact on such properties as strength, stiffness, and glass transition temperatureby simply measuring the viscosity of dilute polymer solutions of varying concentrations. In a laboratory setting, viscosity measurements of dilute polymer solutions are typically made with glass capillary viscometers such as Ubbelohde viscometers that require mL viscometers [6-9] means that such viscosity measurements can eters can thus potentially be used to determine the molecular weight of polymer samples even when sample volumes are severely limited. determine polymer molecular weight, we developed a lowcost laboratory procedure for students to use PDMS micro viscometers to determine the molecular weight of a polymer sample. In addition to the procedure, we present sample data for microviscometer tests run on glycerol solutions and on samples of PEO that match up well with viscometry results obtained with conventional Ubbelohde viscometers. We also discuss the timing and logistics of the lab and the feedback obtained from two sample laboratory sessions run with un dergraduates. Stephen J. Pety received his B.S. in polymer and ber engineering at the Georgia Institute of Technology in 2010 and is currently a graduate student in materials science and engineering at the University of Illinois at Ur bana-Champaign. During his junior and senior years, he was a research assistant working with Dr. Lu and Dr. Thio, where he developed and ran microviscometer laboratory sessions reported here. Yonathan Thio is an assistant professor in polymer, textile, and ber engineering. He received his B.S. in chemical engineering and materials science & engineering from the University of California at Berkeley, and his M.S.C.E.P and Ph.D. in chemical engineering from MIT. He joined Georgia Tech in 2005. His research interests are on the structure and properties of polymer composites, block copolymers, and polymer blends. He has taught courses with topics in polymer characterization and structure-properties of polymers. Hang Lu received her B.S. from U. Illinois, Urbana-Champaign, and M.S.C.E.P and Ph.D. from Massachusetts Institute of Technology, all in chemical engineering. She has been an assistant professor in Chemical & Biomolecular Engineering at Georgia Tech since 2005. Among the courses that she has taught are mass and energy balances, transport phenomena, and microuidics. Her research interest is in microuidics and ap plications in neuroscience, cell biology, and biotechnology. Copyright ChE Division of ASEE 2011 MICROFLUIDICS MEETS DILUTE SOLUTION VISCOMETRY: An Undergraduate Laboratory to Determine Polymer Molecular Weight Using a Microviscometer ChE laboratory STEPHEN J. PETY, HANG LU, AND YONATHAN S. THIO
MATERIALS For soft lithography microchannel fabrication, SU8 2050 negative photoresist and SU-8 developer were molecular weights of ~1 MDa and ~4 MDa were obtained the 1 MDa PEO were prepared by mixing the solutions with a stir bar overnight. Experiments to determine the viscosity of these solutions were performed within eight days of when the solutions were prepared. An aqueous solution of 3 mg/mL of the 4 MDa was prepared by stirring the solution for three days. The shear thinning studies performed using this solution were performed within one day of when the solution was prepared. prepare aqueous glycerol solutions. METHODS Device Fabrication nique.  was created using conven (with the SU-8 layer being 55 of gas-phase 1,1,2-trichlo mixture of PDMS precursor and curing agent was then cast onto the master (about 2.5 mm thickthickness not the PDMS slab was peeled from the master and cut into the PDMS piece with the chan nel imprints were then treated for 30 seconds in an air plasma Figure 1. The PDMS viscometer with two sample channels (SCs) and one reference chan nel (RC) for uid ow. The device was lled with dye for visual effect. Scale bar is 5 mm. Figure 2. Setup for using the mi croviscom eter. After the syringe pump is turned on to pull the syringe back, a camera attached to the mi croscope is used to record the movement of uids through the viscometer.
and bonded together to form the PDMS viscometer (Figure days after their fabrication to reduce the hydrophilicity of the device channels. Experimental Setup The PDMS viscometer consisted of three channels of height total ~ 20.4 cm. The viscometer was prepared for use by using micropipettes channels in the top left of the device. A syringe pump (Har sub-atmospheric pressure within the device channels to drive and the metal pin was inserted into the pressure inlet in the was then used to pull the syringe at a constant rate while the transparent liquids moving through the viscometer caused contrast with the background to decrease as the liquids passed of the test. For the tests on PEO described below, the videos frames. The code operates by subtracting previous images from each frame and detecting the movement of a stream as a change in grayscale intensity that surpasses a certain threshold. Adjacently marked pixels are combined to make up the three streams, and the length of each stream is then found by dividing the total number of pixels in that stream by a constant thickness value. Mechanism and Theory of Microviscometer  since channels, differing mainly in the way the driving pressures are applied. The constant pulling of the syringe attached to the vis cometer generates a continually decreas ing pressure inside the channels of the device that is lower than the air pres sure at the channel described by the Hagen-Poiseuille equation  h is the hydraulic diam eter of the channel related to the height h and width w, d h = constant related to channel geometry, with S = 32 for rectan gular channels; P The pressure drop P consists of two components, i.e., P = P d + P c where P c is the capillary pressure. P d is the at atmospheric pressure P 0 is at the constantly decreasing pressure inside the viscometer P i i.e. P d 0 P i same time, P d where the subscripts s and r refer to the sample and reference T he value of for a given test was thus found by taking where L r s termined from the processing of each video. For the tests on PEO described below, an interval 2 t 1 Figure 3. Microphotographs of the beginning of a viscometry test run with water and PEO solu tions (top row) and the output of the MATLAB code used to track the movement of each stream (bottom row). Scale bar is 2 mm.
Dilute Solution Viscometry For dilute polymer solutions, the addition of higher concentrations of polymer leads to higher solution viscosities in accor dance with the Huggins equation  where cosity of a polymer solution where is the viscos ity of the poly mer solution and is the viscosity of the pure solvent; is the intrinsic viscosity of the polymer solution and is a representation of the hydrody namic volume that the polymer chains take up in solution, and k is Huggins constant. If the viscosities of different concen trations of a polymer in solu tion are known, then a value of for the polymer-solvent pair can be found as the inter cept of a graph of vs. c. The value of can then be re lated to molecular weight using Mark-Houwink relation  = KM a where M is polymer molecular weight and K and a are empirical Houwink constants for a given polymersolvent pair. The values of K and a are known for many common polymers including PEO, having been determined experimentally by measuring values of for a polymer at known molecular weights. For polymers with a molecular weight distribution, the measured value of M through this method is an average known as the viscosity average mo lecular weight M v typically between the number-average M n and the weight-average M w Ubbelohde Viscometry Macroscale viscosity measurements of the glycerol and PEO solutions for validation purpose were made with a Cannon Ubbelohde viscometer of diameter 0.58 mm (State were needed for each test. Water was used as the reference of that solution. Density differences between the dilute PEO solutions and water were negligible, so the relative viscosity of each PEO solution was found simply as the ratio of the Figure 4. Sample plots of [L 2 r (t 2 ) L 2 r (t 1 ) ]/ (t 2 t 1 ) vs. [L 2 s (t 2 ) L 2 s (t 1 ) ]/ (t 2 t 1 ) for aque ous 1 MDa PEO solutions of different concentrations. The relative viscosity of each solu tion is found as the slope of its linear t. Figure 5. Plots of vs. c used to determine values of for the 1 MDa PEO sample using viscosity data from the Ub belohde viscometer and the PDMS viscometers. Linear ts are shown from which values were determined as the intercepts. Only the four highest concentrations were used in the linear t for the PDMS viscometers. Error bars represent the standard deviation of
VALIDATION OF THE DEVICE OPERATION To ensure that the microviscometer produced accurate vis erol solutions as sample streams and water as the reference stream. Pressure was generated with a 50 mL syringe that was pulled at rates ranging from 3.50 mL/min to 21.84 mL/min. The viscosities of the glycerol solutions were measured with be consistent with the Ubbelohde viscometer although the variance in the microviscometer tests is much higher. cometer using dilute 1 MDa PEO solutions as sample streams and water as the reference stream. For these tests, pressure was generated by pulling a 50 mL syringe at an initial vol ume of 25 mL at a rate of 5.46 mL/min. Note that the exact initial volume of the syringe and the pulling rate used in the experiments are not critical, as the viscometer can function over a range of generated pressure gradients. Pressure-induced deformation of the microchannels could occur in a PDMS device such as ours if pressure differences were too large but the maximum pressure gradients across the channels in these experiments were only ~15 kPa for the glycerol tests and ~10 kPa for the PEO tests. No deformation of the channels was observed under the microscope in any test. + sured viscosity values were compared to values obtained with an used to calculate viscosity values in the microviscometer tests are seen in Figure 4. In a few of the microviscometer through the viscometer before the syringe was pulled, suggest ing that the PEO solutions had a positive value of P c, sample i.e. they wet the PDMS surface. This did not interfere with data collection, however, and the results from the viscometer were still valid for times while all fluids were moving. It can be seen from Table 1 that the viscosities of the 1 mg/mL, 1.2 mg/mL, 1.4 mg/mL, and 1.6 mg/mL solutions measured by the Relative viscosity values determined for aqueous solutions of glycerol and PEO vs. water using an Ubbelohde viscometer and PDMS viscometers. Each solution was measured three times with the Ubbelohde viscometer and multiple times with the PDMS viscometers as marked. standard deviation Solution Ubbelohde viscometer PDMS viscometer Number of microviscometry trials 10 % glycerol 1.25 0.003 1.32 0.05 10 20 % glycerol 1.77 0.003 1.80 0.13 12 30 % glycerol 2.38 0.015 2.37 0.12 18 50 % glycerol 6.01 0.012 6.07 0.64 12 0.400 mg/mL PEO 1.26 0.0009 1.22 0.04 5 0.800 mg/mL PEO 1.59 0.002 1.49 0.13 5 1.00 mg/mL PEO 1.76 0.003 1.78 0.05 5 1.20 mg/mL PEO 1.96 0.006 1.94 0.12 5 1.40 mg/mL PEO 2.15 0.005 2.22 0.13 5 1.60 mg/mL PEO 2.40 0.012 2.39 0.20 5 microviscometer matched the results from the Ubbelohde viscometer well while the viscosities of the 0.4 mg/mL and 0.8 mg/mL solutions measured by the microviscometer were somewhat lower than that of the Ubbelohde viscometer, pos sibly due to the high surface areas of microdevices and loss of polymer from the solution to the surface. The variance for the microviscometer is seen to be much greater than that for the Ubbelohde viscometer at all concentrations, which may be due to image processing errors or to the much smaller The viscosity results from the PDMS viscometers and the U for the PEO sample by plotting vs. c and taking as extrapolated to a value of = 0.588 mL/mg. When all th e data for the microviscometer were used, a much lower value of discrepancy in values is caused by the lower viscosi ties plot of to larger differences in To reduce the error in estimation, low concentrations of polymer solution should be avoided in the experiments. As shown in Figure 5, excluding the 0.4 and 0.8 mg/mL microvis cometer data from the extrapolation results in an extrapolated value of = 0.605 mL/mg, which agrees well with the values from Ubbelohde experiments.
Using values of a = 0.78 and K = 12.5 10 -6 mL/mg 1/a for aqueous PEO solutions  and the values above, the Mark-Houwink equation produces values of M = 1,010,000 g/mol for the PDMS viscometers and M = 977,000 g/mol for the Ubbelohde viscometers. These values are in good agreement with each other as well as with the value reported by the manufacturer. LABORATORY IMPLEMENTATION, COST AND LOGISTICS, AND STUDENT FEEDBACK Laboratory Implementation The laboratory procedure consists of a device fabrication demonstration, student-run microviscometer tests on PEO solutions, image processing of the tests using MATLAB, and a shear-thinning demonstration. After the lab session, viscosity used. If time is available, students can also measure the vis cosities of the PEO solutions with macro viscometers such as Ubbelohde viscometers to validate the microviscometer data. vantages of microviscometry in terms of accuracy, precision, Two trials of this procedure were run with volunteer under graduates (mostly junior students who have taken transport of Chemical & Biomolecular Engineering. Each trial had four Several days before the laboratory sessions were held, students were provided with a copy of the procedure as well as a pretheir understanding prior to the lab. The beginning of the labora tory consisted of a microviscometer fabrication demonstration given by the undergraduate teaching assistant. The assistant explained how masks and masters are manufactured, explained how PDMS is mixed, cast, cured, and bonded to form devices, and used the plasma cleaner to bond a device to show to the stu dents. If time allows, this simple micromolding step and device fabrication can be incorporated into the lab, and concepts such treatment can be explained and demonstrated. The students then ran two microviscometer tests where each test used two different concentrations of 1 MDa PEO as sample streams and water as the reference stream. Con centrations of 0.500, 1.00, 1.50, and 2.00 mg/mL were used in the two tests. Pressure was generated by pulling a 50 mL syringe at an initial volume of 25 mL at a rate of 5.46 mL/min (the same conditions as in the validation tests for the PEO Figure 6. Shear thinning display of 4 MDa PEO (middle channel, gray) vs. 60% glycerol (outer channels, black). The top row shows MATLAB output images of a viscometer test run at an average shear rate of ~ 100 s -1 at which the glycerol so lution outraces the PEO solution. The bottom row shows images of a test run at a shear rate of ~ 780 s -1 at which the PEO solution has a lower viscosity than at the slower rate and outraces the glycerol solution. Scale bar is 3 mm.
Image Processing The students then used the pre-written MATLAB code to shooting issues with the image processing can be explained to the students during the lab module to facilitate data process ing. For instance, it is important to take a video that has both uniform contrast (for the streams to be tracked with uniform of the acquired length values. Demonstration of Shear Thinning Fluids To demonstrate both the shear thinning behavior of nondilute polymer solutions and the ability to generate a large range of shear rates in the viscometer using the syringe pump, the students then ran a test with a high pulling rate and a test with a low pulling rate on a sample of 3 mg/mL 4 MDa PEO is run with a syringe initial volume of 40 mL and a pulling rate of 1.7 mL/min, corresponding to an average shear rate ~100 s -1 the 60% glycerol reference is seen to move through the viscometer more quickly than the PEO solution (Figure viscometer more quickly than the 60% glycerol reference when given a higher average shear rate of ~780 s -1 (generated by pulling a syringe at an initial volume of 5 mL at a rate lower viscosity of the PEO solution at a higher shear rate as opposed to the rate-independent viscosity of the Newtonian glycerol solution. The shear thinning behavior of the PEO the viscosity of the PEO solution fell from ~ 14 cP at 100 s -1 to ~ 8.6 cP at 780 s -1 This method can be used to demonstrate shear rates up to 2000 s -1 Cost Estimate and Timing Logistics Assuming that laboratory equipment such as microscopes, cameras, a plasma cleaner, and a syringe pump are available, the laboratory costs come in the materials. The fabrication of a mask and master costs around $150, and samples of the 1 MDa PEO, 4 MDa PEO, glycerol, and PDMS cost ~$30 each for a total startup cost of <$300. Note that other water-soluble poly than glycerol solutions can be used as viscosity standards as long as they do not swell PDMS and their viscosity is known. If needed, we estimate that a simple microscope and camera setup are in the range of $2,000 to $3,000. If a plasma cleaner is not available, it is possible to create devices by pressing a placing the slabs between two glass slides, and then holding the glass slides together using rubber bands. Once the startup materials are present, the individual lab sessions have a very low cost because of the small volumes of chemicals needed. The major repeated cost is in fabricating the PDMS devices which consume ~$1.50 of PDMS per chip. Approximately 5 hours of time were devoted by the under graduate teaching assistant to prepare for each lab session, including device fabrication, solution preparation, and lab set-up. The two lab sessions took about 1 hour and 45 minutes each to complete, including the fabrication demonstration, the completion of four viscometer tests, and the processing of the tests and the description of the MATLAB code. Student Feedback Students who participated in the laboratory experiments provided informal feedback. Most students found the mod ule was effective in introducing the concept of solution had no prior exposure. The students found more background more interesting and more useful. This suggests that the laboratory module should be expanded to multiple sessions to deal with the individual topics in depth. The students also commented that seeing non-Newtonian behavior with a real demonstration could reinforce this concept that they learned in the classroom. CONCLUSIONS We present a procedure for a student laboratory session to and the use of dilute solution viscometry to estimate polymer molecular weight. Overall, the results were reasonably consis tent with those found from conventional Ubbelohde viscometry. stration of the shear thinning behavior of non-dilute polymer solutions. Assuming soft lithography equipment is available, the experimental setup is very quick and affordable. The laboratory polymers, rheology, and image processing while invigorating students with the opportunity to work hands-on in the cutting The combination of written instruction in the pre-lab and procedure, verbal instruction and visual displays from the teaching assistant, and hands-on experience for each student caters to a range of different student learning styles. [15-16] Because it is multi-faceted, this experimen tal platform can be used and re-used in different pedagogical contexts, or it can be a problem-solving based learning tool.  We recommend running the following laboratory modules individually or in combination depending on the need of the processing.
ACKNOWLEDGMENTS We thank M. Li for developing the microviscometer design J. Stirman and M. Crane for their help with the microscope Peterson for use of their facilities. REFERENCES 1. Calvert, P., Inkjet Printing for Materials and Devices, Chemistry of Materials 2. Ansari, A., C.M. Jones, E.R. Henry, J. Hofrichter, and W.A. Eaton, The Changes, Science 2 Drug Delivery 4. McGrath, M.A., and R. Penny, ParaproteinemiaBlood Hyperviscos ity and Clinical Manifestations, J. Clin. Invest. Count are Major Risk Factors for Ischemic Heart DiseaseThe Caer philly and Speedwell Collaborative Heart Disease Studies, Circula tion 83 Newtonian Fluid, J. Micromechanics and Microengineering Analytical Chemistry 77 8. Marinakis, G.N., J.C. Barbenel, A.C. Fisher, and S.G. Tsangaris, A Biorheol ogy Law Fluids, J. Micromechanics and Microengineering 10. Duffy, D.C., J.C. McDonald, O.J.A. Schueller, and G.M. Whitesides, Analytical Chemistry 11. Perry, R.H., and D.W. Green, Perrys Chemical Engineers Handbook 12. Painter, P.C., and M.M. Coleman, Essentials of Polymer Science and Engineering 1st Ed., DEStech Publications, Inc., Lancaster, PA Poly(ethylene oxide) Academic Press, Chem. Eng. Ed. 15. Felder, R.M., and L.K. Silverman, Learning and Teaching Styles in Engineering Education, Eng. Ed. 78 16. Montgomery, S.M., and L.N. Groat, Student Learning Styles and Their Implications for Teaching, CRLT Occasional Papers 17. Major, C.H., and B. Palmer, Assessing the Effectiveness of ProblemAcademic Exchange Quarterly
T he absorption, distribution, metabolism, and excretion trations, are usually represented by compartmental pharmacokinetic models. These compartments correspond to tissues and organs in the human body. The analysis of these processes can be very complex, as in the case of physiologi chemical properties of a compound is necessary to describe i.e. lung, brain, and  Although, in theory, a multi-compartment approach is better suited to describe the dynamics of most drugs in the body, clinicians prefer the simplicity of a one-compartment model  to predict the plasma drug concentrations and to design appropriate dosage regimens. In a one-compartment model, the blood and surrounding tissues are lumped into a single process unit. As soon as the ment, it is uniformly distributed throughout the body.  The mathematical representation of these systems involves a drug injection inlet stream, a constant-volume central compartment, and a clearance term. A series of experiments, inspired by this model, were designed to introduce chemical engineering students to pharmacokinetics and to stimulate their interest in research related to drug delivery.  Continuous intravenous TWO-COMPARTMENT PHARMACOKINETIC MODELS for Chemical Engineers KUMUD KANNEGANTI AND LAURENT SIMON tions were illustrated with activities consisting mostly of a dye placed in a mixing vessel. This contribution focuses on the applications of a twocompartment model for describing drug pharmacokinetics. Although the error in developing dosing regimens based on Laurent Simon is an associate professor of chemical engineering and the associate director of the Pharmaceutical Engineering Program at the New Jersey Institute of Tech nology. He received his Ph.D. in chemical engineering from Colorado State University in 2001. His research and teaching interests involve modeling, analysis, and control of drug-delivery systems. He is the author of Laboratory Online available at
a single-compartment model is acceptable for most drugs, equations for two-compartment kinetics are more appropriate for a few pharmaceutical agents that are potent and/or exhibit a narrow therapeutic range.  Experiments, based on concepts learned in chemical engineering classes, are developed to introduce students to these processes. The learning outcomes total mass and component balances for the two compartments, i.v. bolus administration. LABORATORY DESCRIPTION Theoretical Foundation The schematic of a two-compartment model is shown in Figure 1a. According to this representation, the human body is comprised of a central compartment consisting of the blood/plasma and well-perfused tissues ( e.g. and a peripheral compartment mainly composed of poorly perfused tissues ( e.g. ment. This measurement may be used by the physician to assess the effectiveness of a drug-dosage regimen. and a mass transfer rate constant. The subscripts 1 and 2 repre sent the central and peripheral compartments, respectively. Drug elimination is shown by the subscript el. In addition, the subscript 12 denotes a transfer from compartment 1 to compartment 2 while drug transfer in the opposite direction is shown by 21. The parameter k el rate constant, which is often used to represent clearance. It should be noted that more complex expressions ( e.g. Mi  and with cess designed to mimic the behavior of a two-compartment rates. Fresh water streams are also added to the vessels. At this point, students may be asked to show that component balances around the units lead to the system described by Eqs. objectives i and ii and respectively. The subscripts w1 and w2 indicate the fresh wa ter streams into vessels 1 and 2. Assuming equal and constant densities, we have and Figure 1. Representation of a two-compartment model. Figure 1a is a schematic model of the process as intro duced in a course in pharmacokinetics; Figure 1b is the two-unit process that is assembled to mimic the behavior of the two-compartment model.
hold in order to maintain constant volumes in both tanks. In addition, potassium permanganate balances around the two and 1 with 1 2 As a result, and The initial conditions are C 1 10 and C 2 lus injection. Using the Laplace transforms of the concentra tions C 1 2 i.e. and and and Partial-fraction expansion, or the residue theorem, may be used to invert the ( objective iii Although the satisfaction of the initial conditions, C 1 10 and C 2 In addition, showing that may lead to a discussion on the necessity for administering multiple bolus i.v. doses. and with and Given concentration data in the central compartment (or 12 k 21 and k el ( objective iv calculated using ssion of 1 is written in the form Computational software packages such as Math ematica A, B, and Algebraic manipulations show that  1 l because > Pa rameters B and are obtained from ln[C 1 l t. The variable C 1 l represents the concentration at a suf 1s = where C 1s stands for the concentration a short time after the bolus injection. Parameters A and are estimated from ln[C ls ] t. Any of the methodologies described above is implemented 1 and C 2
Materials and Experimental Procedure Except for the increased number of pumps, the same materials required in the study of the one-compartment experiments  objective v cylinders, pipettes, rubber tubing, magnetic stirrer, magnetic bars, potassium permanganate, spectrophotometer, cuvettes, laboratory stands, and clamps. An i.v. bolus of 1.37 g of potas sium permanganate was administered to the central compart ment. Samples were collected every 15 minutes for both the a spectrophotometer set at 530 nm. A calibration curve was developed to relate the concentration with the absorbance A where y represented the concentration in g/mL and A the absorbance. The volume of each vessel was maintained at 200 mL. Results and Discussions The data for the i.v. bolus administration are shown in Fig ure 3. Pharmacokinetic parameters determined from the three methods are k 12 = 1.80 hr -1 k 21 = 2.94 hr -1 and k el = 0.30 hr -1 12 = 1.42 hr -1 k 21 = 2.37 hr -1 and k el = 0.26 hr -1 (regression in Mathematica 12 = 1.80 hr -1 k 21 = 2.92 hr -1 and k el = 0.27 hr -1 predicted concentrations plotted are the ones derived by the third method. Students may be given a project where they are expected to investigate the effects of the kinetic parameters on C 1 and C 2 the distribution and elimination rate constants. This research also offers the opportunity to address the effects of the dose tions and constant-rate infusions can also be studied after a The choice of one compartment or two compartments may be an important factor when designing appropriate drug-dos ing regimens. To illustrate this point, three bolus injections of 1.10 g, 0.33 g, and 0.33 g of potassium permanganate were added to the central compartment at 0, 1.12, and 3.36 hours, respectively, as recommended by the results of an optimal dosing regimen for KMnO 4 code, based on a two-compartment model and written in the Mathematica errors between the concentrations in the central compartment and a desired KMnO 4 level of 3.46 g/L for an experimental duration of 5.75 hours. The following observations can be 4 concentration around 3.46 g/L. Simulations conducted under the assumption that KMnO 4 obeys one-compartment pharma cokinetics show that the predicted data deviate considerably SUMMARY OF EXPERIENCES A group of six students from an undergraduate course in biotransport worked on this project. The three-credit class is designed for biomedical engineering students pursuing tracks in biomaterials and tissue engineering or biomechanics.  Chemical engineering students may also select the course as was produced after several meetings with the instructor dur ing which the project was discussed. Although a graduate because of time limitation, the group was required to draw a schematic diagram of the process similar to Figure 1b. The on the concentrations in the central and peripheral compart ments. In addition to providing a background of the subject, the students were also responsible for deriving the model equations and estimating the kinetic parameters. They were not told about the methods that could be applied to determine Figure 2. The experi mental setup of the twocompartment model. Potassium permanga nate was added to the beakers. Fresh water in an Erlenmeyer ask was introduced to the two compartments.
these parameters; the kinetic values were rates. The results were also presented to the class and sources of errors, such as CONCLUSIONS Experiments in continuous-stirred vessels were designed to represent drug transport within the body. The processes governing equations were similar to those of a two-compartment model elimination kinetics. These activities gave students the opportunity to apply conservation principles learned in the classroom. In addition, Laplace trans form techniques were implemented to solve the differential equations. Closed-formed expressions for the con centration of potassium permanganate in the central and peripheral compart ment were obtained. Three methods of extracting the pharmacokinetic pa rameters based on experimental data were outlined. After administering an i.v. bolus of 1.37 g of potassium permanganate to the central vessel, pattern analogous to drug transport when a two-compartment model is used. The three parameter estimation methods yield comparable results. Students who worked on the project were able to model the process, solve the governing differential equations, and estimate the kinetics. REFERENCES 1. Clewell, R.A., and H.J. Clewell, Devel cally Based Pharmacokinetic Models for Use in Risk Assessment, Regul. Toxicol. Pharmacol. 2. Schoenwald, R.D., Pharmacokinetic Principles of Dosing Adjustments CRC 3. Simon, L., K. Kanneganti, and K.S. Kim, Drug Transport and Pharmacokinetics for Chemical Engineers, Chem. Eng. Ed. Transport Phenomena in Biological Systems 2nd Ed., Pearson Prentice Hall, 5. Gibaldi, M., and D. Perrier, Pharmacoki netics 2nd Ed., Informa Healthcare, New 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Time (hr)KMnO4 Concentration (g/L) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 0 1 2 3 4 5 6 Time (hr)KMnO4 Concentration (g/L) g g Figure 3. Concentrations of KMnO 4 in the central ( ) and peripheral (+) com partments. The parameters obtained by the method of residuals are k 12 = 1.80 hr 1 k 21 = 2.92 hr -1 and k el = 0.27 hr -1 Predicted concentrations in vessels 1 and 2 are shown by the symbols () and (-----), respectively. Figure 4. Experimental concentrations of KMnO 4 in the central ( ) and peripheral compartments ( ). The predicted data are represented by the solid lines (____). The rate constants for the two-compartment model are k 12 = 1.80 hr -1 k 21 = 2.92 hr -1 and k el = 0.27 hr -1 The elimination rate constant for the one-compartment model (dashed line: -------) is k el = 0.41 hr -1
D istillation is by far the most frequently used industrial is indeed the benchmark with which all newer competitive processes must be compared. Following Null,  distillation should be selected if the relative volatility is greater than 1.05, whereas Nath and Motard  and Douglas  12 greater than 1.10, a more conservative critical value for the relative volatility. Generally, design heuristics point out that processes using energy separation agents should be favored. For the reasons outlined above, distillation experiments are included in the Chemical Engineering Integrated Master curriculum of the Department of Chemistry at University of lation as part of the Separation Processes I course, which is essentially devoted to equilibrium-staged unit operations. Afterwards, experiments are carried out in Laboratrios EQ course intended to provide hands-on experience on separa out the lab exercise and some calculations, and in the sec ond week students do numerical calculations and computer simulations, which require computational support. Student a report prepared by the student groups. In this paper a lab exercise on continuous and batch a number of educational publications concerning distillation calculations, mostly using Excel, Matlab, Hysys, and Math ematica software. [4-6] Moreover, virtual laboratories involving distillation units have been developed in order to enhance the understanding of the process units and to improve the teaching effectiveness. [7, 8] Nonetheless, students are usually uninter experiments in the lab should never be totally replaced by simulated experiments on a computer, notwithstanding its ease and less time-consuming approach. In this work, experiments are performed in an Oldershaw tane under different modes of operation. These modes include CONTINUOUS AND BATCH DISTILLATION IN AN OLDERSHAW TRAY COLUMN CARLOS M. SILVA, RAQUEL V VAZ, ANA S. SANTIAGO, AND PATRCIA F. LITO Copyright ChE Division of ASEE 2011 ChE laboratory Carlos M. Silva is a professor of chemical engineering at the Depart ment of Chemistry, University of Aveiro, Portugal. He received his B.S. and Ph.D. degrees at the School of Engineering, University of Porto, Portugal. His research interests are transport phenomena, membranes, ion exchange, and supercritical uid separation processes. Raquel V. Vaz is a Ph.D. student at the Department of Chemistry, University of Aveiro, Portugal. She received her Masters degree in chemical engineering from the University of Aveiro. Her main research interest focuses on molecular dynamics simulation and modeling of diffusion coefficients of nonpolar and polar systems. Ana S. Santiago is a post-Ph.D. student in the Department of Chemistry, University of Aveiro, Portugal. She received her B.S. degree in chemi cal engineering from the University of Coimbra and Ph.D. in chemical engineering from the University of Aveiro. Her main research interest focuses on bio-renery and membrane separation processes. Patrcia F. Lito is a post-Ph.D. student in the Department of Chemistry, University of Aveiro, Portugal. She received her B.S. and Ph.D. degrees in chemical engineering from the University of Aveiro. Her main research interest focuses on mass transfer, membrane separation processes, ion exchange, and molecular dynamics simulation and modeling of diffusion coefficients of nonpolar and polar systems.
column is a laboratory-scale column equipped with perforated trays. Of special importance is the fact that it exhibits a sepa ration capacity close to that of large industrial columns.  In fact, experimental results show that commercial towers will require a similar number of stages to reach the same separa tion level obtained in the Oldershaw unit.  With this work students practice relevant concepts in troduced earlier in their curriculum, namely vapor-liquid equilibrium, continuous vs. batch operation, McCabe-Thiele results, giving them the opportunity to improve their skills in ing them with those obtained from simulations, students gain insight to this unit operation. LABORATORY DESCRIPTION Experimental Setup Experiments are performed in an Oldershaw tray column instrumented and equipped with a control system supplied by Normschliff Gertebau (similar equipment is available equipment for continuous distillation is offered, for example, (capacity of 2 condenser us ing tap water as cooling fluid, a lateral con denser to re move distillate as liquid, and a solenoid valve to divide the vapor stream distillate un der the partial reflux mode. Additional fea sampling points above each tray to de termine liquid composition; and tempera ture sensors immersed in the reboiler and located in the top condenser allowing the determination of the bottom and head compositions, respectively. The column is used to separate The calibration curvemeasured in this workto deter mine the cyclohexane mole fraction (x 1 is given by x 1 =-309.95 RI 2 + 895.15 RI 645.15. D and T B cyclohexane molar compositions, x D and x B by vapor-liquid equilibrium calculations assuming that the column is kept at atmospheric pressure (pressure drop along the column is This Oldershaw tray column is extremely versatile. It can ing steady state. Such an experiment was carried out at R = 6 for P = 125 W. Once more, T D and T B determine x D and x B Finally, a semi-continuous or batch distillation was performed for R = 6 and P = 125 W. Presently, the distillate is not fed to sitions vary along time. T D and T B were registered during 1 h approximately, to calculate the corresponding x D and x B and the distillate refractive index was measured at the end. HAZARDS AND SAFETY PRECAUTIONS readily form explosive mixtures with air. They are harm ful if swallowed or inhaled, and cause irritation to skin, eyes, and respiratory tract. Attention must be paid during the withdrawal of liquid samples, from the bottom of the column, in order to measure the refractive index. Protec tion equipment, including gloves and glasses, should be used. Students must review the Materials Safety Data Sheet for each chemical before starting the experiment subsequently treated by the DCUA. Figure 1. Oldershaw tray column.
DATA ANALYSIS Vapor-Liquid Equilibrium At low pressure, vapor-liquid equilibrium of a component where y i and x i are the vapor and liquid molar fractions, respectively, i is its activity coef t is total pressure. is computed by the Antoine i by Margules equations, whose constants may be found in the literature. Since the liquid molar fraction may be where x denotes the liquid composition vector. The vapor Number of Equilibrium Stages The number of equilibrium stages is obtained by the wellknown McCabe-Thiele method.  In this work the column where y n+1 and x n are the cyclohexane vapor and liquid frac the operating line coincides with the diagonal line. The num ber of equilibrium stages is given by the number of outlined steps between x D and x B The number of trays is obtained by total number of equilibrium stages. where N ideal is the ideal number of equilibrium stages and N real is the actual number of trays (in this case N real The overall efficiency can be estimated by empirical correlations, namely, those by Drickamer and Bradford  and OConnell.  Drickamer and Bradford  correlate E ov 12 depen 12 is the geometric average of the bottom and top values. Generalized Rayleigh Equation The moles of liquid in the reboiler are related to its residue moles of mixture in the reboiler, respec tively. Knowing experimental pairs of data (x D x B tained by numerical integration. Results and Discussion In Table 1 the re sults obtained at total W are presented. For illustration, the Mc Cabe-Thiele diagram for 125 W is plotted in Figure 2. McCabe-Thiele diagram for a) total reux distillation and b) continuous rectication at par tial reux. T D T B x D x B E ov Exp. Eq. 5 Eq. 6 75 83.9 92.6 0.878 0.281 95.1 53.1 64.8 125 84.2 92.7 0.864 0.273 92.1 53.2 64.9
Figure 2a. The minimum number of equilibrium stages was 4.76 and 4.61 for P = 75 and 125 W, respectively, giving rise to usually unexpected for the students. Actually, higher reboiler it also decreases the mean residence times of both phases in each tray, which has a larger overall impact. Students are Reynolds numbers to large Sherwood values, but neglect the second and more dominant effect in this case. listed in Table 1, and show that both correlations underestimate E ov Students frequently get disappointed with such diverging results. Instructors notice that students almost always doubt their own experimental results, tending to ac cept without hesitation model predictions. At this point it is a complex function of system properties, operating conditions, and column geometric variables, and that common empirical correlations take only some system properties into account, as be encouraged to search data for similar systems to see that data are frequently 10 to 20% higher than OConnells pre dictions.  tion achieved now (0.234 result for all students. Figure 3 shows the evolution of both distillate and residue termined by numerical integration of the Rayleigh equation, equation has upon the numerical solution. For instance, some to scattered positive and negative data. Students calculate B/F also by mass balance using the initial (x B,0 composition of distillate determined by refractive index. The results found are frequently very similar. In this run (see Table and mass balance approaches, respectively. ASPEN SIMULATIONS Aspentech, Inc., a simulator frequently used in industry. TABLE 2 T D T B x D x B E ov 87.8 93.5 0.685 0.234 92.0 TABLE 3 B/F Fraction Obtained by Rayleigh Equation and Mass Balance x D B/F B/F 0.410 0.696 0.667 r Figure 3. Distillate and residue compositions during the batch rectication at P = 125 W and R = 6. Figure 4. Numerical data used for the integration of Rayleigh equation.
This software allows the simulation of distillation columns under different operating conditions and shown in Table 4. For this case, the column is as partial condenser has to be selected in order to purge it from the system. A feed stream was imposed dur ing a predetermined time to charge the tower with the same number of moles that our Oldershaw column contains initially. Subsequently, tions, in order to help students to reproduce our results. an initial charge of feed, and this condition runs until both distillate  In our case, R = 6, the column holdups and pressure drop values are those obtained Simulation Results relative deviations found lie between 1.0 and 19.3%, being higher for R D x B experimental one (x D x B doubt their experimental observations against the simulated results, sug gesting possible experimental errors for the deviations found for R = 6. Nonetheless, in this case such large error may be attributed to the fact that some operating parameters, including pressure drop and holdups, were with simulation results due to the large number of input parameters and CONCLUSIONS This work describes an experiment in which students have the op portunity to study dis tillation, using an Old ershaw tray column, under three different with constant reflux. The effect of the internal Information for Aspen Simulation Column initially empty ( Partial condenser Feed stream to introduce the initial charge of mixture Additional Information number of stages, including reboiler and condenser Reboiler geometry ( dimensions and jacket type pressure, type, area, con Operation Steps Results Column holdups Pressure drop Figure 5. Detail of an Aspen BatchSep 2006.5 window for the total reux simulation.
Tab Setup Pot Geometry Top Hemispherical, bottom Hemispherical Pot Heat Transfer Condenser 2 2 Jacket Heating Pressure Initial Conditions Main Charge Stream Feed Main Operating Step Charge Changed Parameters End Conditions Operating Step Distill Changed Parameters
reboiler power. pared with those obtained by Aspen BatchSep simulations, giving rise to relative deviations between 1.0 and 19.3%. With this work students practice relevant concepts, includ ing vapor-liquid equilibrium, continuous vs. batch operation, Furthermore, they are introduced to the use of simulation software, an important tool for their chemical engineering instruction. ACKNOWLEDGMENTS Patrcia F. Lito and Ana Santiago wish to express their gratitude to Fundao para a Cincia for the grants provided (SFRH/BD/25580/2005 and SFRH/ A. Da Silva for Aspen simulations and pictures support. NOMENCLATURE B Final number of moles of liquid in the reboiler, mol E ov F Initial number of moles of liquid in the reboiler, mol N real Number of real trays N ideal Ideal number of equilibrium stages P Reboiler power, W P t Total pressure, atm P RI Refractive index x Molar fraction of liquid phase y Molar fraction of vapor phase Greek letters 12 Relative volatility Subscripts B Bottom D Top i Component i 0 Initial condition Figure 6. Continuous partial reux simulation owsheet. Total condenser Total initial charge and composition Additional Information Column pressure drop and tray holdups Operating Steps Distillation at R = 6 Results
TABLE 7 Tab Setup Pot Geometry Top Hemispherical, bottom Hemispherical Pot Heat Transfer Condenser Jacket Heating Holdups Initial Conditions Main Initial Charge Charge Stream Distillate Main Operating Step Rpartial Changed Parameters TABLE 8 Bracketed values are relative deviations to the experimental ones. T D T B x D x B 83.8 92.1 85.6 92.4 REFERENCES 1. Null, H.R., Selection of a Separation Process, in Hand book of Separation Process Technology Rousseau, R.W., Ed., Wiley-Interscience, New 2. Nath, R., and R.L. Motard, Evolutionary Synthesis of Separation Processes, AIChE J., 27 3. Douglas, J.M., Conceptual Design of Chemical Process es 4. van der Lee, J.H., D.G. Olsen, An Integrated, Real-Time Computing Environment for Advanced Process Control Development, Chem. Eng. Ed. 5. Binous, H., Equilibrium Staged Separations Using Mat lab and Mathematica, Chem. Eng. Ed. 6. Nasri, Z., and H. Binous, Ap plications of the Peng-Robin son Equation of State Using Matlab, Chem. Eng. Ed. 115 and Implementation of an Inter active Learning Environment for Students of Chemical and Process Engineering, Chem. Eng. Ed. 8. Fleming, P.J., and M.E. Paulai Laboratory, Chem. Eng. Ed. 9. Fair, J.R., H.R. Null, and W.L. Bolles, Scale-up of Plate Efficiency From Laboratory Oldershaw Data, Ind. Eng. Chem. Process Des. Dev. 22 10. Humphrey, J.L., and G.E. Keller, Separation Process Technology McGraw-Hill, 11. Seader, J.D., and E.J. Henley, Separation Process Principles 12. Drickamer, H.G., and J.R. Bradford, Transactions AIChE 39 13. OConnell, H.E., Transactions AIChE 741-755 L.B. Andersen, Principles of Unit Operations 2nd
A ctive learning is an umbrella term for instructional methods used in the classroom in which students are actively engaged in the learning process, as opposed to a traditional lecture in which students play a passive role. Active learning can take many forms such as collaborative learning, cooperative learning, and problem-based learn ing.  Research has shown that such nontraditional methods may lead to improved academic achievement, retention, and student attitudes toward learning, depending on the method [1,2] Indeed, Felder, et al.,  have in cluded active learning methods on their list of teaching meth good match for active-learning techniques (see, for example, In this paper, two active-learning modules targeted for use Materials for both have been designed and made avail interested educators with little time investment. These mod ules involve several of the aforementioned forms of active learning, including both collaborative learning and coopera tive learning. periments using the mechanical energy balance. The students best match the experimental data is also made at the start. The class culminates in the running of the experiments, and allows the students to put their knowledge into practice via active-learning, while also providing a high level of energy Copyright ChE Division of ASEE 2011 ACTIVE LEARNING IN FLUID MECHANICS: YOUTUBE TUBE FLOW AND PUZZLING FLUIDS QUESTIONS CHRISTINE M. HRENYA ChE classroom Christine M. Hrenya received her degrees in chemical engineering from The Ohio State University (B.S.) and Carnegie Mellon Uni versity (Ph.D.), and is currently on faculty at the Department of Chemical and Biological Engineering at the University of Colorado. Her research interests include granular and gas-solid ows, with an emphasis on polydis persity, cohesion, and instabilities.
and enthusiasm due to the contest format. To facilitate use by other instructors, videos with an introduction to the apparatus and the collection of experimental data are available. A spread sheet has also been developed in which group predictions and experimental data can be recorded, which is followed by an after the relevant material has been introduced in the course, week presents a challenge for instructors since any new mate rial will not be assigned as homework and typically will not active learning, creativity, and oral presentation skills, small questions are assigned several weeks prior to the end of the ing demonstrations, videos, etc. A current listing of these questions, which involve current events, sports, hobbies, and a bit of humor, is included below. Also available via the Internet are an example project description, signup sheet, and grading sheet. Given below is a more detailed description of each of these activities and the corresponding course materials. Afterward, followed by concluding remarks. CONTEST: TUBE FLOW EXPERIMENTS ON YOUTUBE Description. chanical problems using the mechanical energy balance is an Typically, the basic equation, friction factor charts, and tables lecture, with another lecture dedicated to example problems. that can be encountered e.g. a simple plug-and-chug solu In this class period, an alternative to the traditional lecture on example problems for the mechanical energy balance is given. Namely, a contest is set up for small groups to to groups with best predictions. The experimental apparatus, tank, in which the height of the water in the tank is maintained tubes located at the base of the tank, each with different wall of the tank, while the third protrudes into the tank. With the dimensions and the materials of the tank and tubes given, from each tube. The mechanical energy balance forms the basis of this calculation  where p refers to pressure, height, and h L where major losses refer to frictional losses over straight piping of length and minor losses refer to frictional losses L component type. Figure 1. Tube ow appara tus. The tank is open to the atmosphere and the water level is main tained at a constant height by means of a pump. Three horizontal tubes of differ ent diameters, length, and entrance types ( i.e. ush vs. inserted) are located near the tank bot tom. The ow rates emanat ing from each of these tubes are measured by means of a graduated cylinder and stopwatch.
it must be determined using the Moody diagram and thus a all are near the transitional region. Accordingly, the student calculations should involve a combination of analytical and trial-and-error approaches, along with the checking of their This exercise can be adapted easily to classes of different durations. In our experience, asking the students to predict 10 minutes to form groups and introduce experiment, 50 minutes for group calculations, and 15 minutes for tallying contest winners. In the last few minutes, the general problem solution is also outlined, with detailed calculations given as handouts at the end of class. For a 50-minute class period, a reasonable variation would be to ask students to predict the consider alerting students one class period beforehand to an upcoming contest, in order to motivate their review of the material ahead of time. a range of straightforward to complex calculations. Course Materials. Below is a listing of the course content ment to the class:
# Question Topic Area 1 Why is sand used in an hourglass instead of a liquid? Hydrostatics 2 Why does a golf ball have dimples? Drag force 3 Why does a knuckleball appear to dance? Drag force 4 If a graduate of this class was hired by the police in 2009 to determine whether Falcon Heene (a.k.a. mended to continue the all-day, costly chase or search for the boy on the ground?* Buoyancy 5 Why can a sailboat travel faster than the wind? Drag force 6 Why can a water bug walk on water when I cant, and how big could the bug be? Surface tension 7 Buoyancy 8 When deep sea diving, why cant a really long snorkel be used for breathing? Hydrostatics 9 home runs hit in Coors Field, and thus high ERAs. In 2002, the Rockies started storing their baseballs in humidors, leading to a dramatic decrease in home runs. Why was the number of home runs in Den ver so high prior to 2002? What caused the reduction? drag force 10 Why is it that I get more snow on my windshield when my car is stopped at a light than when its moving, but I get more rain on my windshield when its moving than when its stopped? Dimensionless numbers 11 How is body fat measured via the immersion method? Buoyancy 12 How do water rockets work? Force balance 13 Denvers runways longer than those of most other airports? Why does this new runway see relatively more use during summer months? 14 Drag force 15 Why do cyclists draft one another? How much does it help / hurt the leader and the followers? Drag force 16 Lift force 17 The Falkirk wheel is a rotating boat lift in Scotland with a capacity of nearly 200,000 gallons. Why does the weight of the wheel remain the same when boats enter or exit? Why does it consume so little power given the huge weight being moved? Buoyancy 18 ishing ball surface? Surface forces 19 How does a hot air balloon work? Buoyancy 20 What is the magic behind the trick in which a piece of cardboard is put on top a glass of water, and Surface tension 21 Why does a curve ball curve? Surface forces 22 Does the distance a discus is thrown depend more on drag or lift or both? Surface forces 23 How do self-righting and self-bailing boats work? Buoyancy / stability 24 Why does a boomerang return to the thrower? Force balance cal answer (since different assumptions may be made in the discuss their topic well in advance. As a result, the reported instructor has the opportunity to give feedback at this stage. occasion with a plethora of entertaining and effective dem the Dead Sea, making hourglasses of both sand and water to demonstrate the linear nature of timekeeping by the former but not the latter, etc. Furthermore, students are encouraged to and indeed several of the questions appearing in Table 1 have been put forth by former students. Additional suggestions are with the community via inclusion on the website indicated
material presented throughout semester is reinforced via peer instruction, including creative, student-generated demonstra ence in written and oral communication, with feedback from the instructor. Course Materials. All materials listed below are avail and to be updated with future suggestions; EVALUATION end of the semester to get feedback from the students on their experiences with these active-learning exercises. Of the 97 students enrolled in the class, 46 students responded to the Overall, the student responses are quite positive, highlighting the learning value of these exercises relative to the traditional experience with group work and the oral communication of technical material. The survey also contained a section for open-ended com ments addressing the best and worst aspects of each activity. Representative comments are included below. Tube Flow ExperimentsBest Aspects how the equations we learn in class can be used in a real-time experiment. assumptions we made in class in order to solve the mechanical energy equation, as well as others ( e.g. nent, and not just things we do to make the problems easier. tion, fusing academia with enthusiasm and a com petitive spirit that promoted comprehension of the subject. Tube Flow ExperimentsWorst Aspects better how to do the problem. results were taken experimentally, somebody could have done the calculations exactly correct and yet not Puzzling Fluids QuestionsBest Aspects lives without even knowing it Figure 2. Student survey results for (a) tube ow experiments and (b) puzzling questions in uid mechanics. See Table 2 for listing of items surveyed. 0 5 10 15 20 25 30 35 40 45 50 strongly disagree disagreeundecidedagreestrongly agreePercentage of Students Q1 Q2 Q3 0 10 20 30 40 50 60 70 strongly disa g ree disagreeundecidedagreestrongly a g reePercentage of Students Q4 Q5 Q6 Q7 Q8
hearing each persons strong points about the particular problem. Groups can have a great deal of creativity with a cumulative effect from each individual. projects Puzzling Fluids QuestionsWorst Aspects to talk and still keep it under 6 minutes an understandable manner CONCLUDING REMARKS In this work, two active-learning exercises appropriate for hundreds of students, it is found that the exercises effec tively promote student interaction, give rise to thoughtful student questions, serve as good learning tools, and last but not least, add quite a bit of enjoyment to the class period for all involved. ACKNOWLEDGMENTS The author would like to express thanks to Will Brewer, The author is also indebted to the students, teaching assistants, this work was provided by the National Science Foundation REFERENCES 1. Prince, M., Does Active Learning Work? A Review of the Research, J. Eng. Ed. 93 2. Smith, K.A., S.D. Sheppard, D. W. Johnson, and R.T. Johnson, Peda J. Eng. Ed. 3. Felder, R., D. Woods, J. Stice, and A. Rugarcia, The Future of En Chem. Eng. Ed. Chem. Eng. Ed. 37 Funda mentals of Fluid Mechanics TABLE 2 Items Used in Student Survey See Figure 2 for responses. # Item Tube Flow Demonstration Q1 This class period was a more valuable learning experi ence than a lecture with example problems. Q2 The contest format ( i.e. more focus and energy on the task than would have been present otherwise. Q3 This class period was the most fun of the semester. Puzzling Questions in Fluid Mechanics Q4 Attending these presentations and working on my mechanics better than other means used during the se mester (examples during lecture, homework problems, Q5 Attending these presentations strengthened my under Q6 This project provided a good learning experience about working in teams. Q7 This project provided a good learning experience for Q8 This project provided a good learning experience in
T exploited in several industrial reactor applications. For example, in the reaction of a gas with a liquid ( e.g.  is continuously bubbled through the batch liquid. Conversely, a gaseous product can be continuously removed from a liquid system ( e.g. CO 2 reactant into another assists in the control of a strong exotherm e.g. nylon  and polypropylene  can be controlled by careful addition of the monomer ( e.g. styrene-butadiene rubber  reactions are an issue ( e.g. substituted alkyl phenols  In spite of its industrial use, the SBR is often ignored in undergraduate reactor engineering classes. Still, the SBR concepts. Haji and Erkey  present an SBR experiment with the exothermic hydrolysis of acetic anhydride. In-situ Fourier transfer infrared spectroscopy is used for monitoring spe cies of interest vs. time. Kinetic analyses are subsequently performed. In this paper, an SBR is used to process the simple reac tion between sodium hypochlorite and hydrogen peroxide. Inexpensive household bleach and pharmaceutical hydrogen peroxide solution serve as the convenient reactants. Product molecular oxygen is monitored through a rotameter. The over all change in solution conductivity is metered with a conduc tivity probe. The reaction exothermicity is monitored through a reactor thermocouple. The elegant model analyses combine reaction kinetics with species and energy balances. REACTION AND KINETICS The reaction used in this experiment is inspired by Shams El Din and Mohammed,  who studied the kinetics of this reaction as a means to remove residual bleach from water H 2 O + NaOCl H 2 O + NaCl + O A + B R + S + T A SEMI-BATCH REACTOR EXPERIMENT for the Undergraduate Laboratory ChE laboratory M D, S B, R B Solmaz Badri was born in Teh ran, Iran, and came to the United States after completing high school. She joined NJIT and graduated in 2009, majoring in chemical engineering. She is now living in New York City, married to a physician, and working as an individual contractor. She dedicates her work and research to her newborn son, Amin Zamanian. Mario Derevjanik graduated from NJIT with a B.S. in chemical engineering in 2008. During his undergraduate career, Mario assisted Dr. Barat in developing new student experiments. Mario is working as a chemical engineer for ConSerTech, a small environmental consulting company. His cur rent responsibilities, including VOC monitor ing, are at the Conoco-Phillips renery in Linden, NJ. Copyright ChE Division of ASEE 2011 The letters representing the species are shown in corre sponding order. The reported rate expression for the disap where r i = reaction rate of species i, k = reaction rate con stant, and C i = molar concentration of i. Because the reaction evolves gaseous O 2 rather rapidly, it is preferable to run it in a semi-batch reactor. To start, a batch vessel contains hydrogen peroxide (H 2 O 2 slowly over time at a constant rate. As shown above, species S and T are NaCl and O 2 respectively. Robert Barat is currently a professor of chemical engineering at NJIT, where he has been a member of the faculty since 1990. He completed his Ph.D. in chemical engineering at MIT in 1990. His research has been in com bustion, reactor engineering, environmental monitoring, applied optics, and is currently in applied catalysis. He is also the faculty coordinator for the chemical engineering laboratories at NJIT.
REACTOR SPECIES BALANCES where F B volume, and N i = moles of i in the batch. A simple batch design where v B = volumetric feed rate of B, B = bleach mass den sity, f B = mass fraction of species B in the feed bleach solution, and W B = molecular weight of B. Since N i = C i The rate of change of the volume is accounted for with a where =mass density of batch solution. It can be rea sonably assumed The volumetric feed rate of B is set at a constant value by the user in the experiment. solved simultaneously. The system is integrated from t = 0 peroxide feed is ended by the user. EVOLUTION OF O 2 Assuming that the bleach solution mixes thoroughly into the peroxide solution, the reaction mixture will likely saturate with O 2 very rapidly. We can assume that the O 2 evolution rate is approximately the same as the reaction rate, and is The Ideal Gas Law can be used to convert F T to a volu metric rate. where T s P s represent standard temperature and pressure to the set of equations to be solved. The volumetric rate of evolved O 2 is one of three possible sources of data in this experiment. The rate r T CONDUCTIVITY CHANGE AND CHLORIDE ION The conductivity of the solution is a weighted sum of the contributions of the ionic species, including NaOCl as the active ingredient, a small amount of NaOH to help prevent degradation of NaOCl to release Cl 2 and residual NaCl from the bleach manufacturing process. We assume that C NaOCl in solution conductivity. Subsequent SBR modeling supports this where C = solution conductivity, = effective molar con ductivity of species i, C i = molar concentration of i, and Cond i = contribution of i to the total conductivity. Accounting for the contribution of NaCl to the solution conductivity requires a species balance, including the pres where f S = mass fraction of NaCl in the feed bleach solution. Molar conductivity data for NaCl aqueous solutions are available  over the temperature range of interest to yield a rfn tb n b n The contribution of the NaOH to the solution conductiv Accounting for the contribution of NaOH to the solution
conductivity requires a non-reactive species balance. Repre where f I = mass fraction of NaOH in the feed bleach solution. Molar conductivity data for NaOH aqueous solutions are available  over the temperature range of interest to yield a rf f fntb bt b The contribution of the NaOH to the solution conductiv ENERGY BALANCE reactor vessel. In a typical experiment, the liquid is in contact with stainless steel walls and internal components ( e.g. agi losses to this metal must be considered. A simple heat loss calibration was performed wherein an electric immersion with water covering the metal parts. A simple heat balance where Q h = electrical heating rate; m w and m m = masses of water and metal parts, respectively; and c pw and c pm = masscessful linear regression of the measured temperature vs. time, loss calibration of m m c pm = 1284 cal/ It can be shown, consistent with Fogler and Gurmen,  that r f where T = reactor temperature, c pj = molar heat capacity of species j, C j = molar concentration of j inside the reactor, F jo = molar feed rate of j, T f rA = heat of constant. It is small compared to the other terms, however, and can be neglected. Selected terms are now examined. where and M are the mean molar heat capacity and molecu lar weight, respectively, of the solution. As an approximation due to the high degree of dilution, the properties of the solvent water can be used. If the mass-based value is used for M is not needed. where T B M B and c pB = temperature, average molecular of the feed bleach. If the mass-based value is used for c pB M B is not needed. of temperature, especially in consideration of the limited temperature range of the experiment. The energy balance in the form used for data modeling is EXPERIMENTAL CONSIDERATIONS experimental system. An agitated reactor vessel is used. The Data PC Bleach conductivity probe Temp thermocouple O2 product Figure 1. Schematic of the semi-batch reactor experiment.
bleach solution, held in an external reservoir, is pumped through since the pump capacity is too large. A magnetic-drive centrifu gal pump is useful since all wetted parts are plastic-coated to avoid corrosion. The vessel has access ports for a stainless steel thermocouple and a conductivity probe. The probe is inserted through a side port to ensure immersion. The vessel is sealed since product O 2 in the current system to measure the pressure in the vapor space considered depending on available equipment. conductivity probe with GoLink interface and Logger Lite data collection and plot ting software are used. A data collection PC is accessed via the USB interface. The probe is calibrated with two conductivity ternative to the conductivity probe. Its membrane requires more care, however, making the ISE not as robust as the all-metal conductivity probe. Hence, the ISE was limited to determination of the chloride content of the bleach, and not inserted into the reactor. Finally, the most likely experimental parameter to vary is the bleach feed rate. Alternative experiments include dilution of either the peroxide or bleach solutions. In either case, care should be taken such that the O 2 evolution rate remains within In a typical run of the present system, a 5 liter agitated (200 refrigerator to improve shelf life. The initial conductivity one liter of laundry bleach is stored in the reservoir. At time lons/hour rate. The O 2 evolution begins almost immediately, and continues until the available bleach is exhausted (~ 350 until the maximum value measurable by the probe (~ 28,000 be used to feed more bleach so as to exhaust the remaining rent runs show reactor pressures of only a few inches of water above atmospheric. The data from this run are shown The Clorox bleach contains ~ 6 wt. % NaOCl as the active ingredient. In addition,  it contains NaOH added to prevent degradation of the NaOCl to Cl 2 The MSDS also quotes a sample of the bleach revealed a pH of 12, corresponding to an NaOH concentration of 0.01 molar or 0.36 wt. %. It also contains residual NaCl from the manufacturing process.  The NaCl concentration in the bleach, determined from an ISE measurement, is 32 grams/liter or 2.9 wt. %. For bleach, B = 1.1 g/cm 3 and c pB DATA, ANALYSIS, AND DISCUSSION of Shams and Mohammed.  r where R = 1.987 cal/mole-K, and T = absolute temperature The analysis approaches the simulation of the experiment a numerical ordinary differential equation solver package. Figures 2 and 3 show experimental and corresponding model results for batch solution conductivity, batch tempera ture, and evolved O 2 rate. The uncertainty bars are based on are reasonable for temperature and O 2 In fact, the heat loss term in the energy balance accounts for ~ 2-3 degree reduction in the observed temperature rise. The model under-prediction of the conductivity suggests that the bleach might contain an additional inert ionic species not accounted for. In addition, modeling results are most sensitive to the bleach rate. An ac As a point of discussion, and lacking direct concentration A and C B are shown monotonically as the bleach is added. The batch concentration then rises slowly, but all at a very low value. These values are consistent with the assumption made earlier. It also is consistent with the claim that NaOCl does not appreciably contribute to the batch conductivity. CONCLUSIONS The reaction H 2 O + NaOCl H 2 O + NaCl + O is a useful system to study in a semi-batch reactor. Generation of a gaseous product offers an opportunity for additional data beyond that of probes. The availability of published conduc tivity data provides a direct means to convert data to con centration of a product. Therefore, unlike most experiments, products are monitored instead of reactants. The multiple species balances required for modeling will challenge the student, but not be out of the realm of undergraduate reactor engineering. This is especially true with the inclusion of an energy balance.
0 5000 10000 15000 20000 25000 30000 0 25 50 75 100 125 150 175 200 T ime (seconds)Conductivity (uS/cm) Exper Model 10 12 14 16 18 20 22 24 26 0 50 100 150 200 250 300 350 Time (seconds)Temperature (oC)0 1 2 3 4 5 6Evolved O2 rate (slm) Model T emp Exp T emp Model O2 Exp O2 Figure 2. (right) Observed and predicted batch solution conduc tivity for bleach / hydrogen peroxide semi-batch run. Figure 3. (below) Observed and predicted batch solution tempera ture and evolved oxygen rate.
REFERENCES Int. J. of Environmental Science and Technology Fuzzy Sets and Systems 3. Seki, H., M. Ogawa, and M. Ohshima, Industrial Application of a in Advance Control of Chemical Processes L.T. Biegler, A. Brambilla, Italy 2000, 2 batch Reactors Using Midcourse Correction Policies, Industrial & Engineering Chemistry Research Reaction Conditions for Complex Kinetics in a Semibatch Reactor, Ind. Eng. Chem. Res. 6. Haji, S., and C. Erkey, Kinetics of Hydrolysis of Acetic Anhydride Laboratory, Chem. Eng. Ed. 39 7. Shams El Din, A.M., and R.A. Mohammed,, Kinetics of the Reaction Between hydrogen Peroxide and Hypochlorite, Desalination 8. Landolt, H., and R. Bornstein, Zahlenwerte und Funktionen aus Natur wissenschaften und Technik Sensing and Control, Freeport, IL 9. Fogler, H.S., and N.M. Gurmen, Elements of Chemical Reaction Engineering 11. Clorox Figure 4. Model-based predicted concentrations of species A (H 2 O 2 ) and B (NaOCl) in the batch. 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 Time (seconds) Batch Concentration of H2O2 (moles/liter) 0 0.5 1 1.5 2 2.5 3 3.5 4 Batch Concentration of NaOCl x 10^11 (mole/liter) CA (H2O2) CB (NaOCl) x 10^11
C to all aspects of chemical engineering design, analysis, and education. In most cases, we cannot apply one without injury, major property damage, and environmental harm, which is a primary focus of industrial chemical engineering practice. We choose to call this third principle conservation and application of process safety and product sustainability introduced to our knowledge by Lewis DeBlois, [1-3] who was safety engineering, with its interests in design, equip ment, organization, supervision, and education bears branches of engineering. This relation is so close, and its need so urgent, that I am convinced that some instruction in the fundamentals of safety engineering should be given a place in the training of every young engineer. He should be taught to think in terms of safety as he now thinks in terms below the conservation of energy. Yet, few of our technical schools and universities offer instruction in this subject, and the graduates go out to their profession with only vague surmises on what all this talk on safety is about. Much of what DeBlois observed and recommended remains universities to determine whether process safety was part of their chemical engineering curricula. Of the universities percent offered an elective process safety course. CSB recommended that the American Institute of Chemical Engineers and the Accreditation Board for Engineering and for chemical engineering education to include greater em CONSERVATION OF LIFE as a Unifying Theme for Process Safety in Chemical Engineering Education JAMES A. KLEIN DuPont, North America Operations RICHARD A. DAVIS James Klein is a Sr. PSM Competency Consultant, North America PSM Co-lead, at DuPont. He has more than 30 years experience in process engineering, research, operations, and safety. He received his chemical engineering degrees from MIT (B.S.) and Drexel (M.S.) and also has an M.S. in management of technology from the University of Minnesota. Richard Davis is a professor of chemical engineering at the University of Minnesota, Duluth, where he teaches computational methods, heat and mass transfer, green engineering, and separations. His current research interests include process modeling and simulation applied to energy conversion, pollution control, and environmental management in mineral processing. He received his chemical engineering degrees from Brigham Young University (B.S.) and the University of California, Santa Barbara (Ph.D.). Copyright ChE Division of ASEE 2011 ChE curriculum
phasis on process safety, in particular awareness of chemical program outcome has been proposed for the general ABET criterion for accrediting undergraduate chemical engineering Engineering programs must demonstrate that their students attain the following outcomes: (l) an awareness of the need to identify, analyze, and mitigate hazards in all aspects of engineering practice, for example design, operational procedures and use policies, hazards detection and response systems, fail-safe systems, life-cycle analyses, etc. COL can be used by universities as a concept and unifying theme for increasing awareness, application, and integration of safety throughout the chemical engineering curriculum and for meeting the revised ABET accreditation criteria. Students need to think of COE, COM, and COL as equally important fundamental principles in engineering design, analysis, and practice. By providing students appropriate tools for evaluating and implementing COL principles, we can help them to better understand what all this safety talk is about, and what their role is in contributing to safety in chemical engineering. COL PRINCIPLES Five COL Principles have been developed and are shown in Figure 1. These principles are based on application of industry standard process safety and product sustainability practices for application in various parts of the chemical engineering curriculum, as discussed further in the following section. 1. Assess material/process hazards required for safe engineering de sign and opera can be defined as a physical or chemical con dition that has the potential for causing harm to people, property, or the environ ment.  Exam ples of material flammability, toxicity, and re activity. Exam ples of process high tempera development of information about a chemical, material, mixing, or interaction of chemicals/materials and about and risk analysis and management. The starting point for on through additional literature and experimental data. 2. Evaluate hazardous events for most chemical processes, based on the material and steps. Consequence analysis and modeling consist of identifying and evaluating the direct, undesirable impacts engineering and/or administrative controls for the process. The purpose of consequence analysis is to help estimate the type, severity, and number of potential injuries, property damage, and environmental harm that could result from different event scenarios.  In conducting events are evaluated for a range of small to catastrophic failure events. A small event could be caused by a smalldiameter hole in a vessel or pipe or possibly a procedural error such as leaving a valve open or in the wrong posi tion. Catastrophic failure events are those where there is a complete and sudden failure of any equipment, structure, or system resulting in major loss of contain ment of chemicals or energy. Even though catastrophic failure events are rare, the consequences of such an event could be sig carefully evaluated.  3. Manage process risks analysis consists of the detailed, methodical evaluation of process equipment, materials, conditions, and op erating steps in order to control and reduce Develop basic data on reactivity, flammability, tox icity, etc. impacts 3. Manage process risks Evaluate risk vs. acceptable risk criteriaApply inherently safer approachesDesign and evaluate multiple layers of protection 4. Consider real-world operations Implement comprehensive PSM systemsLearn from experience Case Histories 5. Ensure product sustainability Implement product safety / stewardship practicesApply life cycle management Figure 1. COL Principles.
failures of process equipment, operating procedures, or re what-if/checklist analysis, failure modes and effects analysis  Risk analysis can range from qualitative to semi-quantitative ( e.g. Layer of  to quantitative,  depending on the potential risks associated with the process. The initial process design and risk analysis activities also provide the greatest opportu nities for consideration and implementation of inherently safer process concepts [12,13] to 4. Consider real-world operations and management is essential to chemical engineering design, but consists of only the initial elements of a sound industrial process safety management program, as shown in Figure 2. Real-world chemical operations must develop and implement systems for operating procedures, training, management of change, equipment main tenance and reliability, etc., [14,15] in order to obtain desired results. In addition, humans make mistakes, so human factors [16-18] must be considered during the initial risk analysis, management of day-to-day opera tions, and emergency response. Incidents and case studies [19,20] also provide opportu nities for learning from previous problems to help prevent their re-occurrence. 5. Ensure product sustainability Chemical products must be de signed and managed for human health and safety throughout the product life cycle from manufac ture to intended use to ultimate disposal without the potential for significant environmental impact. Comprehensive product stewardship programs should include environmental risk assess ment and management, regulatory compliance, life cycle analysis, and stakeholder engagement.  Student awareness and understand ing of the social, environmental, and economic impact of chemical engineering design and analysis is essential for ensuring optimal product sustainability practices. Application of COL principles is intended to help achieve the Assessing Hazards and Risks Process Technology Process Hazards Analysis Managing Operations Operating Procedures Personnel Training Managing Equipment and Facilities Quality Assurance Mechanical Integrity Contractor Safety Managing Incidents Emergency Planning & Response Incident Investigation Managing Change MOC-T,S PSSR MOC-P Figure 2. Elements of a process safety management program. Figure 3. Application of COL in undergraduate chemical engineering curriculum.
mental/social impact associated with chemical engineering practices and products. Awareness and use of these principles by students should help them understand their important roles as engineers in helping make achievement of this goal a real ity. Students may simply wish to think of these concepts as people in = people out. A practical method for measuring the impact of COL in either process or product safety is to consider risk reduction, R is the order of magnitude improvement in risk for the event being evaluated, where R p is the risk level ( e.g. fatalities o is the inherent risk associated with the handling, processing, or considered for implementation, based on application of COL principles. R measures the collective risk improvement, and risk criteria  are typically used to determine if an overall acceptable level of risk has been achieved. APPLICATION OF COL TO CHEMICAL ENGINEERING CURRICULA There are three main reasons for use of COL as a unifying Emphasize importance of safety to students as a funda mental principle that must be considered and evaluated in all aspects of engineering practice equivalent to COE and COM Consistent application and reinforcement of safety integrated throughout the curriculum Meet ABET accreditation changes related to safety. Use of COL will help develop a process safety culture in the curriculum, where students see connections and applications related to COL in most courses. Students will not be able to ity, but will see it as an activity that is inherent to all courses and engineering activities. Using a spiral learning model, COL will build up awareness, understanding, and capability related to safety as students gain experience by revisiting the COL principles at increasing levels of depth and breadth. Ulti mately, students will demonstrate knowledge and application of COL principles in the capstone design course reports and presentations [22-24] Process hazards Hazardous events Hazard/risk analysis Layers of protection Human factors issues Product safety and life-cycle considerations. An example of where COL principles could be applied in the undergraduate chemical engineering curriculum is shown in Figure 3. Additional resource materials for both engineering instruc tors and students for use in applying COL in undergraduate chemical engineering education are planned. Excellent train ing materials currently exist that can be used to get started SACHE modules Engineering texts Incident compilations US Chemical Safety Board investigations Process Safety Beacon Process safety literature ( e.g. Process Safety Progress). A SACHE module introducing COL has been prepared, and materials have been tested in presentations at several universities. Many SACHE modules are currently available,  which can be sorted for application of the COL principles. An example is shown in Figure 4. 1. Assess material/process hazards 1. Assess material/process hazards Chemical reactivity hazards (2005) Chemical reactivity hazards (2005) Dust explosion prevention / control (2006) Dust explosion prevention / control (2006) Explosions (2009) Explosions (2009) Properties of materials (2007) Properties of materials (2007) Reactive and explosive materials (2009) Reactive and explosive materials (2009) Runaway reactions (2003) Runaway reactions (2003) Seminar on fire (2009) Seminar on fire (2009) Etc. Etc. Reaction Engineering Course Reaction Engineering Course Chemical reactivity hazards (2005) Chemical reactivity hazards (2005) Hydroxylamine explosion case (2003) Hydroxylamine explosion case (2003) Reactive and explosive materials (2009) Reactive and explosive materials (2009) Runaway reactions (2003) Runaway reactions (2003) Runaway reactions: Experimental Runaway reactions: Experimental characterization and vent sizing (2005) characterization and vent sizing (2005) Rupture of a Rupture of a nitroaniline nitroaniline reactor (2007) reactor (2007) Etc. Etc. SACHE Modules by COL Principle SACHE Modules by ChemECourse Figure 4. SACHE Mod ules for COL Principles (examples).
EXAMPLE A simple example of a classroom active-learning exercise that reinforces the principles of COL in a separations course was adapted from the April 2003 Process Safety Beacon [33,34] sion originating in an activated carbon drum used to control minal. Starting with COL principle fourconsider real-world operationsthe class is presented with a basic description of COL principles of assessment, evaluation, and management divided into small teams of two or three students and al lowed a short time to work on the problem. Students typically reference the table of Failure Scenarios for Mass Transfer Equipment.  An instructor-led classroom discussion solicits student input and may include the following observations and Assess Hazards : Flammable materials exist in the carbon bed and hydrocarbon vapor, and low thermal conductivity in the carbon bed reduces heat transfer rates with a potential for exceeding the auto-ignition temperature. Evaluate Hazards : Refer as shown in Figure 5, and identify sources for fuel (organic materials), oxygen (air in the tank space) and heat (exother mic heat of adsorption reaction). Manage Risk : Apply LOPA to recommend passive and active design solu minimizing the bed cross sectional area, continuous on detection of high temperature, etc. SUMMARY COL is a fundamental principle equivalent to COE and COM in terms of application to all aspects of chemical engi neering design, analysis, and practice. COL can be used as a concept and unifying theme integrated into the undergraduate consistent application of COL principles, increase student awareness and capabilities, and help meet revised ABET ac creditation requirements. One authors universityUniversity in its undergraduate chemical engineering program. Other REFERENCES 1. DeBlois, L.A., Industrial Safety Organization for Executive and En gineer 2. Petersen, P.B., Lewis A. DeBlois and the Inception of Modern Safety submitted to Academy ofManage ment, Management History, Division, Hagley Museum and Library, Wilmington, DE, ca 1987 3. Klein, J.A., Two Centuries of Process Safety at DuPont, Process Safety Progress 28 4. DeBlois, L.A., The Safety Engineer, American Society of Mechanical Engineers 5. Chemical Safety Board, Investigation Report, T2 Laboratories, Inc., Runaway Reaction Report No. 2008-3-I-FL, Sept. 2009 6. AICHE, Letter to Mr. John Bresland from H. Scott Fogler and June Wispelwey, Dec. 7, 2009 7. Center for Chemical Process Safety, Guidelines for Hazard Evaluation Procedures 8. Center for Chemical Process Safety, Guidelines for Consequence Analysis of Chemical Releases vent Loss of Containment, Process Safety Progress 29 10. Center for Chemical Process Safety, Layer of Protection Analysis: 11. Center for Chemical Process Safety, Guidelines for Chemical Process Quantitative Risk Analysis 12. Center for Chemical Process Safety, Inherently Safer Chemical Pro cesses: A Life Cycle Approach 13. Seay, J.R., and M.R. Eden, Incorporating Risk Assessment and Inher ently Safer Design Practices into Chemical Engineering Education, Chem. Eng. Ed. 14. Center for Chemical Process Safety, Guidelines for Implementing Process Safety Management Systems 15. Center for Chemical Process Safety, Guidelines for Risk Based Process Safety 16. Center for Chemical Process Safety, Human Factors Methods for Improv ing Performance in the Process Industries An Engineers View of Human Error 3rd Ed., IChemE, Rugby, Discipline, Process Safety Progress 27 What Went Wrong? Case Histories of Process Plant Disasters and How They Could Have Been Avoided 20. Atherton, J., and F. Gil, Center 22. Center for Chemical Process Safety, Guidelines for Developing Quan titative Safety Risk Criteria Process Plants: A Handbook for Inherently Safer Design Chem. Eng. Progress July, 2006 Analysis, Synthesis, and Design of Chemical Processes 3rd Ed., Prentice Hall, 26. Louvar, J.F., Safety and Chemical Engineering EducationHistory and Results, Process Safety Progress 28 27.
Richard M. Felder is Hoechst Celanese Professor Emeritus of Chemical Engineering at North Carolina State University. He is coauthor of Elementary Principles of Chemical Processes (Wiley, 2005) and numerous articles on chemical process engineering and engineering and science education, and regularly presents workshops on ef fective college teaching at campuses and conferences around the world. Many of his publications can be seen at
All of the Random Thoughts columns are now available on the World Wide Web at learning, perhaps after attending a workshop or reading a paper or constantly hearing about the superb student responses their gifted colleague always enjoys. He or she tries it and it doesnt go wellthe evaluations are mediocre and some students grumble that their professor made them do all the work instead of teaching them.* Instructors in this situation can easily conclude that the nontraditional methods caused their poor ratings. What that conclusion doesnt explain, however, is how that talented colleague of theirs can use the same methods on the same students and get good performance and glowing reviews. Whenever Ive explored this issue with instructors dis tressed by it, I have invariably found that the teaching method they were trying was not the real problem. It was either that they were making one or more mistakes in implementing the method, or something else was troubling the students and the method was a convenient scapegoat. So, if youve used a learner-centered method, didnt like the outcomes, and would like to do some exploring, you might start with In your student evaluations, were complaints limited to the method, or did they also relate to other things such as the length of your assignments and exams, the clarity of your lecturing, or your lack of availability and/or respect for students? If they did, consider addressing those complaints before abandoning the method. Did you explain to the students why you were using the method? If you tell them youre doing it because research has shown that it leads to improved learning, greater acquisition of skills that potential employers consider valuable, and higher grades, most will set aside Did you use the new method long enough to overcome the learning curve associated with it? It can take most of a semester to become comfortable with and adept at active learning, and if youre using a more com plex technique such as cooperative or problem-based learning and youre not being mentored by an expert, it might take several years. If you got unsatisfactory student ratings, did you check references on the method to see if you were doing something wrong? For example, did you assign smallgroup activities in class that lasted for more than 2 minutes or call for volunteers to respond every time? suggests references you might consult for each of the most common learner-centered methods. the students whether they thought active learning (or whatever you were doing) was (a) helping their learn ing, (b) hindering their learning, or (c) neither helping nor hindering? students objecting vigorously to the method are only a small minority of the class. If thats so, announce the survey results in the next class session. Students who complain about learner-centered methods often imagine that they are speaking for most of their class the way they do, the grumbling tends to disappear immediately. If your answers to any of those questions suggest that making some changes in your approach to the method and trying again might be worthwhile, consider doing it. If you conclude, however, that youve done all you can and going back to traditional teaching is your only viable course of ac totally your call. Best regards, and good luck, Richard Felder BIBLIOGRAPHY 1. Bullard L.G., and R.M. Felder, A Learner-centered Approach to Teach ing Material and Energy Balances. 1. Course Design, Chem. Eng. Ed. pdf> ; Course Instruction and Assessment, Chem. Eng. Ed. 167 2. Felder, R.M., Sermons for Grumpy Campers, Chem. Eng. Ed. 183,
O ne educational goal of the unit operations laboratory is to help students understand fundamental principles by connecting theory and equations in their textbooks to real-world applications. We have found, however, that does not always translate into a good understanding of what is happening inside the pipes.  One problem is that the theoretical development behind the labs is often comprised of approximate methods using lumped parameters that de scribe the results but not the details of the physical process. from an absorption experiment, some students struggle to address this problem, we are using computer simulations to solidify the link between experiment and theory and provide improved learning. [1,2] Commercial software packages like COMSOL Multiphys ics TM allow students to set up and solve the partial differential equations that describe momentum, energy, and mass balances of the processes may not only help reinforce concepts and clarify the underlying physics but it may also help bring to life the mathematics as well as the experiments. With this software, students dont necessarily need to know the details of how to solve complex equations, but they need to know which equations to solve and how to validate the results.  This type of simulation can also extend the range of experi ence beyond what is possible in the lab by allowing studies safety constraints. In this paper we present experiments and computer mod els for studying the environmentally important problem of removing CO 2 from air. Simple models are shown to provide straightforward analysis of the experimental data even when the system is not dilute. In addition, we present more detailed William Clark is an associate professor of chemical engineering at Worcester Polytechnic Institute. He received a B.S. degree from Clemson University and a Ph.D. degree from Rice University, both in chemical engineering. He has more than 20 years of experience teaching thermo dynamics and unit operations laboratory at WPI. In addition to research efforts in teaching and learning, he has conducted disciplinary research in separation processes. Yaminah Jackson graduated from the WPI Chemical Engineering De partment in Spring 2008. She is currently attending graduate school at the University of Southern California. Michael Morin graduated from the WPI Chemical Engineering Depart ment in Spring 2009. He is currently a Ph.D. candidate in mechanical engineering at WPI. Giacomo Ferraro is the laboratory manager in the Chemical Engineering Department at WPI. He is a master machinist and has facilitated equip ment design, fabrication, and use for teaching and research at WPI for more than 30 years. Combining Experiments and Simulation of Gas Absorption for Teaching Mass Transfer Fundamentals: REMOVING CO 2 FROM AIR USING WATER AND NAOH WILLIAM M. CLARK, YAMINAH Z. JACKSON, MICHAEL T MORIN, AND GIACOMO P FERRARO Copyright ChE Division of ASEE 2011 ChE laboratory
into the absorption process. These models help explain the cient and how the process is liquid phase resistance controlled when using water and dependent on the gas phase resistance when using dilute NaOH solution as absorbent. Finally, we provide some discussion of how the simulations have been received by students. LABORATORY EXPERIMENT A few years ago our old 30-foot-tall, 6-inch-diameter, steel absorption tower became clogged with rust and residue from years of use with sodium carbonate solution as absorbent for removing CO 2 from air. Since concerns over global warming are a political reality even if the causes and effects are not clear, we wanted to continue to offer a CO 2 absorption experi ment because of its appeal to student interest as well as its ability to illustrate mass transfer fundamentals. To reduce cost and avoid column fouling in the future, we chose to use pure water as absorbent in our new 6-foot-tall, 3-inch-diameter, glass column packed with 54 inches of -inch glass Raschig rings that we purchased from Hampden Engineering Corpora tion  absorbent focuses the lab on mass transfer concepts without the added complexity of reactions, the limited solubility of CO 2 in water makes it necessary to have accurate analysis of the gas phase and to work with concentrated gas streams to get good results. A Rosemount Analytical, Inc.,  model 880a ment of the CO 2 composition of the gas phase at the column change in the gas phase composition, it is best if the gas rate is low and the water rate is high. Having a low gas rate also provides 2 and emitting less CO 2 to the environment in both the exiting gas and water streams. To illustrate the advantage of combining a chemical reaction with the absorption process, we also built a small-scale column for use with NaOH solution as absorbent. A 1.75-in-diameter, 15-in-long acrylic tube same glass rings used in our larger column. End caps for the acrylic column were made with rubber stop We describe here the analysis of representative sets of ex perimental runs using the two columns. Our students use the the mass transfer process. Experimental data are presented in inlet conditions and room temperature. At present we dont have our students working with NaOH in the lab for safety reasons. Instead, we give them data obtained on the smaller column by a student working on his senior thesis. Table 2 shows the data collected for both water and 1 N NaOH solu condition at room temperature. It can be seen that very little CO 2 is removed in the small column at these conditions with water as absorbent. On the other hand, most of the CO 2 is removed from the gas stream when NaOH is used, even in the small column. Large Column Data and Results for CO 2 Absorption 2 b Water Rate, W L/min Outlet CO 2 y t mole fraction K y a mol/m 3 s 0.53 0.143 0.333 1.06 0.099 0.558 1.58 0.064 0.634 2.11 0.039 0.712 TABLE 2 Small Column Data and Results for CO 2 at Room Temperature y b y b Liquid Rate, W L/min Outlet CO 2 y t mole fraction K y a mol/m 3 s Outlet CO 2 y t mole fraction K y a mol/m 3 s 0.14 0.168 0.237 0.062 2.96 0.23 0.165 0.285 0.050 3.69 0.28 0.164 0.312 0.037 4.09 0.35 0.162 0.349 0.031 4.66 0.40 0.161 0.375 0.027 5.07 TABLE 3 Water Rate L/min H x m H y m mGH x /L m H Oy m k y a mol/m 3 s k x a mol/m 3 s k x a/m mol/m 3 s K y a correlated 0.53 0.193 0.065 0.591 0.656 3.55 557 0.392 0.353 1.06 0.238 0.046 0.363 0.410 5.02 904 0.637 0.565 1.58 0.268 0.038 0.275 0.313 6.13 1196 0.842 0.740 2.11 0.292 0.033 0.225 0.257 7.08 1464 1.031 0.900
TRADITIONAL ANALYSIS If we neglect temperature and pressure effects and assume that CO 2 only is experiencing mass transfer between the gas and the liquid phases, traditional analysis leads to a design equation for our absorber given by  where t and b represent top and bottom of the column, respec tively, Z is the column height, y is the gas phase CO 2 mole fraction, y e is the value of the gas phase CO 2 mole fraction that would be in equilibrium with the liquid phase, K y a is the ing force, G 0 Oy is called the height of a transfer unit, and N Oy is the number of transfer units. Neglecting details of reactions between CO 2 and water and any impurities we can describe the vapor liquid equilibrium with Henrys law using Henrys constant, H = 1420 atm at  Since the height of the laboratory column is known, experimental gas phase composition data can be used in operating conditions. determine x at every value of y before Henrys law can be e at each x that corresponds to each y. This has traditionally been done by plotting the operating line and the ern computing environments like MATLAB TM can be used to from laboratory data as shown in Appendix 1. Results for K y a obtained by this method are given in Table 1 and these can be seen to increase with increasing water rate. The traditional analysis doesnt give much insight into the details of the mass transfer process or the physical reason the mass transfer improves with increasing water rate. To obtain that insight, students are directed to textbooks for an expla  where they learn that the overall resistance to mass transfer can be considered or equivalently, where m is the slope of the equilibrium line, equal to the Hen rys constant here. Geankoplis  gives correlations for H x and H y and the results of these correlations are given in Table 3. Although these correlations are not generally expected to give accurate quantitative predictions, the correlated results for K y a are in reasonably good agreement with the experimentally obtained results. H Oy H x and H y are often thought of as the overall, liquid side, and gas side resistance to mass transfer, respectively. Confusion can result, however, when using these to explain because while H x is larger than H y H x is observed to increase rather than decrease with increasing water rate. Apparently the term mGH x /L is the controlling factor here, but this still doesnt provide a clear physical explanation. SIMPLE MODEL Our simple absorber model uses COMSOL Multiphysics to solve two instances of the convection and diffusion equation simultaneously with appropriate boundary conditions in a R represents a reaction or source term and u is the velocity ates the concentration of solute in the gas phase, c g and the other instance evaluates the concentration of solute in the liquid phase, c l In the simple model, we included a mass transfer term as a reaction and consider that solute leaving the gas phase by this reaction enters the liquid phase by a similar mass transfer reaction. For the gas phase, the mass transfer reaction was written as 2 term in g = v g0 changing gas velocity along the length of the column is eas ily taken into account. This treatment was not needed for the liquid phase because the small amount of solute dissolved in the liquid had a negligible effect on the liquid velocity. The absorber can be modeled equally well in 1-D, 2-D, or 3-D, but we prefer the 2-D axial symmetric implementa A few years ago our old 30-foot-tall, 6-inch-diameter, steel absorption tower became clogged with rust and residue from years of use with sodi um carbonate solution as absorbent for removing CO 2 from air.
tion because it gives the best visual representation of our process. One of the important advantages of the powerful modern computing environments is that there is usually no need for transformation or scaling of variables; we can work with the actual dimensions of the equipment and with SI dimensioned variables. This what-you-seeis-what-you-get philosophy is aimed at making a strong connection between the equations and the physical process and appealing to visual learners. The model results can be presented in a variety of ways including a colorful surface plot of y within the column height as shown in Figure 1. As an example of the wealth of information readily obtained from the model, it is of interest to note that only three of the four experimentally obtained K y a results in Table 1 follow the expected trend of a linear function of water rate raised to the 0.7 power.  rate, did not follow the expected trend may have been chan neling or poor wetting of the packing at this water rate. When we observed the liquid phase mole fraction, x, as a function of column height in our model for this run, however, we saw that the liquid was essentially saturated before reaching the column outlet. Thus, the experimental outlet results can be modeled using a wide range of K y a values including the value of 0.333 mol/m 3 s that we obtained earlier but also the value of 0.480 mol/m 3 s that would fall in line with our other results in a correlation of K y 0.7 Here we have used our model to calculate the outlet con centrations that will occur in the column given an overall the built-in Parametric Solver capability of COMSOL to temperature and pressure effects, and even time dependence, cluded the effect of the chemical reaction between NaOH and CO 2 however. MODEL WITH REACTION The chemical reaction between CO 2 and NaOH is well studied and according to the literature  the rate limiting with second order rate constant given as a function of ionic strength by r where k B is in m 3 /kmol s, T is in K, and I is in kmol/m 3 The ionic strength is calculated as chemical reaction by writing the reaction term for CO 2 in the liquid phase as indicating that CO 2 arrives at the liquid phase from the gas phase by mass transfer and disappears from the liquid phase by reaction. This model also keeps track of the ions, Na + OH and HCO 3 liquid phase. Figure 1 Mole fraction CO 2 in the gas and liquid phases as a function of column height at four different water rates: (a) W = 0.53 L / min, (b) W = 1.06 L/min, (c) W = 1.58 L/min, and (d) W = 2.11 L/min. Upper (a) curve is for K y a = 0.480, lower (a) curve is for K y a = 0.333 mol/m 3 s.
values of K y a needed to make the outlet y results of the model match the experimental y results. The resulting K y a values are shown in Table 2. The dramatic improvement in the mass in K y a with reaction compared to without. QUALITATIVE FALLING FILM MODEL Although our simple absorber model is easier to use than a colorful representation of the composition in the column, it doesnt given much insight into the details of the process or ally occurs inside the column is that solute diffuses through interface to maintain equilibrium there, and diffuses into a boundary condition at the interface. We describe here a quali these concerns and providing a basis for understanding an Inside our packed column are glass rings that have a thin and expensive in computer time to model the exact details of rings randomly packed inside the column. As an illustration, however, it was reasonable to approximate the process with a number of identical glass rods each extending the full height of the column. The water layer around each rod was considered give approximate results that illustrate our points. It was only necessary to model one rod with its surrounding layers axially symmetrically as shown in Figure 2. As before, two instances of the convection and diffusion equation, one for the gas phase and one for the liquid phase, were solved simultaneously. The inlet and outlet boundary conditions are shown in Figure 2. The so-called stiff-spring equilibrium boundary condition  was used at the gas-liquid interface according to Henrys law. That is, the boundary condition on the gas side of the interface was set to and the boundary condition on the liquid side was set to where M is an arbitrary large number; e.g. M = 10000. This the equilibrium condition y e were not used in this diffusion-based model. Instead, carbon dioxide diffuses through the gas phase, crosses the interface, and diffuses into the liquid phase according to molecular diffusion using diffusivities for CO 2 in air and water of 1.6 10 -5 m 2 /s and 1.8 10 -9 m 2 /s, respectively. The velocity built-in Incompressible Navier-Stokes mode of COMSOL. The velocity in the gas phase was considered uniform in the r-direction but decreased as v g0 Figure 2. Falling lm model geometry.
Figure 3 shows the resulting CO 2 the r-direction at a height equal to Z/10 for two different water velocities. Curve a is for a relatively low water rate and the curve b is for a relatively high one. More CO 2 is removed from the gas phase at the high water rate as expected. In both cases, the gas phase concentration is nearly uniform in the r-direc tion. On the other hand, the liquid phase concentration varies changing region close to the interface and a nearly constant re gion in the bulk. The region where the concentration changes is often called the concentration boundary layer.  Figure 3 shows that the thickness of this boundary layer decreases with increasing water rate due to increased convection. In reality, a change in water rate would probably affect the interfacial area as well as the boundary layer thick ness, but we have chosen to illustrate the process with a constant interfacial area. to account for the chemical reaction. In this case, R in CO 2 cates that the thickness of the concentration boundary layer over which the concentration is changing is greatly reduced when the reaction is present in the liquid phase. EXPLICIT TWO-FILM MODEL convection process but does not give accurate pre dictions for outlet compositions because it does not take into account all the details of the non-uniform and provides a physical interpretation of the mass was designed to lump all the complexities of the process into a single parameter accounting for the reciprocal of the average resis tance to mass transfer throughout the column . As shown above, this approach describes absorp tion results well, but doesnt give the same insight into the physical process that a diffusion-based model does. To introduce the mass transfer concept into our diffusion-based model we start by comparing diffusion in a com plex situation to that of diffusion Figure 3. Concentration in the r-direction at z/Z = 0.1 for qualitative falling lm model (a) low water rate, (b) high water rate, (c) NaOH solution rate equal to water rate in (a). Note that the x-axis begins at r = 0.005 m to show only the owing layers in this gure. Figure 4. Model geometry showing two-lm theory.
ation. Note that k c has units of m/s. g or v l of the appropriate thickness on each side of the interface and g or D l Alternatively, and equivalently, we have used an effective dif t Figure 4 shows the geometry and boundary conditions for proach. The appropriate resistance to mass transfer in each priate values for the effective diffusivities requires estimating y a and k x a, accounting for the interfacial area per volume, a, as a separate component of k y a and k x a, and some unit conversions. From Table 3 it can be observed that 1/k y a is a minor con tributor to 1/K y chosen to assume that the correlated values of k y a shown in Table 3 are correct, knowing that uncertainties in these values will not have a strong effect on our subsequent results and interpretations. With this assumption, k x a could be calculated derived values of K y values for k x i ZN R 2 /m 3 R i is the radius of the model at the interface and N R is the number of glass rods. Taking into account unit conversions between c g and y and c l and x yields the following equations rfnt b b r fr r nt where t scribes mass transfer as governed only by molecular diffusion our model to isolate all the resistance to mass transfer in the stagnant layers. Also note that the value of the interfacial area per volume used here is not necessarily a physically cor rect value. It is simply the one that matches thicknesses and associated number of glass rods of our model. the same x and y results as those obtained with our simpler model. In addition, we can observe the concentration at every point in the absorber as shown in Figure 5. By looking at the con Figure 5. Concentration in the r-direc tion for W = 1.58 L/min at various column heights, z/Z = 0, 0.25, 0.5, 0.75, 1.0. Note that the x-axis begins at r = 0.005 m to show only the uid layers in this gure.
centration across the various layers at various heights in the column a student can observe the resistance to mass transfer imposed by equilibrium at the interface. More resistance is indicated by a larger concentration change. In this system, it can be seen that the liquid phase offers considerably more resistance than the gas phase. From Table 4 we see that k x a increases with increasing water rate. This could be due to either k x increasing or the interfacial area, a, increasing or both. The interfacial area probably does increase with increasing water rate because also be thicker. If we assume, however, that a is constant as we have done in our model, we can see that k x increases with increasing water rate. What physical process can account for this? As shown above, k c (and with unit conversions k x be assumed to be equal to the molecular diffusivity divided for convenience in our model, an estimate l theory and subject to the assumptions in our model, the esti ing water rate, thus providing a physical explanation for the more insight into the difference between absorption with and without reaction. To include the chemical reaction, we initially with and without reaction are shown in Figure 6. For the case with no reaction, in Figure 6a, it can be seen that the liquid side resistance dominates the process. For the reaction case, driving force for mass transfer and preventing saturation of the liquid even at low liquid rates. It can also be seen that the resistance in the gas phase is comparable to the resistance in the liquid phase when reaction is present. seen that the chemical reaction has the effect of dramatically (*adjusted to saturation at liquid outlet). Water Rate, W L/min K y a mol/m 3 s H y m k y a mol/m 3 s k x a mol/m 3 s k x mol/m 2 s l m 10 5 g m 10 2 k cl m/s 10 4 k cg m/s 10 4 Large Column No Reaction 0.53 0.480* 0.065 3.55 789 1.18 8.45 13.41 0.213 1.19 1.06 0.558 0.046 5.02 891 1.34 7.48 9.49 0.241 1.69 1.58 0.634 0.038 6.13 1004 1.51 6.64 7.77 0.271 2.06 2.11 0.712 0.033 7.08 1123 1.68 5.93 6.73 0.303 2.38 Small Column No Reaction 0.14 0.237 0.110 6.53 350 0.525 19.1 7.3 0.095 2.19 0.23 0.285 0.086 8.37 419 0.629 15.9 5.7 0.113 2.81 0.28 0.312 0.078 9.23 458 0.687 14.6 5.1 0.124 3.10 0.35 0.349 0.070 10.32 512 0.768 13.0 4.6 0.138 3.47 0.40 0.375 0.065 11.04 551 0.827 12.1 4.3 0.149 3.71 Small Column With Reaction 0.14 2.96 0.110 6.53 7667 11.50 0.870 7.3 2.07 2.19 0.23 3.69 0.086 8.37 9354 14.03 0.713 5.7 2.52 2.81 0.28 4.09 0.078 9.23 10438 15.66 0.639 5.1 2.82 3.10 0.35 4.66 0.070 10.32 12066 18.10 0.552 4.6 3.26 3.47 0.40 5.07 0.065 11.04 13295 19.94 0.501 4.3 3.59 3.71
thicknesses are much larger than the liquid film thick nesses can be explained by the fact that the gas phase dif fusivity is much larger than that in the liquid phase and does not imply that the gas more insight into the resis tance offered by each phase it is instructive to compare the k c values. These values have been calculated from nesses reported in Table 4, but it would be equivalent to calculate them from the k y a and k x a values using ap propriate unit conversions. The resulting values of k cl and k cg shown in Table 4, tell a similar story to the one represented visually in Figure 6. Without reaction, k cl is smaller than k cg indicating that the liquid phase is the controlling resistance. With reaction, the values of k cl and k cg are comparable to one another indicating that the gas phase In the analysis above, we considered the stagnant liquid uid phase separately from the reaction taking place almost since the reaction can take place as soon as the solute crosses the interface. In that case, all the resistance to mass transfer would be in the gas would be equal to the overall modeled that scenario in our y a equal to the K y a values shown in Table 3 and setting the ef fective diffusivity in the r-di shown in Figure 7 gives gas phase concentrations similar to those in Figure 6b. It is possible that Figure 7 is more representative of reality than Figure 6b because the k y a values used to obtain 6b came from a correlation and are not necessarily correct. Figure 3 obtained from our qualitative model suggests that Figure 6b with a small but extant liquid IMPLEMENTATION AND EVALUATION In our unit operations lab, students spend about two weeks collaborate on writing a pre-lab report describing the relevant theory and their plans to conduct the experiment. For the absorber lab, the groups then spend two days of lab work It was disappointing, but revealing, that very few students Figure 7. Concentration prole for W = 0.35 in the small column at z/Z = 0, 0.25, 0.5, 0.75, 1.0 with reaction in the liquid and all mass transfer resistance in the gas lm. Note that the x-axis begins at r = 0.005 m to show only the uid layers in this gure. Figure 6. Concentration prole for W = 0.35 in the small column at z/Z = 0, 0.25, 0.5, 0.75, 1.0: (a) no reaction, (b) with reaction. Note that the x-axis begins at r = 0.005 m to show only the uid layers in this gure.
were offered as a completely optional resource. In the second offering, we required each student to complete an interactive tutorial containing the simulations and them. At the end of the course that year, the students completed a survey regarding their perception of the Students in the course did not build the simulations from scratch but instead re-ran previously developed simulations with different operating conditions. The tutorial walked the students through the pre-built simulations and included several multiple-choice questions requiring simulation results to obtain cor rect answers. For example, one question asked for the numerical value of the mole fraction of CO 2 in the exiting liquid stream according to the simulation under certain conditions. Another question asked for the value that would be obtained if the process were considered dilute with straight equilibrium and oper ating lines. In addition to answering these questions, students were encouraged to experiment with chang ing operating conditions to see the effect on column performance. Students were invited to study the simulations and answer the multiple choice questions on their own time and at their own pace. They were encouraged to study the simulations before complet ing their pre-lab reports but were required to submit the answers to the multiple choice questions on-line report was due. It should be noted that these students were not necessarily COMSOL model builders but did have some familiarity with COMSOL from previous homework assignments using pre-built simulations via tutorials and online questions. The end of course survey revealed that most, but not all, of the students found the simulations to be useful, particularly for illustrating the resistance to mass transfer and increases with increasing water rate. Table 5 shows example questions and the percent of students responding to each of the multiple choice answers for each question. Table 6 provides examples of student comments on the absorber simulations. CONCLUSION Our new absorption experiment provides an effective way of teaching mass transfer fundamentals while using relatively small amounts of CO 2 air, and water. Experiments presented with NaOH as absorbent provide a good demonstration of the dramatic improvement in absorption due to reaction. A simple model made with COMSOL Multiphysics gives accurate calculations, is easier to use than the traditional analysis, and provides a visual representation of the absorption pro Results for Three Survey Questions The percentage of students giving each response is indicated in brackets. the pre-lab and in addition to a written pre-lab report [47%], Example Student Comments About the Absorber Simulation they relate to the physical world makes for a nice learning tool values to what is found experimentally, and why it may vary resistance to mass transfer. By doing this as a simulation, it was easier to see relationships compared to just looking at equations. helped provide a greater understanding of mass transfer principles experiment in an excellent way take and rectify it without wasting much time in the lab. And you can also change constants to see the effect of each on mass transfer. cess. More detailed models that illustrate the concentration with an increase in water rate. These models also make it clear that the improved mass transfer with reaction is due to reduced resistance in the liquid phase as well as maintaining a high driving force and preventing saturation of the liquid. The straightforward and relatively easily obtained solutions together with the richness of information afforded by post processing capabilities in COMSOL can make the details of complex process calculations come alive in comparison to the rare, static, printed examples in text books. Combining the experiments with computer simulations that show the the learning process and help students gain a more complete understanding of mass transfer in an absorber.
ACKNOWLEDGMENTS This material is based on work supported by the National Science Foundation under grant no. DUE-0536342. REFERENCES 1. Clark, W.M., and D. DiBiasio, Computer Simulation of Laboratory Experiments for Enhanced Learning, Proceedings of the ASEE Annual 2. Clark, W.M., COMSOL Multiphysics Models for Teaching Chemical tion of the Two-Film Theory of Mass Transfer, COMSOL Conference 2008 Proceedings 3. Finlayson, B.E., Introduction to Chemical Engineering Computing 6. Cussler, E.L., Diffusion: Mass Transfer in Fluid Systems 3rd Ed., 7. Geankoplis, C.J., Transport Processes and Separation Process Prin ciples (Includes Unit Operations) 4th Ed., Prentice Hall, Upper Saddle 8. Whitman, W.G., The Two-Film Theory of Gas Absorption, Che. Metal. Eng. 29 10. Pohorecki, R., and W. Moniuk, Kinetics of Reaction Between Carbon Dioxide and Hydroxyl Ions in Aqueous Electrolyte Solutions, Chem. Eng. Sci. 11. COMSOL Multiphysics, Chemical Engineering Module Users Guide Separation Through Dialysis Example. 12. Seader, J.D., and E.J. Henley, Separation Process Principles 2nd Ed., APPENDIX The function quadv is a built-in Matlab function that per forms numerical integration of a complex function between % run_absorber.m gas phase xb = 1.2597e-004 NTU = 3.3846 HTU = 0.4054 Kya = 2.0563e+003
O highly mathematical, and sometimes a somewhat obscure discipline. While it is true that many ad can be developed that are suitable for undergraduates at all levels. Two of these problems will be described in this paper, and many others are available on the web.  A pedagogy is described that requires students to identify the trends of the components of the objective function and to understand how trade-offs between these components lead to the existence of the optimum. last generation. Earlier editions of current design textbooks  to close in on the optimum. Now, it is possible to perform few clicks of a mouse, and an entire chemical process can be simulated and results exported to a spreadsheet in a matter of minutes. in undergraduate textbooks; however, the objective func tion does not usually involve economics. These examples include optimum interstage compressor pressure,  optimum insulation thickness,  and identifying conditions for the optimum selectivity.  Qualitative representations of the economic optimum pipe diameter   are are available, but these do not involve an economic objec tive function. [8-10] The problems presented here all involve an economic objective function. TYPES OF PROBLEMS ics are discussed in this paper, and the others are available on the web.  The numbers in parenthesis indicate the number of different versions available for each problem. All of these have been used successfully in a freshman class designed to develop computing skills appropriate for an undergraduate chemical engineering student. Most of these problems would also be suitable for assignments or projects in unit operations OPTIMIZATION PROBLEMS BRIAN J. ANDERSON, ROBIN S. HISSAM, JOSEPH A. SHAEIWITZ, AND RICHARD TURTON Brian J. Anderson is the Verl Purdy Faculty Fellow and an assistant professor in the Department of Chemical Engineering at West Virginia University. His research experience includes sustainable energy and development, economic modeling of energy systems, and geothermal energy development as well as molecular and reservoir modeling. Robin S. Hissam received her B.S. and M.S. degrees in materials science and engineering from Virginia Tech and her Ph.D. in materials science and engineer ing from the University of Delaware. After a post-doctoral fellowship in chemical engineering and applied chemistry at the University of Toronto, Robin joined the Chemical Engineering Department at West Virginia University. Her research is in production of protein polymers for application in tissue engineering, biomin eralization, and biosensors. Copyright ChE Division of ASEE 2011 Available Optimization Problems Single Projects Pipe diam Absorber Generic chemical Reactor/ preheater Batch reactor/pre heater Geothermal energy Staged compressors Fuel production ChE class and home problems Joseph A. Shaeiwitz received his B.S. degree from the University of Delaware and his M.S. and Ph.D. degrees from Carnegie Mellon University. His profes sional interests are in design, design education, and outcomes assessment. Joe is a co-author of the text Analysis, Synthesis, and Design of Chemical Processes (3rd Ed.), published by Prentice Hall in 2009. Richard Turton P.E., has taught the senior design course at West Virginia University for the past 24 years. Prior to this, he spent ve years in the design and construction industry. His main interests are in design education, particulate processing, and modeling of advanced energy processes. Richard is a co-author of the text Analysis, Synthesis, and Design of Chemical Processes (3rd Ed.), published by Prentice Hall in 2009. The object of this column is to enhance our readers collections of interesting and novel problems in chemical engineering. We request problems that can be used to motivate student learning by presenting a particular principle in a new light, can be assigned as novel home problems, are suited for a collaborative learning environment, or dem onstrate a cutting-edge application or principle. Manuscripts should not exceed 14 double-spaced pages and should Lansing, MI 48824-1226.
classes or as problem assignments for the portion of a design Problem 1: Bioreactor Background A liquid-phase, biological reaction is used to produce an intermediate chemical for use in the pharmaceutical indus try. The reaction occurs in a large, well-stirred, isothermal bioreactor, such that the reactor temperature is identical to the inlet temperature. Because this chemical is temperature sensitive, the maximum operating temperature in the reactor maximum temperature. The feed material is fed to the reactor through a heat exchanger that can increase the temperature of the rate of the reaction. This is illustrated in Figure 1. The be adjusted to obtain the desired conversion of reactant. As the temperature in the reactor increases so does the reaction rate, to give the desired conversion. The problem to be solved is to determine the optimal value for the single independent variable; namely, the temperature (T c,2 the purchase costs of the reactor and heat exchanger and the operating cost for the energy to heat the feed. Problem Statement of water ( = 1,000 kg/m 3 C p feed is to be heated with a heating medium that is available The physical properties of the heating medium are = 920 kg/m 3 C p The reaction rate for this reaction, r A is given in terms of the concentration of reactant A (C A where 3 o 3 A is the conversion where and F = log-mean temperature correction factor = 0.8 (assume 2 K The optimum reactor inlet temperature is the one that EAOC is given by where PC i are the purchase equipment costs for the heat factor given by For this problem, use i = 7% and n = 12 years. r 3 The cost of the r fff r fn t where A is the area of the heat exchanger in m 2 The cost of Figure 1. Process ow diagram of the feed preheater and bioreactor.
c,2 and the T c,2 of the reason for the trends on these plots. Problem 2: Batch Bioreactor Background A liquid-phase, biological reaction is used to produce an intermediate chemical for use in the biotech industry. The reaction occurs in a large, well-stirred, isothermal bioreac tor, such that the reactor temperature is identical to the inlet temperature. Because this chemical is temperature sensitive, the maximum operating temperature in the reactor is set to exchanger that increases the temperature of the reactants the reaction. This is illustrated in Figure 2. The reactor runs as a batch operation in which the contents remain in the equipment for a given period of time. The time spent in the bioreactor must be adjusted to obtain the optimal conversion of reactant. Because of the fear of contamination by pathogens and parasitic fungi, the reactor must be cleaned thoroughly between batch operations. The cleaning time per batch (t clean As the time spent in the reactor increases, the amount of product also increases but at a decreasing rate. The problem to be solved is to determine the optimum values of the two independent variables; namely, the time for the products to spend in the reactor, or the batch time, and the costs that vary are the revenues from sales, the reactor cost, and the cost for cleaning. Problem Statement product from the reactor. The feed has the properties of water ( = 1,000 kg/m 3 Cp = be heated with a heating medium that is avail properties of the heating medium are = 920 kg/m 3 C p The reaction rate for this reaction, -r A is given in terms of the concentration of reactant A (C A where where t is the time spent in the reactor and X A is the fractional conversion of reactants to products. The amount of product formed in time t is given as NX A where N is the number of moles of reactant fed to the reactor. The energy balance equation for the heat exchanger is where C p Figure 2. Process ow diagram of feed preheater and bioreactor. Figure 3. Optimization plot for Example 1.
t, and the second plot should show the EAOC should contain a physical explanation of the reason for the trends on these plots. OPTIMIZATION PROBLEMS In Problem 1, the optimum reactor feed temperature is to be determined. There is a trade-off, which is necessary to obtain an absolute maximum or minimum in the In this case, at higher temperatures, it costs more to heat the reactor feed, but, since the reaction rate increases with temperature, the reactor cost is lower because a smaller reac tor is needed. Additionally, at higher reactor feed temperatures, a larger heat exchanger is needed. Students can develop a spreadsheet that varies the reactor inlet temperature and plot the EAOC vs. the reactor inlet tem perature. This plot is illustrated in Figure 3. They can also plot EAOC vs. reactor cost, heating medium cost, and heat exchanger cost to see the trends. This is illustrated in Figure 4. The trend for the heat exchanger clearly illustrates how the as the reactor feed temperature ap proaches the heating medium inlet temperature, causing the log-mean temperature driving force to go to of why it is important for students derstand the trends. It is possible to solve this entire problem on Excel using the Solver tool; however, much of the understanding/synthesis Figure 4. Component optimization trends in Problem 1. 1 and 2 refer to inlet and outlet conditions, respectively. h and c refer to the hot and cold stream, respectively. r where PC i are the purchase equipment costs for the heat exchanger and reactor; UC i feed stream, and the cost of cleaning; and R is the revenue from sales of the product. For this problem, use i = 0.07 and n = 12 years. The purchase cost of the reactor is given by r f 3 The cost of the heat exchanger may r f The price of the feed is $2/mol, the value of the product is $10/mol, and the 3 The cost of cleaning the reactor is given by rf n t and the time to clean a reactor is rf The ability to solve routine optimization problems advances in computing power over the last generation.
trends is essential. It is also possible to illustrate how changes in operating con ditions change the optimum. In a problem similar to Problem 1,  if the reaction kinetics are increased (pre-exponential factor increased to 7.0 and the activation energy reduced to Many different versions of this and other problems can be created by changing some parameters or by changing the economics. We use different versions of these for different groups in the same class. During oral presentations, we ask them to explain why the optima differ. In Problem 2, there are two decision variables (bivariate problem in slightly more complex than Problem 1, and it il lustrates that there may be more than one decision variable. One decision variable is the reactor volume, which in this and the other decision variable is the processing time. The trade-off is that for longer processing times, more product is made, but fewer batches can be made per year. For a larger reactor, more product can be made per batch, but fewer batches can be made per year due to the longer cleaning time. Although this problem does not include it, the reactor feed temperature could also be varied, as in Problem 1, to out that the optimum is the 10,000 L reactor with a reaction time of 9.1 h, at about 97% conversion, as is illustrated in Figure 5. For higher conversions, the additional processing time is long enough to make the annual product revenue drop. This problem also illustrates some of the issues associated with batch processing to students who might be very used to continuous processes. Figure 5 also illustrates a bi one decision variable with several curves indicating the second decision variable. DISCUSSION We believe that an important part of the pedagogy trends of the components of the objective function and to understand how trade-offs between these components lead to the existence of the optimum. That is why and making plots to investigate trends is the trends are understood, Excel Solver can be used to obtain a more exact value of the optimum. We have used these problems as part of a freshman class taken by students who know that they are interested in chemi cal engineering. Other students take a college-wide program ming class. In our class, students are taught computer skills applicable to chemical engineering, mostly using the advanced features of Excel in addition to some elementary program ming techniques and algorithms. All assignments are based on industrially relevant chemical engineering problems. Some of textbook.  Since these problems have been used successfully in a freshman class for several years, we believe they can be used anywhere in the curriculum. Since all students in chemical engineering do not take the class in which these problems are assigned, assessment of job on these problems, and they seem to appreciate the actual chemical engineering application compared to their peers in the programming class. web.  problems could be obtained by manipulating some of the values given in these problems. CONCLUSION suitable for all levels of chemical engineering students have been presented. These problems do not require advanced mathematical techniques; they can be solved using typical software used by students and practitioners, such as Excel. These problems involve an economic objective function with Figure 5. Optimization plot for Example 2. Since these problems have been used successfully in a freshman class for several years, we believe they can be used anywhere in the curriculum.
component capital and operating cost terms. An important part of the pedagogy of these problems is an understanding of how the trends of the components terms in the objective function contribute to the trade-off involved in most optimi REFERENCES php#opt> 2. Peters, M.S., and K.D. Timmerhaus, Plant Design and Economics for Chemical Engineers 10 3. Sandler, S.I., Chemical, Biochemical, and Engineering Thermodynam ics 4. Geankoplis, C., Transport Processes and Separation Principles (4th 5. Fogler, H.S., Elements of Chemical Reaction Engineering Prentice Hall PTR, Upper Saddle River, NJ, 2006, Chapter 6 6. de Nevers, N., Fluid Mechanics for Chemical Engineers 7. Peters, M.S., K.D. Timmerhaus, and R.E. West, Plant Design and Economics for Chemical Engineers Chem. Eng. Ed. 32 Operation, Chem. Eng. Ed. 38 10. Mitsos, A., Design Course for Micropower Generation Devices, Chem. Eng. Ed. Analysis, Synthesis, and Design of Chemical Processes PTR, Upper Saddle River, NJ, 2009, Chapter 14
ChE department ChE at... The University of Houston C hemical engineering at the Uni to advanced materials to energy and sus tainability to the use of bioengineering principles for the betterment of human health. The University of Houston is a young university, founded in 1927 about 3 miles south of downtown Houston. Starting as a junior college, it became a univer sity in 1934, changing hands in 1945 to becoming a part of the State of Texas system in 1963. In 1953 UH gained na tional recognition when it established KUHT, the worlds and is considered one of the most ethnically diverse campuses among U.S. universities. The Department of Chemical & Biomolecular Engineering during the late 1940s and by the 1952/ academic year, a full-time faculty of chemical engineering was formed. During the next three years, under the leadership of Joseph Crump a vision emerged with three short-term program comprising M.S. and Ph.D. degrees supported by an internationally lishment of an accredited undergraduate program with strong industrial ties, and by university administration. During the next 15 years, under the leadership of Frank Tiller (Dean of Engineering, Abe Dukler neering emerged as the young upstart department. Under the leadership of Dan Luss from the mid s, through the s, UH Chemical Engineering became one of the top departments in the United States (ranked 8th by the National Research Jim Rich ardson who chaired the department from 1996-1998. After a challenging period of budget pressures in the mid 1990s, UH attracted one of its former faculty members, Ray Flumerfelt to serve as dean of the Cullen College of Engineering. One of Flumerfelts primary goals was to invest in the Chemi cal Engineering Department to re-establish its prominence. In 2000 Flumerfelt hired one of UHs own, when it underwent the name change to include Biomolecular. The injection of resources has led to a new period of growth and resurgence of the department, now under the leadership of Ramanan Krishnamoorti transforming itself from its unit operations and transport focus to sustained excellence in reaction engineering, and new strengths in materials and biomolecular engineering. The full-time faculty is now ap proaching 20 in number while enhancing its reputation and impact. The most recent 2010 NRC review has the department MICHAEL P HAROLD AND RAMANAN KRISHNAMOORTI Joseph Crump Copyright ChE Division of ASEE 2011
MISSION AND DEGREE PROGRAMS It is this strong foundation and standard that the UH Chemi cal & Biomolecular Engineering Department strives to sustain and build upon. The mission of the department is to produce graduates of the highest scholarship and with skills that will that continually evolves and transforms. The department has and graduate students in chemical engineering through a comprehensive curriculum that emphasizes basic sci ence, mathematics, engineering science, and engineer ing design. UH ChBE faculty members are expected to maintain their reputation as superior teachers and to provide a stimulating educational environment. dents, procure support for this research on a continuous basis, and contribute to the development of fundamental departments varied and aggressively pursued research ensures that our faculty members remain at the technolog ical forefront of their respective areas of specialization. particular, to the City of Houston and the State of Texas, and to provide the local engineering community oppor tunities for advanced and continuing education. ChE) non-thesis M.S. ChE) In addition, the department has administrative responsi bility for a Petroleum Engineering program that confers the The department has traditionally attracted excellent under the diversity of the UH student body as a whole, our under grads are a very diverse group, with under-represented stu 60% of the total. Moreover, the department does very well for working part-time students. Currently there are about 400 students in the program with recent graduation rates of about 35-45 per calendar year. The graduate program numbers ap proximately 100 students, about 25 of whom are part-time students (most have full-time employment and are MChE program numbers about 130 students, equally divided among undergraduate and Masters students, the majority of whom are part-time working professionals. At the undergraduate level, the department has been ef fective in educating students for productive careers in the ergy industry, particularly the upstream sector in recent years. Feedback obtained from local employers reveals that the UH ChBE students are top-performing, typically more mature students from the start. This is testimony to the fundamental focus of the curriculum, the standards of the instructors, and the diversityincluding ageof the student population. Undergraduate enrollments in the program generally follow chemical and petrochemical industries. The strong reputation Left to right: Frank Tiller, William Prengle, Abe Dukler, Dan Luss, Jim Richardson, and Mike Harold. Areas of graduate employment.
of the department, however, has provided a steady stream of high-quality undergraduate students. Recent changes to include biomolecular engineering principles and materials science and engineering in the core undergraduate training along with development of minor options in petroleum engi education and training of the students. THE EARLY YEARS The department was founded in the late 1940s when the University of Houston was at that time a small, private un dergraduate university principally attended by white students ulty members who were, as Jim Richardson refers to them, the instigators. These were Dukler, and Prengle and Dukler were hired from Shell full time faculty in 1952. Dr. Professor of Mechanical Engineering at UH, recalls the important impact that Crump, Dukler, and Prengle had on the department. These three scholars were role models for the rest of the college, says Witte. They showed us how to transform an undergraduate program into a successful graduate research program. In the 1960s they won grant that enabled them to expand and bring in more research. Other departments wanted to emulate their success. An important step for the department and college occurred in 1955 when Frank Tiller was hired from Lamar University as to expand the college, enhance the quality of the faculty, and gain accreditation for the college programs. On arrival only 14% of the engineering faculty had doctoral degrees. Tiller actually sent some of them back to school to earn their Ph.D.s. By 1963, 40% of the college faculty had doctorates. As critical of a leadership role as Dean Tiller provided to the young college, he also became one of the stalwart researchers in the university. Tiller established himself as one of the lead ing academicians who used mathematical methods to solve chemical engineering problems.  His primary interest was in advancing the understanding of solid-liquid systems with doctoral students would study with Tiller and were coveted by industry to improve the many processes involving solids evidenced by the AFS Tiller Award which annually honors a Complementing Tiller was Dukler, who established himself and liquid in vertical pipes. Dukler was elected to the National Academy of Engineering in 1977 for his pioneering advances The department hired from Columbia Uni versity in 1961. Henley has distinguished himself for decades as being an innovator in his research, teaching, and extramural business pursuits. For a period of over two decades and end ing a few years ago upon his retirement, Henley taught the two-course capstone design course to UH senior undergradu ates. This was one of the main reasons why UH graduates engineering design and process economics. Henleys book with J.D. Seader and D. Keith Roper, Separations Process Principles is in its third edition and has established itself as the text of choice for unit operations and separations at chemical engineering departments in the United States and internationally.  During this period, the strong industrial ties to the depart ments research and educational activities were established. As department chair from 1966-1974 and dean of the college from 1976 to 1982, Dukler accelerated the department towards becoming an upstart among chemical engineering depart ments in the United States. In 1968 Dukler landed a $600,000 Center of Excellence Departmental Development Grant from the National Science Foundation, a highly competitive program. These monies were used to hire faculty members and build world-class research laboratories. Prof. Osman I. a faculty member in the Department of Civil and real quantum jump in the direction of research came when Dukler took over. He wanted us to really show a change in one pursuit. Says Stuart Long Professor of Electrical and Computer at UH, Dukler was willing to take the heat for making this the right thing. Around 1975 the department recruited Alkis Payatakes an expert in transport phenomena, from Syracuse. Payatakes would join forces with Flumerfelt to start a center in enhanced oil recovery, which used theory of low Reynolds oil ganglia in porous media. Their approach changed the way the oil industry looked at petroleum recovery and helped to forge closer ties between the upstream energy industry and the department. The departments tradition in multiphase transport would receive a boost with the hiring of two junior faculty in the early 1980s, in 1983 and in 1984. Balakotaiah was one of UHs own, a student of Dan Luss, while Chia was recruited away from UC Santa Barbara. While Bala and Chia had roots in chemical reaction engineering and nonlinear analysis, both
1990s the department recruited Kishore Mohanty away from the oil industry. Mohanty would further solidify the ties with the upstream energy industry with his fundamental focus on transport in porous media applied to oil and gas recovery. Complementing Mohantys efforts was Michael Economides hired from Texas A&M in 1998, who brought more practical aspects of petroleum engineering to the program. THE REACTION ENGINEERING COMPETENCY The hiring of Luss in 1967 was arguably the most important hire in the departments 60+ years. Luss, a highly accom plished student of Neal Amundson at Minnesota, was an expert in chemical reaction engineering. In the same period the department attracted Richardson, an accomplished expert in heterogeneous catalysis, from Exxon. Together their hiring ushered the emergence of chemical reaction engineering as the area in which UH chemical engineering would become Jay Bailey as an assistant professor with primary research inter est in reaction engineering, and broadened the impact of the pioneering research. Bailey applied the principles of chemical reaction engineering and mathematical methods developed and later to biochemical engineering, becoming one of the pre-eminent biochemical engineers. [3,4] Luss became chair of the department in 1975, a position that he held until 1996. It was during Luss tenure as chair that the department would ascend dramatically, thanks to the seeds planted by Dukler, strategic hires by Luss, and a sustained focus on research excellence in chemical engineering science. Indeed, it was Luss who stunned chemical engineering aca deme in 1976 when he attracted his former Ph.D. advisor, Neal Amundson The Chief, to Houston. Amundson brought his expertise in applied mathematics and reaction engineering to the department, and proceeded to graduate about 10 more doctoral students during his second career at UH. Collectively, the department trained a new generation of students who would primarily join industrial research orga the late s the department hired Demetre Economou an expert in electronic materials processing. Economou helped to bridge the gap between reaction engineering and materials, and has become one of the leading researchers in gas-solid re actions in plasma processes. In 2000 another of Luss students, Mike Harold, was recruited to become the sixth department academic at the University of Massachusetts at Amherst, then as a researcher, then a manager at the DuPont Companys Engineering Research labs at the Experimental Station. Ad ditional hires included Roy Jackson from Rice in 1977. In recent years the department has emerged as a leading center for environmental reaction engineering and catalysis. Balakotaiah focused on transport and reaction in catalytic monoliths used in emission aftertreatment systems such as three-way catalytic converters. Harold founded a clean diesel testing and research facility in the early 2000s, now called the Texas Diesel Testing and Research Center and managed by Dr. Charles Rooks who was recruited from industry by Harold. The creation of the diesel center was in response to the regional need to reduce emissions of NOx (NO + NO 2 the exhaust of diesel vehicles and equipment. The Houston area had the dubious distinction of being one of the worst tracted a City of Houston grant of $4 million to create a diesel vehicle testing facility and a few years later a $12 million grant to expand the operation. Today Harold and Rooks lead a team of 15 engineers and staff and collaborate with other faculty members in the ChBE and Mechanical Engineering on basic research and technol ogy development focused on clean diesel. The center has capabilities spanning bench-scale development of emerging technologies to full-scale testing of diesel vehicles. The gies to decrease NOx and particulate matter emissions from on-road and off-road vehicles and equipment. More recently the department has attracted Jeff Rimer from the University of Delaware, an expert in the synthesis of shape-selective enhanced activity and selectivity for the aforementioned lean NOx reduction. Joining the faculty in 2011 will be Bill Epling as an associate professor and as an assistant professor. Epling, with earlier industrial experience from Cummins, Inc., and an established academic from the efforts in environmental reaction engineering. Grabow brings his expertise in molecular modeling of catalysts to apply to a wide range of problems including environmental reaction engineering, biofuels, electrochemistry and development of a new generation of catalyst materials. MATERIALS AND BIOTECHNOLOGY Materials-related research in colloidal, polymeric, and nano materials along with biotechnology and biomolecular engi nature of the discipline. The discovery of high-temperature superconductivity at the University of Houston sparked a materials revolution on campus and the department became a leader in the area Materials Research Science and Engineering Center, led
to the growth of not only inorganic materials but also to the growth of polymeric and nanoscale materials. In 2002, a leading plasma physics expert, joined the department after two decades at AT&Ts Bell Labs. Since then Donnelly and Economou have established the pre-eminent plasma physics and processing laboratory, with both of them ciety. Michael Nikolaou an expert in process control, works closely with them to provide robust control for industrial plasma processes. The proximity of the petrochemical industry and the growth of advanced materials during the last quarter of the 20th century focus. Starting with Raj Rajagopalan an expert in colloids recruited from Syracuse in the mid s, and Jay Scheiber a theoretician working on polymer dynamics, the efforts in soft materials were strengthened by the addition of Ramanan Krishnamoorti and most recently of Manolis Doxastakis Stein Jacinta Conrad and Megan Robertson These faculty have also led the advancement of nanotechnology research at UH with Krishnamoorti becoming a pioneer in the area of polymer nanocomposites. Doxastakis has developed expertise in applying molecular and multiscale modeling to understand entangled polymers, nanocomposites, and lipid-protein inter developing materials for optoelectronics, advanced optical lithography, and organic photovoltaics. Conrad is studying potential applications in petroleum engineering, environmental engineering, and materials engineering. Robertsons research combines novel synthetic polymer chemistry and elucidation of polymer physics to design nanostructured materials to develop a new generation of materials based on renewable resources and in some cases with biomedical applications. Additionally, the development of a ~ 4000 ft 2 class 10/100 nanofabrication facility at UH has enabled the rapid growth of nanoscale soft materials research. Ongoing research to develop advanced materials for energy applications including improved hydro carbon recovery, solar energy capture, and wind energyalong with a focus on sustainability by using natural biodegradable alternatives to petroleum-based materialsis representative of the efforts of the department to address many of the grand challenges facing humanity. The growth of the Texas Medical Center over the last 30 triggered growth of biomolecular and biochemical engineering in the department. Jay Baileys evolution from chemical reaction engineer to the pre-eminent biochemical engineer by the time he left UH in 1980, was the precursor for the current growth in biomolecular research in the department. an tion, joined the department in the late s and is currently the theme leader for the diagnostics thrust of the NIH Western Regional Center of Excellence. Along with Mike Nikolaou (an expert in phase transitions that occur in protein solutions with implications for deadly Navin (quantifying functional human immune re sponses by integrated single cell analysis and developing new Patrick Cirino (protein and metabolic engineering and biocatalysis toward cost-effec tive green chemistry and renewable fuels, bioremediation, to challenging issues involving human health. FEATURES AND OUTLOOK The unique location of the University of Houston and the close relationship between the petroleum, petro-chemical, and materials industry along with the relation with NASA and, more recently, advanced materials companies and the Texas Medical Center, have positioned the department to be at the forefront of chemical engineering. The department has a unique relationship with the chemical industry and medical center through the graduate and research programs as well as the industrial advisory board. The continued vitality of the terest in the short course on polymers, along with the renewed interest in the MChE program for working professionals and doctoral candidates working in the numerous research and the close relationship. These strategic partnerships will continue to drive the suc cess of the students and faculty of the Department of Chemical and Biomolecular Engineering at the University of Houston. The analytical, quantitative, and systems-based approach that was pioneered by Tiller, Dukler, Amundson, and Luss will continue to be the hallmark of the research performed at UH and will be integrated into the developments in cutting-edge applications in materials, human health, and energy. These will also help shape our evolving undergraduate and graduate curricula and maintain excellence in our teaching, service, and research missions. REFERENCES Filtration & Separation 29, 37 2. Seader, J.D., K. Henley, and D. Roper, Separation Process Principles 3. Bailey, J.E., and D.F. Olis, Biochemical Engineering Fundamentals Bio 79 484
ChE book reviews An Introduction to Granular Flow by K. Rao and P. Nott Reviewed by Kimberly H. Henthorn Rose-Hulman Institute of Technology Granular flows are ubiquitous in nature and industry, particularly in systems involving food, pharmaceutical, and chemical processes. Although it is extremely important to be behavior is still not well-understood. A number of theoreti cal and empirical models have been proposed to describe the behavior of particulate systems, but there is still much room on theoretical modeling. The authors focus on continuum models, although there is some attention to discrete models as well. Overall, the book is well-written and provides a thorough The book begins with an introduction that previews a large number of areas including interparticle forces, packing, granu covered in more detail in subsequent chapters. The authors do a lot of external references for further consideration. In my opinion, this chapter could easily be broken into two chapters, with the modeling sections discussed separately, in order to and incomplete because too much information is presented at once. Dividing the material and adding more detail in certain The rest of the book delves into a detailed theoretical was disappointed with the quality and placement of many of good job with Chapter 6 (Flow through Axisymmetric Hop this section were interesting and useful. Since most of the material is based on complex theories, the authors offer several appendices that provide a basic math ematics review. Operations with vectors and tensors, a brief analysis of the stress tensor, and methods to evaluate common integrals are a few topics covered here. I was very happy to see these appendices, because the authors assume the readers have a good understanding of advanced mathematics when discussing the material in the main portion of the text. Each chapter ends with a set of practice problems. These problems were challenging but appropriate for the material in each section. It was interesting to note that many of the prob lems were adapted from other sources. I especially appreciated that each problem was labeled with a heading that described what concept was being tested. I am not sure if the authors offer a solutions manual for this textbook, but it would certainly be useful for instructors adopting the book for a course. I disagree with the authors when they state that this book is appropriate for advanced undergraduates or beginning gradu ate students, at least in the chemical engineering discipline. The material is presented at a much higher level than what I would expect an undergraduate chemical engineering student to be able to handle. The amount of mathematics and model ing background required to understand the material and the appropriate for graduate students concentrating in particle take an introductory particle technology course using an inter mediate text such as Rhodes 1 so that they are better prepared for the material presented in this book. My comments about the incompleteness of certain topics stem from the overwhelming amount of information available without developing a series of texts about the topic. My overall impression of An Introduction to Granular Flow however, is very positive, and I commend the authors for providing a into the appropriate detail in their focus areas. Rhodes, Introduction to Particle Technology Good Mentoring: Fostering Excellent Practice in Higher Education by Jeanne Nakamura and David J Shernoff with Charles H. Hooker Reviewed by Joseph H. Holles University of Wyoming Is good mentoring in the genes? Can successful mentors automatically transmit their knowledge, skills, and values to the next generation of students? If so, how can these at tributes be transmitted in a way that is most useful to their academic offspring? In an effort to better understand how to keep what has been learned from being lost Good Mentoring examines three lineages of scientists and the ability of their skills, values, and practices to be transmitted to their students and successive generations. The general question that the authors are seeking to address
mitments in members of the next generation even as changing sociocultural conditions pose new challenges to the pursuit was a particular emphasis on the transmission of orienting values and principles uniting excellence with responsible practice. The authors postulate that the best chance for their cultivation is likely to lie with teachers who embody these values and practices and the learning environments that the teachers create. Since graduate science education has a strong reliance on learning by apprenticeship, it is an ideal situation for examining mentoring of future generations. While the subtitle is Fostering Excellent Practice in Higher Education, the book is most relevant to a smaller subset of higher ed. In particular, the focus of the book is effective mentoring for supervisors in research. While the case studies focus on mentoring of graduate students and post-doctoral re searchers in academia, the same outcomes are also applicable in any research mentoring situation including undergraduate researchers, government, or industrially sponsored research laboratories. Finally, both mentors and their students can gain insight into successful relationships from this work. Good Mentoring is divided into three distinct parts. Part One presents case studies of each of the three lineages. Part and values across the mentoring generations. Part Three then tions for practitioners and researchers. In Part One, the authors examine three scientists and their lineages through the second and third generation of academic offspring. In perhaps a bit of irony, all three of these academic is to provide a qualitative view of the approaches of each sci entist towards successful research and mentoring. Subsequent discussion of second and third generations then provides insight into what knowledge was successfully passed down. obtain some insight into how individual scientists affected the In Part Two, the authors take a quantitative approach to Categories of memes common to all three lineages were also investigated. From their quantitative analysis, the authors found that even the most widely inherited memes are inherited less from generation to generation. However, this is compensated for by the larger number of offspring in each generation and thus the absolute effect remains high. The mentors in this study transmitted memes the student through verbal exchanges and the mentors indirect impact through student participation in the lab community. example and shaping the culture of the lab was just as important consistent availability and involvement, balance between free student. Good mentoring does not appear to include hectoring, guilt trips, yelling, insults, or subtle jabs. In Part Three, the most important results are discussed and then concrete suggestions for mentor, mentees, and institu tions are presented. For mentors, the most commonly cited resource was to facilitate students building of social capital. For mentees, the authors recommend seeking out multiple Finally, for institutions, good mentoring is a sound investment also need to be places in the institution for advisees to evaluate mentoring experiences similar to the way teaching evaluations provide feedback to classroom instructors. All of the examples and conclusions are drawn from mentor ing relationships between graduate students and their advisor that goes on in higher education outside of what is investigated and discussed in this book, such as advisor/undergraduate researcher relationships and teacher/student classroom relation ships. There are even mentoring relationships between estab lished faculty members and new faculty members. While the authors dont investigate all of the higher education mentoring relationships, the conclusions from this study can help in all. In fact, one of the ripest areas for application of these conclu sions would appear to be in the opportunities for institutions to improve the mentoring of new faculty by senior faculty. How can this book best be used by faculty members today? Clearly, the most direct place is in the laboratory when men toring students. The main results from the study indicate that simply being there for the students, showing a strong work for the student and transmit desired good work practices on to the next generation of researchers. However, the ideas from this book can also be applied in the classroom. In addition, simply providing a welcoming, open, and safe environment for all can have positive results. Since the authors examine the ability of effective mentoring memes to be passed down from advisor to academic offspring, the work becomes very mentor focused. Only in the last chapter do the authors discuss how a mentee should use the results of their study. Again, as a result of their premise, the authors tend to focus on academic offspring who have done well in academia. The applicability of mentoring on non-academic offspring does not appear to be addressed. Finally, while the point of this work was to investigate stars since they were capable of doing good academic work in parallel with performing good mentoring, the ability and effectiveness of non-star researchers to instill respon sible practice in their academic offsprings is still unknown. Copyright ChE Division of ASEE 2011