EDITORIAL ADDRESS: Chemical Engineering Education Chemical Engineering Education c/o Department of Chemical Engineering Volume 45 Number 2 Spring 2011 723 Museum Road University of Florida • Gainesville, FL 32611 PHONE and FAX: 352-392-0861 DEPARTMENT e-mail: [email protected] 150 Chemical Engineering at The University of Houston EDITOR Michael P. Harold and Ramanan Krishnamoorti Tim Anderson CURRICULUM ® ASSOCIATE EDITOR 86 A Freshman Design Course Using Lego NXT Robotics Phillip C. Wankat Bill B. Elmore 101 Two-Compartment Pharmacokinetic Models for Chemical Engineers MANAGING EDITOR Kumud Kanneganti and Laurent Simon Lynn Heasley 126 Conservation of Life as a Unifying Theme for Process Safety in Chemical Engineering Education PROBLEM EDITOR James A. Klein and Richard A. Davis Daina Briedis, Michigan State LABORATORY LEARNING IN INDUSTRY EDITOR 93 Microfluidics Meets Dilute Solution Viscometry: An Undergraduate Lab William J. Koros, Georgia Institute of Technology to Determine Polymer Molecular Weight Using a Microviscometer Stephen J. Pety, Hang Lu, and Yonathan S. Thio PUBLICATIONS BOARD 106 Continuous and Batch Distillation in an Oldershaw Tray Column • CHAIR • Carlos M. Silva, Raquel V. Vaz, Ana S. Santiago, and Patrícia F. Lito C. Stewart Slater 120 A Semi-Batch Reactor Experiment for the Undergraduate Laboratory Rowan University Mario Derevjanik, Solmaz Badri, and Robert Barat • VICE CHAIR• 133 Combining Experiments and Simulation of Gas Absorption for Teaching Jennifer Curtis Mass Transfer Fundamentals: Removing CO2 from Air Using Water and University of Florida NAOH • PAST CHAIR • William M. Clark, Yaminah Z. Jackson, Michael T. Morin, and John O’Connell Giacomo P. Ferraro University of Virginia CLASSROOM • MEMBERS • 114 Active Learning in Fluid Mechanics: YouTube Tube Flow and Puzzling Pedro Arce Fluids Questions Tennessee Tech University Christine M. Hrenya Lisa Bullard North Carolina State RANDOM THOUGHTS Stephanie Farrell 131 Hang in There! Dealing with Student Resistance to Learner-Centered Rowan University Teaching Richard Felder Richard M. Felder North Carolina State Jim Henry CLASS AND HOME PROBLEMS University of Tennessee, Chattanooga 144 Optimization Problems Jason Keith Brian J. Anderson, Robin S. Hissam, Joseph A. Shaeiwitz, and Michigan Technological University Richard Turton Milo Koretsky Oregon State University OTHER CONTENTS Suzanne Kresta inside front cover Teaching Tip, Justin Nijdam and Patrick Jordan University of Alberta Steve LeBlanc 155 Book Reviews University of Toledo by Joseph Holles, Kimberly Henthorn Marcel Liauw Aachen Technical University CHEMICAL ENGINEERING EDUCATION (ISSN 0009-2479) is published quarterly by the Chemical Engineering David Silverstein Division, American Society for Engineering Education, and is edited at the University of Florida. Correspondence regarding University of Kentucky 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 Margot Vigeant Engineering Education. The statements and opinions expressed in this periodical are those of the writers and not necessarily Bucknell University those of the ChE Division, ASEE, which body assumes no responsibility for them. Defective copies replaced if notified within 120 days of publication. Write for information on subscription costs and for back copy costs and availability. POSTMASTER: Send address changes to business address: Chemical Engineering Education, PO Box 142097, Gainesville, FL 32614-2097. Periodicals Postage Paid at Gainesville, Florida, and additional post offices (USPS 101900).
Vol. 45, No. 2, Spring 2011 85 ChE curriculum
A FRESHMAN DESIGN COURSE USING LEGO NXT® ROBOTICS
Bill B. Elmore Mississippi State University • Mississippi State, MS 39762 ivil engineering majors have their concrete canoes and by moving Analysis to the freshman year—using it as a ve- steel bridges and the mechanical engineers have their hicle to incorporate teamwork, experimentation, and project solar cars. Certainly, the discipline of chemical engi- design into the early stages of our curriculum. Cneering is no less visual—we just cannot haul a skid-mounted process unit into the classroom (without raising administrative LEGO® ROBOTICS—FOR CHEMICAL eyebrows and inviting an immediate visit from the campus ENGINEERS? safety officer). What concrete, visible means do we have for The incorporation of problem-based or project-based learn- giving our students a clear picture of chemical engineering? ing strategies into the classroom has swept the educational Pursing K–12 outreach and teaching freshmen for a substantial scene from K–12[1-4] across multiple disciplines in higher part of my career, I’ve journeyed through a maze of options education.[5-7] LEGO® robotics kits have proven to be widely for trying to help students understand what chemical engineers adaptable to a variety of disciplines and learning styles in do in daily practice. Most attempts coalesced into a series engineering education. Building on the work of chemical of chemistry demonstrations accompanied by pictures of engineering educators such as Levien and Rochefort,[8] Moor chemical processing equipment—leaving my audience with and Piergiovanni,[9,10] and Jason Keith,[11] my students and I a conceptual gap between the two. began a journey in the Fall semester of 2006 to incorporate this In the Swalm School of Chemical Engineering at Mis- relatively inexpensive technology into the Analysis course. sissippi State University, the ideal opportunity to tackle At under $300 per base set, the LEGO NXT® robotics kit this problem came with the revision of a three-credit-hour, offers tremendous versatility for designing model engineer- junior-level course—Chemical Engineering Analysis and ing apparatus and processes in the classroom. With modest Simulation (hereafter referred to as Analysis). Originally additional cost for accessories (e.g., valves, tubing, tanks) designed to address the application of numerical methods to a number of units can be built to allow an entire class to be fundamental topics in chemical engineering, the course has pre-requisites that, over time, allowed a shift in class com- Bill Elmore is an associate professor of position to a mixture of underclassmen taking the course “on chemical engineering and the Interim Direc- time” and upperclassmen (typically co-op students) squeezing tor for the School of Chemical Engineering at Mississippi State University. Now in his in the course among other requisite courses. This led to an 22nd year of higher education, his focus is unsatisfactory pressure on the course content (i.e., too difficult primarily on engineering education and the integration of problem-based learning across for one set, too remedial for the other). A general curriculum the curriculum. review revealed an opportunity to strengthen our curriculum
© Copyright ChE Division of ASEE 2011
86 Chemical Engineering Education actively involved in the same design project simultaneously creativity, and precautions to avoid spending an inordinate (in contrast to the traditional Unit Operations laboratory ap- amount of time on their robotics projects, teams of students proach relying on the rotation of student groups through a have consistently pushed the course content forward in subse- single experimental apparatus sequentially). Coupled with the quent semesters—demonstrating the value of a highly visual, LEGO NXT® kits, we chose a series of sensors from Vernier project-based approach to learning engineering fundamentals. (e.g., pH, temperature, dissolved oxygen) that interface with Through several iterations we have constructed projects more the robotics kits for monitoring processes and performing directly oriented to chemical engineering for illustrating the simple control schemes. A significant factor in choosing the importance of fundamental concepts including basic units LEGO NXT® robotics kits is the use of an intuitive graphical and measures, materials balances, and the fundamentals of interface for programming (based on National Instruments process control. Labview® software). This user-friendly programming in- terface removes the focus from programming and places it LEARNING OBJECTIVES AND OUTCOMES on the broader objectives of problem analysis and design of Table 1 describes the learning objectives and outcomes engineering processes. for the Analysis course. Defining a learning objective as a CHE 2213 Chemical Engineering Analysis is a required, specific, targeted description of acquired knowledge or skill three-credit-hour course, offered once per year in the second and a learning outcome as a broader response to particular semester of the freshman year (after a one-hour orientation situations requiring use of that acquired knowledge or skill, and before the sophomore-level Mass & Energy Balances these course objectives and outcomes are being affirmed over course). A large number of students entering the chemical time in coordination with our overall chemical engineering engineering program at Mississippi State University (MSU) program objectives. are community/junior college transfers from an extensive two-year college system throughout the state. Analysis is THE LEARNING ENVIRONMENT AND among the courses required for their first year at MSU. En- COURSE STRUCTURE rollment lies typically between 55-70 students. The course Offered Tuesdays and Thursdays for two 2-hour-and-20- is conducted in a 160-seat auditorium, the adjacent Unit minute sessions, Analysis comprises one credit hour of labora- Operations laboratory, and, with some design competitions, tory and two credit hours of lecture. The learning environment in the connecting hallway for maximum exposure to passing is patterned after a studio setting. I provide instruction on students from other classes. specific topics or skills as needed in a dynamic, laboratory Through loads of laughter and enthusiasm, discovery and environment that allows students to immediately put that knowledge or skill to practice on the current project. Projects TABLE 1 are structured to require use of accumulated knowledge over CHE 2213 Analysis Learning Objectives & Outcomes the course of the semester. Class discussions center around knowledge and skills needed for use on a timely basis. Home- Learning Objectives: work problems are assigned to allow practice of key tools. At the end of this course, you should be able to… Grades come primarily from individual quizzes and the final • Brainstorm a problem quickly within a team setting (or working exam (evaluating their understanding of skills and concepts alone) listing a number of possible solutions over a broad range of learned during design exercises). Some portion of the grade ideas is derived from team participation in oral and written reports • Describe the Engineering Design Cycle as used in this course and (in varying percentages over the semesters since the course’s steps/tools involved in engineering design inception). No grade has yet been assigned for the quality or • Take an idea for solving an engineering problem and bring it to performance of designs. a complete, functioning prototype using the LEGO NXT robotics system and accessories Table 2 (next page) describes the flow and content for • Use Microsoft Excel® tools to collect and analyze data from your Analysis. Up to six in-class quizzes are given at appropri- engineering designs ate junctures, evaluating students’ comprehension and use • Describe the importance and basic elements of conducting a mate- of the concepts, skills, and tools learned to date. Beginning rial balance for and maintaining control of a chemical process. with Team Challenge #2, all designs require quantitative data Learning Outcomes: acquisition and analysis and are accompanied by team written Upon completion of this course, you should be able to… reports, team self-evaluations, and oral reports. • Employ the Design Cycle for both originating an engineering Over the eight semesters we have offered Analysis in its cur- design and for making performance improvements in an existing rent format, a surprising number of students have expressed design little past experience playing with LEGOs®. To put every- • Explain to someone in your family (a non-engineer) what chemi- one at ease at the course outset, student teams construct the cal engineering is all about—giving some very practical examples. LEGO® NXT robotics kits and build a mobile robot of their Vol. 45, No. 2, Spring 2011 87 choice, using as a guide the “Taskbot” design included engineering design principles. Introduction of the Design Cycle with the kit (Figure 1). This enables students unfamiliar (Figure 3) provides teams a guide for iteratively approaching an with LEGO structural elements and the various sensors optimal solution for the problem they are tasked with solving. 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 chemical engineering) experience, thereby strengthen- 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 1. Students becoming familiar with the LEGO NXT® kit. TABLE 2 Course Structure ChE 2213 Analysis comprises approximately 28 studio sessions over 14 weeks. • Course Orientation—one studio session (2 hrs. 20 min. per session) a. Brainstorming b. Using the Engineering Design Cycle c. Data acquisition and analysis using Microsoft Excel® d. Exploration of LEGO NXT® robotics kits • Team Challenge #1 Taskbots & Sumo Wars—four studio sessions a. Learning to use the LEGO NXT® system • Team Challenge #2 Free format Design using LEGO NXT® sensors—five studio sessions 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, ultrasonic, or sound sensors) b. Constraints require clear establishment of an independent/dependent variable with elimination of extraneous parameters (where possible) c. Brainstorming, critical thinking, teaming skills emphasized d. Data acquisition and analysis using Microsoft’s Excel® • Team Challenge #3 Level Control Experiment—five studio sessions 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 d. Level control is measured over time by control valve deflection from an established setpoint • Team Challenge #4 Mixing tank/Continuously stirred tank reactor (CSTR) design—eight studio sessions a. Case 1—Two feed tanks supply two separate components for mixing in a third tank (e.g., deionized water and a salt solution to be mixed to a specified salinity) b. Case 2—Two reactant tanks supply reactants to a CSTR from which a specific product quality must be obtained e.g.( , pH, coloration, dissolved oxygen level) • Individual quizzes—five studio sessions • Final exam
88 Chemical Engineering Education
Communication General Engineering & Engineering Design •Teamwork ChE -specific Topics •Problem definition •Oral reporting •Material Balances •Brainstorming solutions •Written technical summaries •Units/Measurements •Develop prototype from most •Data collection & analysis promising possibilities •Basic concepts for controlling •Test, evaluate, improve processes •Communicate "optimum"
Figure 2. CHE 2213 Analysis—Course content.
and watching for problems that crop up with group dynamics. Envision Additionally, this interaction is an excellent opportunity for get- ting an idea of the broader issues that arise among our chemical engineering students. During this first studio session, we also cover key tools they will be expected to put to use early in the Refine Plan 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 Evaluate Build defined circle while attempting to push the opposing robot out of the ring (Figure 4, next page). A “contest” environment motivates a high-energy response. I have used this team chal- Figure 3. Design Cycle. lenge to bring in upperclassmen and, with loud music and the TEAM DYNAMICS AIChE chapter providing food, the result was a memorable social event. On the opening day, students self-assemble into teams of three members and begin familiarizing themselves with the Team Challenge #2: Free-Format Design robotics kits. In some semesters, I have allowed groups to After the dust settles and emotions subside, a second team remain constant over the course of the semester; in others, challenge opens the door to a more fundamental, and me- group members were reassigned approximately at mid-term. thodical, approach to engineering problem solving. Teams Through frequent, informal interviews and anonymous surveys, are tasked with designing an experiment and constructing a the feedback has been roughly constant for both approaches robot (not necessarily mobile) to demonstrate the performance (i.e., most class members favoring staying in their self-selected of one or more LEGO NXT® sensors of their choice—ac- teams with one or two teams wishing for anyone other than quiring data from a set of independent/dependent variables. their current team members). I interact with individual teams Using available computational tools and the course text,[12] throughout the class periods, coaching and exchanging ideas, teams report raw and processed data in graphical form with Vol. 45, No. 2, Spring 2011 89 appropriate oral and written reports. Student designs have Team Challenge #4: Mixing Tank/Continuously included measuring the volume of liquid dispensed from a Stirred Tank Reactor (CSTR) Design soft drink can as a function of robot “tipping velocity”; the In the latest course iteration, we have strengthened emphasis angle of projection by a ball hit in a robotic batting machine; on chemical engineering process variables (e.g., concentra- and colorimetric sensitivity of the light sensor as a function tion, pH, temperature, pressure) and material balances. Stu- of varying shades. dent teams conduct team challenges using these measures Team Challenge #3: Level Control as indicators of product quality. For example, one challenge requires feeding de-ionized water and a salt solution from The importance of process control in chemical engineering two separate reservoirs to a mixing tank—maintaining a pre- is emphasized in the next team challenge by requiring teams to ® scribed salt concentration in the outlet stream (as indicated by adapt the LEGO NXT system with a bench-scale fluids han- a conductivity sensor). Another challenge allows students to dling system (Figure 5). A submersible pump delivers water feed dilute acid and base solutions (typically vinegar/sodium to a tank through a small needle valve operated by a LEGO bicarbonate) to a mixing tank, maintaining a particular pH motor which in turn is controlled by programming the NXT as an indicator of the product quality. Students are required robotics “Intelligent Brick” (i.e., a 32-bit microprocessor). to conduct calculations using basic stoichiometry and mass Teams must design the system to maintain a prescribed balances to predict their system behavior and to assess actual fluid level in the tank. A sonar sensor, analogous to one type performance. of level-control technology used in industry, detects the In some semesters, we have engaged in “free-form” chal- fluid level feeding the signal through the NXT brick to the lenges—each team deciding on a design depicting some controlling motor. Small adjustments in the liquid level are process of their own choosing with certain guidelines/goals. “amplified” and observed by noting changes in rotational Creative design projects have included building a robotic displacement of the valve stem with an affixed adhesive rule device for titration and assembling a multi-step station for applied to the valve/motor coupling. Students record, as a simulating the application of photo-resist to a silicon wafer, function of time, +/– displacements from an established set spin coating, and wet etching (Figure 6). point. Recorded data is then plotted in a simplified control plot for qualitatively evaluating system control performance. A manual valve on the tank outlet (lower right in Figure 5) allows teams to investigate the capacity of their system (i.e., pump/valve/controller) to maintain adequate control under 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.
Figure 4. Sumo Wars using LEGO “Taskbots.” Figure 5. Elements of level-control system.
90 Chemical Engineering Education
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 can significantly positively impact student self-efficacy[15] and prepara- tion for advanced learning. Using a Service Quality ap- proach,[16] a multi-semester study of Analysis was conducted to assess variances between desired expecta- tions and realized perceptions with a resulting “gap score.” The gap Figure 6. Silicon-wafer treating station. score is the difference between what a customer expects from a service and what the cus- ingly challenging chemical engineering curriculum. A close tomer perceives as being delivered. A negative quality gap match between student perceptions and expectations served score indicates the service is not meeting expectations, while as a primary hypothesis for the study. This hypothesis was a positive score indicates the service exceeds expectations. supported by the survey results. Team efficacy increased over Scores are weighted according to students’ relative expecta- the span of the semester while academic and career efficacy tions from certain characteristics of the course. The study was decreased slightly. While this requires more study, a contribut- structured to examine whether or not an individual student’s ing factor to lowered self-efficacy related to academics and efficacy was impacted by realistic expectations, perceptions career must be the delivery of the final survey during week 15, of the course, preparation, and team experiences. at the end of the semester when multiple exams and projects Multiple surveys were given over the course of each semes- were due across all of their courses. Changes in efficacy and ter—in weeks 3, 8, and 15. Surveys were structured to measure satisfaction, perceived quality, and behavioral intention (i.e., efficacy (the capacity or power to accomplish a desired effect how well a student believes he/she can perform in this chosen or goal) in three areas—academics, team performance, and field) were significantly correlated in the study. career. The service quality surveys, modified from a previ- A perhaps intuitive but valuable and statistically valid ously validated survey instrument, SERVUSE,[17,18] were implication of the study is that making changes to the course structured to evaluate student expectations, their ratings of content to positively influence self- and team-efficacy can the importance of various factors, and their perceptions of lend a positive influence to student satisfaction, perceived various service quality dimensions as related to the course. quality, and behavioral intention. Responses, using a 7-point Likert scale, were then correlated Changes made to the course over its multiple offerings to respondents’ academic preparation in high school and per- include a significant increase in feedback (formal and infor- sonal goals and expectations. Examples of survey questions mal) beyond structured quizzes. Additionally, the instructor included: “In excellent courses, instructors listen carefully to provides opportunities for frequent, informal discussions their students,” and “In ChE 2213, instructors listen carefully across far-ranging questions about the curriculum, co-opera- to their students.” tive education, and general academic issues. As anticipated, students with positive gap scores (i.e., the An equally valuable outcome has been the clarification course met or exceeded their expectations) scored higher in among some students that chemical engineering “isn’t for academic-, self-, and career-efficacy[16]—an indication of them.” While we believe EVERYONE should be a chemical self-confidence needed for moving forward in an increas- engineer (well, not exactly), the earlier a student realizes that
Vol. 45, No. 2, Spring 2011 91 a change of major may best serve their interests, the better for IEEE 5th Intl. Conf. on Evolutionary Computation; IEEE Press, NJ, all concerned. A distinct advantage I have as the instructor for 1998. From the link
92 Chemical Engineering Education ChE laboratory
MICROFLUIDICS MEETS DILUTE SOLUTION VISCOMETRY: An Undergraduate Laboratory to Determine Polymer Molecular Weight Using a Microviscometer
Stephen J. Pety, Hang Lu, and Yonathan S. Thio Georgia Institute of Technology • Atlanta, GA 30332 luid viscosity is an important fluid property to monitor samples of PEO that match up well with viscometry results in industry, research, and medicine. The diverse ap- obtained with conventional Ubbelohde viscometers. We also plications for the rapid measurement of fluid viscosity discuss the timing and logistics of the lab and the feedback Finclude the characterization of inks in ink-jet printing, [1] stud- obtained from two sample laboratory sessions run with un- ies of protein dynamics,[2] the characterization of biomaterials dergraduates. used in drug delivery such as hyaluronic acid (HA), [3] and the clinical detection of diseases such as paraproteinemia[4] Stephen J. Pety received his B.S. in polymer and fiber engineering at the Georgia Institute [5] and ischemic heart disease through the study of blood. of Technology in 2010 and is currently a An additional use of viscometry is in the determination of graduate student in materials science and engineering at the University of Illinois at Ur- the hydrodynamic volume and molecular weight of macro- bana-Champaign. During his junior and senior molecules. Using the data analysis seen later in this paper, a years, he was a research assistant working with Dr. Lu and Dr. Thio, where he developed polymer’s molecular weight can be estimated. It is important and ran microviscometer laboratory sessions to be able to measure a polymer’s molecular weight—because reported here. of its impact on such properties as strength, stiffness, and glass Hang Lu received transition temperature—by simply measuring the viscosity of her B.S. from U. Illinois, Urbana-Champaign, and M.S.C.E.P dilute polymer solutions of varying concentrations. and Ph.D. from Massachusetts Institute of Technology, all in chemical engineering. She In a laboratory setting, viscosity measurements of dilute has been an assistant professor in Chemical polymer solutions are typically made with glass capillary & Biomolecular Engineering at Georgia Tech since 2005. Among the courses that she viscometers such as Ubbelohde viscometers that require mL has taught are mass and energy balances, of fluid for measurement. The development of microfluidic transport phenomena, and microfluidics. Her viscometers[6-9] means that such viscosity measurements can research interest is in microfluidics and ap- plications in neuroscience, cell biology, and now be quickly made with only μL of fluid. Microviscom- biotechnology. Yonathan Thio is an assistant professor in eters can thus potentially be used to determine the molecular polymer, textile, and fiber engineering. He weight of polymer samples even when sample volumes are received his B.S. in chemical engineering and materials science & engineering from the severely limited. University of California at Berkeley, and his M.S.C.E.P and Ph.D. in chemical engineering To illustrate both the use of microfluidics to determine from MIT. He joined Georgia Tech in 2005. fluid viscosity and the use of dilute solution viscometry to His research interests are on the structure and properties of polymer composites, determine polymer molecular weight, we developed a low- block copolymers, and polymer blends. He cost laboratory procedure for students to use PDMS micro- has taught courses with topics in polymer viscometers to determine the molecular weight of a polymer characterization and structure-properties of polymers. sample. In addition to the procedure, we present sample data for microviscometer tests run on glycerol solutions and on © Copyright ChE Division of ASEE 2011
Vol. 45, No. 2, Spring 2011 93 MATERIALS solutions were prepared. An aqueous solution of 3 mg/mL of the 4 MDa was prepared by stirring the solution for three days. For soft lithography microchannel fabrication, SU- The shear thinning studies performed using this solution were 8 2050 negative photoresist and SU-8 developer were performed within one day of when the solution was prepared. acquired from Microchem (Newton, MA). Sylard-184 Glycerol from Fisher Scientific (Pittsburgh, PA) was used to poly(dimethylsiloxane) (PDMS) was obtained from Dow prepare aqueous glycerol solutions. Corning (Midland, MI) and 1,1,2-trichlorosilane (T2492) (a release agent) was obtained from United Chemical Technolo- METHODS gies (Bristol, PA). Samples of PEO with viscosity average molecular weights of ~1 MDa and ~4 MDa were obtained Device Fabrication from Sigma-Aldrich (St. Louis, MO). Aqueous solutions of Microfluidic viscometers (PDMS channel on PDMS flat the 1 MDa PEO were prepared by mixing the solutions with substrate) were fabricated using the rapid prototyping tech- a stir bar overnight. Experiments to determine the viscosity of nique.[10] Briefly, the viscometer device was designed using these solutions were performed within eight days of when the AutoCAD (Autodesk, San Rafael, CA). A silicon-SU-8 master was created using conven- tional UV photolithography (with the SU-8 layer being 55 μm). After surface treatment of gas-phase 1,1,2-trichlo- rosilane (a release agent) on the master, a degassed 10:1 mixture of PDMS precursor and curing agent was then cast onto the master (about 2.5 mm thick—thickness not critical). After being cured at 70 ˚C for at least two hours, the PDMS slab was peeled from the master and cut into devices. A flat PDMS slab and the PDMS piece with the chan- nel imprints were then treated Figure 1. The PDMS viscometer with two sample channels (SCs) and one reference chan- for 30 seconds in an air plasma nel (RC) for fluid flow. The device was filled with dye for visual effect. Scale bar is 5 mm. (Harrick Plasma, Ithaca, NY)
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 fluids through the viscometer.
94 Chemical Engineering Education and bonded together to form the PDMS viscometer (Figure entrances. The laminar flow generated by this pressure can be 1). Tests were not run on the viscometers until at least two described by the Hagen-Poiseuille equation[11]: days after their fabrication to reduce the hydrophilicity of the 2 d ∆P device channels. v = h ()1 Sη L Experimental Setup
The PDMS viscometer consisted of three channels of height where v is the velocity of the fluid; hd is the hydraulic diam- h ~ 55 μm, width w ~ 100 μm, and length L ~ 20.4 cm. eter of the channel related to the height h and width w, dh = total η The viscometer was prepared for use by using micropipettes 2hw / (h+w); is the dynamic viscosity of the fluid; S is a to place two drops of sample fluids and one drop of a refer- constant related to channel geometry, with S = 32 for rectan- ence fluid of known viscosity at the entrances of the three gular channels; ∆P is the pressure drop across the fluid; and channels in the top left of the device. A syringe pump (Har- L is the length of the advancing fluid front. vard Apparatus, Holliston, MA) was then used to generate a The pressure drop ∆P consists of two components, i.e., ∆ ∆ ∆ sub-atmospheric pressure within the device channels to drive P = Pd + Pc, where Pc is the capillary pressure. Pd is the flow. A syringe attached via a Luer stub and polyethylene pressure difference between the fluid inlet, which is constantly tubing (Scientific Commodities, Inc., Lake Havasu City, at atmospheric pressure P0, and the moving fluid front, which AZ) to a bent hollow metal pin was first placed in the pump is at the constantly decreasing pressure inside the viscometer ∆ and the metal pin was inserted into the pressure inlet in the Pi, i.e., Pd(t) = P0 – Pi(t). For a test where a sample fluid bottom right of the device (Figure 2). The syringe pump and a reference fluid are pulled through the viscometer at the ∆ was then used to pull the syringe at a constant rate while the same time, Pd(t) is the same for the two streams and the flow through the channels was tracked with a Moticam 2300 following equations can be written using Eq. (1): camera (Motic, Xiamen, China) mounted on a Stemi SV11 S dissecting microscope (Zeiss, Obercochen, Germany). The η Lt()vt()=−PP()tP+ ()2 d2 ss si0 cs, transparent liquids moving through the viscometer caused h contrast with the background to decrease as the liquids passed S through them (Figure 3). η Lt()vt()=−PP()tP+ ()3 d2 rr ri0 cr, The videos taken from the tests were analyzed with MAT- h LAB to track the length of each fluid stream over the duration where the subscripts s and r refer to the sample and reference of the test. For the tests on PEO described below, the videos streams, respectively. Combining and integrating Eqs. (2) and had a frame rate of 13 to 16 fps and were analyzed every four (3) leads to the equation frames. The code operates by subtracting previous images Lt2 − Lt2 Lt2 − Lt2 PP− from each frame and detecting the movement of a stream rr()2 ()1 ηs ss()2 ()1 2 cr,,cs = + 24d h () as a change in grayscale intensity that surpasses a certain tt21− ηr t2 − t1 Sµr threshold. Adjacently marked pixels are combined to make η up the three streams, and the length of each stream is then The value of s for a given test was thus found by taking η found by dividing the total number of pixels in that stream r 2 2 2 2 Ltrr()2 − Lt()1 Ltss()2 − Lt()1 by a constant thickness value. the slope of a linear fit of vs. tt21− tt21− Mechanism and Theory of Microviscometer where Lr(t) and Ls(t) were determined from the processing of This analysis of fluid flow follows that of Han, et al.,[6] since each video. For the tests on PEO described below, an interval our method and theirs use Poiseuille flows through rectangular of five frames was used for the time interval 2t – t1. 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 Figure 3. Microphotographs of the beginning of a viscometry test run with water and PEO solu- than the air pres- tions (top row) and the output of the MATLAB code used to track the movement of each stream sure at the channel (bottom row). Scale bar is 2 mm.
Vol. 45, No. 2, Spring 2011 95 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[12] η sp 2 = ηη + kc ()5 c
where ηsp is the specific vis- cosity of a polymer solution of concentration c, defined as η solution η ηsp =−1 where solution ηsolvent is the viscosity of the poly-
mer solution and ηsolvent is the viscosity of the pure solvent; η 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 η for the polymer-solvent 2 2 2 2 pair can be found as the inter- Figure 4. Sample plots of [L r (t2) – L r (t1) ]/ (t2 – t1) vs. [L s (t2) – L s (t1) ]/ (t2 – t1) for aque- ηsp ous 1 MDa PEO solutions of different concentrations. The relative viscosity of each solu- cept of a graph of vs. c. tion is found as the slope of its linear fit. c The value of η can then be re- lated to molecular weight using Mark-Houwink relation[12]: η = KMa, where M is polymer molecular weight and K and a are empirical Houwink constants for a given polymer- solvent pair. The values of K and a are known for many common polymers including PEO, having been determined experimentally by measuring values of η 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 Mv, typically between the number-average
Mn and the weight-average Mw. 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 College, PA) in a water bath of 23.0 ˚C. Twelve mL of fluid were needed for each test. Water was used as the reference fluid in the tests. The relative viscosity of each glycerol so- Figure 5. Plots of η / c vs. c used to determine values of η sp lution was found by multiplying the ratio of efflux times of for the 1 MDa PEO sample using viscosity data from the Ub- belohde viscometer and the PDMS viscometers. Linear fits are the solution and the pure solvent by the (measured) density of that solution. Density differences between the dilute PEO shown from which η values were determined as the intercepts. Only the four highest concentrations were used in the linear fit solutions and water were negligible, so the relative viscosity for the PDMS viscometers. Error bars represent the standard of each PEO solution was found simply as the ratio of the deviation of . efflux times of the solution and the pure solvent. ηsp / c
96 Chemical Engineering Education VALIDATION OF THE DEVICE OPERATION microviscometer matched the results from the Ubbelohde viscometer well while the viscosities of the 0.4 mg/mL and To ensure that the microviscometer produced accurate vis- 0.8 mg/mL solutions measured by the microviscometer were cosity readings, tests were first run on the device using glyc- somewhat lower than that of the Ubbelohde viscometer, pos- erol solutions as sample streams and water as the reference sibly due to the high surface areas of microdevices and loss stream. Pressure was generated with a 50 mL syringe that was of polymer from the solution to the surface. The variance for pulled at rates ranging from 3.50 mL/min to 21.84 mL/min. the microviscometer is seen to be much greater than that for Tests were performed at room temperature averaging ~ 23 ˚C. the Ubbelohde viscometer at all concentrations, which may The viscosities of the glycerol solutions were measured with be due to image processing errors or to the much smaller an Ubbelohde viscometer in a 23.0 ˚C bath for comparison sample size. (Table 1). The results from the microviscometer are seen to The viscosity results from the PDMS viscometers and the be consistent with the Ubbelohde viscometer although the Ubbelohde viscometer were then used to find values of η variance in the microviscometer tests is much higher. η sp η Viscosity measurements were then made with the microvis- for the PEO sample by plotting vs. c and taking as c cometer using dilute 1 MDa PEO solutions as sample streams the y-intercept (Figure 5). The Ubbelohde viscometer data and water as the reference stream. For these tests, pressure extrapolated to a value of η = 0.588 mL/mg. When all the was generated by pulling a 50 mL syringe at an initial vol- data for the microviscometer were used, a much lower value of ume of 25 mL at a rate of 5.46 mL/min. Note that the exact η = 0.424 mL/mg was found (extrapolation not shown). This initial volume of the syringe and the pulling rate used in the discrepancy in η values is caused by the lower viscosities experiments are not critical, as the viscometer can function found with the microviscometer at lower c: the error in the over a range of generated pressure gradients. Pressure-induced ηsp plot of is magnified for smaller c, which also corresponds deformation of the microchannels could occur in a PDMS c device such as ours if pressure differences were too large but to larger differences in ηsp. the maximum pressure gradients across the channels in these experiments were only ~15 kPa for the glycerol tests and ~10 To reduce the error in η estimation, low concentrations of kPa for the PEO tests. No deformation of the channels was polymer solution should be avoided in the experiments. As observed under the microscope in any test. shown in Figure 5, excluding the 0.4 and 0.8 mg/mL microvis- The PEO tests were performed at 23.0 ˚C + 0.5 ˚C and the mea- cometer data from the extrapolation results in an extrapolated sured viscosity values were compared to values obtained with an value of η = 0.605 mL/mg, which agrees well with the values Ubbelohde viscometer in a 23.0 ˚C bath (Table 1). Sample plots of from Ubbelohde experiments.
2 2 2 2 Ltrr()2 − Lt()1 Ltss()2 − Lt()1 vs. TABLE 1 tt− tt− 21 21 Relative viscosity values determined for aqueous solutions of glycerol and PEO vs. used to calculate viscosity values water using an Ubbelohde viscometer and PDMS viscometers. Each solution was measured three times with the Ubbelohde viscometer and multiple times with the in the microviscometer tests are PDMS viscometers as marked. seen in Figure 4. η In a few of the microviscometer solution ± standard deviation — — tests, PEO solutions began to flow ηsolvent through the viscometer before the syringe was pulled, suggest- Ubbelohde PDMS Number of Solution ing that the PEO solutions had a viscometer viscometer microviscometry trials positive value of Pc, sample, i.e., they 10 % glycerol 1.25 ± 0.003 1.32 ± 0.05 10 wet the PDMS surface. This did 20 % glycerol 1.77 ± 0.003 1.80 ± 0.13 12 not interfere with data collection, 30 % glycerol 2.38 ± 0.015 2.37 ± 0.12 18 however, and the results from 50 % glycerol 6.01 ± 0.012 6.07 ± 0.64 12 the viscometer were still valid for times while all fluids were 0.400 mg/mL PEO 1.26 ± 0.0009 1.22 ± 0.04 5 moving. 0.800 mg/mL PEO 1.59 ± 0.002 1.49 ± 0.13 5 It can be seen from Table 1 that 1.00 mg/mL PEO 1.76 ± 0.003 1.78 ± 0.05 5 the viscosities of the 1 mg/mL, 1.20 mg/mL PEO 1.96 ± 0.006 1.94 ± 0.12 5 1.2 mg/mL, 1.4 mg/mL, and 1.6 1.40 mg/mL PEO 2.15 ± 0.005 2.22 ± 0.13 5 mg/mL solutions measured by the 1.60 mg/mL PEO 2.40 ± 0.012 2.39 ± 0.20 5
Vol. 45, No. 2, Spring 2011 97 Using values of a = 0.78 and K = 12.5 * 10-6 mL/mg phenomena) from the Georgia Institute of Technology School 1/a [13] (g/mol) for aqueous PEO solutions and the η values of Chemical & Biomolecular Engineering. Each trial had four above, the Mark-Houwink equation produces values of M = students with no microfluidics experience who performed 1,010,000 g/mol for the PDMS viscometers and M = 977,000 the viscometer tests and the first trial had an additional three g/mol for the Ubbelohde viscometers. These values are in students who had worked in a microfluidics laboratory before. good agreement with each other as well as with the value Several days before the laboratory sessions were held, students reported by the manufacturer. were provided with a copy of the procedure as well as a “pre- lab” that provided the background, theory, and a quiz to test LABORATORY IMPLEMENTATION, COST AND their understanding prior to the lab. The beginning of the labora- LOGISTICS, AND STUDENT FEEDBACK tory consisted of a microviscometer fabrication demonstration given by the undergraduate teaching assistant. The assistant Laboratory Implementation explained how masks and masters are manufactured, explained The laboratory procedure consists of a device fabrication how PDMS is mixed, cast, cured, and bonded to form devices, demonstration, student-run microviscometer tests on PEO and used the plasma cleaner to bond a device to show to the stu- solutions, image processing of the tests using MATLAB, and a dents. If time allows, this simple micromolding step and device shear-thinning demonstration. After the lab session, viscosity fabrication can be incorporated into the lab, and concepts such data from different students can be combined and analyzed to as cross-linking, Poisson ratio, Young’s modulus, and surface find an estimate for the molecular weight of the PEO sample treatment can be explained and demonstrated. used. If time is available, students can also measure the vis- The students then ran two microviscometer tests where cosities of the PEO solutions with macro viscometers such as each test used two different concentrations of 1 MDa PEO Ubbelohde viscometers to validate the microviscometer data. as sample streams and water as the reference stream. Con- This allows students to visualize the advantages and disad- centrations of 0.500, 1.00, 1.50, and 2.00 mg/mL were used vantages of microviscometry in terms of accuracy, precision, in the two tests. Pressure was generated by pulling a 50 mL speed, cost, and fluid volume required. syringe at an initial volume of 25 mL at a rate of 5.46 mL/min Two trials of this procedure were run with volunteer under- (the same conditions as in the validation tests for the PEO graduates (mostly junior students who have taken transport solutions).
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.
98 Chemical Engineering Education Image Processing Once the startup materials are present, the individual lab The students then used the pre-written MATLAB code to sessions have a very low cost because of the small volumes analyze their videos. In our experience, some of the trouble- of chemicals needed. The major repeated cost is in fabricating shooting issues with the image processing can be explained to the PDMS devices which consume ~$1.50 of PDMS per chip. the students during the lab module to facilitate data process- Approximately 5 hours of time were devoted by the under- ing. For instance, it is important to take a video that has both graduate teaching assistant to prepare for each lab session, high contrast (for the streams to be located by the code) and including device fabrication, solution preparation, and lab uniform contrast (for the streams to be tracked with uniform set-up. The two lab sessions took about 1 hour and 45 minutes width). Problems with noisy images can be addressed with each to complete, including the fabrication demonstration, the MATLAB filtering of the raw video and with data smoothing completion of four viscometer tests, and the processing of the of the acquired length values. tests and the description of the MATLAB code. Demonstration of Shear Thinning Fluids Student Feedback To demonstrate both the shear thinning behavior of non- Students who participated in the laboratory experiments dilute polymer solutions and the ability to generate a large provided informal feedback. Most students found the mod- range of shear rates in the viscometer using the syringe pump, ule was effective in introducing the concept of solution the students then ran a test with a high pulling rate and a test viscometry and microfluidics, to which most of them had with a low pulling rate on a sample of 3 mg/mL 4 MDa PEO had no prior exposure. The students found more background with 60% glycerol solutions as reference fluids. When a test on microfluidics and microfabrication details would be both is run with a syringe initial volume of 40 mL and a pulling more interesting and more useful. This suggests that the rate of 1.7 mL/min, corresponding to an average shear rate laboratory module should be expanded to multiple sessions ~100 s-1, the 60% glycerol reference is seen to move through to deal with the individual topics in depth. The students also the viscometer more quickly than the PEO solution (Figure commented that seeing non-Newtonian behavior with a real 6). In contrast, the PEO solution is seen to move through the demonstration could reinforce this concept that they learned viscometer more quickly than the 60% glycerol reference in the classroom. 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 CONCLUSIONS of 20 mL/min). This inversion of behavior is caused by the We present a procedure for a student laboratory session to lower viscosity of the PEO solution at a higher shear rate as demonstrate the use of microfluidics to determine fluid viscosity opposed to the rate-independent viscosity of the Newtonian and the use of dilute solution viscometry to estimate polymer glycerol solution. The shear thinning behavior of the PEO molecular weight. Overall, the results were reasonably consis- solution over this range of shear rates was verified using a tent with those found from conventional Ubbelohde viscometry. Physica MCR 3000 rheometer (Anton-Paar, Graz, Austria); The laboratory also allows students to see firsthand how micro- the viscosity of the PEO solution fell from ~ 14 cP at 100 s-1 fluidic devices are fabricated and to observe a visual demon- to ~ 8.6 cP at 780 s-1. This method can be used to demonstrate stration of the shear thinning behavior of non-dilute polymer non-Newtonian behaviors of various fluids in the range of solutions. Assuming soft lithography equipment is available, the shear rates up to 2000 s-1. experimental setup is very quick and affordable. The laboratory serves as an excellent way to generate interest in the fields of Cost Estimate and Timing Logistics polymers, rheology, and image processing while invigorating Assuming that laboratory equipment such as microscopes, students with the opportunity to work hands-on in the “cutting- cameras, a plasma cleaner, and a syringe pump are available, edge” realm of microfluidics.[14] The combination of written the laboratory costs come in the materials. The fabrication of a instruction in the pre-lab and procedure, verbal instruction mask and master costs around $150, and samples of the 1 MDa and visual displays from the teaching assistant, and hands-on PEO, 4 MDa PEO, glycerol, and PDMS cost ~$30 each for a experience for each student caters to a range of different student total startup cost of <$300. Note that other water-soluble poly- learning styles.[15-16] Because it is multi-faceted, this experimen- mers can be substituted for PEO if desired, and fluids other tal platform can be used and re-used in different pedagogical than glycerol solutions can be used as viscosity standards as contexts, or it can be a problem-solving based learning tool.[17] long as they do not swell PDMS and their viscosity is known. We recommend running the following laboratory modules If needed, we estimate that a simple microscope and camera individually or in combination depending on the need of the setup are in the range of $2,000 to $3,000. If a plasma cleaner curricula and time available for the laboratory experiments: (1) is not available, it is possible to create devices by pressing a laminar flow – Hagen-Poiseuille relationship; (2) viscometry; flat PDMS slab against a PDMS slab with channel imprints, (3) demonstration of non-Newtonian flow; (4) microfabrica- placing the slabs between two glass slides, and then holding tion; (5) other concepts of polymer processing; (6) image the glass slides together using rubber bands. processing.
Vol. 45, No. 2, Spring 2011 99 ACKNOWLEDGMENTS for Analyzing Blood Plasma and Other Liquid Samples,” Analytical Chemistry, 77(2) 383 (2005) We thank M. Li for developing the microviscometer design 8. Marinakis, G.N., J.C. Barbenel, A.C. Fisher, and S.G. Tsangaris, “A and the first round of MATLAB code for image processing, New Capillary Viscometer for Whole Blood Viscosimetry,” Biorheol- J. Stirman and M. Crane for their help with the microscope ogy, 36(4) 311 (1999) setup and image processing, and Dr. V. Breedveld and E. 9. Han, Z., and B. Zheng, “A PDMS Viscometer for Microliter Power Law Fluids,” J. Micromechanics and Microengineering, 19(11) 115005 Peterson for use of their facilities. (2009) 10. Duffy, D.C., J.C. McDonald, O.J.A. Schueller, and G.M. Whitesides, REFERENCES “Rapid Prototyping of Microfluidic Systems in Poly(dimethylsiloxane),” Analytical Chemistry, 70(23) 4974 (1998) 1. Calvert, P., “Inkjet Printing for Materials and Devices,” Chemistry of 11. Perry, R.H., and D.W. Green, Perry’s Chemical Engineers’ Handbook, Materials, 13(10) 3299 (2001) 8th Ed., McGraw-Hill, New York (2008) 2. Ansari, A., C.M. Jones, E.R. Henry, J. Hofrichter, and W.A. Eaton, “The 12. Painter, P.C., and M.M. Coleman, Essentials of Polymer Science Role of Solvent Viscosity in the Dynamics of Protein Conformational and Engineering, 1st Ed., DEStech Publications, Inc., Lancaster, PA Changes,” Science, 256(5065) 1796 (1992) (2008) 3. Liao, Y.-H., S.A. Jones, B. Forbes, G.P. Martin, and M.B. Brown, 13. Bailey, F.E., and J.V. Koleske, Poly(ethylene oxide), Academic Press, “Hyaluronan: Pharmaceutical Characterization and Drug Delivery,” New York (1976) Drug Delivery, 12(6) 327 (2005) 14. Young, E.W.K., and C.A. Simmons, “ ‘Student-Lab-on-a-Chip’: In- 4. McGrath, M.A., and R. Penny, “Paraproteinemia—Blood Hyperviscos- tegrating Low-Cost Microfluidics Into Undergraduate Teaching Labs ity and Clinical Manifestations,” J. Clin. Invest., 58(5) 1155 (1976) to Study Multiphase Flow Phenomena in Small Vessels,” Chem. Eng. 5. Yarnell, J.W.G., et al., “Fibrinogen, Viscosity, and White Blood Cell Ed., 43(3) 232 (2009) Count are Major Risk Factors for Ischemic Heart Disease—The Caer- 15. Felder, R.M., and L.K. Silverman, “Learning and Teaching Styles in philly and Speedwell Collaborative Heart Disease Studies,” Circula- Engineering Education,” Eng. Ed., 78(7) 674 (1988) tion, 83(3) 836 (1991) 16. Montgomery, S.M., and L.N. Groat, “Student Learning Styles and Their 6. Han, Z., X. Tang, and B. Zheng, “A PDMS Viscometer for Microliter Implications for Teaching,” CRLT Occasional Papers, 10 (1998) Newtonian Fluid,” J. Micromechanics and Microengineering, 17(9) 17. Major, C.H., and B. Palmer, “Assessing the Effectiveness of Problem- 1828 (2007) Based Learning in Higher Education: Lessons from the Literature,” 7. Srivastava, N., R.D. Davenport, and M.A. Burns, “Nanoliter Viscometer Academic Exchange Quarterly, 5(1) (2001) p
100 Chemical Engineering Education ChE curriculum
TWO-COMPARTMENT PHARMACOKINETIC MODELS for Chemical Engineers
Kumud Kanneganti and Laurent Simon New Jersey Institute of Technology • Newark, NJ 07102 he absorption, distribution, metabolism, and excretion (i.v.) infusion and i.v. bolus (single and multiple) administra- (ADME) of a drug, after single or multiple adminis- tions were illustrated with activities consisting mostly of a trations, are usually represented by compartmental dye placed in a mixing vessel. Tpharmacokinetic models. These compartments correspond to This contribution focuses on the applications of a two- tissues and organs in the human body. The analysis of these compartment model for describing drug pharmacokinetics. processes can be very complex, as in the case of physiologi- Although the error in developing dosing regimens based on cally based pharmacokinetics (PBPK), where information on the weights, blood flows, and physicochemical and bio- Laurent Simon is an associate professor of chemical engineering and the associate chemical properties of a compound is necessary to describe director of the Pharmaceutical Engineering concentration profiles in the tissues (i.e., lung, brain, and Program at the New Jersey Institute of Tech- kidney).[1] Although, in theory, a multi-compartment approach nology. He received his Ph.D. in chemical engineering from Colorado State University is better suited to describe the dynamics of most drugs in the in 2001. His research and teaching interests body, clinicians prefer the simplicity of a one-compartment involve modeling, analysis, and control of [2] drug-delivery systems. He is the author of model to predict the plasma drug concentrations and to Laboratory Online, available at
Vol. 45, No. 2, Spring 2011 101 Component balances in the two compartments (Figure 1a) yield: dC()V 11=−kCVk−+CV kCV ()1 dt el l 112112122 and dC()V 22=−kCVkCV,(2) dt 12 11 21 22 where C is the drug concentration, V is the volume, and k is 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 kel is a first-order elimination rate constant, which is often used to represent clearance. It should be noted that more complex expressions (e.g., Mi- chaelis-Menten kinetics) are often appropriate for certain drugs. Since the volumes are constant, Eqs. (1) and (2) can be written as:[4]
dC()1 =−kCel l −+kC12 12kC12ζ 12 ()3 Figure 1. Representation of a two-compartment model. dt Figure 1a is a schematic model of the process as intro- and duced in a course in pharmacokinetics; Figure 1b is the dC() two-unit process that is assembled to mimic the behavior ζζ2 =−kC kC ()4 of the two-compartment model. 21 dt 12 121212 V a single-compartment model is acceptable for most drugs, ζ = 2 . with 21 equations for two-compartment kinetics are more appropriate V1 for a few pharmaceutical agents that are potent and/or exhibit Figure 1b. corresponds to the flowchart of a two-unit pro- a narrow therapeutic range.[3] Experiments, based on concepts cess designed to mimic the behavior of a two-compartment learned in chemical engineering classes, are developed to model. Several pumps are required to manipulate the flow introduce students to these processes. The learning outcomes rates. Fresh water streams are also added to the vessels. At of this project are to: i) illustrate a two-compartment pharma- this point, students may be asked to show that component cokinetic model using continuous-stirred vessels, ii) derive balances around the units lead to the system described by Eqs. total mass and component balances for the two compartments, (3) and (4) (objectives i and ii). A total mass balance around iii) solve the derived differential equations using Laplace vessels 1 and 2 yields: ρ transform methodologies, iv) calculate the pharmacokinetic dV()11 =+FFρ ρρρ−−FF ()5 parameters, and v) conduct experiments to simulate a single dt ww11 21 21el l 21 i.v. bolus administration. and LABORATORY DESCRIPTION dV()ρ 22=+FFρρ− F ρ ,(6) Theoretical Foundation dt ww22 12 1212 The schematic of a two-compartment model is shown in respectively. The subscripts w1 and w2 indicate the fresh wa- Figure 1a. According to this representation, the human body ter streams into vessels 1 and 2. Assuming equal and constant is comprised of a central compartment consisting of the densities, we have ρρ12==ρρww12= . The relationships: blood/plasma and well-perfused tissues (e.g., liver, heart), and a peripheral compartment mainly composed of poorly FFel +=12 FFw12+ 1 ()7 perfused tissues (e.g., skeletal muscles). Analysis of a blood sample would reveal the concentration in the first compart- and ment. This measurement may be used by the physician to FF21 =+w 21F 2 ()8 assess the effectiveness of a drug-dosage regimen.
102 Chemical Engineering Education hold in order to maintain constant volumes in both tanks. In Although the satisfaction of the initial conditions, C1(0) = C10 addition, potassium permanganate balances around the two and C2(0) = 0, is not sufficient to guarantee the accuracy of units yield: Eqs. (15) and (16), these equalities are necessary conditions.
In addition, showing that Ct12()→∞ =→Ct()∞ = 0 may dC()11V =−FC FC− FC ()9 lead to a discussion on the necessity for administering multiple dt 21 21el l 21 bolus i.v. doses. and + − ()λ + kC21 10 + ()λ + kC21 10 − Ct= eλ t − eλ t ()17 dC()V 1 () +− + − 22=−FC FC.(10) ()λλ− ()λ −λ dt 12 1212 and Dividing Eqs. (9) and (10) by V1 results in Eqs. (3) and (4) F F F V kC +−kC ==12 21 ==el ζ 2 Ct= 12 10 eλλtt− 12 10 e (818) with k12 ,,k21 kel ,.21 2 () +− +− V1 V21V V1 ()λλ− ()λλ−
The experiments are conducted with V1=V2. As a result, with Eqs. (3) and (4) become: 2 −+()kk+ kk++()kk+ −4kk dC() + 12 21 el 12 21 el el 21 1 =−kC−+kC kC ()11 λ = (919) dt el l 12 1212 2 and and 2 dC −+kk+ kk−+kk+ −4kk ()2 − ()12 21 el ()12 21 el el 21 =−kC12 12kC12 ()12 λ = (020) dt 2
The initial conditions are C1(0) = C10 and C2(0) = 0 for a bo- Given concentration data in the central compartment (or lus injection. Using the Laplace transforms of the concentra- vessel 1), Eq. (17) can be applied to estimate k12, k21, and k el ∞ −st (objective iv). Students may be given the opportunity to L{}Ct1 () = Cs11()= Ct()edt tions C1(t) and C2(t) (i.e., ∫ choose among three methods to compute these parameters: 0 ∞ 1) Measurement of the flow rates: the pharmacokinetics are L Ct= Cs= Cted−st t and {}2 () 22() ∫ () ) and applying the F F F calculated using k ==12 ,,k 21 andk = el . 0 12 V 21 V el V Laplace operator to both sides of Eqs. (11) and (12), the fol- 1 21 2) Regression of Eq. (17) to experimental C1(t) data: Eq. (17) lowing equations are obtained: −−αβtt is written in the form Ct1 ()=+Ae Be with αβ> . −=−+ + sC11Ck01()21kCel kC21 2 ()13 Computational software packages such as Math- ematica® (Wolfram Research, Inc., IL) or Matlab® and (The MathWorks, Inc., MA) can be adopted to estimate α β sC21=−kC21 kC21 2 ()14 A, B, , and . Algebraic manipulations show that ABβα+ αβ k = ,.k ==andk αβ+−kk− The system formed by Eqs. (13) and (14) is solved to 21 AB+ el k 12 21 el give: 21 sk+ C 3) Methods of residuals[5]: Data collected at long times are ()21 10 −βt C1 = ()15 fitted to the equation C (t) = Be because α > β. Pa- 2 +++ + 1l ()sk()12 kk21 el skelk21 β β rameters B and are obtained from ln[C1l(t)]=ln(B)–
t. The variable C1l represents the concentration at a suf- and ficiently long time. Similarly, data gathered at short times −βt −αt kC12 10 are fitted to C (t)– Be = Ae where C stands for C = ()16 1s 1s 2 2 the concentration a short time after the bolus injection. ()sk++()12 kk21 + el sk+ elk21 α −βt Parameters A and are estimated from ln[Cls(t)– Be ] Partial-fraction expansion, or the residue theorem, may =ln(A)– αt. be used to invert the Ca12nd C (objective iii). Students are Any of the methodologies described above is implemented also encouraged to apply Laplace transform initial and final to study the influences of pharmacokinetic parameters on C1 value theorems to verify the correctness of Eqs. (15) and (16). and C2.
Vol. 45, No. 2, Spring 2011 103 Materials and Experimental Procedure ing regimens. To illustrate this point, three bolus injections of Except for the increased number of pumps, the same 1.10 g, 0.33 g, and 0.33 g of potassium permanganate were materials required in the study of the one-compartment added to the central compartment at 0, 1.12, and 3.36 hours, experiments[3] are used in this project (Figure 2) (objective respectively, as recommended by the results of an optimal v): variable flow-rate pumps, beakers, stopwatch, graduated dosing regimen for KMnO4 (Figure 4). The optimization cylinders, pipettes, rubber tubing, magnetic stirrer, magnetic code, based on a two-compartment model and written in the ® bars, potassium permanganate, spectrophotometer, cuvettes, Mathematica environment, minimized the sum of squared laboratory stands, and clamps. An i.v. bolus of 1.37 g of potas- errors between the concentrations in the central compartment sium permanganate was administered to the central compart- and a desired KMnO4 level of 3.46 g/L for an experimental ment. Samples were collected every 15 minutes for both the duration of 5.75 hours. The following observations can be central and the peripheral compartments and analyzed with made: i) The predicted and experimental data agree very well a spectrophotometer set at 530 nm. A calibration curve was and ii) the calculated doses were able to maintain the KMnO4 developed to relate the concentration with the absorbance concentration around 3.46 g/L. Simulations conducted under reading: y = 0.0163A where y represented the concentration the assumption that KMnO4 obeys one-compartment pharma- in g/mL and A the absorbance. The volume of each vessel was cokinetics show that the predicted data deviate considerably maintained at 200 mL. from the true profile (Figure 4). Results and Discussions SUMMARY OF EXPERIENCES The data for the i.v. bolus administration are shown in Fig- A group of six students from an undergraduate course in ure 3. Pharmacokinetic parameters determined from the three biotransport worked on this project. The three-credit class is -1 -1 -1 methods are k12 = 1.80 hr , k21 = 2.94 hr , and kel = 0.30 hr designed for biomedical engineering students pursuing tracks -1 -1 [3] (measurement of the flow rates); 12k = 1.42 hr , k21 = 2.37 hr , in biomaterials and tissue engineering or biomechanics. -1 ® -1 and kel = 0.26 hr (regression in Mathematica ); k12 = 1.80 hr , Chemical engineering students may also select the course as -1 -1 k21 = 2.92 hr , and kel = 0.27 hr (methods of residuals). The an elective toward their degree requirements. A final report predicted concentrations plotted are the ones derived by the was produced after several meetings with the instructor dur- third method. Students may be given a project where they are ing which the project was discussed. Although a graduate expected to investigate the effects of the kinetic parameters on assistant helped design the experimental setup (Figure 2) C1 and C2 to understand how drug transport is influenced by because of time limitation, the group was required to draw a the distribution and elimination rate constants. This research schematic diagram of the process similar to Figure 1b. The also offers the opportunity to address the effects of the dose specific assignment was to study the effects of loading doses size on the plasma blood concentration. Multiple bolus-injec- on the concentrations in the central and peripheral compart- tions and constant-rate infusions can also be studied after a ments. In addition to providing a background of the subject, slight modification of the model and initial conditions. the students were also responsible for deriving the model The choice of one compartment or two compartments may equations and estimating the kinetic parameters. They were be an important factor when designing appropriate drug-dos- not told about the methods that could be applied to determine
Figure 2. The experi- mental setup of the two- compartment model. Potassium permanga- nate was added to the beakers. Fresh water in an Erlenmeyer flask was introduced to the two compartments.
104 Chemical Engineering Education
these parameters; the kinetic values were estimated from measurement of the flow 8.0 rates. The results were also presented to the class and sources of errors, such as 7.0 flow fluctuations, were identified. 6.0 CONCLUSIONS 5.0 Experiments in continuous-stirred vessels were designed to represent drug 4.0 transport within the body. The processes Concentration (g/L) governing equations were similar to 4 3.0 those of a two-compartment model with linear first-order distribution and KMnO 2.0 elimination kinetics. These activities gave students the opportunity to apply 1.0 conservation principles learned in the classroom. In addition, Laplace trans- 0.0 form techniques were implemented 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 to solve the differential equations. Time (hr) Closed-formed expressions for the con- j centration of potassium permanganate Figure 3. Concentrations of KMnO4 in the central ( ) and peripheral (+) com- partments. The parameters obtained by the method of residuals are k = 1.80 in the central and peripheral compart- 12 hr- 1, k = 2.92 hr -1, and k = 0.27 hr -1. Predicted concentrations in vessels 1 and ment were obtained. Three methods 21 el 2 are shown by the symbols (—) and (-----), respectively. of extracting the pharmacokinetic pa- rameters based on experimental data were outlined. After administering an i.v. bolus of 1.37 g of potassium 6.00 1st dose: 1.10 g permanganate to the central vessel, 5.50 the concentration profiles showed a 2nd dose: 0.332 g pattern analogous to drug transport 5.00 when a two-compartment model is 4.50 3rd dose: 0.330 g used. The three parameter estimation methods yield comparable results. 4.00 Students who worked on the project 3.50 were able to model the process, solve the governing differential equations, 3.00 and estimate the kinetics. 2.50
REFERENCES 2.00 1. Clewell, R.A., and H.J. Clewell, “Devel-
opment and Specification of Physiologi- KMnO4 Concentration (g/L) 1.50 cally Based Pharmacokinetic Models for Use in Risk Assessment,” Regul. Toxicol. 1.00 Pharmacol., 50(1), 129 (2008) 2. Schoenwald, R.D., Pharmacokinetic 0.50 Principles of Dosing Adjustments, CRC Press, Boca Raton (2001) 0.00 3. Simon, L., K. Kanneganti, and K.S. Kim, “Drug Transport and Pharmacokinetics 0 1 2 3 4 5 6 for Chemical Engineers,” Chem. Eng. Time (hr) Ed., 44(4), 262 (2010) 4. Truskey, G.A., F. Yuan, and D.F. Katz, d Transport Phenomena in Biological Figure 4. Experimental concentrations of KMnO4 in the central ( ) and peripheral Systems, 2nd Ed., Pearson Prentice Hall, compartments (j). The predicted data are represented by the solid lines (____). The Upper Saddle River, NJ (2009) -1 -1 rate constants for the two-compartment model are k12 = 1.80 hr , k21 = 2.92 hr , 5. Gibaldi, M., and D. Perrier, Pharmacoki- -1 and kel = 0.27 hr . The elimination rate constant for the one-compartment model netics, 2nd Ed., Informa Healthcare, New (dashed line: ------) is k = 0.41 hr -1. York (2007) p el Vol. 45, No. 2, Spring 2011 105 ChE laboratory
CONTINUOUS AND BATCH DISTILLATION IN AN OLDERSHAW TRAY COLUMN
Carlos M. Silva, Raquel V. Vaz, Ana S. Santiago, and Patrícia F. Lito Universidade de Aveiro, Campus de Santiago • 3810-193 Aveiro, PORTUGAL istillation is by far the most frequently used industrial calculations, mostly using Excel, Matlab, Hysys, and Math- separation process. Although not energy-efficient, it ematica software.[4-6] Moreover, virtual laboratories involving has a simple flowsheet and is a low-risk process. It distillation units have been developed in order to enhance the Dis indeed the benchmark with which all newer competitive understanding of the process units and to improve the teaching processes must be compared. Following Null,[1] distillation effectiveness.[7, 8] Nonetheless, students are usually uninter- should be selected if the relative volatility is greater than 1.05, ested in a problem unless they can visualize it in practice, so [2] [3] whereas Nath and Motard and Douglas indicate α12 greater experiments in the lab should never be totally replaced by than 1.10, a more conservative critical value for the relative simulated experiments on a computer, notwithstanding its volatility. Generally, design heuristics point out that processes ease and less time-consuming approach. using energy separation agents should be favored. In this work, experiments are performed in an Oldershaw For the reasons outlined above, distillation experiments column with five sieve trays to separate cyclohexane/n-hep- are included in the Chemical Engineering Integrated Master tane under different modes of operation. These modes include curriculum of the Department of Chemistry at University of total reflux, continuous rectification with partial reflux, and Aveiro (DCUA). Students start receiving lectures on distil- lation as part of the Separation Processes I course, which is Carlos M. Silva is a professor of chemical engineering at the Depart- ment of Chemistry, University of Aveiro, Portugal. He received his B.S. essentially devoted to equilibrium-staged unit operations. and Ph.D. degrees at the School of Engineering, University of Porto, Afterwards, experiments are carried out in Laboratórios EQ Portugal. His research interests are transport phenomena, membranes, (Chemical Engineering Laboratory), a weekly six-hour lab ion exchange, and supercritical fluid separation processes. course intended to provide hands-on experience on separa- Raquel V. Vaz is a Ph.D. student at the Department of Chemistry, tions, reaction, and control. Each experiment lasts two weeks: University of Aveiro, Portugal. She received her Master’s degree in chemical engineering from the University of Aveiro. Her main research in the first week students—divided into groups of three—carry interest focuses on molecular dynamics simulation and modeling of out the lab exercise and some calculations, and in the sec- diffusion coefficients of nonpolar and polar systems. ond week students do numerical calculations and computer Ana S. Santiago is a post-Ph.D. student in the Department of Chemistry, simulations, which require computational support. Student University of Aveiro, Portugal. She received her B.S. degree in chemi- cal engineering from the University of Coimbra and Ph.D. in chemical assessment is based on a very short individual oral quiz and engineering from the University of Aveiro. Her main research interest a report prepared by the student groups. focuses on bio-refinery and membrane separation processes. In this paper a lab exercise on continuous and batch Patrícia 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 rectification developed at DCUA is presented. Papers with in chemical engineering from the University of Aveiro. Her main research experimental work in the distillation field are scarce and ac- interest focuses on mass transfer, membrane separation processes, ion exchange, and molecular dynamics simulation and modeling of cordingly this communication intends to fill this gap. There are diffusion coefficients of nonpolar and polar systems. a number of educational publications concerning distillation
© Copyright ChE Division of ASEE 2011
106 Chemical Engineering Education batch rectification with constant reflux. An Oldershaw tray ture sensors immersed in the reboiler and located in the top column is a laboratory-scale column equipped with perforated condenser allowing the determination of the bottom and head trays. Of special importance is the fact that it exhibits a sepa- compositions, respectively. The column is used to separate ration capacity close to that of large industrial columns.[9] In c.a. 800 mL of a cyclohexane (Lab-Scan, 99%) / n-heptane fact, experimental results show that commercial towers will (Lab-Scan, 99%) mixture with 30% (mol) of cyclohexane. require a similar number of stages to reach the same separa- The calibration curve—measured in this work—to deter- tion level obtained in the Oldershaw unit.[10] mine the cyclohexane mole fraction (x1) in a cyclohexane–n- With this work students practice relevant concepts in- heptane mixture at 30 ˚C as function of refractive index (RI), 2 troduced earlier in their curriculum, namely vapor-liquid is given by x1=-309.95 RI + 895.15 RI – 645.15. equilibrium, continuous vs. batch operation, McCabe-Thiele Experiments at Total Reflux, R = ∞ graphical method, column efficiency, and application of the generalized Rayleigh equation. Moreover, students use in- Rectifications at total reflux were performed at two distinct dustrial simulation software (Aspen) to predict experimental effective reboiler powers (P = 75 and 125 W) to evaluate results, giving them the opportunity to improve their skills in the effect of the internal molar flow upon separation and this field, too. By examining experimental results and compar- column efficiency. The invariance of the top and bottom (TD ing them with those obtained from simulations, students gain and TB) temperatures was used to detect the steady state. Ad- insight to this unit operation. ditionally, they were utilized to determine the corresponding cyclohexane molar compositions, xD and xB, by vapor-liquid LABORATORY DESCRIPTION equilibrium calculations assuming that the column is kept at atmospheric pressure (pressure drop along the column is Experimental Setup considered negligible). Experiments are performed in an Oldershaw tray column instrumented and equipped with a control system supplied Continuous Rectification at Partial Reflux by Normschliff Gerätebau (similar equipment is available This Oldershaw tray column is extremely versatile. It can from Normag GmbH Imenau). Other commercial teaching be operated continuously under partial reflux. With simple equipment for continuous distillation is offered, for example, modifications, the distillate may be directly fed to the reboiler by Armfield, Ltd. (
Vol. 45, No. 2, Spring 2011 107 DATA ANALYSIS subtracting one stage (corresponding to reboiler) from the Vapor-Liquid Equilibrium total number of equilibrium stages. At low pressure, vapor-liquid equilibrium of a component Overall Efficiencies i may be represented by: The experimental overall efficiency is given by: yP = xxγ PTσ ()1 it ii()i () Nideal Eov ()%(=×100 4) Nreal where yi and xi are the vapor and liquid molar fractions, σ respectively, Pi is its vapor pressure, γ is its activity coef- i where Nideal is the ideal number of equilibrium stages and Nreal Pσ ficient, and Pt is total pressure. i is computed by the Antoine is the actual number of trays (in this case Nreal = 5). equation and γi by Margules equations, whose constants may The overall efficiency can be estimated by empirical be found in the literature. correlations, namely, those by Drickamer and Bradford[12] == [13] [12] Since ∑∑xyii1, the liquid molar fraction may be and O’Connell. Drickamer and Bradford correlate Eov determined for any temperature by the relation: with the feed viscosity, μ, at the average temperature of the σσcolumn: Pxt = 11γγ()xP11()Tx+−()1222()xP()T () Ecov ()%.=−13 3668.logµ()P ()5 where x denotes the liquid composition vector. The vapor molar fraction can be then determined by Eq. (1). O’Connell used a viscosity and relative volatility, α12, depen- Number of Equilibrium Stages dence. His graphical result can be fit with −0.226 The number of equilibrium stages is obtained by the well- Ec%.=×50 36αµP () ov () 12 () known McCabe-Thiele method.[11] In this work the column has a rectifying section only, hence the operating line is: where α12 is the geometric average of the bottom and top R 1 values. y = x + x (33) nn+1 D R +1 R +1 Generalized Rayleigh Equation
The moles of liquid in the reboiler are related to its residue where yn+1 and xn are the cyclohexane vapor and liquid frac- composition by the Generalized Rayleigh equation: tions of trays n+1 and n, respectively. At total reflux (R = ∞) the operating line coincides with the diagonal line. The num- xBf, inal B dx ber of equilibrium stages is given by the number of outlined ln = B ()7 F ∫ xx− steps between x and x . The number of trays is obtained by DB D B xB,0
TABLE 1 where F and B are the initial and final Experimental Conditions and Results for the Experiments at Total Reflux moles of mixture in the reboiler, respec- tively. Knowing experimental pairs of Eov(%) P(W) T (˚C) T (˚C) x x D B D B data (x , x ), B/F fraction may be ob- Exp. Eq. 5 Eq. 6 D B tained by numerical integration. 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 Results and Discussion
1.0 1.0 In Table 1 the re- P = 125 W P = 125 W sults obtained at total 0.9 2a 0.9 2b R = ∞ R = 6 reflux at 75 and 125 0.8 0.8 W are presented. For 0.7 0.7 illustration, the Mc- 0.6 0.6 Cabe-Thiele diagram Operating line y y 0.5 0.5 for 125 W is plotted in
0.4 0.4 Figure 2. 0.3 0.3 McCabe-Thiele 0.2 0.2 diagram for a) total 0.1 0.1 reflux distillation x = x B x = x D x = x B x = x D 0.0 0.0 and b) continuous 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 rectification at par- x x tial reflux.
108 Chemical Engineering Education
Figure 2a. The minimum number of equilibrium stages was 0.7 4.76 and 4.61 for P = 75 and 125 W, respectively, giving rise to overall efficiencies of 95.1% and 92.1%. These results indicate 0.6 the column is more efficient when operated at 75 W, which is usually unexpected for the students. Actually, higher reboiler 0.5 powers generate higher internal flows. Although such effect Distillate may lead to a foreseen increase of mass transfer coefficients, 0.4 it also decreases the mean residence times of both phases 0.3 in each tray, which has a larger overall impact. Students are Composition frequently aware of the first effect, since they associate large 0.2 Residue Reynolds numbers to large Sherwood values, but neglect the second and more dominant effect in this case. 0.1
The experimental and predicted overall efficiencies are 0.0 listed in Table 1, and show that both correlations under- 0 5 10 15 20 25 30 35 40 45 50 55 estimate Eov. Students frequently get disappointed with such time (min) diverging results. Instructors notice that students almost Figure 3. Distillate and residue compositions during the always doubt their own experimental results, tending to ac- cept without hesitation model predictions. At this point it is batch rectification at P = 125 W and R = 6. essential to keep in mind that the overall column efficiency is a complex function of system properties, operating conditions, 5.0 y = -12137x4 + 4060.2x3 - 320.08x2 - 22.27x + 5.2365 and column geometric variables, and that common empirical R2 = 0.977 correlations take only some system properties into account, as 4.0 is the case of Eqs. (5) and (6) adopted here. Students should be encouraged to search data for similar systems to see that )
B 3.0
data are frequently 10 to 20% higher than O’Connell’s pre- x -
[11] D
dictions. x
The results obtained for the continuous rectification at 1/( 2.0 partial reflux (P = 125 W and R = 6) are given in Table 2 and Figure 2b. As may be observed, the overall efficiency 1.0 achieved is about the same of that obtained at total reflux for the same power (92.0% vs. 92.1%). Furthermore, the separa- 0.0 → tion achieved now (0.234 0.685) is inferior to that obtained 0.00 0.05 0.10 0.15 0.20 → at R = ∞ (0.273 0.864; see Table 1), which is the expected x B result for all students. Figure 3 shows the evolution of both distillate and residue Figure 4. Numerical data used for the integration of molar compositions during the batch rectification (P = 125 Rayleigh equation. W and R = 6). As expected, the cyclohexane content of the residue approaches zero since it is the lighter component. using a polynomial fitted to experimental data (see Figure 4). The fraction of undistilled liquid in the flask, B/F, was de- Many times students are not aware of the impact that the fitted termined by numerical integration of the Rayleigh equation, equation has upon the numerical solution. For instance, some groups try to integrate by the trapezoid rule, which gives rise TABLE 2 to scattered positive and negative data. Results for Continuous Rectification Experiment Students calculate B/F also by mass balance using the initial at P = 125 W and R = 6 (xB,0) and final (xB,final) residue compositions, and the average T (˚C) T (˚C) x x E (%) D B D B ov composition of distillate determined by refractive index. The 87.8 93.5 0.685 0.234 92.0 results found are frequently very similar. In this run (see Table 3) they found B/F = 0.696 and 0.667 using the Rayleigh TABLE 3 and mass balance approaches, respectively. B/F Fraction Obtained by Rayleigh Equation and Mass Balance Results for Experiment at P =125 W and R-6 ASPEN SIMULATIONS
xD B/F B/F Distillations at total reflux and partial reflux (P = (RI) (Rayleigh Eq.) (mass balance) 125 W) may be simulated using BatchSep 2006.5 by 0.410 0.696 0.667 Aspentech, Inc., a simulator frequently used in industry.
Vol. 45, No. 2, Spring 2011 109 This software allows the simulation of distillation ing a predetermined time to charge the tower with the same number columns under different operating conditions and of moles that our Oldershaw column contains initially. Subsequently, modes of operation. The embedded VLE calculations a null distillate flow must be imposed to reach total reflux condition were based on the RK-SOAVE method. (see Figure 5). The simulation is carried out in two consecutive steps: Total Reflux Simulation i) column charge and ii) distillation at total reflux. Table 5 compiles the pertinent data and options selection for the total reflux calcula- The total reflux simulation is carried out using tions, in order to help students to reproduce our results. the input specifications and additional information shown in Table 4. For this case, the column is as- Simulation of Continuous Rectification at Partial Reflux sumed to be initially filled with nitrogen, therefore a The continuous rectification at partial reflux (R = 6) is computed partial condenser has to be selected in order to purge with the input specifications and additional information compiled in it from the system. A feed stream was imposed dur- Table 6 ( page 112). For this simulation, the column has to be initially at total reflux and only then submitted to R = 6. Students should TABLE 4 realize this approach is in accordance with industrial columns start- Information for Aspen Simulation at Total Reflux up: distillation towers are frequently started up at total reflux, after an initial charge of feed, and this condition runs until both distillate Input Specifications and bottom compositions reach the desired project specifications; - Column initially empty (initially filled with N ) [14] 2 only then is the finite reflux ratio implemented. In our case, R = - Partial condenser 6, the column holdups and pressure drop values are those obtained - Feed stream to introduce the initial charge of mixture previously from the total reflux simulation, and the feed stream is the - Null distillate flow to get ∞ = R distillate recycled to column (see Figure 6, page 112). Table 7 (page Additional Information 113) compiles data and options for the continuous rectification at partial reflux calculations. - Column configuration number( of stages, including reboiler and condenser) Simulation Results - Reboiler geometry (dimensions and jacket type) The simulation results, presented in Table 8 (page 113) for both total - Power (P = 125 W) and partial reflux, are in good agreement with the measured values; the
- Condenser specifications pressure,( type, area, con- relative deviations found lie between 1.0 and 19.3%, being higher for R densing coefficient, coolant inlet temperature, coolant = 6. The calculated separation for R = ∞ (xD – xB = 0.584) is very near the mass flow, and coolant heat capacity) experimental one (xD – xB = 0.591) whereas it diverges for R = 6 (0.484 - Tray specifications and dimensions against 0.451, respectively). It is curious to notice that students usually
Operation Steps doubt their experimental observations against the simulated results, sug- gesting possible experimental errors for the deviations found for R = 6. - i) Column charge - ii) Distillation at ∞=R Nonetheless, in this case such large error may be attributed to the fact that some operating parameters, including pressure drop and holdups, were Results calculated at R = ∞ and assumed to be the same in the continuous partial - Column holdups reflux simulation. On the whole, students and instructors are amazed
- Pressure drop with simulation results due to the large number of input parameters and - Composition profile specifications, particularly those for geometrical variables.
- Temperature profile 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 modes of operation: total reflux, continuous partial reflux, and batch with constant reflux. The effect of the internal Figure 5. Detail of an Aspen BatchSep 2006.5 window for the total reflux simulation. molar flows on column
110 Chemical Engineering Education TABLE 5 Specification and Options Selection for the Total Reflux Simulation Carried Out With Aspen Batchsep 2006.5 Window Tab Specifications/Selections Number of stages: 7 Configuration Valid phases: Vapor-Liquid Pot orientation: vertical Pot head type: Pot Geometry Top Hemispherical, bottom Hemispherical Diameter: 0.18m Height: 0.18m Jacket: Heating, Jacket covers head Pot Heat Transfer Setup Top height: 0.08m Condenser type: Partial Partial condenser spec: Coolant temperature Condensing coefficient: 100 cal/hr/m2 Condenser Area: 0.15 m2 Coolant inlet temperature: 18 ˚C Coolant mass flow: 100 kg/hr Coolant heat capacity: 4.18 kJ/kg/K Reflux Distillate mass flow rate: 0 kg/hr Heating option: Specified duty Jacket Heating Jacket Heating Duty: 0.125 kW Pressure/Holdups Pressure Pressure profile and holdups: Calculated Section: Start stage: 2 End stage: 6
Tray Specifications: Diameter: 0.03m Internal 1 Specification Spacing: 0.025m Weir height: 0.005m Lw/D: 0.83 % Active area: 90 % Hole area: 15 Discharge coefficient: 0.8 Initial condition: Empty Initial Conditions Main Initial temperature: 20 ˚C Initial pressure: 1.01325 bar Charge stage: 7 Valid phases: Liquid-Only Feed convention: On-stage Type: Fresh feed Flow rate basis: Mole
Conditions: Charge Stream Feed Main Temperature: 20 ˚C Pressure: 1.01325 bar
Composition: Composition basis: Mole-Frac CYCLO-01: 0.3 N-HEP-01: 0.7 N2: 0 Location: Charge stream/Feed Charge stream/Feed/Mole flow rate: 0.75 mol/min Changed Parameters Jacket/Heating/Duty: 0 kW Operating Step Charge Condenser/Coolant mass flow: 0 kg/hr Step end condition: Elapsed time End Conditions Duration: 10 min Location: Charge stream/Feed Charge stream/Feed/Mole flow rate: 0 mol/min Operating Step Distill Changed Parameters Jacket/Heating/Duty: 0.125 kW Condenser/Coolant mass flow: 100 kg/hr
Vol. 45, No. 2, Spring 2011 111 performance was investigated at total reflux by changing well as data analysis with the generalized Rayleigh equation. reboiler power. Furthermore, they are introduced to the use of simulation Results show that the efficiency decreases slightly with software, an important tool for their chemical engineering increasing flows. Moreover, column efficiency measured at instruction. partial reflux is analogous to that obtained at total reflux. For batch distillation, the application of the generalized Rayleigh ACKNOWLEDGMENTS equation provides good results. The results at infinite reflux Patrícia F. Lito and Ana Santiago wish to express their and for the continuous rectification at partial reflux were com- gratitude to Fundação para a Ciência e Tecnologia (Portugal) pared with those obtained by Aspen BatchSep simulations, for the grants provided (SFRH/BD/25580/2005 and SFRH/ giving rise to relative deviations between 1.0 and 19.3%. BPD/48258/2008), and to B.R. Figueiredo and Professor F. With this work students practice relevant concepts, includ- A. Da Silva for Aspen simulations and pictures support. ing vapor-liquid equilibrium, continuous vs. batch operation, McCabe-Thiele graphical method, and column efficiency as NOMENCLATURE B Final number of moles of liquid in the reboiler, mol
TABLE 6 Eov Overall efficiency, % Information for Aspen Simulation of the Continuous Rectification F Initial number of moles of liquid in the reboiler, mol
at Partial Reflux Nreal Number of real trays Input Specifications Nideal Ideal number of equilibrium stages P Reboiler power, W - Column initially at total reflux Pt Total pressure, atm - Total condenser Pσ Vapor pressure, atm - Total initial charge and composition R Reflux ratio RI Refractive index - Distillate flow to get R = 6 T Temperature, ˚C Additional Information x Molar fraction of liquid phase - Column configuration (number of stages, including reboiler and condenser ) y Molar fraction of vapor phase - Reboiler geometry (dimensions and jacket type) Greek letters α Relative volatility - Power (P = 125 W) 12 γ Activity coefficient - Column pressure drop and tray holdups μ Molar average liquid viscosity, cP - Distillate charge stream (charge stage, type, temperature, pressure) Subscripts Operating Steps B Bottom D Top - Distillation at R = 6 final Final condition Results i Component i - Composition profile 0 Initial condition - Temperature profile
Figure 6. Continuous partial reflux simulation flowsheet.
112 Chemical Engineering Education REFERENCES TABLE 7 1. Null, H.R., “Selection of a Specification and Options Selection for the Continuous Rectification Separation Process,” in Hand- at Partial Reflux Simulation Carried Out With Aspen Batchsep 2006.5 book of Separation Process Technology, Rousseau, R.W., Window Tab Specifications/Selections Ed., Wiley-Interscience, New Number of stages: 7 Configuration York (1987) Valid phases: Vapor-Liquid 2. Nath, R., and R.L. Motard, “Evolutionary Synthesis of Pot orientation: vertical Separation Processes,” AIChE Pot head type: J., 27, 578-587 (1981) Pot Geometry 3. Douglas, J.M., Conceptual Top Hemispherical, bottom Hemispherical Design of Chemical Process- Setup Diameter: 0.18m es, McGraw-Hill, New York Height: 0.18m (1988) Jacket: Heating, Jacket covers head Pot Heat Transfer 4. van der Lee, J.H., D.G. Olsen, Top height: 0.08m B.R. Young, and W.Y. Svrcek, “An Integrated, Real-Time Condenser Condenser type: Total Computing Environment for Reflux Reflux ratio: 6 Advanced Process Control Heating option: Specified duty Development,” Chem. Eng. Jacket Heating Jacket Heating Duty: 0.125 kW Ed., 35(3) 172 (2001) 5. Binous, H., “Equilibrium Holdup basis: Mole Staged Separations Using Mat- Pressure/Holdups Holdups Start Stage: 2 lab and Mathematica,” Chem. Stage Holdup: 5E-5 kmol Eng. Ed., 42(2) 69 (2008) Initial condition: Total reflux 6. Nasri, Z., and H. Binous, “Ap- Initial drum liquid volume fraction: 0.5 plications of the Peng-Robin- Main Initial temperature: 20 ˚C son Equation of State Using Initial pressure: 1.01325 bar Matlab,” Chem. Eng. Ed., 43(2) Initial Conditions 115 (2009) Composition basis: Mole-frac Total initial charge: 0.0075 kmol 7. Santoro, M., and M. Mazzotti, Initial Charge “HYPER-TVT: Development CYCLO-01: 0.3 and Implementation of an Inter- N-HEP-01: 0.7 active Learning Environment Charge stage: 7 for Students of Chemical and Valid phases: Liquid-Only Process Engineering,” Chem. Feed convention: On-stage Eng. Ed., 43(2) 175 (2009) Type: Distillate receiver recycle 8. Fleming, P.J., and M.E. Paulai- Charge Stream Flow rate basis: Mole Main tis, “A Virtual Unit Operations Distillate Laboratory,” Chem. Eng. Ed., Conditions: Temperature: 80 ˚C 36(2) 166 (2002) Pressure: 1.01325 bar 9. Fair, J.R., H.R. Null, and W.L. Bolles, “Scale-up of Plate Distillate receiver: 1 Efficiency From Laboratory Oldershaw Data,” Ind. Eng. Location: Charge stream/Distillate Charge stream/Distillate/Mole flow rate: 0.1 mol/s Chem. Process Des. Dev., 22, Operating Step Changed Parameters Liquid distillate receiver: 1 53-58 (1983) Rpartial 10. Humphrey, J.L., and G.E. Condenser pressure: 1.01325 Keller, Separation Process Jacket/Heating/Duty: 0.125 kW Technology, McGraw-Hill, New York (1997) 11. Seader, J.D., and E.J. Henley, Separation Process TABLE 8 Principles, 2nd Ed., John Wiley & Sons, New York Total Reflux and Continuous Rectification Simulations Results (2006) Bracketed values are relative deviations to the experimental ones. 12. Drickamer, H.G., and J.R. Bradford, Transactions T (˚C) T (˚C) x x AIChE, 39, 319-360 (1943) D B D B 42 Total reflux 13. O’Connell, H.E., Transactions AIChE, , 741-755 83.8 92.1 0.873 (1.0%) 0.289 (5.9%) (1946) (R = ∞) 14. Foust, A.S., L.A. Wenzel, C.W. Clump, L. Maus, and Cont. rectification 85.6 92.4 0.774 (13.0%) 0.290 (19.3%) L.B. Andersen, Principles of Unit Operations, 2nd (R = 6) Ed., John Wiley & Sons, New York (1980) p
Vol. 45, No. 2, Spring 2011 113 ChE classroom
ACTIVE LEARNING IN FLUID MECHANICS: YOUTUBE TUBE FLOW AND PUZZLING FLUIDS QUESTIONS
Christine M. Hrenya University of Colorado • Boulder, CO 80309-0424 ctive learning is an umbrella term for instructional The first activity involves a contest among small groups methods used in the classroom in which students are of students to correctly predict the outcome of tube-flow ex- actively engaged in the learning process, as opposed periments using the mechanical energy balance. The students Ato a traditional lecture in which students play a passive role. are first introduced to the experimental apparatus (gravity- Active learning can take many forms such as collaborative driven flow from a tank), and then charged with predicting learning, cooperative learning, and problem-based learn- the outlet flow rates from various tubes. An announcement [1] ing. Research has shown that such nontraditional methods that prizes will be awarded to groups with predictions that may lead to improved academic achievement, retention, and best match the experimental data is also made at the start. student attitudes toward learning, depending on the method The class culminates in the running of the experiments, and [1,2] [3] of active learning utilized. Indeed, Felder, et al., have in- real-time identification of the “winners.” This class period cluded active learning methods on their list of teaching meth- allows the students to put their knowledge into practice via ods that work. Courses on fluid mechanics are a particularly active-learning, while also providing a high level of energy good match for active-learning techniques (see, for example, Reference 4), since everyday examples are ubiquitous. Christine M. Hrenya received her degrees in chemical engineering from The Ohio State In this paper, two active-learning modules targeted for use University (B.S.) and Carnegie Mellon Uni- in an undergraduate fluid mechanics course are described. versity (Ph.D.), and is currently on faculty at Materials for both have been designed and made avail- the Department of Chemical and Biological Engineering at the University of Colorado. able via the Internet (
Vol. 45, No. 2, Spring 2011 115 rate (for which they are solving). An analytical expression for swer unique questions related to a puzzling fluid mechanical f in terms of Re is only possible for laminar flow; otherwise, phenomena seen in everyday life, the answers to which draw it must be determined using the Moody diagram and thus a on the course content throughout the semester: buoyancy, tur- trial-and-error solution for the flow rate is required. The tubes bulence, drag force, hydrostatics, surface tension, mechanical are designed such that flow rate from each is different, but energy balance, dimensionless numbers, surface forces, etc. all are near the transitional region. Accordingly, the student The questions are assigned several weeks prior to the end of calculations should involve a combination of analytical and the semester. During the final week of class, each group turns trial-and-error approaches, along with the checking of their in a short report on their findings, and gives a 6-10 minute initial assumptions (laminar vs. turbulent). presentation to the entire class, in which illustrative calcula- This exercise can be adapted easily to classes of different tions, demonstrations, and videos are encouraged. durations. In our experience, asking the students to predict Table 1 contains a listing of the project questions, along flow rates from all three tubes is doable in a 1.25-hour period: with the general topic area. Before the questions are revealed 10 minutes to form groups and introduce experiment, 50 to the class, a sheet is passed around for students to sign up in minutes for group calculations, and 15 minutes for tallying self-selected groups, with each group having a unique group of predictions, running of experiments, and identification of number. The project questions assigned to each group are then contest winners. In the last few minutes, the general problem read aloud, generating a considerable amount of enthusiasm solution is also outlined, with detailed calculations given as given the perplexing and often humorous nature of the ques- handouts at the end of class. For a 50-minute class period, a tions. Because an aim of the presentation is to “teach” the class reasonable variation would be to ask students to predict the a variety of topics, students are asked to relate their content flow rate from only one of the tubes. Either way, one may to the material presented previously during the course. Also, consider alerting students one class period beforehand to an because of the varying degree of difficulty associated with the upcoming “contest,” in order to motivate their review of the project questions, students are asked to make their own deci- material ahead of time. sion as to whether a full analysis with example calculations is possible, or whether the bulk of the material will be presented Benefits. The benefits of this exercise include: (i) active in a qualitative manner. Finally, students are encouraged to learning with an ad hoc group of peers, (ii) in-class collec- be creative in their presentations, using videos and in-class tion of data (via video) provides experimental verification of demonstrations where appropriate. the mechanical energy balance, (iii) high level of motivation instilled due to contest format, and (iv) complexity of example The presentations are intentionally brief. First, practical problems not sacrificed, as the three-part experiment provides time constraints exist. Most recently, this project has been a range of straightforward to complex calculations. used with a class of 100 students forming 18 groups (five to six students / group). This breakdown allowed for 6-minute Course Materials. Below is a listing of the course content presentations (1 minute per student) and two additional for use by educators in their own classes: minutes for questions and transition, which consume nearly 1) a YouTube video introducing the experi- the entire 150 minutes (for a three-credit course) during the ment to the class:
116 Chemical Engineering Education cal answer (since different assumptions may be made in the the Dead Sea, making hourglasses of both sand and water to analysis), students are encouraged to come to office hours to demonstrate the linear nature of timekeeping by the former discuss their topic well in advance. As a result, the reported but not the latter, etc. Furthermore, students are encouraged to findings are generally scientifically sound, but regardless the add to the list of puzzling questions for use in future courses, instructor has the opportunity to give feedback at this stage. and indeed several of the questions appearing in Table 1 have On a final note, past students have more than risen to the been put forth by former students. Additional suggestions are occasion with a plethora of entertaining and effective dem- welcome (send to [email protected]), and will be shared onstrations, like watching an egg sink in tap water but float with the community via inclusion on the website indicated in salt water to demonstrate the principles behind floating in below (see course materials).
TABLE 1 Puzzling Fluids Questions for End-of-Semester Project # 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. Buoyancy “Balloon Boy”) could be supported by his parents’ homemade contraption, would he/she have recom- mended to continue the all-day, costly chase or search for the boy on the ground?* 5 Why can a sailboat travel faster than the wind? Drag force 6 Why can a water bug walk on water when I can’t, and how big could the bug be? Surface tension 7 Why is it easy to float in the Dead Sea and not in the ocean? Buoyancy 8 When deep sea diving, why can’t a really long snorkel be used for breathing? Hydrostatics 9 Prior to 2002, the Colorado Rockies had difficulties recruiting pitchers due to the large number of Fluid properties (density) / home runs hit in Coors Field, and thus high ERA’s. In 2002, the Rockies started storing their baseballs drag force 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? 10 Why is it that I get more snow on my windshield when my car is stopped at a light than when it’s Dimensionless numbers moving, but I get more rain on my windshield when it’s moving than when it’s stopped? (Stokes) 11 How is body fat measured via the immersion method? Buoyancy 12 How do water rockets work? Force balance 13 In 2003, Denver taxpayers justified spending $165 million to build the longest runway in the United Fluid properties (density) States (~3miles) to ensure the airport’s competitiveness in attracting wide and heavy aircraft. Why are Denver’s runways longer than those of most other airports? Why does this new runway see relatively more use during summer months? 14 What basic techniques should a swimmer use to maximize her efficiency? Drag force 15 Why do cyclists draft one another? How much does it help / hurt the leader and the followers? Drag force 16 Why is the aerofoil (wing) shape mounted upside down in race cars relative to its mounting in planes? Lift force 17 The Falkirk wheel is a rotating boat lift in Scotland with a capacity of nearly 200,000 gallons. Why Buoyancy 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? 18 What are the effects of some “dirty tricks” in baseball: (i) lubrication of ball and (ii) roughening/pol- Surface forces ishing ball surface? 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 then the cardboard/water stays in place when the glass is flipped? 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 *Some useful assumptions: (i) balloon was constructed with tarps (typically made from HDPE) and duct tape and then filled with helium, (ii) au- thorities said the silver balloon, 20 feet long and 5 feet high, at times reached 7,000 feet above the ground while adrift (
Vol. 45, No. 2, Spring 2011 117 Benefits. The benefits of this project include: (i) course The survey also contained a section for open-ended com- material presented throughout semester is reinforced via peer ments addressing the best and worst aspects of each activity. instruction, including creative, student-generated demonstra- Representative comments are included below. tions; (ii) students are exposed to a wide range of everyday Tube Flow Experiments—Best Aspects applications of fluid mechanics, including current news stories; (iii) students work in self-selected group on question • “Cannot overstate the benefit of actually observing with open-ended nature; and (iv) students gain early experi- how the equations we learn in class can be used in a ence in written and oral communication, with feedback from real-time experiment.” the instructor. • “the fact that we were able to see how the simplifying Course Materials. All materials listed below are avail- assumptions we made in class in order to solve the able at the website
50 70 45 Q1 (a) (b) 60 Q4 40 Q2 Q5 50 35 Q3 Q6 30 40 Q7 25 Q8 20 30
15 20 Percentage of Percentage Students Percentage of StudentsPercentage 10 10 5 0 0 strongly disagree undecided agree strongly strongly disagree undecided agree strongly disa gree a gree disa gree agree
Figure 2. Student survey results for (a) tube flow experiments and (b) puzzling questions in fluid mechanics. See Table 2 for listing of items surveyed.
118 Chemical Engineering Education hearing each person’s strong points about the particular to talk and still keep it under 6 minutes” problem. Groups can have a great deal of creativity • “having to talk in front of our peers (it was scary!)” with a cumulative effect from each individual.” • “not all of the groups had applied fluids equations in • “learning about not only our project but other an understandable manner” projects” • “The projects were just plain fun.” CONCLUDING REMARKS Puzzling Fluids Questions—Worst Aspects In this work, two active-learning exercises appropriate for • “trying to get everyone to agree on ideas.” an undergraduate course in fluid mechanics are presented. Based on firsthand experience using these exercises with • “difficult to try to explain the concept and for everyone hundreds of students, it is found that the exercises effec- tively promote student interaction, give rise to thoughtful TABLE 2 student questions, serve as good learning tools, and last but Items Used in Student Survey not least, add quite a bit of enjoyment to the class period for See Figure 2 for responses. all involved. # Item Tube Flow Demonstration ACKNOWLEDGMENTS Q1 This class period was a more valuable learning experi- The author would like to express thanks to Will Brewer, ence than a lecture with example problems. who prepared the sample calculations and YouTube video. Q2 The contest format (i.e., prizes for winners) provided The author is also indebted to the students, teaching assistants, more focus and energy on the task than would have been present otherwise. and colleagues who have contributed to the list of puzzling fluid-mechanics questions over the years. Funding support for Q3 This class period was the most fun of the semester. this work was provided by the National Science Foundation Puzzling Questions in Fluid Mechanics (CBET-0658903). Q4 Attending these presentations and working on my own project illustrated the everyday relevance of fluid mechanics better than other means used during the se- REFERENCES mester (examples during lecture, homework problems, 1. Prince, M., “Does Active Learning Work? A Review of the Research,” etc.). J. Eng. Ed., 93, 223-231 (2004) 2. Smith, K.A., S.D. Sheppard, D. W. Johnson, and R.T. Johnson, “Peda- Q5 Attending these presentations strengthened my under- gogies of Engagement: Classroom-Based Practices,” J. Eng. Ed., 94, standing of basic fluid mechanical principles. 87-101 (2005) Q6 This project provided a good learning experience about 3. Felder, R., D. Woods, J. Stice, and A. Rugarcia, “The Future of En- working in teams. gineering Education: II. Teaching Methods That Work,” Chem. Eng. Ed., 34, 26-39 (2000) Q7 This project provided a good learning experience for 4. Ford, L.P. , “Water Day: An Experiential Lecture for Fluid Mechanics,” the oral communication of scientific ideas to peers. Chem. Eng. Ed., 37, (2003) Q8 This project provided a good learning experience in 5. Munson, B.R., D.F. Young, T.H. Okiishi, and W.W. Huebsch, Funda- written communication of scientific ideas. mentals of Fluid Mechanics, Wiley, New York (2009) p
Vol. 45, No. 2, Spring 2011 119 ChE laboratory
A SEMI-BATCH REACTOR EXPERIMENT for the Undergraduate Laboratory
Mario Derevjanik, Solmaz Badri, and Robert Barat New Jersey Institute of Technology • Newark, NJ 0102 he advantages of the semi-batch reactor (SBR) are The letters representing the species are shown in corre- exploited in several industrial reactor applications. For sponding order. The reported rate expression for the disap- example, in the reaction of a gas with a liquid (e.g., pearance of A is second order: [1] Tozonation of industrial wastewater to remove dyes ), the gas −= =− == rkCC rrr ()1 is continuously bubbled through the batch liquid. Conversely, AABBST a gaseous product can be continuously removed from a liquid where ri = reaction rate of species i, k = reaction rate con- system (e.g., CO2 in fermentation). The slow addition of one stant, and Ci = molar concentration of i. Because the reaction reactant into another assists in the control of a strong exotherm evolves gaseous O rather rapidly, it is preferable to run it in a [2] 2 in the SBR, such as in polymerization reactors (e.g., nylon semi-batch reactor. To start, a batch vessel contains hydrogen and polypropylene[3]). Polymer molecular weight distributions peroxide (H2O2 – species A) in a water solution. The aqueous can be controlled by careful addition of the monomer (e.g., solution of sodium hypochlorite (NaOCl – species B) is fed [4] styrene-butadiene rubber ). The SBR can be used to maxi- slowly over time at a constant rate. As shown above, species mize selectivity, especially where byproducts or competing S and T are NaCl and O , respectively. reactions are an issue (e.g., substituted alkyl phenols[5]). 2 In spite of its industrial use, the SBR is often ignored in undergraduate reactor engineering classes. Still, the SBR Mario Derevjanik graduated from NJIT offers a useful opportunity to combine both batch and flow with a B.S. in chemical engineering in [6] 2008. During his undergraduate career, concepts. Haji and Erkey present an SBR experiment with Mario assisted Dr. Barat in developing new the exothermic hydrolysis of acetic anhydride. In-situ Fourier student experiments. Mario is working as a chemical engineer for ConSerTech, a small transfer infrared spectroscopy is used for monitoring spe- environmental consulting company. His cur- cies of interest vs. time. Kinetic analyses are subsequently rent responsibilities, including VOC monitor- ing, are at the Conoco-Phillips refinery in performed. Linden, NJ. In this paper, an SBR is used to process the simple reac- Solmaz Badri tion between sodium hypochlorite and hydrogen peroxide. was born in Teh- ran, Iran, and came to the United States after Inexpensive household bleach and pharmaceutical hydrogen completing high school. She joined NJIT and peroxide solution serve as the convenient reactants. Product graduated in 2009, majoring in chemical engineering. She is now living in New York molecular oxygen is monitored through a rotameter. The over- City, married to a physician, and working all change in solution conductivity is metered with a conduc- as an individual contractor. She dedicates her work and research to her newborn son, tivity probe. The reaction exothermicity is monitored through Amin Zamanian. a reactor thermocouple. The elegant model analyses combine reaction kinetics with species and energy balances. Robert Barat is currently a professor of chemical engineering at NJIT, where he has been a member of the faculty since 1990. He REACTION AND KINETICS completed his Ph.D. in chemical engineering at MIT in 1990. His research has been in com- The reaction used in this experiment is inspired by Shams bustion, reactor engineering, environmental El Din and Mohammed,[7] who studied the kinetics of this monitoring, applied optics, and is currently reaction as a means to remove residual bleach from water in applied catalysis. He is also the faculty coordinator for the chemical engineering purification equipment. laboratories at NJIT. → H2O2(aq) + NaOCl(aq) H2O(l) + NaCl(aq) + O2(g) A + B → R + S + T © Copyright ChE Division of ASEE 2011 120 Chemical Engineering Education REACTOR SPECIES BALANCES The Ideal Gas Law can be used to convert FT to a volu- A semi-batch design equation applies for B: metric rate.
rVTsRT dN B ≈ v 10 Fr+=V 2 T () BB () P dt s where FB = molar flow rate inlet to the batch, V = batch liquid where Ts, Ps represent standard temperature and pressure volume, and Ni = moles of i in the batch. A simple batch design conditions (298 K, 1 atm), respectively, and R = ideal gas equation applies for A: constant (0.0821 liter-atm/mole-K). Eq. (10) can be added dN to the set of equations to be solved. The volumetric rate of rV= A ()3 evolved O is one of three possible sources of data in this A dt 2 experiment. The rate rT is obtained from Eq. (1). The inlet molar flow rate of B can be written in more con- venient volumetric terms: CONDUCTIVITY CHANGE AND CHLORIDE ION vfρ The conductivity of the solution is a weighted sum of the F = BBB ()4 contributions of the ionic species, including NaOCl as the B W B active ingredient, a small amount of NaOH to help prevent degradation of NaOCl to release Cl , and residual NaCl from where v = volumetric feed rate of B, ρ = bleach mass den- 2 B B the bleach manufacturing process. We assume that C in sity, f = mass fraction of species B in the feed bleach solution, NaOCl B the SBR is very small, and insignificantly contributes to the and W = molecular weight of B. Since N = C V, and V = f(t) B i i solution conductivity. Subsequent SBR modeling supports this in the most general semi-batch case, Eq. (3) becomes: claim. The batch solution conductivity can be estimated as: C dC AAdV Cc≈≡Cc= Cc+ CCond + Cond rA −= ()5 ∑ i iNaClNaClNaOHNaOHNaCl NaOH V dt dt i 11 and Eq. (2) becomes: ()
FB CBBdV dC where C = solution conductivity, ci = effective molar con- +−rB = ()6 V V dt dt ductivity of species i, Ci = molar concentration of i, and Condi = contribution of i to the total conductivity. The rate of change of the volume is accounted for with a Accounting for the contribution of NaCl to the solution transient mass balance: conductivity requires a species balance, including the pres- dV()ρ ence of NaCl in the bleach feed: v BBρ = ()7 dt F C dV dC s +−r ss= ()12 where ρ =mass density of batch solution. It can be rea- V s V dt dt sonably assumed ρ is constant; then, Eq. (7) reduces to: The inlet molar flow rate of S can be written in more con- ρ dV v B = ()8 venient volumetric terms: B ρ dt vfBBρ S The volumetric feed rate of B is set at a constant value by the FS = ()13 W user in the experiment. S
Eqs. (1), (4)-(6), and (8) form a partial system that will be where fS = mass fraction of NaCl in the feed bleach solution. solved simultaneously. The system is integrated from t = 0 Molar conductivity data for NaCl aqueous solutions are (when the reactant B solution flow begins) to whenever the available[8] over the temperature range of interest to yield a peroxide feed is ended by the user. relationship valid up to 0.85 molar concentration: O 2 ΛS =+0..0117TTcc1 3737 +51.665 EVOLUTION OF O2 (ΛO in mS/cm/mmolar, temperature T in C) Assuming that the bleach solution mixes thoroughly into S c the peroxide solution, the reaction mixture will likely saturate The contribution of the NaOH to the solution conductiv- with O very rapidly. We can assume that the O evolution 2 2 ity is: rate is approximately the same as the reaction rate, and is O given by: CondSS= Λ CS ()14 Fr≈ V 9 TT () Accounting for the contribution of NaOH to the solution
Vol. 45, No. 2, Spring 2011 121 conductivity requires a non-reactive species balance. Repre- species j, Cj = molar concentration of j inside the reactor, Fjo senting NaOH as the inert I, the balance is: = molar feed rate of j, Tf = feed temperature, ΔHrA = heat of reaction per mole of A, and P = system pressure. The final F C dV dC II−=I ()15 term in the numerator is included since the fluid volume is not V V dt dt constant. It is small compared to the other terms, however, and The inlet molar flow rate of I can be written in more con- can be neglected. Selected terms are now examined. venient volumetric terms: c ρ cC= p ()19 ∑ pjj vfBBρ I j M FI = ()16 WI where cp and M are the mean molar heat capacity and molecu- where fI = mass fraction of NaOH in the feed bleach solution. lar weight, respectively, of the solution. As an approximation O Molar conductivity ΛI data for NaOH aqueous solutions are due to the high degree of dilution, the properties of the solvent [11] available over the temperature range of interest to yield a water can be used. If the mass-based value is used for cp , relationship valid up to 0.3 molar concentration: M is not needed. O 2 T Λ =−0..0241TT++5 0658 111.13 vcBBρ p I cc FcdT ≈−B ()TT ()20 O ∑ joj∫ p B (Λ in mS/cm//molar, temperature T in C) j M B I c Tf
The contribution of the NaOH to the solution conductiv- where TB, MB, and cpB = temperature, average molecular ity is: weight, and mean heat capacity (mole-based), respectively, O of the feed bleach. If the mass-based value is used for cpB, Cond II= Λ CI ()17 MB is not needed. The standard heat of reaction (-37.2 kcal/mole at 25 ˚C for ENERGY BALANCE the reaction as written earlier) is assumed to be independent The energy balance should reflect the configuration of the of temperature, especially in consideration of the limited reactor vessel. In a typical experiment, the liquid is in contact temperature range of the experiment. with stainless steel walls and internal components (e.g., agi- The energy balance in the form used for data modeling is tator, probes). An air-filled jacket surrounds the walls. Heat now written as: losses to this metal must be considered. A simple heat loss calibration was performed wherein an electric immersion −−vcρ ()TT+−Vr()−∆H dT BBpBBAAr() = ()21 heater of known wattage was placed into the vessel filled + ρ dt mcmpmpVc with water covering the metal parts. A simple heat balance of this calibration is: dT Q EXPERIMENTAL CONSIDERATIONS = h ()18a dt mcwpwm+ mcpm Figure 1 illustrates the basic configuration of the current experimental system. An agitated reactor vessel is used. The where Qh = electrical heating rate; mw and mm = masses of O product water and metal parts, respectively; and cpw and cpm = mass- 2 based specific heats of water and metal, respectively. A suc- cessful linear regression of the measured temperature vs. time, according to the integrated form of Eq. (18a), yielded a heat loss calibration of mmcpm = 1284 cal/ ˚C. It can be shown, consistent with Fogler and Gurmen,[9] that the reactor energy balance is: conductivity Bleach probe T dV −+FcdT VP()−−rH∆ + ∑ joj∫ p Ar()A dt dT i T f = ()18b dt mc + VcC thermocouple m ppm ∑ pjj j Data PC Temp
where T = reactor temperature, cpj = molar heat capacity of Figure 1. Schematic of the semi-batch reactor experiment.
122 Chemical Engineering Education bleach solution, held in an external reservoir, is pumped through contains residual NaCl from the manufacturing process.[11] a calibrated flow meter, and into the reactor. A bypass is used The NaCl concentration in the bleach, determined from an since the pump capacity is too large. A magnetic-drive centrifu- ISE measurement, is 32 grams/liter or 2.9 wt. %. For bleach, ρ 3 gal pump is useful since all wetted parts are plastic-coated to B = 1.1 g/cm , and cpB = 0.9 cal/g- ˚C (estimated). avoid corrosion. The vessel has access ports for a stainless steel thermocouple and a conductivity probe. The probe is inserted DATA, ANALYSIS, AND DISCUSSION through a side port to ensure immersion. The vessel is sealed The rate constant used in Eq. (1) is estimated from the data [7] since product O2 gas is vented through a calibrated flow meter. of Shams and Mohammed. A differential pressure gauge (not shown in the figure) is used 12 −11800 in the current system to measure the pressure in the vapor space k ≈⋅210 exp/ litermole−seec ()22 RT in the vessel during a run. Variations on this setup should be considered depending on available equipment. where R = 1.987 cal/mole-K, and T = absolute temperature In the present system, a Vernier® conductivity probe with (K). ® ® GoLink interface and Logger Lite data collection and plot- The analysis approaches the simulation of the experiment ting software are used. A data collection PC is accessed via the as a design problem. In this approach, the model defined by USB interface. The probe is calibrated with two conductivity Eqs. (1), (4)-(6), (8), (10)-(17), (21), and (22) is solved with ® standard solutions available from Vernier . a numerical ordinary differential equation solver package. ® A Vernier chloride ion specific electrode (ISE) is an al- Figures 2 and 3 show experimental and corresponding ternative to the conductivity probe. Its membrane requires model results for batch solution conductivity, batch tempera- more care, however, making the ISE not as robust as the ture, and evolved O2 rate. The uncertainty bars are based on all-metal conductivity probe. Hence, the ISE was limited to estimated precisions of the measuring devices. Relative fits determination of the chloride content of the bleach, and not are reasonable for temperature and O2. In fact, the heat loss inserted into the reactor. term in the energy balance accounts for ~ 2-3 degree reduction Finally, the most likely experimental parameter to vary is in the observed temperature rise. The model under-prediction the bleach feed rate. Alternative experiments include dilution of the conductivity suggests that the bleach might contain an of either the peroxide or bleach solutions. In either case, care additional inert ionic species not accounted for. In addition, should be taken such that the O2 evolution rate remains within modeling results are most sensitive to the bleach rate. An ac- the useful range of the flow meter. curate measure of the bleach flow rate is critical. In a typical run of the present system, a 5 liter agitated (200 As a point of discussion, and lacking direct concentration rpm) vessel is filled with 3 liters of over-the-counter hydrogen measurements, the model profiles for CA and CB are shown peroxide solution (3%) that had been stored in a laboratory in Figure 4 (page 125). The peroxide concentration drops refrigerator to improve shelf life. The initial conductivity monotonically as the bleach is added. The batch concentration reading is ≈ 0 and the initial temperature is ≈ 13 ˚C. About of the bleach jumps initially as the bleach is first added, and one liter of laundry bleach is stored in the reservoir. At time then rises slowly, but all at a very low value. These values t = 0, the bleach is flowed into the batch at a constant 4 gal- are consistent with the lons/hour rate. The O evolution begins almost immediately, 2 CHO >>CNaOCl and continues until the available bleach is exhausted (~ 350 22 assumption made earlier. It also is consistent with the claim seconds). The batch solution conductivity rises monotonically that NaOCl does not appreciably contribute to the batch until the maximum value measurable by the probe (~ 28,000 conductivity. μS/cm) is reached. A larger or second bleach reservoir can be used to feed more bleach so as to exhaust the remaining CONCLUSIONS peroxide. Consuming ≈ 1 liter of bleach in this system causes → The reaction H2O2(aq) + NaOCl(aq) H2O(l) + NaCl(aq) + O2(g) the batch temperature to rise to ≈ 8 centigrade degrees. Cur- is a useful system to study in a semi-batch reactor. Generation rent runs show reactor pressures of only a few inches of of a gaseous product offers an opportunity for additional data water above atmospheric. The data from this run are shown beyond that of probes. The availability of published conduc- in Figures 2 and 3 (page 124). tivity data provides a direct means to convert data to con- The Clorox® bleach contains ~ 6 wt. % NaOCl as the active centration of a product. Therefore, unlike most experiments, ingredient. In addition,[10] it contains NaOH added to prevent products are monitored instead of reactants. The multiple degradation of the NaOCl to Cl2. The MSDS also quotes a species balances required for modeling will challenge the specific gravity of 1.1 and pH of ~ 11.4 for the bleach. A student, but not be out of the realm of undergraduate reactor sample of the bleach revealed a pH of 12, corresponding to engineering. This is especially true with the inclusion of an an NaOH concentration of 0.01 molar or 0.36 wt. %. It also energy balance.
Vol. 45, No. 2, Spring 2011 123 30000
Figure 2. (right) 25000 Observed and predicted batch solution conduc- 20000 tivity for bleach / hydrogen peroxide semi-batch run. 15000
10000 Conductivity (uS/cm)
Figure 3. (below) Exper Model 5000 Observed and predicted batch solution tempera- ture and evolved 0 oxygen rate. 0 25 50 75 100 125 150 175 200 Time (seconds)
Model Temp Exp Temp Model O2 Exp O2
26 6
24 5
22
4 20
18 3
16 Temperature (oC) 2 Evolved O2 rate (slm)
14
1 12
10 0 0 50 100 150 200 250 300 350 Time (seconds)
124 Chemical Engineering Education REFERENCES 1. Gharbani, P., S.M. Tabatabaii, and A. Mehrizad, “Removal of Congo Ind. Eng. Chem. Res., 36(12), 5196 (1997) Red from Textile Wastewater by Ozonation,” Int. J. of Environmental 6. Haji, S., and C. Erkey, “Kinetics of Hydrolysis of Acetic Anhydride Science and Technology, 5(4), 495 (2008) by In-Situ FTIR Spectroscopy: An Experiment for the Undergraduate 2. Wakabayashi, C., M. Embiruçu, C. Fontes, and R. Kalid, “Fuzzy Laboratory,” Chem. Eng. Ed., 39(1), 56 (2005) Control of a Nylon Polymerization Semi-Batch Reactor,” Fuzzy Sets 7. Shams El Din, A.M., and R.A. Mohammed,, “Kinetics of the Reaction and Systems, 160(4), 537 (2009) Between hydrogen Peroxide and Hypochlorite,” Desalination, 115, 3. Seki, H., M. Ogawa, and M. Ohshima, “Industrial Application of a 145-153 (1998) Nonlinear Predictive Control to a Semi-Batch Polymerization Reactor,” 8. Landolt, H., and R. Bornstein, Zahlenwerte und Funktionen aus Natur- in Advance Control of Chemical Processes, L.T. Biegler, A. Brambilla, wissenschaften und Technik., K.H. Hellwege (ed.), Volume 2, Part. and G. Marchetti (eds.), Proceedings of the IFAC Symposium, Pisa, Volume 6, Springer-Verlag, Berlin (1987)—obtained via Honeywell Italy 2000, 2, 539-544 (2001) Sensing and Control, Freeport, IL 4. Yabuki, Y., and J.F. MacGregor, “Product Quality Control in Semi- 9. Fogler, H.S., and N.M. Gurmen, Elements of Chemical Reaction batch Reactors Using Midcourse Correction Policies,” Industrial & Engineering, 4th Ed., Prentice-Hall (2006) Engineering Chemistry Research, 36(4), 1268 (1997) 10.
0.9 4
3.5 0.8
3
0.7 2.5
0.6 2
1.5 0.5
CA (H2O2) 1
Batch Concentration of H2O2 (moles/liter) CB (NaOCl) x 10^11 Batch Concentration of NaOCl x 10^11 (mole/liter) 0.4 0.5
0.3 0 0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 Time (seconds)
Figure 4. Model-based predicted concentrations of species A (H2O2) and B (NaOCl) in the batch.
Vol. 45, No. 2, Spring 2011 125 ChE curriculum
CONSERVATION OF LIFE as a Unifying Theme for Process Safety in Chemical Engineering Education
James A. Klein DuPont, North America Operations Richard A. Davis University of Minnesota, Duluth • Duluth, MN onservation of energy (COE) and conservation of mass Much of what DeBlois observed and recommended remains (COM)—both are fundamental principles that apply true today, over 90 years later, as identified by the U.S. Chemi- to all aspects of chemical engineering design, analysis, cal Safety Board (CSB) in their report on the T2 Laboratories Cand education. In most cases, we cannot apply one without incident in 2009: consideration of the other. Yet, a third fundamental principle In 2006, the Mary Kay O’Connor Process Safety Center exists that is too often not recognized as on the same level of surveyed 180 chemical engineering departments at U.S. importance as COE and COM: prevention of serious human universities to determine whether process safety was part injury, major property damage, and environmental harm, of their chemical engineering curricula. Of the universities which is a primary focus of industrial chemical engineering surveyed, only 11 percent required process safety educa- tion in the core baccalaureate curriculum. An additional 13 practice. We choose to call this third principle “conservation [5] of life” (COL), reflecting the need for fundamental awareness percent offered an elective process safety course. and application of process safety and product sustainability CSB recommended that the American Institute of Chemical concepts in chemical engineering education. COL was first Engineers and the Accreditation Board for Engineering and introduced to our knowledge by Lewis DeBlois,[1-3] who was Technology (ABET) work together to improve requirements DuPont’s first corporate safety manager and later president of for chemical engineering education to include greater em- the National Safety Council, when he wrote in 1918:
… safety engineering, with its interests in design, equip- James Klein is a Sr. PSM Competency Consultant, North America PSM ment, organization, supervision, and education … bears Co-lead, at DuPont. He has more than 30 years experience in process as well a very definite and important relation to all other engineering, research, operations, and safety. He received his chemical branches of engineering. This relation is so close, and its 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. need so urgent, that I am convinced that some instruction in the fundamentals of safety engineering should be given a Richard Davis is a professor of chemical engineering at the University of Minnesota, Duluth, where he teaches computational methods, heat place in the training of every young engineer. He should be and mass transfer, green engineering, and separations. His current taught to think in terms of safety as he now thinks in terms research interests include process modeling and simulation applied to of efficiency. Conservation of life should surely not be rated energy conversion, pollution control, and environmental management in mineral processing. He received his chemical engineering degrees below the conservation of energy. Yet, few of our technical from Brigham Young University (B.S.) and the University of California, schools and universities offer instruction in this subject, Santa Barbara (Ph.D.). and the graduates go out to their profession with only vague [4] surmises on “what all this talk on safety is about.” © Copyright ChE Division of ASEE 2011
126 Chemical Engineering Education phasis on process safety, in particular awareness of chemical ture, high pressure, and mechanical energy. Hazards reactivity hazards. In response, the following additional assessment can be defined as the detailed evaluation and program outcome has been proposed for the general ABET development of information about a chemical, material, criterion for accrediting undergraduate chemical engineering mixing, or interaction of chemicals/materials and about programs: any operating conditions that can create process hazards. Engineering programs must demonstrate that their students Hazards assessment therefore provides the basic under- attain the following outcomes: (l) an awareness of the need standing and data for conducting further process hazards to identify, analyze, and mitigate hazards in all aspects and risk analysis and management. The starting point for of engineering practice, for example design, operational hazards assessment is often the Material Safety Data Sheet procedures and use policies, hazards detection and response (MSDS), but the MSDS should be considered only for [6] systems, fail-safe systems, life-cycle analyses, etc. initial information, which should be verified and expanded COL can be used by universities as a concept and unifying on through additional literature and experimental data. theme for increasing awareness, application, and integration 2. Evaluate hazardous events of safety throughout the chemical engineering curriculum Multiple hazardous events, such as loss of containment, and for meeting the revised ABET accreditation criteria. fires, explosions, runaway reactions, etc., can be described Students need to think of COE, COM, and COL as equally for most chemical processes, based on the material and important fundamental principles in engineering design, process hazards and intended or accidental processing analysis, and practice. By providing students appropriate steps. Consequence analysis and modeling consist of tools for evaluating and implementing COL principles, we identifying and evaluating the direct, undesirable impacts can help them to better understand “what all this safety talk of potentially hazardous events, resulting from failure of is about,” and what their role is in contributing to safety in engineering and/or administrative controls for the process. chemical engineering. The purpose of consequence analysis is to help estimate the type, severity, and number of potential injuries, COL PRINCIPLES property damage, and environmental harm that could Five COL Principles have been developed and are shown in result from different event scenarios.[8] In conducting Figure 1. These principles are based on application of industry consequence analysis, the impacts of possible hazardous standard process safety and product sustainability practices events are evaluated for a range of small to catastrophic and are intended to organize COL concepts and methodologies failure events. A small event could be caused by a small- for application in various parts of the chemical engineering diameter hole in a vessel or pipe or possibly a procedural curriculum, as discussed further in the following section. error such as leaving a valve open or in the wrong posi- 1. Assess material/process hazards tion. Catastrophic failure events are those where there is a complete and sudden failure of any equipment, structure, A basic understanding of material and process hazards is or system resulting in required for safe major loss of contain- engineering de- 1. Assess material/process hazards ment of chemicals or sign and opera- – Develop basic data on reactivity, flammability, toxicity, etc. energy. Even though tions. A hazard catastrophic failure can be defined 2. Evaluate hazardous events events are rare, the as a physical or – Apply methodologies to estimate potential hazardous impacts consequences of such chemical con- 3. Manage process risks an event could be sig- dition that has – Evaluate risk vs. acceptable risk criteria nificant and should be the potential for carefully evaluated.[9] causing harm to – Apply inherently safer approaches people, property, – Design and evaluate multiple layers of protection 3. Manage process or the environ- 4. Consider real-world operations risks ment.[7] Exam- – Implement comprehensive PSM systems Process hazards/risk ples of material analysis consists of the – Recognize importance of human factors hazards include detailed, methodical flammability, – Learn from experience – Case Histories evaluation of process toxicity, and re- 5. Ensure product sustainability equipment, materials, activity. Exam- – Implement product safety / stewardship practices conditions, and op- ples of process erating steps in order – Apply life cycle management hazards include to control and reduce high tempera- Figure 1. COL Principles. process risks. Specific Vol. 45, No. 2, Spring 2011 127 failures of process equipment, operating procedures, or re- methods include hazard and operability analysis (HAZOP), lated systems that can lead to potentially hazardous events what-if/checklist analysis, failure modes and effects analysis must be identified and evaluated to ensure that appropriate (FMEA), and fault tree analysis (FTA).[7] Risk analysis can and reliable safeguards (layers of protection) are provided range from qualitative to semi-quantitative (e.g., Layer of to achieve acceptable risk levels. Typical hazards evaluation Protection Analysis)[10] to quantitative,[11] depending on the potential risks associated with the process. The initial process design and risk analysis Assessing Hazards and Risks activities also provide the greatest opportu- Process nities for consideration and implementation Process Hazards [12,13] Technology of inherently safer process concepts to Analysis significantly reduce process risks.
Managing Operations 4. Consider real-world operations Operating Personnel Process hazard identification, evaluation, Procedures Training and management is essential to chemical engineering design, but consists of only Managing Equipment and Facilities the initial elements of a sound industrial Quality Mechanical Contractor process safety management program, as Assurance Integrity Safety shown in Figure 2. Real-world chemical operations must develop and implement Managing Change systems for operating procedures, training, management of change, equipment main- MOC-T,S PSSR MOC-P tenance and reliability, etc.,[14,15] in order to obtain desired results. In addition, humans make mistakes, so human factors[16-18] Managing Incidents must be considered during the initial risk Emergency Incident analysis, management of day-to-day opera- Planning & Investigation Response tions, and emergency response. Incidents and case studies[19,20] also provide opportu- nities for learning from previous problems Figure 2. Elements of a process safety management program. 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.[21] 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 Figure 3. Application of COL in undergraduate chemical engineering curriculum. is intended to help achieve “the 128 Chemical Engineering Education SACHE Modules by COL Principle SACHE Modules by ChemE Course
1. Assess material/process hazards Reaction Engineering Course Figure 4. – Chemical reactivity hazards (2005) – Chemical reactivity hazards (2005) SACHE Mod- – Dust explosion prevention / control (2006) – Hydroxylamine explosion case (2003) ules for COL Principles – Explosions (2009) – Reactive and explosive materials (2009) (examples). – Properties of materials (2007) – Runaway reactions (2003) – Reactive and explosive materials (2009) – Runaway reactions: Experimental – Runaway reactions (2003) characterization and vent sizing (2005) – Seminar on fire (2009) – Rupture of a nitroaniline reactor (2007) – Etc. – Etc. goal is zero” with respect to injuries, incidents, and environ- easily compartmentalize COL as a separate, unrelated activ- mental/social impact associated with chemical engineering ity, but will see it as an activity that is inherent to all courses practices and products. Awareness and use of these principles and engineering activities. Using a spiral learning model, by students should help them understand their important roles COL will build up awareness, understanding, and capability as engineers in helping make achievement of this goal a real- related to safety as students gain experience by revisiting the ity. Students may simply wish to think of these concepts as COL principles at increasing levels of depth and breadth. Ulti- “people in = people out.” mately, students will demonstrate knowledge and application of COL principles in the capstone design course reports and A practical method for measuring the impact of COL in [22-24] either process or product safety is to consider risk reduction, presentations by addressing subjects such as: such as shown in Eq. (1): • Process hazards • Hazardous events ∆=RRlog(()opR 1) • Hazard/risk analysis ∆R is the order of magnitude improvement in risk for the • Layers of protection event being evaluated, where Rp is the risk level (e.g., fatalities Human factors issues per year) when COL principles have been applied, and Ro is • the inherent risk associated with the handling, processing, or • Product safety and life-cycle considerations. use of potentially hazardous materials or products. Cost-ef- fective risk reduction improvements should be identified and An example of where COL principles could be applied in considered for implementation, based on application of COL the undergraduate chemical engineering curriculum is shown principles. ∆R measures the collective risk improvement, and in Figure 3. risk criteria[22] are typically used to determine if an overall Additional resource materials for both engineering instruc- acceptable level of risk has been achieved. tors and students for use in applying COL in undergraduate chemical engineering education are planned. Excellent train- APPLICATION OF COL TO ing materials currently exist that can be used to get started CHEMICAL ENGINEERING CURRICULA with COL immediately, including: There are three main reasons for use of COL as a unifying • SACHE modules[26,27] concept and •theme in undergraduate chemical engineering • Engineering texts[28-31] education: • Incident compilations[19,20] • Emphasize importance of safety to students as a funda- mental principle that must be considered and evaluated • US Chemical Safety Board investigations[32] in all aspects of engineering practice equivalent to • Process Safety Beacon[33,34] COE and COM • Process safety literature (e.g., Process Safety • Consistent application and reinforcement of safety Progress). integrated throughout the curriculum A SACHE module introducing COL has been prepared, • Meet ABET accreditation changes related to safety. and materials have been tested in presentations at several Use of COL will help develop a process safety culture in the universities. Many SACHE modules are currently available,[27] curriculum, where students see connections and applications which can be sorted for application of the COL principles. An related to COL in most courses. Students will not be able to example is shown in Figure 4.
Vol. 45, No. 2, Spring 2011 129 EXAMPLE gineer, McGraw Hill (1926) 2. Petersen, P.B., Lewis A. DeBlois and the Inception of Modern Safety A simple example of a classroom active-learning exercise Management at DuPont, 1907-1926, submitted to Academy ofManage- that reinforces the principles of COL in a separations course ment, Management History, Division, Hagley Museum and Library, was adapted from the April 2003 Process Safety Beacon.[33,34] Wilmington, DE, ca 1987 3. Klein, J.A., “Two Centuries of Process Safety at DuPont,” Process The article describes an incident involving a fire and explo- Safety Progress, 28(24) (2009) sion originating in an activated carbon drum used to control 4. DeBlois, L.A., “The Safety Engineer,” American Society of Mechanical hydrocarbon emissions from a flammable liquids storage ter- Engineers, Hagley Museum and Library, Wilmington, DE (1918) minal. Starting with COL principle four—consider real-world 5. Chemical Safety Board, Investigation Report, T2 Laboratories, Inc., Runaway Reaction, Report No. 2008-3-I-FL, Sept. 2009 operations—the class is presented with a basic description of 6. AICHE, Letter to Mr. John Bresland from H. Scott Fogler and June the incident, and then asked to work through the first three Wispelwey, Dec. 7, 2009 COL principles of assessment, evaluation, and management 7. Center for Chemical Process Safety, Guidelines for Hazard Evaluation of process hazards applied to this case study. The class is Procedures, 3rd Ed., John Wiley & Sons (2008) 8. Center for Chemical Process Safety, Guidelines for Consequence divided into small teams of two or three students and al- Analysis of Chemical Releases, AICHE (1999) lowed a short time to work on the problem. Students typically 9. Dharmavaram, S., and J.A. Klein, “Using Hazards Assessment to Pre- reference the table of Failure Scenarios for Mass Transfer vent Loss of Containment,” Process Safety Progress, 29(4) (2010) Equipment.[7] An instructor-led classroom discussion solicits 10. Center for Chemical Process Safety, Layer of Protection Analysis: Simplified Process Risk Assessment, John Wiley & Sons (2001) student input and may include the following observations and 11. Center for Chemical Process Safety, Guidelines for Chemical Process recommendations: Quantitative Risk Analysis, AICHE (1999) 1. Assess Hazards: Flammable materials exist in the carbon 12. Center for Chemical Process Safety, Inherently Safer Chemical Pro- cesses: A Life Cycle Approach, 2nd Ed., John Wiley & Sons (2008) bed and hydrocarbon vapor, and low thermal conductivity in 13. Seay, J.R., and M.R. Eden, “Incorporating Risk Assessment and Inher- the carbon bed reduces heat transfer rates with a potential ently Safer Design Practices into Chemical Engineering Education,” for exceeding the auto-ignition temperature. Chem. Eng. Ed., 42(3) (2008) 2. Evaluate Hazards: Refer- 14. Center for Chemical Process Safety, Guidelines for Implementing ence the fire triangle, Process Safety Management Systems, AICHE (1993) as shown in Figure 5, FUEL OXYGEN 15. Center for Chemical Process Safety, Guidelines for Risk Based Process Safety, John Wiley & Sons (2007) and identify sources for 16. Center for Chemical Process Safety, Human Factors Methods for Improv- fuel (organic materials), ing Performance in the Process Industries, John Wiley & Sons (2007) oxygen (air in the tank 17. Kletz, T., An Engineer’s View of Human Error, 3rd Ed., IChemE, Rugby, space) and heat (exother- UK (2001) mic heat of adsorption HEAT 18. Klein, J.A., and B.K. Vaughen, “A Revised Model for Operational Discipline,” Process Safety Progress, 27(1) (2008) reaction). Figure 5. Fire triangle. 19. Kletz, T., What Went Wrong? Case Histories of Process Plant Disasters 3. Manage Risk: Apply and How They Could Have Been Avoided, 5th Ed., Elsevier (2009) [10] LOPA to recommend passive and active design solu- 20. Atherton, J., and F. Gil, Incidents That Define Process Safety, Center tions that include: proper flow distribution in the bed, for Chemical Process Safety, John Wiley & Sons (2008) minimizing the bed cross sectional area, continuous 21.
130 Chemical Engineering Education Random Thoughts . . .
HANG IN THERE! Dealing with Student Resistance to Learner-Centered Teaching
Richard M. Felder Dear Dr. Felder, like to check the research for yourself, the attached bibli- ography suggests some good starting points.) It’s true that What can I do about low teaching evaluations from students many students want us to simply tell them up front in our I teach actively when what they clearly want is much more lectures everything they need to know for the exam rather traditional (passive ride, smooth highway please)? I’m about than challenging them to figure any of it out for themselves. ready to give up and return to just lecturing, as I am sure If we give them that, though, we are failing those who have students will evaluate my courses higher if I do. Thank you an aptitude for high-level thinking and problem solving but for your time and consideration. might not develop those skills without the guidance, practice, Sincerely, ______and feedback learner-centered methods provide. That failure is a high price for us to pay to get better student ratings—and we might not even get them by staying traditional. Teachers * * * whose evaluations are not all that high to begin with com- Dear ______, monly see their ratings increase when they adopt a more Before I respond to your question, let me assure you that learner-centered approach. I get it. Learner-centered teaching methods like active and I don’t know what your institution is like, but here’s the cooperative and problem-based learning make students take way things go at the universities and colleges I’ve visited. more responsibility for their learning than traditional teacher- Most instructors teach traditionally but there are quite a few centered methods do, and the students are not necessarily who use active learning and other learner-centered methods, thrilled about it. All college instructors who have tried the including some of the best teachers on the campus—the ones former methods have experienced student resistance—and who routinely get excellent performance and high ratings from if they were getting high evaluations when they taught tradi- their students, teaching awards, and wedding invitations and tionally, their ratings may have dropped when they made the birth announcements from their former students. At some switch. As you’ve discovered, it doesn’t feel good when that point another faculty member may decide to try, say, active happens, so it will be understandable if you decide to go back to teaching classes where you just lecture and the students Richard M. Felder is Hoechst Celanese Professor Emeritus of Chemical Engineering just listen (or text or surf or daydream or sleep). at North Carolina State University. He is co- author of Elementary Principles of Chemical Please think about a couple of things before you make your Processes (Wiley, 2005) and numerous decision, however. An important part of our job as teachers is articles on chemical process engineering equipping as many of our students as possible with high-level and engineering and science education, and regularly presents workshops on ef- problem-solving and thinking skills, including critical and fective college teaching at campuses and creative thinking. If there’s broad agreement about anything conferences around the world. Many of his publications can be seen at
Vol. 45, No. 2, Spring 2011 131 learning, perhaps after attending a workshop or reading a • In your midterm evaluations, did you specifically ask paper or constantly hearing about the superb student responses the students whether they thought active learning (or their gifted colleague always enjoys. He or she tries it and whatever you were doing) was (a) helping their learn- it doesn’t go well—the evaluations are mediocre and some ing, (b) hindering their learning, or (c) neither helping students grumble that their professor made them do all the nor hindering? If you do this, you may find that the work instead of teaching them.* Instructors in this situation students objecting vigorously to the method are only can easily conclude that the nontraditional methods caused a small minority of the class. If that’s so, announce their poor ratings. What that conclusion doesn’t explain, the survey results in the next class session. Students however, is how that talented colleague of theirs can use the who complain about learner-centered methods often same methods on the same students and get good performance imagine that they are speaking for most of their class- and glowing reviews. mates. Once they find out that very few others feel Whenever I’ve explored this issue with instructors dis- the way they do, the grumbling tends to disappear tressed by it, I have invariably found that the teaching method immediately. they were trying was not the real problem. It was either that If your answers to any of those questions suggest that they were making one or more mistakes in implementing making some changes in your approach to the method and the method, or something else was troubling the students trying again might be worthwhile, consider doing it. If you and the method was a convenient scapegoat. So, if you’ve conclude, however, that you’ve done all you can and going used a learner-centered method, didn’t like the outcomes, back to traditional teaching is your only viable course of ac- and would like to do some exploring, you might start with tion, then so be it. I hope you choose the first option, but it’s these questions: totally your call. • In your student evaluations, were complaints limited Best regards, and good luck, to the method, or did they also relate to other things Richard Felder 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 BIBLIOGRAPHY method. 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., • Did you explain to the students why you were using 41(2), 93
132 Chemical Engineering Education ChE laboratory
Combining Experiments and Simulation of Gas Absorption for Teaching Mass Transfer Fundamentals:
REMOVING CO2 FROM AIR USING WATER AND NAOH
William M. Clark, Yaminah Z. Jackson, Michael T. Morin, and Giacomo P. Ferraro Worcester Polytechnic Institute, Worcester MA 01609 ne educational goal of the unit operations laboratory which equations to solve and how to validate the results.[3] is to help students understand fundamental principles This type of simulation can also extend the range of experi- by connecting theory and equations in their textbooks ence beyond what is possible in the lab by allowing studies Oto real-world applications. We have found, however, that that would otherwise be prohibited by time, financial, or collecting data and analyzing it with empirical correlations safety constraints. does not always translate into a good understanding of what [1] In this paper we present experiments and computer mod- is happening inside the pipes. One problem is that the els for studying the environmentally important problem of theoretical development behind the labs is often comprised removing CO2 from air. Simple models are shown to provide of approximate methods using lumped parameters that de- straightforward analysis of the experimental data even when scribe the results but not the details of the physical process. the system is not dilute. In addition, we present more detailed For example, when a mass transfer coefficient is obtained models that illustrate the two-film theory and provide insight from an absorption experiment, some students struggle to explain what the mass transfer coefficient represents and William Clark is an associate professor of chemical engineering at why it increases with increasing absorbent flow rate. To Worcester Polytechnic Institute. He received a B.S. degree from Clemson University and a Ph.D. degree from Rice University, both in chemical address this problem, we are using computer simulations to engineering. He has more than 20 years of experience teaching thermo- solidify the link between experiment and theory and provide dynamics and unit operations laboratory at WPI. In addition to research [1,2] efforts in teaching and learning, he has conducted disciplinary research improved learning. in separation processes. Commercial software packages like COMSOL Multiphys- Yaminah Jackson graduated from the WPI Chemical Engineering De- icsTM allow students to set up and solve the partial differential partment in Spring 2008. She is currently attending graduate school at the University of Southern California. equations that describe momentum, energy, and mass balances Michael Morin graduated from the WPI Chemical Engineering Depart- and also to visualize the velocity, pressure, temperature, and ment in Spring 2009. He is currently a Ph.D. candidate in mechanical concentration profiles within the equipment. Visualization engineering at WPI. of the processes may not only help reinforce concepts and Giacomo Ferraro is the laboratory manager in the Chemical Engineering clarify the underlying physics but it may also help “bring to Department at WPI. He is a master machinist and has facilitated equip- ment design, fabrication, and use for teaching and research at WPI for life” the mathematics as well as the experiments. With this more than 30 years. software, students don’t necessarily need to know the details of how to solve complex equations, but they need to know © Copyright ChE Division of ASEE 2011 Vol. 45, No. 2, Spring 2011 133 into the absorption process. These models help explain the End caps for the acrylic column were made with rubber stop- absorbent flow rate dependence of the mass transfer coeffi- pers fitted with liquid and gas inlet and outlets. cient and how the process is liquid phase resistance controlled We describe here the analysis of representative sets of ex- when using water and dependent on the gas phase resistance perimental runs using the two columns. Our students use the when using dilute NaOH solution as absorbent. Finally, we larger column to determine the effect of water flow rate on provide some discussion of how the simulations have been the mass transfer process. Experimental data are presented in received by students. Table 1 for four different water flow rates at fixed gas phase inlet conditions and room temperature. At present we don’t LABORATORY EXPERIMENT have our students working with NaOH in the lab for safety A few years ago our old 30-foot-tall, 6-inch-diameter, steel reasons. Instead, we give them data obtained on the smaller absorption tower became clogged with rust and residue from column by a student working on his senior thesis. Table 2 years of use with sodium carbonate solution as absorbent for shows the data collected for both water and 1 N NaOH solu- removing CO2 from air. Since concerns over global warming tion at five different liquid rates and a fixed gas phase inlet are a political reality even if the causes and effects are not condition at room temperature. It can be seen that very little clear, we wanted to continue to offer a CO2 absorption experi- CO2 is removed in the small column at these conditions with ment because of its appeal to student interest as well as its water as absorbent. On the other hand, most of the CO2 is ability to illustrate mass transfer fundamentals. To reduce cost removed from the gas stream when NaOH is used, even in and avoid column fouling in the future, we chose to use pure the small column. water as absorbent in our new 6-foot-tall, 3-inch-diameter, glass column packed with 54 inches of ¼-inch glass Raschig TABLE 1 rings that we purchased from Hampden Engineering Corpora- Large Column Data and Results for CO2 Absorption tion[4] and modified to suit our needs. Although using water as from Air Using Water at Room Temperature Air Rate, A = 1.42 L/min; Inlet CO , y = 0.185 absorbent focuses the lab on mass transfer concepts without 2 b the added complexity of reactions, the limited solubility of Water Rate, W Outlet CO2, yt Kya L/min mole fraction mol/m3s CO2 in water makes it necessary to have accurate analysis of the gas phase and to work with concentrated gas streams to 0.53 0.143 0.333 get good results. A Rosemount Analytical, Inc.,[5] model 880a 1.06 0.099 0.558 infrared analyzer provides accurate and reliable measure- 1.58 0.064 0.634 ment of the CO2 composition of the gas phase at the column 2.11 0.039 0.712 entrance and exit. To measure a significant change in the gas phase composition, it is TABLE 2 best if the gas rate is low and the water rate Small Column Data and Results for CO2 Absorption From Air Using Water is high. Having a low gas rate also provides or 1 N NaOH at Room Temperature the benefit of consuming less CO2 (and air) Air Rate, A = 1.5 L/min and emitting less CO2 to the environment in both the exiting gas and water streams. yb = 0.175 (water) yb = 0.178 (NaOH)
Liquid Rate, W Outlet CO2 , yt Kya Outlet CO2, yt Kya To illustrate the advantage of combining 3 3 a chemical reaction with the absorption L/min mole fraction mol/m s mole fraction mol/m s process, we also built a small-scale column 0.14 0.168 0.237 0.062 2.96 for use with NaOH solution as absorbent. 0.23 0.165 0.285 0.050 3.69 A 1.75-in-diameter, 15-in-long acrylic tube 0.28 0.164 0.312 0.037 4.09 was filled to a height of 12.75 in with the 0.35 0.162 0.349 0.031 4.66 same glass rings used in our larger column. 0.40 0.161 0.375 0.027 5.07
TABLE 3 Heights of Transfer Units and Mass Transfer Coefficients for Large Column
Water Rate Hx Hy mGHx/L HOy kya kxa kxa/m Kya L/min m m m m mol/m3s mol/m3s mol/m3s 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
134 Chemical Engineering Education TRADITIONAL ANALYSIS A few years ago our old 30-foot-tall, If we neglect temperature and pressure effects and assume that CO2 only is experiencing mass transfer between the gas 6-inch-diameter, steel absorption and the liquid phases, traditional analysis leads to a design equation for our absorber given by[6]: tower became clogged with rust and
Z G yt dy residue from years of use with sodi- Zd==z 0 = HN ()1 ∫∫0 y 2 Oy Oy Kay b 1− yy− y ()()e um carbonate solution as absorbent where t and b represent top and bottom of the column, respec- for removing CO2 from air. tively, Z is the column height, y is the gas phase CO2 mole fraction, ye is the value of the gas phase CO2 mole fraction that would be in equilibrium with the liquid phase, Kya is the Although these correlations are not generally expected to give overall mass transfer coefficient based on the gas phase driv- accurate quantitative predictions, the correlated results for Kya ing force, G0 is the solute free gas flux, OyH is called the height are in reasonably good agreement with the experimentally of a transfer unit, and NOy is the number of transfer units. obtained results.
Neglecting details of reactions between CO2 and water and HOy, Hx, and Hy are often thought of as the overall, liquid any impurities we can describe the vapor liquid equilibrium side, and gas side resistance to mass transfer, respectively. with Henry’s law using Henry’s constant, H = 1420 atm at Confusion can result, however, when using these to explain 20 ˚C.[7] Since the height of the laboratory column is known, the water rate dependence of the mass transfer coefficient, experimental gas phase composition data can be used in because while Hx is larger than Hy, Hx is observed to increase Eq. (1) to solve for the mass transfer coefficient at various rather than decrease with increasing water rate. Apparently operating conditions. the term mGHx/L is the controlling factor here, but this still Integrating Eq. (1) is tedious since a mass balance in the doesn’t provide a clear physical explanation. form of an operating line equation must first be used to determine x at every value of y before Henry’s law can be SIMPLE MODEL used to find ye at each x that corresponds to each y. This has Our simple absorber model uses COMSOL Multiphysics to traditionally been done by plotting the operating line and the solve two instances of the convection and diffusion equation equilibrium line and then graphically integrating Eq. (1). Mod- simultaneously with appropriate boundary conditions in a ern computing environments like MATLABTM can be used to cylinder with the dimensions of our column: integrate this equation and back out mass transfer coefficients ∇−i()Dc∇ =−Rui∇c(4) from laboratory data as shown in Appendix 1. Results for K a y obtained by this method are given in Table 1 and these can R represents a reaction or source term and u is the velocity be seen to increase with increasing water rate. vector in the convection term. One instance of Eq. (4) evalu-
The traditional analysis doesn’t give much insight into the ates the concentration of solute in the gas phase, cg, and the details of the mass transfer process or the physical reason the other instance evaluates the concentration of solute in the mass transfer improves with increasing water rate. To obtain liquid phase, cl. In the simple model, we included a mass that insight, students are directed to textbooks for an expla- transfer term as a “reaction” and consider that solute leaving nation of the two-film theory of Whitman[8] where they learn the gas phase by this “reaction” enters the liquid phase by a that the overall resistance to mass transfer can be considered similar mass transfer “reaction.” For the gas phase, the mass to be made of a gas phase film resistance and a liquid phase transfer “reaction” was written as film resistance: RK=− yeay()15− ()yy− () G HH=+m H ()2 2 Oy yxL The quantity (1-y) accounts for part of the (1-y) term in Eq. (1) while the other part is accounted for by setting the or equivalently, gas velocity in the z-direction to vg = vg0 / (1-y). Thus, the 11m changing gas velocity along the length of the column is eas- =+ ()3 Ka ka ka ily taken into account. This treatment was not needed for the yyx liquid phase because the small amount of solute dissolved in where m is the slope of the equilibrium line, equal to the Hen- the liquid had a negligible effect on the liquid velocity. [7] ry’s constant here. Geankoplis gives correlations for Hx and The absorber can be modeled equally well in 1-D, 2-D,
Hy and the results of these correlations are given in Table 3. or 3-D, but we prefer the 2-D axial symmetric implementa-
Vol. 45, No. 2, Spring 2011 135 tion because it gives the best visual representation of our include variable mass transfer coefficients, multiple solutes, process. One of the important advantages of the powerful temperature and pressure effects, and even time dependence, modern computing environments is that there is usually but these modifications were not needed here. We have in- no need for transformation or scaling of variables; we cluded the effect of the chemical reaction between NaOH can work with the actual dimensions of the equipment and CO2, however. and with SI dimensioned variables. This what-you-see- is-what-you-get philosophy is aimed at making a strong MODEL WITH REACTION connection between the equations and the physical process The chemical reaction between CO2 and NaOH is well and appealing to visual learners. studied and according to the literature[10] the rate limiting The model results can be presented in a variety of ways step in this reaction is: including a colorful surface plot of y within the column −− CO23+→OH HCO ()6 geometry (not shown here) and plots of y and x vs. column height as shown in Figure 1. As an example of the wealth of and the rate of reaction can be expressed as: information readily obtained from the model, it is of interest rk= CC− ()7 to note that only three of the four experimentally obtained BCO2 OH Kya results in Table 1 follow the expected trend of a linear with second order rate constant given as a function of ionic [6] function of water rate raised to the 0.7 power. At first, we strength by rationalized that the reason the first point, at the lowest water log.kT=−11 875 2382 /.+−0 221 II0.(016 2 8) rate, did not follow the expected trend may have been chan- ()B neling or poor wetting of the packing at this water rate. When 3 3 we observed the liquid phase mole fraction, x, as a function where kB is in m /kmol s, T is in K, and I is in kmol/m . The of column height in our model for this run, however, we saw ionic strength is calculated as that the liquid was essentially saturated before reaching the IC=+05.(+−CC+ 49− ) ()Na OH HCO3 column outlet. Thus, the experimental outlet results can be modeled using a wide range of Kya values including the value Our absorber model was easily modified to account for this of 0.333 mol/m3s that we obtained earlier but also the value chemical reaction by writing the “reaction” term for CO2 in 3 of 0.480 mol/m s that would fall in line with our other results the liquid phase as 0.7 in a correlation of Kya vs. (W) . RK=−ay yk− CC− ()10 ye()BCO2 OH Here we have used our model to calculate the outlet con- centrations that will occur in the column given an overall indicating that CO2 arrives at the liquid phase from the gas mass transfer coefficient. We could just as easily have used phase by mass transfer and disappears from the liquid phase the built-in Parametric Solver capability of COMSOL to by reaction. This model also keeps track of the ions, Na+, – – find the values of the mass transfer coefficients that fit our OH , and HCO3 , by solving Eq. (4) for each species in the experimental data. Our model could be easily modified to liquid phase.
Figure 1. Mole fraction CO2 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 3 Kya = 0.480, lower (a) curve is for Kya = 0.333 mol/m s. 136 Chemical Engineering Education The Parametric Solver in COMSOL was used to find the the column. The water layer around each rod was considered values of Kya needed to make the outlet y results of the model to flow downward in laminar flow and the gas layer around match the experimental y results. The resulting Kya values that was considered to flow upward in plug flow. The thick- are shown in Table 2. The dramatic improvement in the mass ness and velocities in these flowing layers were selected to transfer process due to the reaction is reflected in the increase give approximate results that illustrate our points. It was only in Kya with reaction compared to without. necessary to model one rod with its surrounding layers axially symmetrically as shown in Figure 2. QUALITATIVE FALLING FILM MODEL As before, two instances of the convection and diffusion Although our simple absorber model is easier to use than equation, one for the gas phase and one for the liquid phase, the traditional analysis and has the added benefit of showing were solved simultaneously. The inlet and outlet boundary a colorful representation of the composition in the column, it conditions are shown in Figure 2. The so-called “stiff-spring” doesn’t given much insight into the details of the process or equilibrium boundary condition[11] was used at the gas-liquid help explain why the mass transfer coefficients increase with interface according to Henry’s law. That is, the boundary increasing water flow rate. The physical process that actu- condition on the gas side of the interface was set to ally occurs inside the column is that solute diffuses through Flux =−My− y ()11 a flowing gas phase to the gas-liquid interface, crosses the ()e interface to maintain equilibrium there, and diffuses into a and the boundary condition on the liquid side was set to flowing liquid phase. To model this process more directly we should solve Eq. (4) with R = 0 and use the actual diffusion Flux =−My()ye ()12 coefficients in the gas and liquid phases and an appropriate boundary condition at the interface. We describe here a quali- where M is an arbitrary large number; e.g., M = 10000. This tative diffusion-based falling film model aimed at addressing assures a continuous flux across the interface and enforces these concerns and providing a basis for understanding an the equilibrium condition ye = H x. Mass transfer coefficients explicit two-film model presented below. were not used in this diffusion-based model. Instead, carbon Inside our packed column are glass rings that have a thin dioxide diffuses through the gas phase, crosses the interface, layer of water flowing down over them surrounded by gas and diffuses into the liquid phase according to molecular flowing upward. Although it can be done, it is complicated diffusion using diffusivities for CO2 in air and water of 1.6 and expensive in computer time to model the exact details of 3 10-5 m2/s and 1.8 3 10-9 m2/s, respectively. The velocity the fluid flow and mass transfer that takes place around these profile in the liquid phase was given by the solution to the rings randomly packed inside the column. As an illustration, built-in Incompressible Navier-Stokes mode of COMSOL. however, it was reasonable to approximate the process with a The velocity in the gas phase was considered uniform in the number of identical glass rods each extending the full height of r-direction but decreased as vg0 / (1-y) in the z-direction.
Figure 2. Falling film model geometry.
Vol. 45, No. 2, Spring 2011 137 Figure 3 shows the resulting CO2 concentration profile in tion. On the other hand, the liquid phase concentration varies the r-direction at a height equal to Z/10 for two different water in the r-direction and can be characterized as having a rapidly velocities. Curve a is for a relatively low water rate and the changing region close to the interface and a nearly constant re- curve b is for a relatively high one. More CO2 is removed from gion in the bulk. The region where the concentration changes the gas phase at the high water rate as expected. In both cases, is often called the concentration boundary layer.[12] Figure 3 the gas phase concentration is nearly uniform in the r-direc- 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. Our qualitative falling film model was also modified to account for the chemical reaction. In this case, R in the liquid phase was given by Eq. (7). The resulting
CO2 concentration profile shown in Figure 3c indi- 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 Our falling film model illustrates the diffusion and 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 Figure 3. Concentration in the r-direction at z/Z = 0.1 for qualitative packing and flow patterns in the column. We describe falling film model (a) low water rate, (b) high water rate, (c) NaOH here an explicit two-film model that gives accurate solution rate equal to water rate in (a). Note that the x-axis begins at outlet compositions, illustrates the two-film theory, r = 0.005 m to show only the flowing layers in this figure. and provides a physical interpretation of the mass transfer coefficient. The mass transfer coefficient 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.[6] As shown above, this approach describes absorp- tion results well, but doesn’t 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 across a stagnant 1-D film. The steady state flux across a 1-D film is given by Fick’s law: D Flux =∆c ()13 l Figure 4. Model geometry showing two-film theory. where l is the film thickness and
138 Chemical Engineering Education Δc is the concentration difference across the film. The mass values of the individual mass transfer coefficients, kya and kxa, transfer coefficient was defined to give a similar simple equa- accounting for the interfacial area per volume, a, as a separate tion for the flux for more complex situations: component of kya and kxa, and some unit conversions.
Flux =∆kcc ()14 From Table 3 it can be observed that 1/kya is a minor con-
tributor to 1/Kya in Eq. (3), for this system. We have, therefore, One way to understand what the mass transfer coefficient chosen to assume that the correlated values of kya shown in represents is to compare Eqs. (13) and (14) and let Table 3 are correct, knowing that uncertainties in these values D will not have a strong effect on our subsequent results and k = ()15 c δ interpretations. With this assumption, kxa could be calculated from Eq. (3) using the previously obtained experimentally where δ is some equivalent stagnant film (or concentration derived values of Kya at each liquid flow rate. The resulting boundary layer) thickness that can be viewed as controlling values for kxa are given in Table 4 (next page). For our model, 2 3 (providing resistance to) the mass transfer in a complex situ- the interfacial area per volume is 2πRiZNR/V = 667 m /m . ation. Note that kc has units of m/s. Ri is the radius of the model at the interface and NR is the To introduce the two-film concept into our diffusion-based number of glass rods. Taking into account unit conversions between cg and y and cl and x yields the following equations model we could incorporate a stagnant film (with gv or vl = 0) of the appropriate thickness on each side of the interface and for effective diffusivities in the gas and liquid films. use Eq. (4) (with R = 0 and D = D or D ) over those films. 3 g l kayf()tmilm ()8./314 Pa molK Alternatively, and equivalently, we have used an effective dif- Dg = ()(17 eff aP101325 a fusivity acting over an arbitrarily established film thickness, () tfilm, instead of the actual diffusivity over a film thickness, δ, 3 kaxf()tcilm ()1000 mL/ that would need to be adjusted to fit each data point: D = ()18 leff 3 D D am()55./556 ol L ()100cm / m = eff ()16 δ tfilm where tfilm is the thickness of the stagnant gas and liquid films Figure 4 shows the geometry and boundary conditions for used in the model (arbitrarily set to 0.001 m). our two-film model based on this effective diffusivity ap- Note that although we have used mass transfer coefficients proach. The appropriate resistance to mass transfer in each in defining our effective diffusivities, our two-film model does film has been established by setting the effective diffusivity not use the mass transfer coefficient approach but instead de- in the r-direction of the film to be equal to the individual mass scribes mass transfer as governed only by molecular diffusion transfer coefficient times the film thickness. Obtaining appro- through stagnant films, equilibrium at the interface, and con- priate values for the effective diffusivities requires estimating vection in the flowing layers (assumed to be in plug flow). We have also artificially increased the diffusivities in the r-direction in the two flowing layers of 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 the arbitrarily chosen flowing layer and film thicknesses and associated number of glass rods of our model. Solving our explicit two-film model gives 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 fluid layers in this figure.
Vol. 45, No. 2, Spring 2011 139 TABLE 4 Mass Transfer Coefficients and Film Thicknesses (*adjusted to saturation at liquid outlet). Water K a H k a k a k δ δ k k Rate, W y y y x x l g cl cg mol/m3s m mol/m3s mol/m3s mol/m2s m 3 105 m 3 102 m/s 3 104 m/s 3 104 L/min Large No Column 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 No Column 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 With Column 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 centration across the various layers at various heights in the stagnant film thicknesses are fictitious constructs of the film column a student can observe the resistance to mass transfer theory and subject to the assumptions in our model, the esti- in each of the films as well as the concentration difference mated film thicknesses can be seen to decrease with increas- imposed by equilibrium at the interface. More resistance is ing water rate, thus providing a physical explanation for the indicated by a larger concentration change. In this system, it observed dependence of mass transfer on water flow rate. can be seen that the liquid phase offers considerably more Our explicit two-film model can also be used to provide resistance than the gas phase. more insight into the difference between absorption with and
From Table 4 we see that kxa increases with increasing without reaction. To include the chemical reaction, we initially water rate. This could be due to either kx increasing or the used Eq. (7) in the flowing liquid layer only. The resulting interfacial area, a, increasing or both. The interfacial area concentration profiles at various heights in the small column probably does increase with increasing water rate because with and without reaction are shown in Figure 6. For the case more of the packing is wetted and the flowing liquid layer may with no reaction, in Figure 6a, it can be seen that the liquid also be thicker. If we assume, however, that a is constant as side resistance dominates the process. For the reaction case, we have done in our model, we can see that kx increases with shown in Figure 6b, the concentration in the flowing liquid increasing water rate. What physical process can account for is essentially zero everywhere providing a consistently high this? As shown above, kc (and with unit conversions kx) can driving force for mass transfer and preventing saturation of be assumed to be equal to the molecular diffusivity divided the liquid even at low liquid rates. It can also be seen that the by the stagnant film thickness. Since we used an arbitrary resistance in the gas phase is comparable to the resistance in film thickness, tfilm, for convenience in our model, an estimate the liquid phase when reaction is present. of the stagnant liquid film thickness in our absorber can be Estimates of the effective film thicknesses in the small obtained by solving Eq. (16) for δl. column obtained from Eq. (16) are given in Table 4. In ac- Results for this stagnant film (or concentration boundary cordance with our qualitative falling film model, it can be layer) thickness estimated by this approach are given in Table seen that the chemical reaction has the effect of dramatically 4 at each of the absorbent flow rates studied. Even though the reducing the liquid film thickness. The fact that the gas film 140 Chemical Engineering Education Figure 6. Concentration profile 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 fluid layers in this figure. thicknesses are much larger the interface. In that case, than the liquid film thick- all the resistance to mass nesses can be explained by transfer would be in the gas the fact that the gas phase dif- film and the individual gas fusivity is much larger than film mass transfer coefficient that in the liquid phase and would be equal to the overall does not imply that the gas mass transfer coefficient. We film offers more resistance modeled that scenario in our than the liquid film. To gain two-film model by setting kya more insight into the resis- equal to the Kya values shown tance offered by each phase in Table 3 and setting the ef- it is instructive to compare fective diffusivity in the r-di- the kc values. These values rection in our liquid film to an have been calculated from artificially large number. The Eq. (15) using film thick- resulting concentration profile nesses reported in Table 4, shown in Figure 7 gives gas but it would be equivalent phase concentrations similar to calculate them from the Figure 7. Concentration profile for W = 0.35 in the small to those in Figure 6b. It is column at z/Z = 0, 0.25, 0.5, 0.75, 1.0 with reaction in the kya and kxa values using ap- possible that Figure 7 is more propriate unit conversions. liquid and all mass transfer resistance in the gas film. Note representative of reality than The resulting values of k that the x-axis begins at r = 0.005 m to show only the fluid Figure 6b because the k a cl layers in this figure. y and kcg shown in Table 4, values used to obtain 6b came tell a similar story to the from a correlation and are not one represented visually in Figure 6. Without reaction, kcl necessarily correct. Figure 3 obtained from our qualitative is smaller than kcg indicating that the liquid phase is the model suggests that Figure 6b with a small but extant liquid controlling resistance. With reaction, the values of kcl and kcg film might be more realistic than Figure 7, however. are comparable to one another indicating that the gas phase resistance plays a significant role. IMPLEMENTATION AND EVALUATION In the analysis above, we considered the stagnant liquid In our unit operations lab, students spend about two weeks film to account for resistance due to diffusion into the liq- on each experiment. Groups of three or four students first uid phase separately from the reaction taking place almost collaborate on writing a pre-lab report describing the relevant instantaneously in the flowing liquid layer. Another way to theory and their plans to conduct the experiment. For the analyze this type of fast reaction process is to consider that absorber lab, the groups then spend two days of lab work there is no liquid film (or no resistance in the liquid film) collecting data that they analyze and include in a final report. since the reaction can take place as soon as the solute crosses It was disappointing, but revealing, that very few students Vol. 45, No. 2, Spring 2011 141 bothered to use the simulations the first year they TABLE 5 were offered as a completely optional resource. In the Results for Three Survey Questions second offering, we required each student to complete The percentage of students giving each response is indicated in brackets. an interactive tutorial containing the simulations and (1) The learning tool helped me to understand mass transfer, in general: an associated online quiz that asked questions about (a) not at all [13%], (b) just a little [13%], (c) somewhat [40%], (d) much [27%], them. At the end of the course that year, the students (e) very much [7%]. completed a survey regarding their perception of the (2) It helped me understand how the mass transfer coefficient varies with water benefits of using the simulations. flow rate: (a) not at all [7%], (b) just a little [7%], (c) somewhat [20%], (d) much [53%], Students in the course did not build the simulations (e) very much [13%]. from scratch but instead re-ran previously developed (3) The best time to use this learning tool would be: (a) as a homework before simulations with different operating conditions. The the pre-lab and in addition to a written pre-lab report [47%], tutorial walked the students through the pre-built (b) at the pre-lab stage instead of a written pre-lab report [27%], simulations and included several multiple-choice (c) after a written pre-lab and the lab itself are complete, as an aid to writing a good final report [13%], questions requiring simulation results to obtain cor- (d) after a written pre-lab and the lab itself are complete, to be used instead of a rect answers. For example, one question asked for final report [0%], (e) not necessary for the average student to spend time on this at any point [13%] the numerical value of the mole fraction of CO2 in the exiting liquid stream according to the simulation under certain conditions. Another question asked for TABLE 6 the value that would be obtained if the process were Example Student Comments About the Absorber Simulation considered dilute with straight equilibrium and oper- • “it allowed me to visualize the diffusion of gas into the liquid” ating lines. In addition to answering these questions, • “it allowed me to see the connection between the theoretical equation and how students were encouraged to experiment with chang- they relate to the physical world” ing operating conditions to see the effect on column • “being able to adjust the values and quickly observing changes in the system performance. Students were invited to study the makes for a nice learning tool” simulations and answer the multiple choice questions • “I would not have remembered as much about mass transfer if I didn’t have it” on their own time and at their own pace. They were • “really helped me visualize what is occurring and then linking the theoretical encouraged to study the simulations before complet- values to what is found experimentally, and why it may vary” ing their pre-lab reports but were required to submit • “It allowed me to understand how changing variables could affect the final the answers to the multiple choice questions on-line resistance to mass transfer. By doing this as a simulation, it was easier to see after the pre-lab was completed and before the final relationships compared to just looking at equations.” report was due. It should be noted that these students • “the ability to change variables and investigate their effects on mass transfer were not necessarily COMSOL model builders but did helped provide a greater understanding of mass transfer principles” have some familiarity with COMSOL from previous • “it basically showed me what the lab would be like … and prepared me for the homework assignments using pre-built simulations experiment in an excellent way” via tutorials and online questions. • “It helps you visualize the process and makes it easier for you to make a mis- take and rectify it without wasting much time in the lab. And you can also change The end of course survey revealed that most, but not constants to see the effect of each on mass transfer.” all, of the students found the simulations to be useful, particularly for illustrating the resistance to mass transfer and cess. More detailed models that illustrate the concentration providing a physical feel for why the mass transfer coefficient boundary layer and the two-film theory provide a physical increases with increasing water rate. Table 5 shows example feel for the observed increase in the mass transfer coefficient questions and the percent of students responding to each of the with an increase in water rate. These models also make it multiple choice answers for each question. Table 6 provides clear that the improved mass transfer with reaction is due to examples of student comments on the absorber simulations. reduced resistance in the liquid phase as well as maintaining a high driving force and preventing saturation of the liquid. CONCLUSION The straightforward and relatively easily obtained solutions Our new absorption experiment provides an effective way together with the richness of information afforded by post of teaching mass transfer fundamentals while using relatively processing capabilities in COMSOL can make the details of small amounts of CO2, air, and water. Experiments presented complex process calculations “come alive” in comparison to with NaOH as absorbent provide a good demonstration of the the rare, static, printed examples in text books. Combining dramatic improvement in absorption due to reaction. A simple the experiments with computer simulations that show the model made with COMSOL Multiphysics gives accurate concentration profile within the equipment appears to benefit calculations, is easier to use than the traditional analysis, the learning process and help students gain a more complete and provides a visual representation of the absorption pro- understanding of mass transfer in an absorber.
142 Chemical Engineering Education ACKNOWLEDGMENTS gas phase % mass transfer coefficient, Kya, and the HTU and This material is based on work supported by the National NTU for an absorber Science Foundation under grant no. DUE-0536342. % input is Z, packing height (m); S, cross sec- tional area (m^2); REFERENCES % L0, liquid flux, (mol/m2s), G0, non absorbing 1. Clark, W.M., and D. DiBiasio, “Computer Simulation of Laboratory gas flux (mol/m2s); Experiments for Enhanced Learning,” Proceedings of the ASEE Annual % yb, inlet gas mole fraction solute; yt, outlet Conference, Honolulu, Hawaii, June 24-27, (2007) gas mole fraction solute. 2. Clark, W.M., “COMSOL Multiphysics Models for Teaching Chemical % inlet liquid is assumed pure solvent Engineering Fundamentals: Absorption Column Models and Illustra- % outlet liquid xb is obtained from mass balance tion of the Two-Film Theory of Mass Transfer,” COMSOL Conference % ye = ystar = H*x 2008 Proceedings, Boston, October (2008) % Kya is in mol/m^3h 3. Finlayson, B.E., Introduction to Chemical Engineering Computing, global L0 G0 xb yb yt H Wiley-Interscience, Hoboken, NJ, (2006) H=1420; 4.
Vol. 45, No. 2, Spring 2011 143 ChE class and home problems
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 be accompanied by the originals of any figures or photographs. Please submit them to Dr. Daina Briedis (e-mail: [email protected]), Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824-1226.
OPTIMIZATION PROBLEMS
Brian J. Anderson, Robin S. Hissam, Joseph A. Shaeiwitz, and Richard Turton West Virginia University • Morgantown, WV 26506-6102 ptimization is often considered to be an advanced, economic optimum pipe diameter[6] and reflux ratio[7] are highly mathematical, and sometimes a somewhat also available. Other examples of optimization problems obscure discipline. While it is true that many ad- are available, but these do not involve an economic objec- Ovanced optimization techniques exist, optimization problems tive function.[8-10] The problems presented here all involve can be developed that are suitable for undergraduates at all an economic objective function. levels. Two of these problems will be described in this paper, and many others are available on the web.[1] A pedagogy is TYPES OF PROBLEMS described that requires students to identify the trends of the Three types of optimization problems are available, and components of the objective function and to understand how they are summarized in Table 1. The ones highlighted in ital- trade-offs between these components lead to the existence ics are discussed in this paper, and the others are available on [1] of the optimum. the web. The numbers in parenthesis indicate the number of different versions available for each problem. All of these The ability to solve “routine” optimization problems has have been used successfully in a freshman class designed to been simplified by advances in computing power over the develop computing skills appropriate for an undergraduate last generation. Earlier editions of current design textbooks[2] chemical engineering student. Most of these problems would presented a sequence of optimization techniques aimed at also be suitable for assignments or projects in unit operations minimizing the number of cases that had to be considered to close in on the optimum. Now, it is possible to perform TABLE 1 optimization calculations involving numerous cases with a Available Optimization Problems few clicks of a mouse, and an entire chemical process can be Single Multi-variable Projects simulated and results exported to a spreadsheet in a matter Variable of minutes. Pipe diam- Absorber Generic chemical Several optimization examples are routinely discussed eter (2) process (2) in undergraduate textbooks; however, the objective func- Reactor/ Batch reactor/pre- Geothermal energy tion does not usually involve economics. These examples preheater heater (2) include optimum interstage compressor pressure,[3] optimum (2) insulation thickness,[4] and identifying conditions for the Reflux ratio Staged compressors Fuel production from biomass (4) optimum selectivity.[5] Qualitative representations of the
Brian J. Anderson is the Verl Purdy Faculty Fellow and an assistant professor in Joseph A. Shaeiwitz received his B.S. degree from the University of Delaware the Department of Chemical Engineering at West Virginia University. His research and his M.S. and Ph.D. degrees from Carnegie Mellon University. His profes- experience includes sustainable energy and development, economic modeling sional interests are in design, design education, and outcomes assessment. Joe of energy systems, and geothermal energy development as well as molecular is a co-author of the text Analysis, Synthesis, and Design of Chemical Processes and reservoir modeling. (3rd Ed.), published by Prentice Hall in 2009. Robin S. Hissam received her B.S. and M.S. degrees in materials science and Richard Turton, P.E., has taught the senior design course at West Virginia engineering from Virginia Tech and her Ph.D. in materials science and engineer- University for the past 24 years. Prior to this, he spent five years in the design ing from the University of Delaware. After a post-doctoral fellowship in chemical and construction industry. His main interests are in design education, particulate engineering and applied chemistry at the University of Toronto, Robin joined the processing, and modeling of advanced energy processes. Richard is a co-author Chemical Engineering Department at West Virginia University. Her research is of the text Analysis, Synthesis, and Design of Chemical Processes (3rd Ed.), in production of protein polymers for application in tissue engineering, biomin- published by Prentice Hall in 2009. eralization, and biosensors. © Copyright ChE Division of ASEE 2011
144 Chemical Engineering Education classes or as problem assignments for the portion of a design class where optimization is taught. 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 Figure 1. Process flow diagram of the feed preheater sensitive, the maximum operating temperature in the reactor and bioreactor. is limited to 65 ˚C by using a heating medium available at this maximum temperature. The feed material is fed to the reactor The design equation for the heat exchanger is given by: through a heat exchanger that can increase the temperature of QM= CT−−TM= CT T = UAF∆T ()4 the reactants (contents of the reactor), which in turn increases cp,,cc()21ch,,ph()hh,,12 llm the rate of the reaction. This is illustrated in Figure 1. The where time spent in the bioreactor (known as the space time) must be adjusted to obtain the desired conversion of reactant. As the ()TT− −−()TT ∆=T hc,,21 hc,,12 ()5 temperature in the reactor increases so does the reaction rate, lm ()TT− thereby decreasing the size (and cost) of the reactor required ln hc,,21 − to give the desired conversion. The problem to be solved is ()Th, 12Tc, to determine the optimal value for the single independent and variable; namely, the temperature (Tc,2) at which to maintain the reactor (preheat the feed). The costs to be considered are F = log-mean temperature correction factor = 0.8 (assume the purchase costs of the reactor and heat exchanger and the that this is constant for all cases) operating cost for the energy to heat the feed. U = overall heat transfer coefficient = 400 W/m2K Problem Statement The optimum reactor inlet temperature is the one that It is desired to optimize the preheat temperature for a re- minimizes the equivalent annual operating cost (EAOC). The actant feed flow of 5,000 gal/h. The feed has the properties EAOC is given by ρ 3 2 of water ( = 1,000 kg/m , Cp = 4.18 kJ/kg ˚C) and enters EAOC $/yP = CA$/ Pi,,ny1/ + UC $/y ()6 the heat exchanger at a temperature of 20 ˚C. The reactor ∑ i () i=1 feed is to be heated with a heating medium that is available at a temperature of 65 ˚C and must leave the heat exchanger where PCi are the purchase equipment costs for the heat at 30 ˚C. Therefore, the desired reactor inlet temperature is exchanger and reactor, UC is the operating (utility) cost for adjusted by changing the flowrate of the heating medium. the heating medium, and (A/P, i, n) is the capital recovery The physical properties of the heating medium are ρ = 920 factor given by 3 kg/m , C = 2.2 kJ/kg ˚C. n p ii()1+ The reaction rate for this reaction, –rA, is given in terms of the = ()AP/,in,(n 7) concentration of reactant A (CA) by the following equation: ()11+ i −
−=rkAAC ()1 For this problem, use i = 7% and n = 12 years. where The purchase cost of the reactor is given by: 08. 5 −1 3,500 PC = $,17 000V ()8 ks =−25.exp ()2) reactor TK 3 where V is the volume of the reactor in m . The cost of the The design equation for the reactor is given by: heat exchanger is: 05. 7 2 vXoA PC = $,12 000 Am ()9 V = ()3 exchanger {} kX()1− A where A is the area of the heat exchanger in m2. The cost of 3 where V is the reactor volume (m ), vo is the volumetric flow- the heating medium is: rate of fluid into the reactor (m3/s), and X is the conversion A UC $/hQ =×$/51016 kJ h ()0 (assumed to be 80% or 0.8 for this reaction).
Vol. 45, No. 2, Spring 2011 145 The results should be presented as two plots. The first should by the following equation: show how each term in Eq. (6) changes with Tc,2, and the −=rkC ()11 second plot should show the EAOC (y-axis) as a function of AA
Tc,2 (x-axis). The report should contain a physical explanation where of the reason for the trends on these plots. −1 3,500 ks =−25.exp (212) Problem 2: Batch Bioreactor TK Background A liquid-phase, biological reaction is used to produce an The design equation for the reactor is given by: intermediate chemical for use in the biotech industry. The 11 ts = ln ()13 reaction occurs in a large, well-stirred, isothermal bioreac- −1 1− X ks A tor, such that the reactor temperature is identical to the inlet temperature. Because this chemical is temperature sensitive, where t is the time spent in the reactor and XA is the fractional the maximum operating temperature in the reactor is set to conversion of reactants to products. The amount of product 55 ˚C. The feed material is fed to the reactor through a heat formed in time t is given as NXA, where N is the number of exchanger that increases the temperature of the reactants moles of reactant fed to the reactor. (contents of the reactor), which in turn increases the rate of The energy balance equation for the heat exchanger is the reaction. This is illustrated in Figure 2. given by: The reactor runs as a batch operation in which the contents QM=−cpCT,,cc()21TMch,,=−CTph()hh,,12T ()14 remain in the equipment for a given period of time. The time spent in the bioreactor must be adjusted to obtain the optimal where conversion of reactant. Because of the fear of contamination M is the mass of fluid to be heated or cooled (kg) by pathogens and parasitic fungi, the reactor must be cleaned Cp is the specific heat capacity of the fluid (kJ/kg ˚C) thoroughly between batch operations. The cleaning time per batch (tclean) and the cost of cleaning both vary based on the size of the reactor used. 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 reactor size. For this problem, it is assumed that only standard size Figure 2. Process flow diagram of feed preheater and bioreactor. vessels are available (1,000, 5,000, or 10,000 gallons), and that the costs of the feed are fixed. Therefore, the costs that vary are the revenues from sales, the reactor cost, and the cost for cleaning.
Problem Statement It is desired to optimize the production of product from the reactor. The feed has the properties of water ( ρ = 1,000 kg/m3, Cp = 4.18 kJ/kg ˚C) and enters the heat exchanger at a temperature of 20 ˚C. The reactor feed is to be heated with a heating medium that is avail- able at a temperature of 65 ˚C and must leave the heat exchanger at 30 ˚C. The desired reactor inlet temperature is fixed at 55 ˚C. The physical properties of the heating medium are ρ = 920 3 kg/m , Cp = 2.2 kJ/kg ˚C.
The reaction rate for this reaction, -rA, is given in terms of the concentration of reactant A (CA) Figure 3. Optimization plot for Example 1.
146 Chemical Engineering Education T is the temperature (˚C) The ability to solve “routine” 1 and 2 refer to inlet and outlet conditions, respectively. h and c refer to the hot and cold stream, respectively. optimization problems The optimal reactor configuration is the one that minimizes the equivalent has been simplified by annual operating cost (EAOC). The EAOC is given by: 2 3 advances in computing power EAOC $/yP = CA$/ Pi,,ny1/ + UC $/yR − $/y ()15 ∑ i () ∑ i i=1 i1= over the last generation. where PC are the purchase equipment costs for the heat exchanger and reactor; i The final results should be presented as two UCi are the operating (utility) costs for the heating medium, the cost of the feed stream, and the cost of cleaning; and R is the revenue from sales of the plots. The first plot should show how each product. For this problem, use i = 0.07 and n = 12 years. term in Eq. (15) changes with the batch time, t, and the second plot should show the EAOC The purchase cost of the reactor is given by (y- axis) as a function of t (x-axis). The report = 08. 5 PCreactor $,17 000V ()16 should contain a physical explanation of the reason for the trends on these plots. where V is the volume of the reactor in m3. The cost of the heat exchanger may be taken to be equal to 20% of the cost of the reactor from Eq. (16). OPTIMIZATION PROBLEMS The cost of the heating medium is given by: In Problem 1, the optimum reactor feed UC $/hQ =×5101−6 kJ /(h 7) heating temperature is to be determined. There is a trade-off, which is necessary to obtain where Q is the heat duty obtained from Eq. (14). an absolute maximum or minimum in the The price of the feed is $2/mol, the value of the product is $10/mol, and the 3 objective function (EAOC) as the decision molar density (concentration) of both feed and product is 100 mol/m . The variable (reactor feed temperature) varies. cost of cleaning the reactor is given by In this case, at higher temperatures, it costs Vgreactor al more to heat the reactor feed, but, since the UC $/cleaningc =1,$000 / leaning10+ .5 ()18 clean reaction rate increases with temperature, the 1,000 gal reactor cost is lower because a smaller reac- and the time to clean a reactor is tor is needed. Additionally, at higher reactor feed temperatures, a larger heat exchanger is Vg al reactor needed. Students can develop a spreadsheet th = 41h + 05. ()19 clean that varies the reactor inlet temperature and 1,000 gal 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 heat exchanger cost goes to infinity as the reactor feed temperature ap- proaches the heating medium inlet temperature, causing the log-mean temperature driving force to go to zero and the heat exchanger area to become infinite. This is an example of why it is important for students to analyze a series of data and un- derstand the trends. It is possible to solve this entire problem on Excel using the Solver tool; however, Figure 4. Component optimization trends in Problem 1. much of the understanding/synthesis
Vol. 45, No. 2, Spring 2011 147 Since these problems have been used components lead to the existence of the optimum. That is why methods, such as using the Excel Solver, are not emphasized, successfully in a freshman class for and making plots to investigate trends is emphasized. Once the trends are understood, Excel Solver can be used to obtain several years, we believe they can be a more exact value of the optimum. We have used these problems as part of a freshman class used anywhere in the curriculum. taken by students who know that they are interested in chemi- cal engineering. Other students take a college-wide program- of the problem is lost. We believe that optimization is more ming class. In our class, students are taught computer skills than finding an answer. An understanding of the underlying applicable to chemical engineering, mostly using the advanced trends is essential. features of Excel in addition to some elementary program- ming techniques and algorithms. All assignments are based on It is also possible to illustrate how changes in operating con- industrially relevant chemical engineering problems. Some of ditions change the optimum. In a problem similar to Problem [11] these problems also appear in the optimization chapter of our 1, if the reaction kinetics are increased (pre-exponential textbook.[11] Since these problems have been used successfully factor increased to 7.0 and the activation energy reduced to in a freshman class for several years, we believe they can be 3300), the optimum temperature shifts down to about 35 ˚C. used anywhere in the curriculum. Many different versions of this and other problems can be created by changing some parameters or by changing the Since all students in chemical engineering do not take the economics. We use different versions of these for different class in which these problems are assigned, assessment of groups in the same class. During oral presentations, we ask their long-term impact is difficult. The freshmen do a good them to explain why the optima differ. job on these problems, and they seem to appreciate the actual chemical engineering application compared to their peers in In Problem 2, there are two decision variables (bivariate the programming class. optimization) due to the batch processing. Therefore, this problem in slightly more complex than Problem 1, and it il- Additional optimization problems are available on the [1] lustrates that there may be more than one decision variable. web. It is observed that virtually an infinite source of these One decision variable is the reactor volume, which in this problems could be obtained by manipulating some of the case is limited to three standard sizes (an arbitrary number), values given in these problems. and the other decision variable is the processing time. The trade-off is that for longer processing times, more product is CONCLUSION made, but fewer batches can be made per year. For a larger Two example optimization problems that are believed to be reactor, more product can be made per batch, but fewer suitable for all levels of chemical engineering students have batches can be made per year due to the longer cleaning been presented. These problems do not require advanced time. Although this problem does not include it, the reactor mathematical techniques; they can be solved using typical feed temperature could also be varied, as in Problem 1, to software used by students and practitioners, such as Excel. create a three-variable optimization. In this problem, it turns These problems involve an economic objective function with 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- variate optimization plot, with the x-axis containing one decision variable with several curves indicating the second decision variable.
DISCUSSION We believe that an important part of the pedagogy of optimization is for students to understand the trends of the components of the objective function and to understand how trade-offs between these Figure 5. Optimization plot for Example 2.
148 Chemical Engineering Education component capital and operating cost terms. An important Ed.), Prentice Hall PTR, Upper Saddle River, NJ, 2003, Chapter 4.3F part of the pedagogy of these problems is an understanding 5. Fogler, H.S., Elements of Chemical Reaction Engineering (4th Ed.), Prentice Hall PTR, Upper Saddle River, NJ, 2006, Chapter 6 of how the trends of the components terms in the objective 6. de Nevers, N., Fluid Mechanics for Chemical Engineers (3rd Ed.), function contribute to the trade-off involved in most optimi- McGraw Hill, New York, 2005, Chapter 6 zation problems. 7. Peters, M.S., K.D. Timmerhaus, and R.E. West, Plant Design and Economics for Chemical Engineers, (4th Ed.), McGraw Hill, New York, 2003, Chapter 9 REFERENCES 8. Barolo, M., “Batch Distillation Optimization Made Easy,” Chem. Eng. 1.
Vol. 45, No. 2, Spring 2011 149 ChE department
ChE at... The University of Houston
Michael P. Harold and Ramanan Krishnamoorti hemical engineering at the Uni- versity of Houston has reflected the growth and diversification of Cthe field: from traditional petrochemicals 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 become a private university and finally becoming a part of the State of Texas system in 1963. In 1953 UH gained na- tional recognition when it established KUHT, the world’s leadership of Dan Luss from the mid ’70s, through the ’80s, first educational television station. Today, the University of UH Chemical Engineering became one of the top departments Houston is the flagship of the University of Houston System in the United States (ranked 8th by the National Research and is considered one of the most ethnically diverse campuses Council in 1982). The leadership was passed to Jim Rich- among U.S. universities. ardson, who chaired the department from 1996-1998. After a The Department of Chemical & Biomolecular Engineering challenging period of budget pressures in the mid 1990s, UH (ChBE) at the University of Houston started as a program attracted one of its former faculty members, Ray Flumerfelt, during the late 1940s and by the 1952/’53 academic year, a to serve as dean of the Cullen College of Engineering. One full-time faculty of chemical engineering was formed. During of Flumerfelt’s primary goals was to invest in the Chemi- the next three years, under the leadership of Joseph Crump, cal Engineering Department to re-establish its prominence. a vision emerged with three short-term In 2000 Flumerfelt hired one of UH’s own, Mike Harold goals: (i) establishment of a graduate (Ph.D., 1985) who chaired the department from 2000 to 2008 program comprising M.S. and Ph.D. when it underwent the name change to include Biomolecular. degrees supported by an internationally The injection of resources has led to a new period of growth recognized research program, (ii) estab- and resurgence of the department, now under the leadership lishment of an accredited undergraduate of Ramanan Krishnamoorti—transforming itself from its program with strong industrial ties, and unit operations and transport focus to sustained excellence (iii) growth of a department supported in reaction engineering, and new strengths in materials and by university administration. During biomolecular engineering. The full-time faculty is now ap- Joseph Crump the next 15 years, under the leadership proaching 20 in number while enhancing its reputation and of Frank Tiller (Dean of Engineering, impact. The most recent 2010 NRC review has the department 1955 to 1963) and Abe Dukler (Chair), UH Chemical Engi- ranked 18th (based on the more objective “S” ranking). neering emerged as the young upstart department. Under the © Copyright ChE Division of ASEE 2011
150 Chemical Engineering Education 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 enable them to prosper in their careers and to adapt to a field that continually evolves and transforms. The department has three specific aims: 1. To provide a high-quality education for undergraduate 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. 2. To engage in research programs that train graduate stu- dents, procure support for this research on a continuous basis, and contribute to the development of fundamental Areas of graduate employment. knowledge in the field of chemical engineering. The department’s varied and aggressively pursued research ensures that our faculty members remain at the technolog- dents (African-American, Hispanic, Asian) making up about ical forefront of their respective areas of specialization. 60% of the total. Moreover, the department does very well 3. To be of service to the community at large and, in in attracting female students and provides a flexible program particular, to the City of Houston and the State of Texas, for working part-time students. Currently there are about 400 and to provide the local engineering community oppor- students in the program with recent graduation rates of about tunities for advanced and continuing education. 35-45 per calendar year. The graduate program numbers ap- proximately 100 students, about 25 of whom are part-time The department currently confers the following degrees: students (most have full-time employment and are MChE • Bachelor of Science in Chemical Engineering (B.S. students). Current enrollment in the Petroleum Engineering ChE) program numbers about 130 students, equally divided among • Master of Chemical Engineering (non-thesis; MChE) undergraduate and Master’s students, the majority of whom • Master of Science in Chemical Engineering (thesis and are part-time working professionals. non-thesis M.S. ChE) • Doctorate in Chemical Engineering (Ph.D. ChE). At the undergraduate level, the department has been ef- In addition, the department has administrative responsi- fective in educating students for productive careers in the bility for a Petroleum Engineering program that confers the chemical process industry, process design firms, and the en- following degrees: ergy industry, particularly the upstream sector in recent years. Feedback obtained from local employers reveals that the UH • Bachelor of Science in Petroleum Engineering (B.S. PE) ChBE students are top-performing, typically more mature • Master of Science in Petroleum Engineering (M.S. PE) students from the start. This is testimony to the fundamental • Master of Petroleum Engineering (non-thesis, MPE). focus of the curriculum, the standards of the instructors, and The department has traditionally attracted excellent under- the diversity—including age—of the student population. graduate students who are among the best at UH. Reflecting Undergraduate enrollments in the program generally follow the diversity of the UH student body as a whole, our under- national trends influenced by the hiring dynamics in the grads are a very diverse group, with under-represented stu- chemical and petrochemical industries. The strong reputation
Left to right: Frank Tiller, William Prengle, Abe Dukler, Dan Luss, Jim Richardson, and Mike Harold.
Vol. 45, No. 2, Spring 2011 151 of the department, however, has provided a steady stream and liquid in vertical pipes. Dukler was elected to the National of high-quality undergraduate students. Recent changes to Academy of Engineering in 1977 for his pioneering advances include biomolecular engineering principles and materials in high Reynolds number multiphase flow. science and engineering in the core undergraduate training The department hired Ernest Henley from Columbia Uni- along with development of minor options in petroleum engi- versity in 1961. Henley has distinguished himself for decades neering and nanomaterials engineering have diversified the as being an innovator in his research, teaching, and extramural education and training of the students. business pursuits. For a period of over two decades and end- ing a few years ago upon his retirement, Henley taught the THE EARLY YEARS two-course capstone design course to UH senior undergradu- The department was founded in the late 1940s when the ates. This was one of the main reasons why UH graduates University of Houston was at that time a small, private un- were coveted by industry: UH graduates knew chemical dergraduate university principally attended by white students engineering design and process economics. Henley’s book from more affluent families of the greater Houston area. with J.D. Seader and D. Keith Roper, Separations Process Crump, the first department chair, recruited several key fac- Principles, is in its third edition and has established itself ulty members who were, as Jim Richardson refers to them, as the text of choice for unit operations and separations at “the instigators.” These were William Prengle, Dukler, and chemical engineering departments in the United States and Frank Worley. Prengle and Dukler were hired from Shell internationally.[2] Oil Company and at first were part-time lecturers and became During this period, the strong industrial ties to the depart- full time faculty in 1952. ment’s research and educational activities were established. Dr. Larry Witte, Professor of Mechanical Engineering at As department chair from 1966-1974 and dean of the college UH, recalls the important impact that Crump, Dukler, and from 1976 to 1982, Dukler accelerated the department towards Prengle had on the department. “These three scholars were becoming an upstart among chemical engineering depart- role models for the rest of the college,” says Witte. “They ments in the United States. In 1968 Dukler landed a $600,000 showed us how to transform an undergraduate program into a “Center of Excellence Departmental Development Grant” successful graduate research program. In the 1960s they won from the National Science Foundation, a highly competitive a National Science Foundation (NSF) matching excellence program. These monies were used to hire faculty members grant that enabled them to expand and bring in more research. and build world-class research laboratories. Prof. Osman I. Other departments wanted to emulate their success.” Ghazzaly, a faculty member in the Department of Civil and Environmental Engineering since 1966, points out that: “The An important step for the department and college occurred real quantum jump in the direction of research came when in 1955 when Frank Tiller was hired from Lamar University as Dukler took over. He wanted us to really show a change in the first dean of the College of Engineering. Dean Tiller set out direction, and he emphasized that research was the number to expand the college, enhance the quality of the faculty, and one pursuit.” gain accreditation for the college programs. On arrival only 14% of the engineering faculty had doctoral degrees. Tiller Says Stuart Long, Professor of Electrical and Computer actually sent some of them back to school to earn their Ph.D.’s. Engineering and currently Interim Vice President for Research By 1963, 40% of the college faculty had doctorates. at UH, “Dukler was willing to take the heat for making this transition. He was willing to sacrifice his popularity to do As critical of a leadership role as Dean Tiller provided to the the right thing.” Around 1975 the department recruited Alkis young college, he also became one of the stalwart researchers Payatakes, an expert in transport phenomena, from Syracuse. in the university. Tiller established himself as one of the lead- Payatakes would join forces with Flumerfelt to start a center ing academicians who used mathematical methods to solve in enhanced oil recovery, which used theory of low Reynolds [1] chemical engineering problems. His primary interest was fluid dynamics to understand the movement and recovery of in advancing the understanding of solid-liquid systems with oil ganglia in porous media. Their approach changed the way application to separations, notably filtration. A long string of the oil industry looked at petroleum recovery and helped to doctoral students would study with Tiller and were coveted forge closer ties between the upstream energy industry and by industry to improve the many processes involving solids the department. The department’s tradition in multiphase and their purification. Tiller helped to establish and grow transport would receive a boost with the hiring of two junior the American Filtration and Separations Society (AFS) as faculty in the early 1980s, Vemuri Balakotaiah in 1983 and evidenced by the AFS Tiller Award which annually honors a Hsieh Chia Chang in 1984. Balakotaiah was one of UH’s top engineer in the field. own, a student of Dan Luss, while Chia was recruited away Complementing Tiller was Dukler, who established himself from UC Santa Barbara. While Bala and Chia had roots in as the leading expert in multiphase flow. Dukler advanced the chemical reaction engineering and nonlinear analysis, both high-speed laser Doppler velocimetry method for flow of gas applied their skills to the inherent nonlinearities of wavy flows
152 Chemical Engineering Education and flows in porous media, among other systems. During the In recent years the department has emerged as a leading 1990s the department recruited Kishore Mohanty away from center for environmental reaction engineering and catalysis. the oil industry. Mohanty would further solidify the ties with Balakotaiah focused on transport and reaction in catalytic the upstream energy industry with his fundamental focus on monoliths used in emission aftertreatment systems such as transport in porous media applied to oil and gas recovery. three-way catalytic converters. Harold founded a clean diesel Complementing Mohanty’s efforts was Michael Economides, testing and research facility in the early 2000s, now called hired from Texas A&M in 1998, who brought more practical the Texas Diesel Testing and Research Center and managed aspects of petroleum engineering to the program. by Dr. Charles Rooks who was recruited from industry by Harold. The creation of the diesel center was in response to the THE REACTION ENGINEERING COMPETENCY regional need to reduce emissions of NOx (NO + NO2) from the exhaust of diesel vehicles and equipment. The Houston The hiring of Luss in 1967 was arguably the most important area had the dubious distinction of being one of the worst hire in the department’s 60+ years. Luss, a highly accom- offenders of the Clean Air Act’s ozone standard. Harold at- plished student of Neal Amundson at Minnesota, was an tracted a City of Houston grant of $4 million to create a diesel expert in chemical reaction engineering. In the same period vehicle testing facility and a few years later a $12 million the department attracted Richardson, an accomplished expert grant to expand the operation. in heterogeneous catalysis, from Exxon. Together their hiring Today Harold and Rooks lead a team of 15 engineers and ushered the emergence of chemical reaction engineering as staff and collaborate with other faculty members in the ChBE the area in which UH chemical engineering would become and Mechanical Engineering on basic research and technol- the recognized national leader. In 1971, UH attracted Jay ogy development focused on clean diesel. The center has Bailey as an assistant professor with primary research inter- capabilities spanning bench-scale development of emerging est in reaction engineering, and broadened the impact of the technologies to full-scale testing of diesel vehicles. The pioneering research. Bailey applied the principles of chemical main focus of the testing activities is on retrofit technolo- reaction engineering and mathematical methods developed gies to decrease NOx and particulate matter emissions from in chemical engineering first to enzyme catalyzed reactions on-road and off-road vehicles and equipment. More recently and later to biochemical engineering, becoming one of the [3,4] the department has attracted Jeff Rimer from the University pre-eminent biochemical engineers. of Delaware, an expert in the synthesis of shape-selective Luss became chair of the department in 1975, a position that crystalline materials such as zeolites. Rimer and Harold he held until 1996. It was during Luss’ tenure as chair that the are joining forces to discover new zeolitic materials with department would ascend dramatically, thanks to the seeds enhanced activity and selectivity for the aforementioned planted by Dukler, strategic hires by Luss, and a sustained lean NOx reduction. Joining the faculty in 2011 will be Bill focus on research excellence in chemical engineering science. Epling as an associate professor and Lars Grabow as an Indeed, it was Luss who stunned chemical engineering aca- assistant professor. Epling, with earlier industrial experience deme in 1976 when he attracted his former Ph.D. advisor, Neal from Cummins, Inc., and an established academic from the Amundson, “The Chief,” to Houston. Amundson brought his University of Waterloo, will be a perfect fit in the department’s expertise in applied mathematics and reaction engineering efforts in environmental reaction engineering. Grabow brings to the department, and proceeded to graduate about 10 more his expertise in molecular modeling of catalysts to apply to doctoral students during his second career at UH. a wide range of problems including environmental reaction Collectively, the department trained a new generation of engineering, biofuels, electrochemistry and development of students who would primarily join industrial research orga- a new generation of catalyst materials. nizations and help to change the way that chemical reactors in particular would be analyzed, modeled, and designed. In MATERIALS AND BIOTECHNOLOGY the late ’80s the department hired Demetre Economou, an Materials-related research in colloidal, polymeric, and nano expert in electronic materials processing. Economou helped materials along with biotechnology and biomolecular engi- to bridge the gap between reaction engineering and materials, neering have become significant strengths of the department and has become one of the leading researchers in gas-solid re- over the last three decades and in part reflect the changing actions in plasma processes. In 2000 another of Luss’ students, nature of the discipline. Mike Harold, was recruited to become the sixth department The discovery of high-temperature superconductivity at chair. Harold had established a strong reputation first as an the University of Houston sparked a materials revolution academic at the University of Massachusetts at Amherst, then on campus and the department became a leader in the area as a researcher, then a manager at the DuPont Company’s of oxide materials. The significant investments in materials Engineering Research labs at the Experimental Station. Ad- characterization, developed in part as a result of the NSF ditional hires included Roy Jackson from Rice in 1977. Materials Research Science and Engineering Center, led
Vol. 45, No. 2, Spring 2011 153 to the growth of not only inorganic materials but also to expert in biomolecular recognition and nucleic acid purifica- the growth of polymeric and nanoscale materials. In 2002, tion, joined the department in the late ’80s and is currently the Vince Donnelly, a leading plasma physics expert, joined the theme leader for the diagnostics thrust of the NIH Western department after two decades at AT&T’s Bell Labs. Since then Regional Center of Excellence. Along with Mike Nikolaou Donnelly and Economou have established the pre-eminent (drug delivery), Peter Vekilov (an expert in phase transitions plasma physics and processing laboratory, with both of them that occur in protein solutions with implications for deadly receiving the highest honors from the American Vacuum So- diseases including sickle cell anemia and Alzheimer’s and for ciety. Michael Nikolaou, an expert in process control, works pharmaceutical drug preparation), and most recently Navin closely with them to provide robust control for industrial Varadarajan (quantifying functional human immune re- plasma processes. sponses by integrated single cell analysis and developing new The proximity of the petrochemical industry and the growth cancer therapeutics and vaccines) and Patrick Cirino (protein of advanced materials during the last quarter of the 20th century and metabolic engineering and biocatalysis toward cost-effec- were reflected in the UH Chemical Engineering department’s tive “green” chemistry and renewable fuels, bioremediation, focus. Starting with Raj Rajagopalan, an expert in colloids and “next-generation” therapeutics), the department is well- recruited from Syracuse in the mid ’80s, and Jay Scheiber, positioned to grow biomolecular research and find solutions a theoretician working on polymer dynamics, the efforts in to challenging issues involving human health. soft materials were strengthened by the addition of Ramanan FEATURES AND OUTLOOK Krishnamoorti and most recently of Manolis Doxastakis, Gila Stein, Jacinta Conrad, and Megan Robertson. These faculty The unique location of the University of Houston and the have also led the advancement of nanotechnology research close relationship between the petroleum, petro-chemical, at UH with Krishnamoorti becoming a pioneer in the area of and materials industry along with the relation with NASA polymer nanocomposites. Doxastakis has developed expertise and, more recently, advanced materials companies and the in applying molecular and multiscale modeling to understand Texas Medical Center, have positioned the department to be entangled polymers, nanocomposites, and lipid-protein inter- at the forefront of chemical engineering. The department has actions. Stein is an expert in polymer thin films, working on a unique relationship with the chemical industry and medical developing materials for optoelectronics, advanced optical center through the graduate and research programs as well as lithography, and organic photovoltaics. Conrad is studying the industrial advisory board. The continued vitality of the the interaction between complex fluids such as polymers and short course on heterogeneous catalysis and the significant in- colloids and the surfaces that confine or support them with terest in the short course on polymers, along with the renewed potential applications in petroleum engineering, environmental interest in the MChE program for working professionals and engineering, and materials engineering. Robertson’s research the significant interest in the part-time Ph.D. program (with combines novel synthetic polymer chemistry and elucidation of doctoral candidates working in the numerous research and polymer physics to design nanostructured materials to develop development centers in the greater Houston area), demonstrate a new generation of materials based on renewable resources the close relationship. and in some cases with biomedical applications. Additionally, These strategic partnerships will continue to drive the suc- the development of a ~ 4000 ft2 class 10/100 nanofabrication cess of the students and faculty of the Department of Chemical facility at UH has enabled the rapid growth of nanoscale soft and Biomolecular Engineering at the University of Houston. materials research. Ongoing research to develop advanced The analytical, quantitative, and systems-based approach that materials for energy applications including improved hydro- was pioneered by Tiller, Dukler, Amundson, and Luss will carbon recovery, solar energy capture, and wind energy—along continue to be the hallmark of the research performed at UH with a focus on sustainability by using natural biodegradable and will be integrated into the developments in cutting-edge alternatives to petroleum-based materials—is representative applications in materials, human health, and energy. These of the efforts of the department to address many of the grand will also help shape our evolving undergraduate and graduate challenges facing humanity. curricula and maintain excellence in our teaching, service, The growth of the Texas Medical Center over the last 30 and research missions. years, starting from the pioneering efforts to produce the first REFERENCES artificial heart to the latest innovations in treating cancer, has 1. Yelshin, A., Filtration & Separation, 29, 37 triggered growth of biomolecular and biochemical engineering 2. Seader, J.D., K. Henley, and D. Roper, Separation Process Principles, in the department. Jay Bailey’s evolution from chemical reaction 3rd Ed., Wiley (2011) engineer to the pre-eminent biochemical engineer by the time 3. Bailey, J.E., and D.F. Olis, Biochemical Engineering Fundamentals, McGraw-Hill, Inc., New York (1986) he left UH in 1980, was the precursor for the current growth in 4. Reardon, K.F., K.H. Lee, K.D. Wittrup, and V. Hatzimanikatis, Bio- biomolecular research in the department. Richard Willson, an technology and Bioengineering 2002, 79, 484 p
154 Chemical Engineering Education Each chapter ends with a set of practice problems. These ChE book reviews problems were challenging but appropriate for the material in each section. It was interesting to note that many of the prob- An Introduction to Granular Flow lems were adapted from other sources. I especially appreciated that each problem was labeled with a heading that described by K. Rao and P. Nott what concept was being tested. I am not sure if the authors offer Cambridge (2009) $155.00 a solutions manual for this textbook, but it would certainly be useful for instructors adopting the book for a course. Reviewed by I disagree with the authors when they state that this book is Kimberly H. Henthorn appropriate for advanced undergraduates or beginning gradu- Rose-Hulman Institute of Technology ate students, at least in the chemical engineering discipline. Granular flows are ubiquitous in nature and industry, The material is presented at a much higher level than what I particularly in systems involving food, pharmaceutical, and would expect an undergraduate chemical engineering student chemical processes. Although it is extremely important to be to be able to handle. The amount of mathematics and model- able to characterize and model these systems, granular flow ing background required to understand the material and the behavior is still not well-understood. A number of theoreti- authors’ use of specialized vocabulary makes this book more cal and empirical models have been proposed to describe the appropriate for graduate students concentrating in particle behavior of particulate systems, but there is still much room technology related fields. I would recommend students first for refinement. This book gives a solid discussion of a broad take an introductory particle technology course using an inter- range of topics related to granular flow, with much emphasis mediate text such as Rhodes1 so that they are better prepared on theoretical modeling. The authors focus on continuum for the material presented in this book. models, although there is some attention to discrete models as My comments about the incompleteness of certain topics well. Overall, the book is well-written and provides a thorough stem from the overwhelming amount of information available overview of the current state of granular flow research. on granular flows. It would be impossible to cover everything The book begins with an introduction that previews a large without developing a series of texts about the topic. My overall number of areas including interparticle forces, packing, granu- impression of An Introduction to Granular Flow, however, lar statics and flow, and modeling, with most of these topics is very positive, and I commend the authors for providing a covered in more detail in subsequent chapters. The authors do solid reference for those interested in granular flows. They a good job of briefly describing each of these topics, and offer do a nice job of summarizing peripheral topics while going a lot of external references for further consideration. In my into the appropriate detail in their focus areas. opinion, this chapter could easily be broken into two chapters, 1 Rhodes, Introduction to Particle Technology, John Wiley & Sons, 2nd with the modeling sections discussed separately, in order to ed., 2008 better organize the material. Some portions are a bit choppy and incomplete because too much information is presented at Good Mentoring: Fostering Excellent once. Dividing the material and adding more detail in certain places would definitely help with this. Practice in Higher Education The rest of the book delves into a detailed theoretical by Jeanne Nakamura and David J Shernoff discussion of slow plane and three-dimensional flows, flows with Charles H. Hooker through hoppers and bunkers, and rapid flows. The material Josey-Bass, 303 pages, $40 (2009) seemed a little unorganized and incomplete in places, and I was disappointed with the quality and placement of many of Reviewed by the figures and tables. I think the authors did an especially Joseph H. Holles good job with Chapter 6 (Flow through Axisymmetric Hop- University of Wyoming pers and Bunkers), however. They provided a good mix of Is good mentoring in the genes? Can successful mentors theory and experimental data, and I thought their figures in automatically transmit their knowledge, skills, and values this section were interesting and useful. to the next generation of students? If so, how can these at- Since most of the material is based on complex theories, the tributes be transmitted in a way that is most useful to their authors offer several appendices that provide a basic math- academic offspring? In an effort to better understand “how to ematics review. Operations with vectors and tensors, a brief keep what has been learned from being lost” Good Mentoring analysis of the stress tensor, and methods to evaluate common examines three lineages of scientists and the ability of their integrals are a few topics covered here. I was very happy to skills, values, and practices to be transmitted to their students see these appendices, because the authors assume the readers and successive generations. have a good understanding of advanced mathematics when The general question that the authors are seeking to address discussing the material in the main portion of the text. is: “Can one generation’s ‘good workers’ nurture similar com- Vol. 45, No. 2, Spring 2011 155 mitments in members of the next generation even as changing as intense personal interactions. The defining characteristic of sociocultural conditions pose new challenges to the pursuit positive mentoring was supportiveness. Supportiveness included: of excellence and ethics in a field?” Included in this question consistent availability and involvement, balance between free- was a particular emphasis on the transmission of orienting dom and guidance, frequent and specific positive feedback, treat- values and principles uniting excellence with responsible ment as respected colleagues, and individualized interest in the practice. The authors postulate that “the best chance for their student. Good mentoring does not appear to include hectoring, cultivation is likely to lie with teachers who embody these guilt trips, yelling, insults, or subtle jabs. values and practices and the learning environments that the In Part Three, the most important results are discussed and teachers create.” Since graduate science education has a strong then concrete suggestions for mentor, mentees, and institu- reliance on learning by apprenticeship, it is an ideal situation tions are presented. For mentors, the most commonly cited for examining mentoring of future generations. resource was to facilitate students’ building of social capital. While the subtitle is “Fostering Excellent Practice in Higher For mentees, the authors recommend seeking out multiple Education,” the book is most relevant to a smaller subset of influences since many of the worst cases of mentoring occur higher ed. In particular, the focus of the book is effective when a single person has significant control over the student. mentoring for supervisors in research. While the case studies Finally, for institutions, good mentoring is a sound investment focus on mentoring of graduate students and post-doctoral re- for the future and the reward structure should reflect this. There searchers in academia, the same outcomes are also applicable also need to be places in the institution for advisees to evaluate in any research mentoring situation including undergraduate mentoring experiences similar to the way teaching evaluations researchers, government, or industrially sponsored research provide feedback to classroom instructors. laboratories. Finally, both mentors and their students can gain All of the examples and conclusions are drawn from mentor- insight into successful relationships from this work. ing relationships between graduate students and their advisor Good Mentoring is divided into three distinct parts. Part (a faculty member). There is a significant amount of mentoring One presents case studies of each of the three lineages. Part that goes on in higher education outside of what is investigated Two summarizes the transmission of knowledge, practices, and discussed in this book, such as advisor/undergraduate and values across the mentoring generations. Part Three then researcher relationships and teacher/student classroom relation- summarizes the key lessons learned and draws out implica- ships. There are even mentoring relationships between estab- tions for practitioners and researchers. lished faculty members and new faculty members. While the In Part One, the authors examine three scientists and their authors don’t investigate all of the higher education mentoring lineages through the second and third generation of academic relationships, the conclusions from this study can help in all. offspring. In perhaps a bit of irony, all three of these academic In fact, one of the ripest areas for application of these conclu- lineages are in the field of genetics. The goal of these chapters sions would appear to be in the opportunities for institutions to is to provide a qualitative view of the approaches of each sci- improve the mentoring of new faculty by senior faculty. entist towards successful research and mentoring. Subsequent How can this book best be used by faculty members today? discussion of second and third generations then provides Clearly, the most direct place is in the laboratory when men- insight into what knowledge was successfully passed down. toring students. The main results from the study indicate that From these second- and third-generation profiles, we also simply being there for the students, showing a strong work obtain some insight into how individual scientists affected the ethic, and being flexible will result in a positive experience overall memes (building blocks of culture) of the lineage. for the student and transmit desired good work practices on to In Part Two, the authors take a quantitative approach to the next generation of researchers. However, the ideas from complement the previous profiles. Values and practices spe- this book can also be applied in the classroom. In addition, cific to each lineage are identified and the successful transi- simply providing a welcoming, open, and safe environment tion of these memes through three generations is quantified. for all can have positive results. Categories of memes common to all three lineages were also Since the authors examine the ability of effective mentoring investigated. memes to be passed down from advisor to academic offspring, From their quantitative analysis, the authors found that even the the work becomes very mentor focused. Only in the last chapter most widely inherited memes are inherited less from generation do the authors discuss how a mentee should use the results of to generation. However, this is compensated for by the larger their study. Again, as a result of their premise, the authors tend number of offspring in each generation and thus the absolute to focus on academic offspring who have done well in academia. effect remains high. The mentors in this study transmitted memes The applicability of mentoring on non-academic offspring does “through two intertwined aspects: mentor’s direct impact on not appear to be addressed. Finally, while the point of this work the student through verbal exchanges and the mentor’s indirect was to investigate “stars” since they were capable of doing good impact through student participation in the lab community.” academic work in parallel with performing good mentoring, the Contrary to the author’s expectation, the influence on students by ability and effectiveness of “non-star” researchers to instill respon- example and shaping the culture of the lab was just as important sible practice in their academic offsprings is still unknown. p © Copyright ChE Division of ASEE 2011 156 Chemical Engineering Education