EDITORIAL ADDRESS:
Chemical Engineering Education
c/o Department of Chemical Engineering
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EDITOR
Tim Anderson
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Phillip C. Wankat
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Daina Briedis, Michigan State
LEARNING IN INDUSTRY EDITOR
William J. Koros, Georgia Institute of Technology
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Vol. 45, No. 2, Spring 2011
Chemical Engineering Education
Volume 45
Number 2
Spring 2011
 DEPARTMENT
150 Chemical Engineering at The University of Houston
Michael P. Harold and Ramanan Krishnamoorti
 CURRICULUM
86 A Freshman Design Course Using Lego NXT® Robotics
Bill B. Elmore
101 Two-Compartment Pharmacokinetic Models for Chemical Engineers
Kumud Kanneganti and Laurent Simon
126 Conservation of Life as a Unifying Theme for Process Safety in
Chemical Engineering Education
James A. Klein and Richard A. Davis
 LABORATORY
93 Microfluidics Meets Dilute Solution Viscometry: An Undergraduate Lab
to Determine Polymer Molecular Weight Using a Microviscometer
Stephen J. Pety, Hang Lu, and Yonathan S. Thio
106 Continuous and Batch Distillation in an Oldershaw Tray Column
Carlos M. Silva, Raquel V. Vaz, Ana S. Santiago, and Patrícia F. Lito
120 A Semi-Batch Reactor Experiment for the Undergraduate Laboratory
Mario Derevjanik, Solmaz Badri, and Robert Barat
133 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
 CLASSROOM
114 Active Learning in Fluid Mechanics: YouTube Tube Flow and Puzzling
Fluids Questions
Christine M. Hrenya
 RANDOM THOUGHTS
131 Hang in There! Dealing with Student Resistance to Learner-Centered
Teaching
Richard M. Felder
 CLASS AND HOME PROBLEMS
144 Optimization Problems
Brian J. Anderson, Robin S. Hissam, Joseph A. Shaeiwitz, and
Richard Turton
 OTHER CONTENTS
inside front cover
Teaching Tip, Justin Nijdam and Patrick Jordan
155 Book Reviews
by Joseph Holles, Kimberly Henthorn
CHEMICAL ENGINEERING EDUCATION (ISSN 0009-2479) is published quarterly by the Chemical Engi­neering
Division, American Society for Engineering Education, and is edited at the University of Florida. Cor­respondence regarding
editorial matter, circulation, and changes of address should be sent to CEE, Chemical Engineering Department, University
of Florida, Gainesville, FL 32611-6005. Copyright © 2011 by the Chemical Engineering Division, American Society for
Engineering Education. The statements and opinions expressed in this periodical are those of the writers and not necessarily
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:
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Periodicals Postage Paid at Gainesville, Florida, and additional post offices (USPS 101900).
85
ChE curriculum
A FRESHMAN DESIGN COURSE
USING LEGO NXT® ROBOTICS
Bill B. Elmore
C
Mississippi State University • Mississippi State, MS 39762
ivil engineering majors have their concrete canoes and
steel bridges and the mechanical engineers have their
solar cars. Certainly, the discipline of chemical engineering is no less visual—we just cannot haul a skid-mounted
process unit into the classroom (without raising administrative
eyebrows and inviting an immediate visit from the campus
safety officer). What concrete, visible means do we have for
giving our students a clear picture of chemical engineering?
Pursing K–12 outreach and teaching freshmen for a substantial
part of my career, I’ve journeyed through a maze of options
for trying to help students understand what chemical engineers
do in daily practice. Most attempts coalesced into a series
of chemistry demonstrations accompanied by pictures of
chemical processing equipment—leaving my audience with
a conceptual gap between the two.
In the Swalm School of Chemical Engineering at Mississippi State University, the ideal opportunity to tackle
this problem came with the revision of a three-credit-hour,
junior-level course—Chemical Engineering Analysis and
Simulation (hereafter referred to as Analysis). Originally
designed to address the application of numerical methods to
fundamental topics in chemical engineering, the course has
pre-requisites that, over time, allowed a shift in class composition to a mixture of underclassmen taking the course “on
time” and upperclassmen (typically co-op students) squeezing
in the course among other requisite courses. This led to an
unsatisfactory pressure on the course content (i.e., too difficult
for one set, too remedial for the other). A general curriculum
review revealed an opportunity to strengthen our curriculum
by moving Analysis to the freshman year—using it as a vehicle to incorporate teamwork, experimentation, and project
design into the early stages of our curriculum.
LEGO® ROBOTICS—FOR CHEMICAL
ENGINEERS?
The incorporation of problem-based or project-based learning strategies into the classroom has swept the educational
scene from K–12[1-4] across multiple disciplines in higher
education.[5-7] LEGO® robotics kits have proven to be widely
adaptable to a variety of disciplines and learning styles in
engineering education. Building on the work of chemical
engineering educators such as Levien and Rochefort,[8] Moor
and Piergiovanni,[9,10] and Jason Keith,[11] my students and I
began a journey in the Fall semester of 2006 to incorporate this
relatively inexpensive technology into the Analysis course.
At under $300 per base set, the LEGO NXT® robotics kit
offers tremendous versatility for designing model engineering apparatus and processes in the classroom. With modest
additional cost for accessories (e.g., valves, tubing, tanks)
a number of units can be built to allow an entire class to be
Bill Elmore is an associate professor of
chemical engineering and the Interim Director for the School of Chemical Engineering
at Mississippi State University. Now in his
22nd year of higher education, his focus is
primarily on engineering education and the
integration of problem-based learning across
the curriculum.
© Copyright ChE Division of ASEE 2011
86
Chemical Engineering Education
actively involved in the same design project simultaneously
(in contrast to the traditional Unit Operations laboratory approach relying on the rotation of student groups through a
single experimental apparatus sequentially). Coupled with the
LEGO NXT® kits, we chose a series of sensors from Vernier
(e.g., pH, temperature, dissolved oxygen) that interface with
the robotics kits for monitoring processes and performing
simple control schemes. A significant factor in choosing the
LEGO NXT® robotics kits is the use of an intuitive graphical
interface for programming (based on National Instruments
Labview® software). This user-friendly programming interface removes the focus from programming and places it
on the broader objectives of problem analysis and design of
engineering processes.
CHE 2213 Chemical Engineering Analysis is a required,
three-credit-hour course, offered once per year in the second
semester of the freshman year (after a one-hour orientation
and before the sophomore-level Mass & Energy Balances
course). A large number of students entering the chemical
engineering program at Mississippi State University (MSU)
are community/junior college transfers from an extensive
two-year college system throughout the state. Analysis is
among the courses required for their first year at MSU. Enrollment lies typically between 55-70 students. The course
is conducted in a 160-seat auditorium, the adjacent Unit
Operations laboratory, and, with some design competitions,
in the connecting hallway for maximum exposure to passing
students from other classes.
Through loads of laughter and enthusiasm, discovery and
TABLE 1
CHE 2213 Analysis
Learning Objectives & Outcomes
Learning Objectives:
At the end of this course, you should be able to…
• Brainstorm a problem quickly within a team setting (or working
alone) listing a number of possible solutions over a broad range of
ideas
• Describe the Engineering Design Cycle as used in this course and
steps/tools involved in engineering design
• Take an idea for solving an engineering problem and bring it to
a complete, functioning prototype using the LEGO NXT robotics
system and accessories
• Use Microsoft Excel tools to collect and analyze data from your
engineering designs
®
• Describe the importance and basic elements of conducting a material balance for and maintaining control of a chemical process.
Learning Outcomes:
Upon completion of this course, you should be able to…
• Employ the Design Cycle for both originating an engineering
design and for making performance improvements in an existing
design
• Explain to someone in your family (a non-engineer) what chemical engineering is all about—giving some very practical examples.
Vol. 45, No. 2, Spring 2011
creativity, and precautions to avoid spending an inordinate
amount of time on their robotics projects, teams of students
have consistently pushed the course content forward in subsequent semesters—demonstrating the value of a highly visual,
project-based approach to learning engineering fundamentals.
Through several iterations we have constructed projects more
directly oriented to chemical engineering for illustrating the
importance of fundamental concepts including basic units
and measures, materials balances, and the fundamentals of
process control.
LEARNING OBJECTIVES AND OUTCOMES
Table 1 describes the learning objectives and outcomes
for the Analysis course. Defining a learning objective as a
specific, targeted description of acquired knowledge or skill
and a learning outcome as a broader response to particular
situations requiring use of that acquired knowledge or skill,
these course objectives and outcomes are being affirmed over
time in coordination with our overall chemical engineering
program objectives.
THE LEARNING ENVIRONMENT AND
COURSE STRUCTURE
Offered Tuesdays and Thursdays for two 2-hour-and-20minute sessions, Analysis comprises one credit hour of laboratory and two credit hours of lecture. The learning environment
is patterned after a studio setting. I provide instruction on
specific topics or skills as needed in a dynamic, laboratory
environment that allows students to immediately put that
knowledge or skill to practice on the current project. Projects
are structured to require use of accumulated knowledge over
the course of the semester. Class discussions center around
knowledge and skills needed for use on a timely basis. Homework problems are assigned to allow practice of key tools.
Grades come primarily from individual quizzes and the final
exam (evaluating their understanding of skills and concepts
learned during design exercises). Some portion of the grade
is derived from team participation in oral and written reports
(in varying percentages over the semesters since the course’s
inception). No grade has yet been assigned for the quality or
performance of designs.
Table 2 (next page) describes the flow and content for
Analysis. Up to six in-class quizzes are given at appropriate junctures, evaluating students’ comprehension and use
of the concepts, skills, and tools learned to date. Beginning
with Team Challenge #2, all designs require quantitative data
acquisition and analysis and are accompanied by team written
reports, team self-evaluations, and oral reports.
Over the eight semesters we have offered Analysis in its current format, a surprising number of students have expressed
little past experience playing with LEGOs®. To put everyone at ease at the course outset, student teams construct the
LEGO® NXT robotics kits and build a mobile robot of their
87
choice, using as a guide the “Taskbot” design included
with the kit (Figure 1). This enables students unfamiliar
with LEGO structural elements and the various sensors
included in the kit to quickly learn something about the
capabilities and limits of both the building components
and the available sensor technology.
engineering design principles. Introduction of the Design Cycle
(Figure 3) provides teams a guide for iteratively approaching an
optimal solution for the problem they are tasked with solving.
Key aspects of the course content are shown in Figure 2. The Analysis course was placed in the second
semester of the freshman year to engage our chemical
engineering students in team-oriented, “real engineering” projects at a critical stage of their collegiate (and
chemical engineering) experience, thereby strengthening 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 integration 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
•Teamwork
•Oral reporting
•Written technical summaries
General Engineering &
ChE -specific Topics
•Material Balances
•Units/Measurements
•Data collection & analysis
•Basic concepts for controlling
processes
Engineering Design
•Problem definition
•Brainstorming solutions
•Develop prototype from most
promising possibilities
•Test, evaluate, improve
•Communicate "optimum"
Figure 2. CHE 2213 Analysis—Course content.
Envision
Refine
Plan
and watching for problems that crop up with group dynamics.
Additionally, this interaction is an excellent opportunity for getting 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
course including brainstorming for initial problem solving, using the Engineering Design Cycle, and use of Microsoft Excel®
for data acquisition and analysis.
Team Challenge #1: Taskbots and Sumo Wars
Evaluate
Build
Figure 3. Design Cycle.
TEAM DYNAMICS
On the opening day, students self-assemble into teams of
three members and begin familiarizing themselves with the
robotics kits. In some semesters, I have allowed groups to
remain constant over the course of the semester; in others,
group members were reassigned approximately at mid-term.
Through frequent, informal interviews and anonymous surveys,
the feedback has been roughly constant for both approaches
(i.e., most class members favoring staying in their self-selected
teams with one or two teams wishing for anyone other than
their current team members). I interact with individual teams
throughout the class periods, coaching and exchanging ideas,
Vol. 45, No. 2, Spring 2011
The team challenge announced to the class is a “Sumo war”
requiring teams to build a robot capable of staying within a
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 challenge to bring in upperclassmen and, with loud music and the
AIChE chapter providing food, the result was a memorable
social event.
Team Challenge #2: Free-Format Design
After the dust settles and emotions subside, a second team
challenge opens the door to a more fundamental, and methodical, approach to engineering problem solving. Teams
are tasked with designing an experiment and constructing a
robot (not necessarily mobile) to demonstrate the performance
of one or more LEGO NXT® sensors of their choice—acquiring data from a set of independent/dependent variables.
Using available computational tools and the course text,[12]
teams report raw and processed data in graphical form with
89
appropriate oral and written reports. Student designs have
included measuring the volume of liquid dispensed from a
soft drink can as a function of robot “tipping velocity”; the
angle of projection by a ball hit in a robotic batting machine;
and colorimetric sensitivity of the light sensor as a function
of varying shades.
Team Challenge #3: Level Control
The importance of process control in chemical engineering
is emphasized in the next team challenge by requiring teams to
adapt the LEGO® NXT system with a bench-scale fluids handling system (Figure 5). A submersible pump delivers water
to a tank through a small needle valve operated by a LEGO
motor which in turn is controlled by programming the NXT
robotics “Intelligent Brick” (i.e., a 32-bit microprocessor).
Teams must design the system to maintain a prescribed
fluid level in the tank. A sonar sensor, analogous to one type
of level-control technology used in industry, detects the
fluid level feeding the signal through the NXT brick to the
controlling motor. Small adjustments in the liquid level are
“amplified” and observed by noting changes in rotational
displacement of the valve stem with an affixed adhesive rule
applied to the valve/motor coupling. Students record, as a
function of time, +/– displacements from an established set
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. Additionally, this challenge touches, to some degree, on each of
the course objectives.
Figure 4. Sumo Wars using LEGO “Taskbots.”
90
Team Challenge #4: Mixing Tank/Continuously
Stirred Tank Reactor (CSTR) Design
In the latest course iteration, we have strengthened emphasis
on chemical engineering process variables (e.g., concentration, pH, temperature, pressure) and material balances. Student teams conduct team challenges using these measures
as indicators of product quality. For example, one challenge
requires feeding de-ionized water and a salt solution from
two separate reservoirs to a mixing tank—maintaining a prescribed salt concentration in the outlet stream (as indicated by
a conductivity sensor). Another challenge allows students to
feed dilute acid and base solutions (typically vinegar/sodium
bicarbonate) to a mixing tank, maintaining a particular pH
as an indicator of the product quality. Students are required
to conduct calculations using basic stoichiometry and mass
balances to predict their system behavior and to assess actual
performance.
In some semesters, we have engaged in “free-form” challenges—each team deciding on a design depicting some
process of their own choosing with certain guidelines/goals.
Creative design projects have included building a robotic
device for titration and assembling a multi-step station for
simulating the application of photo-resist to a silicon wafer,
spin coating, and wet etching (Figure 6).
Figure 5. Elements of level-control system.
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 “centers of security” and opportunities
for growth beyond their level of
comfort. When conducted in a supportive/collaborative environment,
this approach to student learning
can significantly positively impact
student self-efficacy[15] and preparation for advanced learning.
Using a Service Quality approach,[16] a multi-semester study
of Analysis was conducted to assess
variances between desired expectations and realized perceptions with
Figure 6. Silicon-wafer treating station.
a resulting “gap score.” The gap
score is the difference between
ingly challenging chemical engineering curriculum. A close
what a customer expects from a service and what the cusmatch between student perceptions and expectations served
tomer perceives as being delivered. A negative quality gap
as a primary hypothesis for the study. This hypothesis was
score indicates the service is not meeting expectations, while
supported by the survey results. Team efficacy increased over
a positive score indicates the service exceeds expectations.
the span of the semester while academic and career efficacy
Scores are weighted according to students’ relative expectadecreased slightly. While this requires more study, a contributtions from certain characteristics of the course. The study was
ing factor to lowered self-efficacy related to academics and
structured to examine whether or not an individual student’s
career must be the delivery of the final survey during week 15,
efficacy was impacted by realistic expectations, perceptions
at the end of the semester when multiple exams and projects
of the course, preparation, and team experiences.
were due across all of their courses. Changes in efficacy and
Multiple surveys were given over the course of each semessatisfaction, perceived quality, and behavioral intention (i.e.,
ter—in weeks 3, 8, and 15. Surveys were structured to measure
how well a student believes he/she can perform in this chosen
efficacy (the capacity or power to accomplish a desired effect
field) were significantly correlated in the study.
or goal) in three areas—academics, team performance, and
A perhaps intuitive but valuable and statistically valid
career. The service quality surveys, modified from a previ[17,18]
implication
of the study is that making changes to the course
ously validated survey instrument, SERVUSE,
were
content
to
positively
influence self- and team-efficacy can
structured to evaluate student expectations, their ratings of
lend
a
positive
influence
to student satisfaction, perceived
the importance of various factors, and their perceptions of
quality,
and
behavioral
intention.
various service quality dimensions as related to the course.
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 perinclude a significant increase in feedback (formal and inforsonal 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-operato their students.”
tive education, and general academic issues.
An equally valuable outcome has been the clarification
As anticipated, students with positive gap scores (i.e., the
among
some students that chemical engineering “isn’t for
course met or exceeded their expectations) scored higher in
[16]
them.”
While
we believe EVERYONE should be a chemical
academic-, self-, and career-efficacy —an indication of
engineer
(well,
not exactly), the earlier a student realizes that
self-confidence needed for moving forward in an increasVol. 45, No. 2, Spring 2011
91
a change of major may best serve their interests, the better for
all concerned. A distinct advantage I have as the instructor for
this course is that I also serve as the undergraduate coordinator
for our chemical engineering program. As a result, I can also
maintain ongoing academic/career advisement—regularly
discussing with individual students their academic progress,
interest, and preparation for participating in cooperative
education, etc. We generally maintain an open, free-flowing communication that allows students to readily express
concerns or doubts about their major—sorting out critical
decisions before too much “time on task” has elapsed before
switching fields of study.
Additional improvements include informal team surveys
and individual interviews to assess the impact of projects.
Through this process, and with enthusiastic inventiveness
of many students, the team challenges have continuously
improved. In several instances, students returning from their
co-op experience have reported that the work with spreadsheets and the design approach have had a significant impact
on their job preparation and performance. Additional feedback
from co-op students has been re-invested into the course for
making continual improvements.
SUMMARY
The placement of CHE 2213 Chemical Engineering Analysis in the second semester of the freshman year has enabled
our program to maintain a steady, continuous contact with our
freshmen throughout that critical first year. The significant
numbers of transfer students taking the course benefit by being immersed in teamwork and engineering design, thereby
solidifying their working relationships with others in their
class and adapting to engineering problem solving. Projectbased learning proves to be a worthy vehicle for integrating
seemingly disjointed concepts studied in calculus, chemistry,
and physics into practical problem solving— and it is much
more fun than merely lecturing!
ACKNOWLEDGMENTS
Sincere thanks go to Dr. Lesley Strawderman, assistant
professor in Mississippi State’s Department of Industrial
Engineering, and her doctoral student, Arash Salehi, for their
Service Quality experimental design and data analysis.
REFERENCES
1. Lund, H.H., O. Miglino, L. Pagliarini, A. Billard, and A. Ijspeert,
“Evolutionary Robotics—A Children’s Game,” In Proceedings of
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IEEE 5th Intl. Conf. on Evolutionary Computation; IEEE Press, NJ,
1998. From the link <http://citeseerx.ist.psu.edu/viewdoc/download?
doi=10.1.1.35.6283&rep=rep1&type=ps> (1998)
2. Chambers, J., M. Carbonaro, and M. Rex, “Scaffolding Knowledge
Construction Through Robotic Technology: A Middle School Case
Study,” Electronic J. for the Integration of Technology in Education,
6, 55–70. From <http://ejite.isu.edu/Volume6/Chambers.pdf> (2007)
3. Carbonaro, M., M. Rex, and J. Chambers, “Using LEGO Robotics
in a Project-Based Learning Environment,” from <http://imej.wfu.
edu/articles/2004/1/02/index.asp>
4. Kolodner, J., P. Camp, D. Crismond, B. Fasse, J. Gray, J. Holbrook, S.
Puntambekar, and M. Ryan, “Problem-Based Learning Meets CaseBased Reasoning in the Middle-School Science Classrom: Putting
Learning by DesignTM into Practice,” J. of Learning Sciences, 12(4) 495
(2003)
5. Hmelo-Silver, C.E., “Problem-Based Learning: What and How Do
Students Learn?,” Edu. Psych. Rev., 16(3) 235 (Sept. 2004)
6. Thomas, J.W., “A Review of Project-Based Learning,” 1-45, found
at <http://www.bobpearlman.org/BestPractices/PBL_Research.pdf>
(March 2000)
7. Gijbels, D., F. Dochy, P.V.D. Bossche, and M. Segers, “Effects of
Problem-Based Learning: A Meta-Analysis from the Angle of Assessment,” Rev. Edu. Rsrch., 75(1) 27 (Spring 2005)
8. Levien, K., and W.E. Rochefort, “Lessons with LEGO®—Engaging
Students in Chemical Engineering Courses,” Proceedings of the ASEE
Annual Conf. & Exp., 2002; found at <http://www.rowan.edu/colleges/engineering/clinics/asee/papers/2002/1672>
9. Moor, S., P.R. Piergiovanni, and M. Metzger, “Learning Process
Control with LEGOs®,” Proceedings of the 2004 ASEE Annual Conf.
& Exp.; found at <http://soa.asee.org/paper/conference/paper-view.
cfm?id=19879>
10. Moor, S., P.R. Piergiovanni, and D. Keyser, “Design—Build—Test:
Flexible Process Control Kits for the Classroom,” Proceedings of the
2003 ASEE Conf. & Exp; found at <http://www.ni.com/pdf/academic/
us/journals/Design_Build_Test.pdf>
11. Keith, J.M., “Learning “Outside the Toy Box,” Proceedings of the
2002 ASEE Annual Conf. & Exp.; <found at http://www.ni.com/pdf/
academic/us/journals/lv02_43.pdf>
12. Larsen, R.W., Engineering with Excel, 3rd ed., Pearson Prentice Hall
(2009)
13. Fink, L.D., Creating Significant Learning Experiences, Jossey-Bass,
Wiley and Sons (2003)
14. Donovan, M.S., and J.D. Bransford (eds.), How Students Learn:
History, Mathematics and Science in the Classroom, The National
Academies Press (2005)
15. Strawderman, L., B.B. Elmore, and A. Aslehi, “Exploring the Impact
of First-Year Engineering Student Perceptions on Student Efficacy,”
AC2009-62; Second Place—ASEE First-year Programs Division;
presented at the 2009 ASEE Annual Meeting
16. Voss, R., T. Gruber, and I. Szmigin, “Service Quality in Higher Education: The Role of Student Expectations,” J. of Bus. Rsrch., 60; 949-959
(2007)
17. Strawderman, L., and R. Koubek, “Quality and Usability in a Student
Health Clinic,” Intl. J. of Health Care Quality Assurance, 19, 225-236
(2006)
18. Strawderman, L., and R. Koubek, “Human Factors and Usability in
Service Quality Measurement,” Human Factors and Ergonomics in
Manufacturing, 18, 454-463 (2008) p
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
F
Georgia Institute of Technology • Atlanta, GA 30332
luid viscosity is an important fluid property to monitor
in industry, research, and medicine. The diverse applications for the rapid measurement of fluid viscosity
include the characterization of inks in ink-jet printing, [1] studies of protein dynamics,[2] the characterization of biomaterials
used in drug delivery such as hyaluronic acid (HA), [3] and
the clinical detection of diseases such as paraproteinemia[4]
and ischemic heart disease[5] through the study of blood.
An additional use of viscometry is in the determination of
the hydrodynamic volume and molecular weight of macromolecules. Using the data analysis seen later in this paper, a
polymer’s molecular weight can be estimated. It is important
to be able to measure a polymer’s molecular weight—because
of its impact on such properties as strength, stiffness, and glass
transition temperature—by simply measuring the viscosity of
dilute polymer solutions of varying concentrations.
In a laboratory setting, viscosity measurements of dilute
polymer solutions are typically made with glass capillary
viscometers such as Ubbelohde viscometers that require mL
of fluid for measurement. The development of microfluidic
viscometers[6-9] means that such viscosity measurements can
now be quickly made with only μL of fluid. Microviscometers can thus potentially be used to determine the molecular
weight of polymer samples even when sample volumes are
severely limited.
To illustrate both the use of microfluidics to determine
fluid viscosity and the use of dilute solution viscometry to
determine polymer molecular weight, we developed a lowcost laboratory procedure for students to use PDMS microviscometers to determine the molecular weight of a polymer
sample. In addition to the procedure, we present sample data
for microviscometer tests run on glycerol solutions and on
Vol. 45, No. 2, Spring 2011
samples of PEO that match up well with viscometry results
obtained with conventional Ubbelohde viscometers. We also
discuss the timing and logistics of the lab and the feedback
obtained from two sample laboratory sessions run with undergraduates.
Stephen J. Pety received his B.S. in polymer
and fiber engineering at the Georgia Institute
of Technology in 2010 and is currently a
graduate student in materials science and
engineering at the University of Illinois at Urbana-Champaign. During his junior and senior
years, he was a research assistant working
with Dr. Lu and Dr. Thio, where he developed
and ran microviscometer laboratory sessions
reported here.
Hang Lu received
her B.S. from U.
Illinois, Urbana-Champaign, and M.S.C.E.P
and Ph.D. from Massachusetts Institute of
Technology, all in chemical engineering. She
has been an assistant professor in Chemical
& Biomolecular Engineering at Georgia Tech
since 2005. Among the courses that she
has taught are mass and energy balances,
transport phenomena, and microfluidics. Her
research interest is in microfluidics and applications in neuroscience, cell biology, and
biotechnology.
Yonathan Thio is an assistant professor in
polymer, textile, and fiber engineering. He
received his B.S. in chemical engineering
and materials science & engineering from the
University of California at Berkeley, and his
M.S.C.E.P and Ph.D. in chemical engineering
from MIT. He joined Georgia Tech in 2005.
His research interests are on the structure
and properties of polymer composites,
block copolymers, and polymer blends. He
has taught courses with topics in polymer
characterization and structure-properties
of polymers.
© Copyright ChE Division of ASEE 2011
93
MATERIALS
For soft lithography microchannel fabrication, SU8 2050 negative photoresist and SU-8 developer were
acquired from Microchem (Newton, MA). Sylard-184
poly(dimethylsiloxane) (PDMS) was obtained from Dow
Corning (Midland, MI) and 1,1,2-trichlorosilane (T2492) (a
release agent) was obtained from United Chemical Technologies (Bristol, PA). Samples of PEO with viscosity average
molecular weights of ~1 MDa and ~4 MDa were obtained
from Sigma-Aldrich (St. Louis, MO). Aqueous solutions of
the 1 MDa PEO were prepared by mixing the solutions with
a stir bar overnight. Experiments to determine the viscosity of
these solutions were performed within eight days of when the
solutions were prepared. An aqueous solution of 3 mg/mL of
the 4 MDa was prepared by stirring the solution for three days.
The shear thinning studies performed using this solution were
performed within one day of when the solution was prepared.
Glycerol from Fisher Scientific (Pittsburgh, PA) was used to
prepare aqueous glycerol solutions.
METHODS
Device Fabrication
Microfluidic viscometers (PDMS channel on PDMS flat
substrate) were fabricated using the rapid prototyping technique.[10] Briefly, the viscometer device was designed using
AutoCAD (Autodesk, San Rafael, CA). A silicon-SU-8 master
was created using conventional UV photolithography
(with the SU-8 layer being 55
μm). After surface treatment
of gas-phase 1,1,2-trichlorosilane (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 channel 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
(Harrick Plasma, Ithaca, NY)
nel (RC) for fluid flow. The device was filled with dye for visual effect. Scale bar is 5 mm.
Figure 2.
Setup for
using the microviscometer. After
the syringe
pump is
turned on
to pull the
syringe back,
a camera
attached to
the microscope
is used to
record the
movement
of fluids
through the
viscometer.
94
Chemical Engineering Education
and bonded together to form the PDMS viscometer (Figure
1). Tests were not run on the viscometers until at least two
days after their fabrication to reduce the hydrophilicity of the
device channels.
entrances. The laminar flow generated by this pressure can be
described by the Hagen-Poiseuille equation[11]:
The PDMS viscometer consisted of three channels of height
h ~ 55 μm, width w ~ 100 μm, and length Ltotal ~ 20.4 cm.
The viscometer was prepared for use by using micropipettes
to place two drops of sample fluids and one drop of a reference fluid of known viscosity at the entrances of the three
channels in the top left of the device. A syringe pump (Harvard Apparatus, Holliston, MA) was then used to generate a
sub-atmospheric pressure within the device channels to drive
flow. A syringe attached via a Luer stub and polyethylene
tubing (Scientific Commodities, Inc., Lake Havasu City,
AZ) to a bent hollow metal pin was first placed in the pump
and the metal pin was inserted into the pressure inlet in the
bottom right of the device (Figure 2). The syringe pump
was then used to pull the syringe at a constant rate while the
flow through the channels was tracked with a Moticam 2300
camera (Motic, Xiamen, China) mounted on a Stemi SV11
dissecting microscope (Zeiss, Obercochen, Germany). The
transparent liquids moving through the viscometer caused
contrast with the background to decrease as the liquids passed
through them (Figure 3).
where v is the velocity of the fluid; dh is the hydraulic diameter of the channel related to the height h and width w, dh =
2hw / (h+w); η is the dynamic viscosity of the fluid; S is a
constant related to channel geometry, with S = 32 for rectangular channels; ∆P is the pressure drop across the fluid; and
L is the length of the advancing fluid front.
Experimental Setup
The videos taken from the tests were analyzed with MATLAB to track the length of each fluid stream over the duration
of the test. For the tests on PEO described below, the videos
had a frame rate of 13 to 16 fps and were analyzed every four
frames. The code operates by subtracting previous images
from each frame and detecting the movement of a stream
as a change in grayscale intensity that surpasses a certain
threshold. Adjacently marked pixels are combined to make
up the three streams, and the length of each stream is then
found by dividing the total number of pixels in that stream
by a constant thickness value.
Mechanism and Theory of Microviscometer
v=
d 2h ∆P
Sη L
(1)
The pressure drop ∆P consists of two components, i.e.,
∆P = ∆P d + Pc, where Pc is the capillary pressure. ∆Pd is the
pressure difference between the fluid inlet, which is constantly
at atmospheric pressure P0, and the moving fluid front, which
is at the constantly decreasing pressure inside the viscometer
Pi, i.e., ∆P d(t) = P0 – Pi(t). For a test where a sample fluid
and a reference fluid are pulled through the viscometer at the
same time, ∆Pd(t) is the same for the two streams and the
following equations can be written using Eq. (1):
S
ηs Ls ( t) v s ( t) = P0 − Pi ( t) + Pc ,s
d 2h
( 2)
S
ηr L r ( t) v r ( t) = P0 − Pi ( t) + Pc ,r
d 2h
(3)
where the subscripts s and r refer to the sample and reference
streams, respectively. Combining and integrating Eqs. (2) and
(3) leads to the equation
L2r ( t 2 ) − L2r ( t1 )
t 2 − t1
=
2
2
P − Pc ,s
η s L s ( t 2 ) − L s ( t1 )
+ 2d 2h c ,r
( 4)
t 2 − t1
ηr
Sµ r
ηs
for a given test was thus found by taking
ηr
L2 ( t ) − L2r ( t1 )
L2 ( t ) − L2s ( t1 )
vs. s 2
the slope of a linear fit of r 2
t 2 − t1
t 2 − t1
The value of
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 t2 – t1.
channels, differing
mainly in the way the
driving pressures are
applied. The constant
pulling of the syringe
attached to the viscometer generates a
continually decreasing 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 soluthan the air prestions (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 accordance with the Huggins equation[12]
ηsp
2
= η + k η c
(5)
c
where ηsp is the specific viscosity of a polymer solution
of concentration c, defined as
η
ηsp = solution −1 where ηsolution
ηsolvent
is the viscosity of the polymer 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 hydrodynamic volume that the polymer
chains take up in solution, and
k is Huggins’ constant. If the
viscosities of different concentrations of a polymer in solution are known, then a value
of η for the polymer-solvent
pair can be found as the interFigure 4. Sample plots of [L2r (t2) – L2r (t1) ]/ (t2 – t1) vs. [L2s (t2) – L2s (t1) ]/ (t2 – t1) for aqueη
ous 1 MDa PEO solutions of different concentrations. The relative viscosity of each solucept of a graph of sp vs. c.
c
tion is found as the slope of its linear fit.
The value of η can then be related 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 polymersolvent 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 molecular weight Mv, typically between the number-average
Mn and the weight-average Mw.
Figure 5. Plots of ηsp / c vs. c used to determine values of η
for the 1 MDa PEO sample using viscosity data from the Ubbelohde viscometer and the PDMS viscometers. Linear fits are
shown from which η values were determined as the intercepts.
Only the four highest concentrations were used in the linear fit
for the PDMS viscometers. Error bars represent the standard
deviation of ηsp / c .
96
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 solution was found by multiplying the ratio of efflux times of
the solution and the pure solvent by the (measured) density
of that solution. Density differences between the dilute PEO
solutions and water were negligible, so the relative viscosity
of each PEO solution was found simply as the ratio of the
efflux times of the solution and the pure solvent.
Chemical Engineering Education
VALIDATION OF THE DEVICE OPERATION
To ensure that the microviscometer produced accurate viscosity readings, tests were first run on the device using glycerol solutions as sample streams and water as the reference
stream. Pressure was generated with a 50 mL syringe that was
pulled at rates ranging from 3.50 mL/min to 21.84 mL/min.
Tests were performed at room temperature averaging ~ 23 ˚C.
The viscosities of the glycerol solutions were measured with
an Ubbelohde viscometer in a 23.0 ˚C bath for comparison
(Table 1). The results from the microviscometer are seen to
be consistent with the Ubbelohde viscometer although the
variance in the microviscometer tests is much higher.
Viscosity measurements were then made with the microviscometer using dilute 1 MDa PEO solutions as sample streams
and water as the reference stream. For these tests, pressure
was generated by pulling a 50 mL syringe at an initial volume of 25 mL at a rate of 5.46 mL/min. Note that the exact
initial volume of the syringe and the pulling rate used in the
experiments are not critical, as the viscometer can function
over a range of generated pressure gradients. Pressure-induced
deformation of the microchannels could occur in a PDMS
device such as ours if pressure differences were too large but
the maximum pressure gradients across the channels in these
experiments were only ~15 kPa for the glycerol tests and ~10
kPa for the PEO tests. No deformation of the channels was
observed under the microscope in any test.
The PEO tests were performed at 23.0 ˚C + 0.5 ˚C and the measured viscosity values were compared to values obtained with an
Ubbelohde viscometer in a 23.0 ˚C bath (Table 1). Sample plots of
L2r ( t 2 ) − L2r ( t1 )
t 2 − t1
vs.
microviscometer matched the results from the Ubbelohde
viscometer well while the viscosities of the 0.4 mg/mL and
0.8 mg/mL solutions measured by the microviscometer were
somewhat lower than that of the Ubbelohde viscometer, possibly due to the high surface areas of microdevices and loss
of polymer from the solution to the surface. The variance for
the microviscometer is seen to be much greater than that for
the Ubbelohde viscometer at all concentrations, which may
be due to image processing errors or to the much smaller
sample size.
The viscosity results from the PDMS viscometers and the
Ubbelohde viscometer were then used to find values of η
η
for the PEO sample by plotting sp vs. c and taking η as
c
the y-intercept (Figure 5). The Ubbelohde viscometer data
extrapolated to a value of η = 0.588 mL/mg. When all the
data for the microviscometer were used, a much lower value of
η = 0.424 mL/mg was found (extrapolation not shown). This
 
discrepancy in η values is caused by the lower viscosities
found with the microviscometer at lower c: the error in the
η
plot of sp is magnified for smaller c, which also corresponds
c
to larger differences in ηsp.
To reduce the error in η estimation, low concentrations of
polymer solution should be avoided in the experiments. As
shown in Figure 5, excluding the 0.4 and 0.8 mg/mL microviscometer data from the extrapolation results in an extrapolated
value of η = 0.605 mL/mg, which agrees well with the values
from Ubbelohde experiments.
L2s ( t 2 ) − L2s ( t1 )
t 2 − t1
used to calculate viscosity values
in the microviscometer tests are
seen in Figure 4.
In a few of the microviscometer
tests, PEO solutions began to flow
through the viscometer before
the syringe was pulled, suggesting that the PEO solutions had a
positive value of Pc, sample, i.e., they
wet the PDMS surface. This did
not interfere with data collection,
however, and the results from
the viscometer were still valid
for times while all fluids were
moving.
It can be seen from Table 1 that
the viscosities of the 1 mg/mL,
1.2 mg/mL, 1.4 mg/mL, and 1.6
mg/mL solutions measured by the
Vol. 45, No. 2, Spring 2011
TABLE 1
Relative viscosity values determined for aqueous solutions of glycerol and PEO vs.
water using an Ubbelohde viscometer and PDMS viscometers. Each solution was
measured three times with the Ubbelohde viscometer and multiple times with the
PDMS viscometers as marked.
—
ηsolution
ηsolvent
± standard deviation
—
Solution
Ubbelohde
viscometer
PDMS
viscometer
Number of
microviscometry trials
10 % glycerol
1.25 ± 0.003
1.32 ± 0.05
10
20 % glycerol
1.77 ± 0.003
1.80 ± 0.13
12
30 % glycerol
2.38 ± 0.015
2.37 ± 0.12
18
50 % glycerol
6.01 ± 0.012
6.07 ± 0.64
12
0.400 mg/mL PEO
1.26 ± 0.0009
1.22 ± 0.04
5
0.800 mg/mL PEO
1.59 ± 0.002
1.49 ± 0.13
5
1.00 mg/mL PEO
1.76 ± 0.003
1.78 ± 0.05
5
1.20 mg/mL PEO
1.96 ± 0.006
1.94 ± 0.12
5
1.40 mg/mL PEO
2.15 ± 0.005
2.22 ± 0.13
5
1.60 mg/mL PEO
2.40 ± 0.012
2.39 ± 0.20
5
97
Using values of a = 0.78 and K = 12.5 * 10-6 mL/mg
(g/mol)1/a for aqueous PEO solutions[13] and the η values
above, the Mark-Houwink equation produces values of M =
1,010,000 g/mol for the PDMS viscometers and M = 977,000
g/mol for the Ubbelohde viscometers. These values are in
good agreement with each other as well as with the value
reported by the manufacturer.
LABORATORY IMPLEMENTATION, COST AND
LOGISTICS, AND STUDENT FEEDBACK
Laboratory Implementation
The laboratory procedure consists of a device fabrication
demonstration, student-run microviscometer tests on PEO
solutions, image processing of the tests using MATLAB, and a
shear-thinning demonstration. After the lab session, viscosity
data from different students can be combined and analyzed to
find an estimate for the molecular weight of the PEO sample
used. If time is available, students can also measure the viscosities of the PEO solutions with macro viscometers such as
Ubbelohde viscometers to validate the microviscometer data.
This allows students to visualize the advantages and disadvantages of microviscometry in terms of accuracy, precision,
speed, cost, and fluid volume required.
Two trials of this procedure were run with volunteer undergraduates (mostly junior students who have taken transport
phenomena) from the Georgia Institute of Technology School
of Chemical & Biomolecular Engineering. Each trial had four
students with no microfluidics experience who performed
the viscometer tests and the first trial had an additional three
students who had worked in a microfluidics laboratory before.
Several days before the laboratory sessions were held, students
were provided with a copy of the procedure as well as a “prelab” that provided the background, theory, and a quiz to test
their understanding prior to the lab. The beginning of the laboratory consisted of a microviscometer fabrication demonstration
given by the undergraduate teaching assistant. The assistant
explained how masks and masters are manufactured, explained
how PDMS is mixed, cast, cured, and bonded to form devices,
and used the plasma cleaner to bond a device to show to the students. If time allows, this simple micromolding step and device
fabrication can be incorporated into the lab, and concepts such
as cross-linking, Poisson ratio, Young’s modulus, and surface
treatment can be explained and demonstrated.
The students then ran two microviscometer tests where
each test used two different concentrations of 1 MDa PEO
as sample streams and water as the reference stream. Concentrations of 0.500, 1.00, 1.50, and 2.00 mg/mL were used
in the two tests. Pressure was generated by pulling a 50 mL
syringe at an initial volume of 25 mL at a rate of 5.46 mL/min
(the same conditions as in the validation tests for the PEO
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 solution 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
sessions have a very low cost because of the small volumes
of chemicals needed. The major repeated cost is in fabricating
the PDMS devices which consume ~$1.50 of PDMS per chip.
Approximately 5 hours of time were devoted by the undergraduate teaching assistant to prepare for each lab session,
including device fabrication, solution preparation, and lab
set-up. The two lab sessions took about 1 hour and 45 minutes
each to complete, including the fabrication demonstration, the
completion of four viscometer tests, and the processing of the
tests and the description of the MATLAB code.
Demonstration of Shear Thinning Fluids
Student Feedback
The students then used the pre-written MATLAB code to
analyze their videos. In our experience, some of the troubleshooting issues with the image processing can be explained to
the students during the lab module to facilitate data processing. For instance, it is important to take a video that has both
high contrast (for the streams to be located by the code) and
uniform contrast (for the streams to be tracked with uniform
width). Problems with noisy images can be addressed with
MATLAB filtering of the raw video and with data smoothing
of the acquired length values.
To demonstrate both the shear thinning behavior of nondilute polymer solutions and the ability to generate a large
range of shear rates in the viscometer using the syringe pump,
the students then ran a test with a high pulling rate and a test
with a low pulling rate on a sample of 3 mg/mL 4 MDa PEO
with 60% glycerol solutions as reference fluids. When a test
is run with a syringe initial volume of 40 mL and a pulling
rate of 1.7 mL/min, corresponding to an average shear rate
~100 s-1, the 60% glycerol reference is seen to move through
the viscometer more quickly than the PEO solution (Figure
6). In contrast, the PEO solution is seen to move through the
viscometer more quickly than the 60% glycerol reference
when given a higher average shear rate of ~780 s-1 (generated
by pulling a syringe at an initial volume of 5 mL at a rate
of 20 mL/min). This inversion of behavior is caused by the
lower viscosity of the PEO solution at a higher shear rate as
opposed to the rate-independent viscosity of the Newtonian
glycerol solution. The shear thinning behavior of the PEO
solution over this range of shear rates was verified using a
Physica MCR 3000 rheometer (Anton-Paar, Graz, Austria);
the viscosity of the PEO solution fell from ~ 14 cP at 100 s-1
to ~ 8.6 cP at 780 s-1. This method can be used to demonstrate
non-Newtonian behaviors of various fluids in the range of
shear rates up to 2000 s-1.
Cost Estimate and Timing Logistics
Assuming that laboratory equipment such as microscopes,
cameras, a plasma cleaner, and a syringe pump are available,
the laboratory costs come in the materials. The fabrication of a
mask and master costs around $150, and samples of the 1 MDa
PEO, 4 MDa PEO, glycerol, and PDMS cost ~$30 each for a
total startup cost of <$300. Note that other water-soluble polymers can be substituted for PEO if desired, and fluids other
than glycerol solutions can be used as viscosity standards as
long as they do not swell PDMS and their viscosity is known.
If needed, we estimate that a simple microscope and camera
setup are in the range of $2,000 to $3,000. If a plasma cleaner
is not available, it is possible to create devices by pressing a
flat PDMS slab against a PDMS slab with channel imprints,
placing the slabs between two glass slides, and then holding
the glass slides together using rubber bands.
Vol. 45, No. 2, Spring 2011
Students who participated in the laboratory experiments
provided informal feedback. Most students found the module was effective in introducing the concept of solution
viscometry and microfluidics, to which most of them had
had no prior exposure. The students found more background
on microfluidics and microfabrication details would be both
more interesting and more useful. This suggests that the
laboratory module should be expanded to multiple sessions
to deal with the individual topics in depth. The students also
commented that seeing non-Newtonian behavior with a real
demonstration could reinforce this concept that they learned
in the classroom.
CONCLUSIONS
We present a procedure for a student laboratory session to
demonstrate the use of microfluidics to determine fluid viscosity
and the use of dilute solution viscometry to estimate polymer
molecular weight. Overall, the results were reasonably consistent with those found from conventional Ubbelohde viscometry.
The laboratory also allows students to see firsthand how microfluidic devices are fabricated and to observe a visual demonstration of the shear thinning behavior of non-dilute polymer
solutions. Assuming soft lithography equipment is available, the
experimental setup is very quick and affordable. The laboratory
serves as an excellent way to generate interest in the fields of
polymers, rheology, and image processing while invigorating
students with the opportunity to work hands-on in the “cuttingedge” realm of microfluidics.[14] The combination of written
instruction in the pre-lab and procedure, verbal instruction
and visual displays from the teaching assistant, and hands-on
experience for each student caters to a range of different student
learning styles.[15-16] Because it is multi-faceted, this experimental platform can be used and re-used in different pedagogical
contexts, or it can be a problem-solving based learning tool.[17]
We recommend running the following laboratory modules
individually or in combination depending on the need of the
curricula and time available for the laboratory experiments: (1)
laminar flow – Hagen-Poiseuille relationship; (2) viscometry;
(3) demonstration of non-Newtonian flow; (4) microfabrication; (5) other concepts of polymer processing; (6) image
processing.
99
ACKNOWLEDGMENTS
We thank M. Li for developing the microviscometer design
and the first round of MATLAB code for image processing,
J. Stirman and M. Crane for their help with the microscope
setup and image processing, and Dr. V. Breedveld and E.
Peterson for use of their facilities.
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Newtonian Fluid,” J. Micromechanics and Microengineering, 17(9)
1828 (2007)
7. Srivastava, N., R.D. Davenport, and M.A. Burns, “Nanoliter Viscometer
100
for Analyzing Blood Plasma and Other Liquid Samples,” Analytical
Chemistry, 77(2) 383 (2005)
8. Marinakis, G.N., J.C. Barbenel, A.C. Fisher, and S.G. Tsangaris, “A
New Capillary Viscometer for Whole Blood Viscosimetry,” Biorheology, 36(4) 311 (1999)
9. Han, Z., and B. Zheng, “A PDMS Viscometer for Microliter Power
Law Fluids,” J. Micromechanics and Microengineering, 19(11) 115005
(2009)
10. Duffy, D.C., J.C. McDonald, O.J.A. Schueller, and G.M. Whitesides,
“Rapid Prototyping of Microfluidic Systems in Poly(dimethylsiloxane),”
Analytical Chemistry, 70(23) 4974 (1998)
11. Perry, R.H., and D.W. Green, Perry’s Chemical Engineers’ Handbook,
8th Ed., McGraw-Hill, New York (2008)
12. Painter, P.C., and M.M. Coleman, Essentials of Polymer Science
and Engineering, 1st Ed., DEStech Publications, Inc., Lancaster, PA
(2008)
13. Bailey, F.E., and J.V. Koleske, Poly(ethylene oxide), Academic Press,
New York (1976)
14. Young, E.W.K., and C.A. Simmons, “ ‘Student-Lab-on-a-Chip’: Integrating Low-Cost Microfluidics Into Undergraduate Teaching Labs
to Study Multiphase Flow Phenomena in Small Vessels,” Chem. Eng.
Ed., 43(3) 232 (2009)
15. Felder, R.M., and L.K. Silverman, “Learning and Teaching Styles in
Engineering Education,” Eng. Ed., 78(7) 674 (1988)
16. Montgomery, S.M., and L.N. Groat, “Student Learning Styles and Their
Implications for Teaching,” CRLT Occasional Papers, 10 (1998)
17. Major, C.H., and B. Palmer, “Assessing the Effectiveness of ProblemBased Learning in Higher Education: Lessons from the Literature,”
Academic Exchange Quarterly, 5(1) (2001) p
Chemical Engineering Education
ChE curriculum
TWO-COMPARTMENT
PHARMACOKINETIC MODELS
for Chemical Engineers
Kumud Kanneganti and Laurent Simon
T
New Jersey Institute of Technology • Newark, NJ 07102
he absorption, distribution, metabolism, and excretion
(ADME) of a drug, after single or multiple administrations, are usually represented by compartmental
pharmacokinetic models. These compartments correspond to
tissues and organs in the human body. The analysis of these
processes can be very complex, as in the case of physiologically based pharmacokinetics (PBPK), where information
on the weights, blood flows, and physicochemical and biochemical properties of a compound is necessary to describe
concentration profiles in the tissues (i.e., lung, brain, and
kidney).[1] Although, in theory, a multi-compartment approach
is better suited to describe the dynamics of most drugs in the
body, clinicians prefer the simplicity of a one-compartment
model[2] to predict the plasma drug concentrations and to
design appropriate dosage regimens.
In a one-compartment model, the blood and surrounding
tissues are lumped into a single process unit. As soon as the
active pharmaceutical ingredient (API) enters this compartment, it is uniformly distributed throughout the body.[2] The
mathematical representation of these systems involves a drug
injection inlet stream, a constant-volume central compartment,
and a clearance term. A series of experiments, inspired by
this model, were designed to introduce chemical engineering
students to pharmacokinetics and to stimulate their interest in
research related to drug delivery.[3] Continuous intravenous
Vol. 45, No. 2, Spring 2011
(i.v.) infusion and i.v. bolus (single and multiple) administrations were illustrated with activities consisting mostly of a
dye placed in a mixing vessel.
This contribution focuses on the applications of a twocompartment model for describing drug pharmacokinetics.
Although the error in developing dosing regimens based on
Laurent Simon is an associate professor
of chemical engineering and the associate
director of the Pharmaceutical Engineering
Program at the New Jersey Institute of Technology. He received his Ph.D. in chemical
engineering from Colorado State University
in 2001. His research and teaching interests
involve modeling, analysis, and control of
drug-delivery systems. He is the author of
Laboratory Online, available at <http://laurentsimon.com/>, a series of educational and
interactive modules to enhance engineering
knowledge in drug-delivery technologies
and underlying engineering principles.
Kumud Kanneganti is pursuing a Master’s
degree in the Otto H. York Department of
Chemical, Biological, and Pharmaceutical
Engineering. He received a B. Tech. degree
in chemical engineering from Nirma University of Science and Technology (NU), India.
His research focus is in the design of drug
delivery strategies using well-stirred vessel
experiments.
© Copyright ChE Division of ASEE 2011
101
Component balances in the two compartments (Figure 1a)
yield:
d (C1V1 )
= −k el Cl V1 − k12 C1V1 + k 21C2 V2
(1)
dt
and
d (C2 V2 )
dt
Figure 1. Representation of a two-compartment model.
Figure 1a is a schematic model of the process as introduced in a course in pharmacokinetics; Figure 1b is the
two-unit process that is assembled to mimic the behavior
of the two-compartment model.
a single-compartment model is acceptable for most drugs,
equations for two-compartment kinetics are more appropriate
for a few pharmaceutical agents that are potent and/or exhibit
a narrow therapeutic range.[3] Experiments, based on concepts
learned in chemical engineering classes, are developed to
introduce students to these processes. The learning outcomes
of this project are to: i) illustrate a two-compartment pharmacokinetic model using continuous-stirred vessels, ii) derive
total mass and component balances for the two compartments,
iii) solve the derived differential equations using Laplace
transform methodologies, iv) calculate the pharmacokinetic
parameters, and v) conduct experiments to simulate a single
i.v. bolus administration.
LABORATORY DESCRIPTION
102
( 2)
where C is the drug concentration, V is the volume, and k is
a mass transfer rate constant. The subscripts 1 and 2 represent 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., Michaelis-Menten kinetics) are often appropriate for certain
drugs. Since the volumes are constant, Eqs. (1) and (2) can
be written as:[4]
d (C1 )
= −k el Cl − k12 C1 + k 21ζ 21C2
(3)
dt
and
ζ 21
d (C 2 )
dt
= k12 C1 − k 21ζ 21C2
( 4)
V2
.
V1
Figure 1b. corresponds to the flowchart of a two-unit process designed to mimic the behavior of a two-compartment
model. Several pumps are required to manipulate the flow
rates. Fresh water streams are also added to the vessels. At
this point, students may be asked to show that component
balances around the units lead to the system described by Eqs.
(3) and (4) (objectives i and ii). A total mass balance around
vessels 1 and 2 yields:
d (ρ1V1 )
= Fw 1ρw 1 + F21ρ2 − Fel ρl − F12 ρ1
(5)
dt
with ζ 21 =
and
Theoretical Foundation
The schematic of a two-compartment model is shown in
Figure 1a. According to this representation, the human body
is comprised of a central compartment consisting of the
blood/plasma and well-perfused tissues (e.g., liver, heart),
and a peripheral compartment mainly composed of poorly
perfused tissues (e.g., skeletal muscles). Analysis of a blood
sample would reveal the concentration in the first compartment. This measurement may be used by the physician to
assess the effectiveness of a drug-dosage regimen.
= k12 C1V1 − k 21C2 V2 ,
d (ρ2 V2 )
dt
= Fw 2 ρw 2 + F12 ρ1 − F21ρ2 ,
(6)
respectively. The subscripts w1 and w2 indicate the fresh water streams into vessels 1 and 2. Assuming equal and constant
densities, we have ρ1 = ρ2 = ρw 1 = ρw 2 . The relationships:
Fel + F12 = Fw 1 + F21
(7 )
F21 = Fw 2 + F12
(8)
and
Chemical Engineering Education
hold in order to maintain constant volumes in both tanks. In
addition, potassium permanganate balances around the two
units yield:
d (C1V1 )
dt
= F21C2 − Fel Cl − F12 C1
(9)
and
Although the satisfaction of the initial conditions, C1(0) = C10
and C2(0) = 0, is not sufficient to guarantee the accuracy of
Eqs. (15) and (16), these equalities are necessary conditions.
In addition, showing that C1 ( t → ∞) = C2 ( t → ∞) = 0 may
lead to a discussion on the necessity for administering multiple
bolus i.v. doses.
(λ + k ) C
C ( t) =
(λ − λ )
+
d (C2 V2 )
dt
21
= F12 C1 − F21C2 .
1
(10)
Dividing Eqs. (9) and (10) by V1 results in Eqs. (3) and (4)
F
F
F
V
with k12 = 12 , k 21 = 21 , k el = el , ζ 21 = 2 .
V1
V2
V1
V1
and
The experiments are conducted with V1=V2. As a result,
Eqs. (3) and (4) become:
with
d (C1 )
dt
= −k el Cl − k12 C1 + k 21C2
(11)
C2 ( t) =
+
λ =
e
−
k12 C10
(λ − λ
+
λ+ t
+
−
−(k12 + k 21 + k el ) +
)
21
10
+
eλ t −
−
k12 C10
(λ − λ
+
−
−
)
2
(k12 + k 21 + kel )
eλ
−
eλ
t
t
− 4k el k 21
2
(17 )
(18)
(19)
and
and
d (C 2 )
dt
= k12 C1 − k 21C2
(12)
The initial conditions are C1(0) = C10 and C2(0) = 0 for a bolus injection. Using the Laplace transforms of the concentra∞
−st
tions C1(t) and C2(t) (i.e., L {C1 ( t)} = C1 (s) = ∫ C1 ( t) e dt
0
∞
and L {C2 ( t)} = C2 (s) = ∫ C2 ( t) e dt ) and applying the
−st
0
Laplace operator to both sides of Eqs. (11) and (12), the following equations are obtained:
sC1 − C10 = −(k12 + k el ) C1 + k 21C2
(13)
sC2 = k12 C1 − k 21C2
(14)
and
The system formed by Eqs. (13) and (14) is solved to
give:
(s + k 21 )C10
C1 = 2
(s + (k12 + k21 + kel )s + kel k21 )
(15)
and
C2 =
+
(λ + k ) C
−
(λ − λ )
−
10
(s + ( k
2
12
k12 C10
+ k 21 + k el ) s + k el k 21 )
(16)
Partial-fraction expansion, or the residue theorem, may
be used to invert the C1 and C2 (objective iii). Students are
also encouraged to apply Laplace transform initial and final
value theorems to verify the correctness of Eqs. (15) and (16).
Vol. 45, No. 2, Spring 2011
−
λ =
−(k12 + k 21 + k el ) −
2
(k12 + k 21 + kel )
2
− 4k el k 21
( 20)
Given concentration data in the central compartment (or
vessel 1), Eq. (17) can be applied to estimate k12, k21, and k el
(objective iv). Students may be given the opportunity to
choose among three methods to compute these parameters:
1) Measurement of the flow rates: the pharmacokinetics are
F
F
F
calculated using k12 = 12 , k 21 = 21 , and k el = el .
V1
V2
V1
2) Regression of Eq. (17) to experimental C1(t) data: Eq. (17)
is written in the form C1 ( t) = Ae−αt + Be−βt with α > β.
Computational software packages such as Mathematica® (Wolfram Research, Inc., IL) or Matlab®
(The MathWorks, Inc., MA) can be adopted to estimate
A, B, α, and β. Algebraic manipulations show that
Aβ + Bα
αβ
k 21 =
, k el =
and k12 = α + β − k 21 − k el .
A+B
k 21
3) Methods of residuals[5]: Data collected at long times are
fitted to the equation C1l(t) = Be−βt because α > β. Parameters B and β are obtained from ln[C1l(t)]=ln(B)– β
t. The variable C1l represents the concentration at a sufficiently long time. Similarly, data gathered at short times
are fitted to C1s(t)– Be−βt = Ae−αt where C1s stands for
the concentration a short time after the bolus injection.
Parameters A and α are estimated from ln[Cls(t)– Be−βt ]
=ln(A)– αt.
Any of the methodologies described above is implemented
to study the influences of pharmacokinetic parameters on C1
and C2.
103
Materials and Experimental Procedure
Except for the increased number of pumps, the same
materials required in the study of the one-compartment
experiments[3] are used in this project (Figure 2) (objective
v): variable flow-rate pumps, beakers, stopwatch, graduated
cylinders, pipettes, rubber tubing, magnetic stirrer, magnetic
bars, potassium permanganate, spectrophotometer, cuvettes,
laboratory stands, and clamps. An i.v. bolus of 1.37 g of potassium permanganate was administered to the central compartment. Samples were collected every 15 minutes for both the
central and the peripheral compartments and analyzed with
a spectrophotometer set at 530 nm. A calibration curve was
developed to relate the concentration with the absorbance
reading: y = 0.0163A where y represented the concentration
in g/mL and A the absorbance. The volume of each vessel was
maintained at 200 mL.
ing regimens. To illustrate this point, three bolus injections of
1.10 g, 0.33 g, and 0.33 g of potassium permanganate were
added to the central compartment at 0, 1.12, and 3.36 hours,
respectively, as recommended by the results of an optimal
dosing regimen for KMnO4 (Figure 4). The optimization
code, based on a two-compartment model and written in the
Mathematica® environment, minimized the sum of squared
errors between the concentrations in the central compartment
and a desired KMnO4 level of 3.46 g/L for an experimental
duration of 5.75 hours. The following observations can be
made: i) The predicted and experimental data agree very well
and ii) the calculated doses were able to maintain the KMnO4
concentration around 3.46 g/L. Simulations conducted under
the assumption that KMnO4 obeys one-compartment pharmacokinetics show that the predicted data deviate considerably
from the true profile (Figure 4).
Results and Discussions
SUMMARY OF EXPERIENCES
The data for the i.v. bolus administration are shown in Figure 3. Pharmacokinetic parameters determined from the three
methods are k12 = 1.80 hr-1, k21 = 2.94 hr-1, and kel = 0.30 hr-1
(measurement of the flow rates); k12 = 1.42 hr-1, k21 = 2.37 hr-1,
and kel = 0.26 hr-1 (regression in Mathematica®); k12 = 1.80 hr-1,
k21 = 2.92 hr-1, and kel = 0.27 hr-1(methods of residuals). The
predicted concentrations plotted are the ones derived by the
third method. Students may be given a project where they are
expected to investigate the effects of the kinetic parameters on
C1 and C2 to understand how drug transport is influenced by
the distribution and elimination rate constants. This research
also offers the opportunity to address the effects of the dose
size on the plasma blood concentration. Multiple bolus-injections and constant-rate infusions can also be studied after a
slight modification of the model and initial conditions.
The choice of one compartment or two compartments may
be an important factor when designing appropriate drug-dos-
A group of six students from an undergraduate course in
biotransport worked on this project. The three-credit class is
designed for biomedical engineering students pursuing tracks
in biomaterials and tissue engineering or biomechanics.[3]
Chemical engineering students may also select the course as
an elective toward their degree requirements. A final report
was produced after several meetings with the instructor during which the project was discussed. Although a graduate
assistant helped design the experimental setup (Figure 2)
because of time limitation, the group was required to draw a
schematic diagram of the process similar to Figure 1b. The
specific assignment was to study the effects of loading doses
on the concentrations in the central and peripheral compartments. In addition to providing a background of the subject,
the students were also responsible for deriving the model
equations and estimating the kinetic parameters. They were
not told about the methods that could be applied to determine
Figure 2. The experimental setup of the twocompartment model.
Potassium permanganate was added to the
beakers. Fresh water
in an Erlenmeyer flask
was introduced to the
two compartments.
104
Chemical Engineering Education
Experiments in continuous-stirred
vessels were designed to represent drug
transport within the body. The processes
governing equations were similar to
those of a two-compartment model
with linear first-order distribution and
elimination kinetics. These activities
gave students the opportunity to apply
conservation principles learned in the
classroom. In addition, Laplace transform techniques were implemented
to solve the differential equations.
Closed-formed expressions for the concentration of potassium permanganate
in the central and peripheral compartment were obtained. Three methods
of extracting the pharmacokinetic parameters based on experimental data
were outlined. After administering
an i.v. bolus of 1.37 g of potassium
permanganate to the central vessel,
the concentration profiles showed a
pattern analogous to drug transport
when a two-compartment model is
used. The three parameter estimation
methods yield comparable results.
Students who worked on the project
were able to model the process, solve
the governing differential equations,
and estimate the kinetics.
REFERENCES
1. Clewell, R.A., and H.J. Clewell, “Development and Specification of Physiologically Based Pharmacokinetic Models for
Use in Risk Assessment,” Regul. Toxicol.
Pharmacol., 50(1), 129 (2008)
2. Schoenwald, R.D., Pharmacokinetic
Principles of Dosing Adjustments, CRC
Press, Boca Raton (2001)
3. Simon, L., K. Kanneganti, and K.S. Kim,
“Drug Transport and Pharmacokinetics
for Chemical Engineers,” Chem. Eng.
Ed., 44(4), 262 (2010)
4. Truskey, G.A., F. Yuan, and D.F. Katz,
Transport Phenomena in Biological
Systems, 2nd Ed., Pearson Prentice Hall,
Upper Saddle River, NJ (2009)
5. Gibaldi, M., and D. Perrier, Pharmacokinetics, 2nd Ed., Informa Healthcare, New
York (2007) p
Vol. 45, No. 2, Spring 2011
7.0
KMnO4 Concentration (g/L)
CONCLUSIONS
8.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Time (hr)
Figure 3. Concentrations of KMnO4 in the central (j) and peripheral (+) compartments. The parameters obtained by the method of residuals are k12 = 1.80
hr- 1, k21 = 2.92 hr -1, and kel = 0.27 hr -1. Predicted concentrations in vessels 1 and
2 are shown by the symbols (—) and (-----), respectively.
6.00
1st dose: 1.10 g
5.50
2nd dose: 0.332 g
5.00
KMnO4 Concentration (g/L)
these parameters; the kinetic values were
estimated from measurement of the flow
rates. The results were also presented to
the class and sources of errors, such as
flow fluctuations, were identified.
3rd dose: 0.330 g
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
0
1
2
3
4
5
6
Time (hr)
Figure 4. Experimental concentrations of KMnO4 in the central (d) and peripheral
compartments (j). The predicted data are represented by the solid lines (____). The
rate constants for the two-compartment model are k12 = 1.80 hr -1, k21 = 2.92 hr -1,
and kel = 0.27 hr -1. The elimination rate constant for the one-compartment model
(dashed line: -------) is kel = 0.41 hr -1.
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
D
Universidade de Aveiro, Campus de Santiago • 3810-193 Aveiro, PORTUGAL
istillation is by far the most frequently used industrial
separation process. Although not energy-efficient, it
has a simple flowsheet and is a low-risk process. It
is indeed the benchmark with which all newer competitive
processes must be compared. Following Null,[1] distillation
should be selected if the relative volatility is greater than 1.05,
whereas Nath and Motard[2] and Douglas[3] indicate α12 greater
than 1.10, a more conservative critical value for the relative
volatility. Generally, design heuristics point out that processes
using energy separation agents should be favored.
For the reasons outlined above, distillation experiments
are included in the Chemical Engineering Integrated Master
curriculum of the Department of Chemistry at University of
Aveiro (DCUA). Students start receiving lectures on distillation as part of the Separation Processes I course, which is
essentially devoted to equilibrium-staged unit operations.
Afterwards, experiments are carried out in Laboratórios EQ
(Chemical Engineering Laboratory), a weekly six-hour lab
course intended to provide hands-on experience on separations, reaction, and control. Each experiment lasts two weeks:
in the first week students—divided into groups of three—carry
out the lab exercise and some calculations, and in the second week students do numerical calculations and computer
simulations, which require computational support. Student
assessment is based on a very short individual oral quiz and
a report prepared by the student groups.
In this paper a lab exercise on continuous and batch
rectification developed at DCUA is presented. Papers with
experimental work in the distillation field are scarce and accordingly this communication intends to fill this gap. There are
a number of educational publications concerning distillation
calculations, mostly using Excel, Matlab, Hysys, and Mathematica software.[4-6] Moreover, virtual laboratories involving
distillation units have been developed in order to enhance the
understanding of the process units and to improve the teaching
effectiveness.[7, 8] Nonetheless, students are usually uninterested in a problem unless they can visualize it in practice, so
experiments in the lab should never be totally replaced by
simulated experiments on a computer, notwithstanding its
ease and less time-consuming approach.
In this work, experiments are performed in an Oldershaw
column with five sieve trays to separate cyclohexane/n-heptane under different modes of operation. These modes include
total reflux, continuous rectification with partial reflux, and
Carlos M. Silva is a professor of chemical engineering at the Department of Chemistry, University of Aveiro, Portugal. He received his B.S.
and Ph.D. degrees at the School of Engineering, University of Porto,
Portugal. His research interests are transport phenomena, membranes,
ion exchange, and supercritical fluid separation processes.
Raquel V. Vaz is a Ph.D. student at the Department of Chemistry,
University of Aveiro, Portugal. She received her Master’s degree in
chemical engineering from the University of Aveiro. Her main research
interest focuses on molecular dynamics simulation and modeling of
diffusion coefficients of nonpolar and polar systems.
Ana S. Santiago is a post-Ph.D. student in the Department of Chemistry,
University of Aveiro, Portugal. She received her B.S. degree in chemical engineering from the University of Coimbra and Ph.D. in chemical
engineering from the University of Aveiro. Her main research interest
focuses on bio-refinery and membrane separation processes.
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
in chemical engineering from the University of Aveiro. Her main research
interest focuses on mass transfer, membrane separation processes,
ion exchange, and molecular dynamics simulation and modeling of
diffusion coefficients of nonpolar and polar systems.
© Copyright ChE Division of ASEE 2011
106
Chemical Engineering Education
batch rectification with constant reflux. An Oldershaw tray
column is a laboratory-scale column equipped with perforated
trays. Of special importance is the fact that it exhibits a separation capacity close to that of large industrial columns.[9] In
fact, experimental results show that commercial towers will
require a similar number of stages to reach the same separation level obtained in the Oldershaw unit.[10]
With this work students practice relevant concepts introduced earlier in their curriculum, namely vapor-liquid
equilibrium, continuous vs. batch operation, McCabe-Thiele
graphical method, column efficiency, and application of the
generalized Rayleigh equation. Moreover, students use industrial simulation software (Aspen) to predict experimental
results, giving them the opportunity to improve their skills in
this field, too. By examining experimental results and comparing them with those obtained from simulations, students gain
insight to this unit operation.
LABORATORY DESCRIPTION
Experimental Setup
Experiments are performed in an Oldershaw tray column
instrumented and equipped with a control system supplied
by Normschliff Gerätebau (similar equipment is available
from Normag GmbH Imenau). Other commercial teaching
equipment for continuous distillation is offered, for example,
by Armfield, Ltd. (<www.discoverarmfield.co.uk>), De
Dietrich-QVF (<www.ddpsinc.com>), and Phywe (<www.
phywe-systeme.com>). The unit used is shown in Figure 1 and
comprises five perforated plates (3 cm of diameter), a reboiler
(capacity of 2
L), a total top
condenser using tap water as
cooling fluid,
a lateral condenser to remove distillate
as liquid, and a
solenoid valve
to divide the
vapor stream
into reflux and
distillate under the partial
reflux mode.
Additional features include:
sampling
points above
each tray to determine liquid
composition;
Figure 1. Oldershaw tray column.
and temperaVol. 45, No. 2, Spring 2011
ture sensors immersed in the reboiler and located in the top
condenser allowing the determination of the bottom and head
compositions, respectively. The column is used to separate
c.a. 800 mL of a cyclohexane (Lab-Scan, 99%) / n-heptane
(Lab-Scan, 99%) mixture with 30% (mol) of cyclohexane.
The calibration curve—measured in this work—to determine the cyclohexane mole fraction (x1) in a cyclohexane–nheptane mixture at 30 ˚C as function of refractive index (RI),
is given by x1=-309.95 RI2 + 895.15 RI – 645.15.
Experiments at Total Reflux, R = ∞
Rectifications at total reflux were performed at two distinct
effective reboiler powers (P = 75 and 125 W) to evaluate
the effect of the internal molar flow upon separation and
column efficiency. The invariance of the top and bottom (TD
and TB) temperatures was used to detect the steady state. Additionally, they were utilized to determine the corresponding
cyclohexane molar compositions, xD and xB, by vapor-liquid
equilibrium calculations assuming that the column is kept
at atmospheric pressure (pressure drop along the column is
considered negligible).
Continuous Rectification at Partial Reflux
This Oldershaw tray column is extremely versatile. It can
be operated continuously under partial reflux. With simple
modifications, the distillate may be directly fed to the reboiler
(see path A in Figure 1), allowing us to reach the corresponding steady state. Such an experiment was carried out at R
= 6 for P = 125 W. Once more, TD and TB were utilized to
determine xD and xB.
Batch Rectification at Constant Partial Reflux
Finally, a semi-continuous or batch distillation was performed
for R = 6 and P = 125 W. Presently, the distillate is not fed to
the reboiler, but collected in the independent flask shown in
Figure 1 (see path B). Under such mode of operation, compositions vary along time. TD and TB were registered during 1 h
approximately, to calculate the corresponding xD and xB, and
the distillate refractive index was measured at the end.
HAZARDS AND SAFETY PRECAUTIONS
Cyclohexane (CAS registry number: 110-82-7) and
n-heptane (CAS registry number: 142-82-5) are stable
liquids at room temperature, highly flammable, and may
readily form explosive mixtures with air. They are harmful if swallowed or inhaled, and cause irritation to skin,
eyes, and respiratory tract. Attention must be paid during
the withdrawal of liquid samples, from the bottom of the
column, in order to measure the refractive index. Protection equipment, including gloves and glasses, should be
used. Students must review the Materials Safety Data
Sheet for each chemical before starting the experiment
and are instructed to collect wastes in specific tanks to be
subsequently treated by the DCUA.
107
subtracting one stage (corresponding to reboiler) from the
total number of equilibrium stages.
DATA ANALYSIS
Vapor-Liquid Equilibrium
At low pressure, vapor-liquid equilibrium of a component
i may be represented by:
yi Pt = x i γ i ( x ) Piσ (T)
(1)
where yi and xi are the vapor and liquid molar fractions,
respectively, Piσ is its vapor pressure, γi is its activity coefficient, and Pt is total pressure. Piσ is computed by the Antoine
equation and γi by Margules equations, whose constants may
be found in the literature.
Since ∑ x i = ∑ yi = 1, the liquid molar fraction may be
determined for any temperature by the relation:
Pt = x1γ1 ( x ) P1σ (T) + (1− x1 ) γ 2 ( x ) P2σ (T)
( 2)
where x denotes the liquid composition vector. The vapor
molar fraction can be then determined by Eq. (1).
Number of Equilibrium Stages
The number of equilibrium stages is obtained by the wellknown McCabe-Thiele method.[11] In this work the column
has a rectifying section only, hence the operating line is:
 R 
 1 
 x n + 

(33)
y n+1 = 
 R + 1 x D
 R + 1
where yn+1 and xn are the cyclohexane vapor and liquid fractions of trays n+1 and n, respectively. At total reflux (R = ∞)
the operating line coincides with the diagonal line. The number of equilibrium stages is given by the number of outlined
steps between xD and xB. The number of trays is obtained by
Overall Efficiencies
The experimental overall efficiency is given by:
E ov (%) =
The overall efficiency can be estimated by empirical
correlations, namely, those by Drickamer and Bradford[12]
and O’Connell.[13] Drickamer and Bradford[12] correlate Eov
with the feed viscosity, μ, at the average temperature of the
column:
E ov (%) = 13.3 − 66.8 log µ (cP)
E ov (%) = 50.3 α12 × µ (cP)


The moles of liquid in the reboiler are related to its residue
composition by the Generalized Rayleigh equation:
B
ln =
F
Eov(%)
TB(˚C)
xD
xB
Eq. 5
Eq. 6
75
83.9
92.6
0.878
0.281
95.1
53.1
64.8
125
84.2
92.7
0.864
0.273
92.1
53.2
64.9
0.9
0.8
0.7
0.7
0.6
0.6
y
y
P = 125 W
2a
0.8
0.5
0.4
0.3
0.3
0.2
0.2
x = xB
x = xD
∫
x B,0
0.0 0.1 0.2
0.3 0.4 0.5 0.6 0.7
x
0.8 0.9 1.0
(7 )
Results and Discussion
In Table 1 the results obtained at total
reflux at 75 and 125
W are presented. For
illustration, the McCabe-Thiele diagram
for 125 W is plotted in
2b
R=6
Operating line
0.1
x = xB
dx B
xD − xB
where F and B are the initial and final
moles of mixture in the reboiler, respectively. Knowing experimental pairs of
data (xD, xB), B/F fraction may be obtained by numerical integration.
x = xD
0.0
0.0
108
0.5
0.4
0.1
x B,final
1.0
R =∞
(6)
Generalized Rayleigh Equation
TD(˚C)
P = 125 W
−0.226
where α12 is the geometric average of the bottom and top
values.
P(W)
0.9
(5)
O’Connell used a viscosity and relative volatility, α12, dependence. His graphical result can be fit with
TABLE 1
1.0
( 4)
where Nideal is the ideal number of equilibrium stages and Nreal
is the actual number of trays (in this case Nreal = 5).
Experimental Conditions and Results for the Experiments at Total Reflux
Exp.
N ideal
×100
N real
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
x
Figure 2.
McCabe-Thiele
diagram for a) total
reflux distillation
and b) continuous
rectification at partial reflux.
Chemical Engineering Education
TABLE 2
Results for Continuous Rectification Experiment
at P = 125 W and R = 6
TD(˚C)
TB(˚C)
87.8
93.5
xD
0.685
xB
0.234
Eov(%)
92.0
TABLE 3
B/F Fraction Obtained by Rayleigh Equation and Mass Balance
Results for Experiment at P =125 W and R-6
xD
(RI)
B/F
(Rayleigh Eq.)
B/F
(mass balance)
0.410
0.696
0.667
Vol. 45, No. 2, Spring 2011
0.7
0.6
Composition
0.5
Distillate
0.4
0.3
0.2
Residue
0.1
0.0
0
5
10
15
20 25 30
time (min)
35
40
45
50
55
Figure 3. Distillate and residue compositions during the
batch rectification at P = 125 W and R = 6.
5.0
y = -12137x4 + 4060.2x3 - 320.08x2 - 22.27x + 5.2365
R2 = 0.977
4.0
1/(x D-x B )
Figure 2a. The minimum number of equilibrium stages was
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
the column is more efficient when operated at 75 W, which is
usually unexpected for the students. Actually, higher reboiler
powers generate higher internal flows. Although such effect
may lead to a foreseen increase of mass transfer coefficients,
it also decreases the mean residence times of both phases
in each tray, which has a larger overall impact. Students are
frequently aware of the first effect, since they associate large
Reynolds numbers to large Sherwood values, but neglect the
second and more dominant effect in this case.
The experimental and predicted overall efficiencies are
listed in Table 1, and show that both correlations underestimate Eov. Students frequently get disappointed with such
diverging results. Instructors notice that students almost
always doubt their own experimental results, tending to accept without hesitation model predictions. At this point it is
essential to keep in mind that the overall column efficiency is
a complex function of system properties, operating conditions,
and column geometric variables, and that common empirical
correlations take only some system properties into account, as
is the case of Eqs. (5) and (6) adopted here. Students should
be encouraged to search data for similar systems to see that
data are frequently 10 to 20% higher than O’Connell’s predictions.[11]
The results obtained for the continuous rectification at
partial reflux (P = 125 W and R = 6) are given in Table 2
and Figure 2b. As may be observed, the overall efficiency
achieved is about the same of that obtained at total reflux for
the same power (92.0% vs. 92.1%). Furthermore, the separation achieved now (0.234 → 0.685) is inferior to that obtained
at R = ∞ (0.273 → 0.864; see Table 1), which is the expected
result for all students.
Figure 3 shows the evolution of both distillate and residue
molar compositions during the batch rectification (P = 125
W and R = 6). As expected, the cyclohexane content of the
residue approaches zero since it is the lighter component.
The fraction of undistilled liquid in the flask, B/F, was determined by numerical integration of the Rayleigh equation,
3.0
2.0
1.0
0.0
0.00
0.05
0.10
xB
0.15
0.20
Figure 4. Numerical data used for the integration of
Rayleigh equation.
using a polynomial fitted to experimental data (see Figure 4).
Many times students are not aware of the impact that the fitted
equation has upon the numerical solution. For instance, some
groups try to integrate by the trapezoid rule, which gives rise
to scattered positive and negative data.
Students calculate B/F also by mass balance using the initial
(xB,0) and final (xB,final) residue compositions, and the average
composition of distillate determined by refractive index. The
results found are frequently very similar. In this run (see Table
3) they found B/F = 0.696 and 0.667 using the Rayleigh
and mass balance approaches, respectively.
ASPEN SIMULATIONS
Distillations at total reflux and partial reflux (P =
125 W) may be simulated using BatchSep 2006.5 by
Aspentech, Inc., a simulator frequently used in industry.
109
This software allows the simulation of distillation
columns under different operating conditions and
modes of operation. The embedded VLE calculations
were based on the RK-SOAVE method.
Total Reflux Simulation
The total reflux simulation is carried out using
the input specifications and additional information
shown in Table 4. For this case, the column is assumed to be initially filled with nitrogen, therefore a
partial condenser has to be selected in order to purge
it from the system. A feed stream was imposed durTABLE 4
Information for Aspen Simulation
at Total Reflux
Input Specifications
- Column initially empty (initially filled with N2)
- Partial condenser
- Feed stream to introduce the initial charge of mixture
- Null distillate flow to get ∞ = R
Additional Information
- Column configuration (number of stages, including
reboiler and condenser)
- Reboiler geometry (dimensions and jacket type)
- Power (P = 125 W)
- Condenser specifications (pressure, type, area, condensing coefficient, coolant inlet temperature, coolant
mass flow, and coolant heat capacity)
- Tray specifications and dimensions
Operation Steps
- i) Column charge
- ii) Distillation at ∞=R
Results
- Column holdups
- Pressure drop
- Composition profile
- Temperature profile
ing a predetermined time to charge the tower with the same number
of moles that our Oldershaw column contains initially. Subsequently,
a null distillate flow must be imposed to reach total reflux condition
(see Figure 5). The simulation is carried out in two consecutive steps:
i) column charge and ii) distillation at total reflux. Table 5 compiles
the pertinent data and options selection for the total reflux calculations, in order to help students to reproduce our results.
Simulation of Continuous Rectification at Partial Reflux
The continuous rectification at partial reflux (R = 6) is computed
with the input specifications and additional information compiled in
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
realize this approach is in accordance with industrial columns startup: distillation towers are frequently started up at total reflux, after
an initial charge of feed, and this condition runs until both distillate
and bottom compositions reach the desired project specifications;
only then is the finite reflux ratio implemented.[14] In our case, R =
6, the column holdups and pressure drop values are those obtained
previously from the total reflux simulation, and the feed stream is the
distillate recycled to column (see Figure 6, page 112). Table 7 (page
113) compiles data and options for the continuous rectification at partial
reflux calculations.
Simulation Results
The simulation results, presented in Table 8 (page 113) for both total
and partial reflux, are in good agreement with the measured values; the
relative deviations found lie between 1.0 and 19.3%, being higher for R
= 6. The calculated separation for R = ∞ (xD – xB = 0.584) is very near the
experimental one (xD – xB = 0.591) whereas it diverges for R = 6 (0.484
against 0.451, respectively). It is curious to notice that students usually
doubt their experimental observations against the simulated results, suggesting possible experimental errors for the deviations found for R = 6.
Nonetheless, in this case such large error may be attributed to the fact that
some operating parameters, including pressure drop and holdups, were
calculated at R = ∞ and assumed to be the same in the continuous partial
reflux simulation. On the whole, students and instructors are amazed
with simulation results due to the large number of input parameters and
specifications, particularly those for geometrical variables.
CONCLUSIONS
Figure 5. Detail of an Aspen BatchSep 2006.5 window for the total reflux simulation.
110
This work describes
an experiment in which
students have the opportunity to study distillation, using an Oldershaw tray column,
under three different
modes of operation:
total reflux, continuous
partial reflux, and batch
with constant reflux.
The effect of the internal
molar flows on column
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
Configuration
Number of stages: 7
Valid phases: Vapor-Liquid
Pot Geometry
Pot orientation: vertical
Pot head type:
Top Hemispherical, bottom Hemispherical
Diameter: 0.18m
Height: 0.18m
Pot Heat Transfer
Jacket: Heating, Jacket covers head
Top height: 0.08m
Condenser
Condenser type: Partial
Partial condenser spec: Coolant temperature
Condensing coefficient: 100 cal/hr/m2
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
Jacket Heating
Jacket Heating
Heating option: Specified duty
Duty: 0.125 kW
Pressure/Holdups
Pressure
Pressure profile and holdups: Calculated
Setup
Section:
Start stage: 2
End stage: 6
Internal 1
Specification
Initial Conditions
Main
Tray Specifications:
Diameter: 0.03m
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 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
Charge Stream Feed
Main
Conditions:
Temperature: 20 ˚C
Pressure: 1.01325 bar
Composition:
Composition basis: Mole-Frac
CYCLO-01: 0.3
N-HEP-01: 0.7
N2: 0
Operating Step Charge
Operating Step Distill
Vol. 45, No. 2, Spring 2011
Changed Parameters
Location: Charge stream/Feed
Charge stream/Feed/Mole flow rate: 0.75 mol/min
Jacket/Heating/Duty: 0 kW
Condenser/Coolant mass flow: 0 kg/hr
End Conditions
Step end condition: Elapsed time
Duration: 10 min
Changed Parameters
Location: Charge stream/Feed
Charge stream/Feed/Mole flow rate: 0 mol/min
Jacket/Heating/Duty: 0.125 kW
Condenser/Coolant mass flow: 100 kg/hr
111
performance was investigated at total reflux by changing
reboiler power.
Results show that the efficiency decreases slightly with
increasing flows. Moreover, column efficiency measured at
partial reflux is analogous to that obtained at total reflux. For
batch distillation, the application of the generalized Rayleigh
equation provides good results. The results at infinite reflux
and for the continuous rectification at partial reflux were compared with those obtained by Aspen BatchSep simulations,
giving rise to relative deviations between 1.0 and 19.3%.
With this work students practice relevant concepts, including vapor-liquid equilibrium, continuous vs. batch operation,
McCabe-Thiele graphical method, and column efficiency as
TABLE 6
well as data analysis with the generalized Rayleigh equation.
Furthermore, they are introduced to the use of simulation
software, an important tool for their chemical engineering
instruction.
ACKNOWLEDGMENTS
Patrícia F. Lito and Ana Santiago wish to express their
gratitude to Fundação para a Ciência e Tecnologia (Portugal)
for the grants provided (SFRH/BD/25580/2005 and SFRH/
BPD/48258/2008), and to B.R. Figueiredo and Professor F.
A. Da Silva for Aspen simulations and pictures support.
NOMENCLATURE
Information for Aspen Simulation of the Continuous Rectification
at Partial Reflux
Input Specifications
- Column initially at total reflux
- Total condenser
- Total initial charge and composition
- Distillate flow to get R = 6
Additional Information
- Column configuration (number of stages, including reboiler and condenser )
- Reboiler geometry (dimensions and jacket type)
- Power (P = 125 W)
- Column pressure drop and tray holdups
- Distillate charge stream (charge stage, type, temperature, pressure)
Operating Steps
- Distillation at R = 6
Results
- Composition profile
B
Eov F real N
Nideal P Pt Pσ R RI T x y Final number of moles of liquid in the reboiler, mol
Overall efficiency, %
Initial number of moles of liquid in the reboiler, mol
Number of real trays
Ideal number of equilibrium stages
Reboiler power, W
Total pressure, atm
Vapor pressure, atm
Reflux ratio
Refractive index
Temperature, ˚C
Molar fraction of liquid phase
Molar fraction of vapor phase
12 α
γ μ Relative volatility
Activity coefficient
Molar average liquid viscosity, cP
B D
final i 0 Bottom
Top
Final condition
Component i
Initial condition
Greek letters
Subscripts
- 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 Handat Partial Reflux Simulation Carried Out With Aspen Batchsep 2006.5
book of Separation Process
Window
Tab
Specifications/Selections
Technology, Rousseau, R.W.,
Ed., Wiley-Interscience, New
Number of stages: 7
Configuration
York (1987)
Valid phases: Vapor-Liquid
2. Nath, R., and R.L. Motard,
Pot orientation: vertical
“Evolutionary Synthesis of
Separation Processes,” AIChE
Pot head type:
J., 27, 578-587 (1981)
Pot Geometry
Top Hemispherical, bottom Hemispherical
3. Douglas, J.M., Conceptual
Setup
Diameter: 0.18m
Design of Chemical ProcessHeight: 0.18m
es, McGraw-Hill, New York
(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,
Condenser
Condenser type: Total
“An Integrated, Real-Time
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)
Holdup basis: Mole
5. Binous, H., “Equilibrium
Pressure/Holdups
Holdups
Start Stage: 2
Staged Separations Using MatStage Holdup: 5E-5 kmol
lab and Mathematica,” Chem.
Eng. Ed., 42(2) 69 (2008)
Initial condition: Total reflux
6. Nasri, Z., and H. Binous, “ApInitial drum liquid volume fraction: 0.5
Main
plications of the Peng-RobinInitial temperature: 20 ˚C
son Equation of State Using
Initial pressure: 1.01325 bar
Matlab,” Chem. Eng. Ed., 43(2)
Initial Conditions
Composition basis: Mole-frac
115 (2009)
Total initial charge: 0.0075 kmol
7. Santoro, M., and M. Mazzotti,
Initial Charge
CYCLO-01: 0.3
“HYPER-TVT: Development
N-HEP-01: 0.7
and Implementation of an Interactive 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. PaulaiCharge 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
Location: Charge stream/Distillate
Oldershaw Data,” Ind. Eng.
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
Condenser pressure: 1.01325
10. Humphrey, J.L., and G.E.
Jacket/Heating/Duty: 0.125 kW
Keller, Separation Process
Technology, McGraw-Hill,
New York (1997)
TABLE 8
11. Seader, J.D., and E.J. Henley, Separation Process
Total Reflux and Continuous Rectification Simulations Results
Principles, 2nd Ed., John Wiley & Sons, New York
Bracketed values are relative deviations to the experimental ones.
(2006)
12. Drickamer, H.G., and J.R. Bradford, Transactions
TD(˚C)
TB(˚C)
xD
xB
AIChE, 39, 319-360 (1943)
Total reflux
13. O’Connell, H.E., Transactions AIChE, 42, 741-755
83.8
92.1
0.873 (1.0%)
0.289 (5.9%)
(R = ∞)
(1946)
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
A
University of Colorado • Boulder, CO 80309-0424
ctive learning is an umbrella term for instructional
methods used in the classroom in which students are
actively engaged in the learning process, as opposed
to a traditional lecture in which students play a passive role.
Active learning can take many forms such as collaborative
learning, cooperative learning, and problem-based learning.[1] Research has shown that such nontraditional methods
may lead to improved academic achievement, retention, and
student attitudes toward learning, depending on the method
of active learning utilized.[1,2] Indeed, Felder, et al.,[3] have included active learning methods on their list of teaching methods that work. Courses on fluid mechanics are a particularly
good match for active-learning techniques (see, for example,
Reference 4), since everyday examples are ubiquitous.
In this paper, two active-learning modules targeted for use
in an undergraduate fluid mechanics course are described.
Materials for both have been designed and made available via the Internet (<http://hrenya.colorado.edu/Hrenya.
php?page=teaching>) so that they can be incorporated by
interested educators with little time investment. These modules involve several of the aforementioned forms of active
learning, including both collaborative learning and cooperative learning.
114
The first activity involves a contest among small groups
of students to correctly predict the outcome of tube-flow experiments using the mechanical energy balance. The students
are first introduced to the experimental apparatus (gravitydriven flow from a tank), and then charged with predicting
the outlet flow rates from various tubes. An announcement
that prizes will be awarded to groups with predictions that
best match the experimental data is also made at the start.
The class culminates in the running of the experiments, and
real-time identification of the “winners.” This class period
allows the students to put their knowledge into practice via
active-learning, while also providing a high level of energy
Christine M. Hrenya received her degrees
in chemical engineering from The Ohio State
University (B.S.) and Carnegie Mellon University (Ph.D.), and is currently on faculty at
the Department of Chemical and Biological
Engineering at the University of Colorado.
Her research interests include granular and
gas-solid flows, with an emphasis on polydispersity, cohesion, and instabilities.
© Copyright ChE Division of ASEE 2011
Chemical Engineering Education
and enthusiasm due to the contest format. To facilitate use by
other instructors, videos with an introduction to the apparatus
and the collection of experimental data are available. A spreadsheet has also been developed in which group predictions and
experimental data can be recorded, which is followed by an
automated identification of the contest winners.
Unlike the tube-flow experiments which are best used just
after the relevant material has been introduced in the course,
the second activity is targeted at the final week of class. This
week presents a challenge for instructors since any new material will not be assigned as homework and typically will not
be covered on the final exam. As an alternative that involves
active learning, creativity, and oral presentation skills, small
groups of students are assigned a unique, puzzling question
involving fluid mechanics and found in everyday life. These
questions are assigned several weeks prior to the end of the
semester, and each group presents its findings to the entire
class during a short presentation (~6 minutes), often involving demonstrations, videos, etc. A current listing of these
questions, which involve current events, sports, hobbies,
and a bit of humor, is included below. Also available via the
Internet are an example project description, signup sheet,
and grading sheet.
Given below is a more detailed description of each of these
activities and the corresponding course materials. Afterward,
a student-based evaluation of both activities is summarized,
followed by concluding remarks.
CONTEST: TUBE FLOW EXPERIMENTS ON
YOUTUBE
Description. Knowing how to identify and solve fluid mechanical problems using the mechanical energy balance is an
essential tool for engineers with a training in fluid mechanics.
Typically, the basic equation, friction factor charts, and tables
with loss coefficients for fittings, etc., are introduced in one
lecture, with another lecture dedicated to example problems.
The latter is justified given the different level of complexities
that can be encountered—e.g., a simple plug-and-chug solution when finding the pressure drop for laminar tube flow to
a trial-and-error solution for sizing pipe diameters when the
flow is turbulent.
In this class period, an alternative to the traditional lecture
on example problems for the mechanical energy balance
is given. Namely, a “contest” is set up for small groups to
correctly predict the outcome of a tube flow experiment.
The class takes three parts: (i) introduction of the tube flow
experiment, including the specific measurements to be taken,
(ii) small groups work to make predictions of the experimental
outcome, and (iii) experiment is run, with small prizes given
to groups with best predictions. The experimental apparatus,
as shown in Figure 1, consists of gravity-driven flow from a
tank, in which the height of the water in the tank is maintained
constant. The water drains from the tank via three horizontal
Vol. 45, No. 2, Spring 2011
tubes located at the base of the tank, each with different
lengths and diameters. Two of the tubes are flush with the
wall of the tank, while the third protrudes into the tank. With
the dimensions and the materials of the tank and tubes given,
students are asked to predict the volumetric flow rate exiting
from each tube. The mechanical energy balance forms the
basis of this calculation[5]:
2
Pout α out Vout
α V2
P
+
+ z out = in + in in + z in − h L
γ
2g
γ
2g
(1)
where p refers to pressure, γ refers to specific weight, α is the
kinetic energy coefficient (α =1 for uniform velocity profile
and α =2 for laminar flow), g is gravity, z refers to vertical
height, and hL refers to the overall head loss:
h L = h L ,major + h L ,minor = f
2
V2
V
+ KL
D 2g
2g
( 2)
where major losses refer to frictional losses over straight
piping of length , and minor losses refer to frictional losses
associated with additional components (valves, bends, etc.); f
is the friction coefficient, D is the pipe diameter, and the loss
coefficient KL is available from graphs and tables specific to
component type.
To solve for the flow rate using the mechanical energy balance, students need to find a value for friction coefficient f,
which depends on the Reynolds number Re, and hence on flow
Figure 1. Tube
flow apparatus. The tank
is open to the
atmosphere
and the water
level is maintained at a
constant height
by means of a
pump. Three
horizontal
tubes of different diameters,
length, and
entrance types
(i.e., flush vs.
inserted) are
located near
the tank bottom. The flow
rates emanating from each
of these tubes
are measured
by means of
a graduated
cylinder and
stopwatch.
115
rate (for which they are solving). An analytical expression for
f in terms of Re is only possible for laminar flow; otherwise,
it must be determined using the Moody diagram and thus a
trial-and-error solution for the flow rate is required. The tubes
are designed such that flow rate from each is different, but
all are near the transitional region. Accordingly, the student
calculations should involve a combination of analytical and
trial-and-error approaches, along with the checking of their
initial assumptions (laminar vs. turbulent).
This exercise can be adapted easily to classes of different
durations. In our experience, asking the students to predict
flow rates from all three tubes is doable in a 1.25-hour period:
10 minutes to form groups and introduce experiment, 50
minutes for group calculations, and 15 minutes for tallying
of predictions, running of experiments, and identification of
contest winners. In the last few minutes, the general problem
solution is also outlined, with detailed calculations given as
handouts at the end of class. For a 50-minute class period, a
reasonable variation would be to ask students to predict the
flow rate from only one of the tubes. Either way, one may
consider alerting students one class period beforehand to an
upcoming “contest,” in order to motivate their review of the
material ahead of time.
Benefits. The benefits of this exercise include: (i) active
learning with an ad hoc group of peers, (ii) in-class collection of data (via video) provides experimental verification of
the mechanical energy balance, (iii) high level of motivation
instilled due to contest format, and (iv) complexity of example
problems not sacrificed, as the three-part experiment provides
a range of straightforward to complex calculations.
Course Materials. Below is a listing of the course content
for use by educators in their own classes:
1)
a YouTube video introducing the experiment to the class: <http://www.youtube.com/
watch?v=cwcVnEMyCNU>;
2) an Excel spreadsheet that can be used to record the
predictions of each group, record the experimental results [as obtained from video, see item (3) below], and
then automatically determine contest winners: <http://
hrenya.colorado.edu/Hrenya.php?page=teaching>;
3) a separate video showing the experiment being run
and a “solutions” document with detailed calculations from the mechanical energy balance; interested
educators should e-mail hrenya@colorado.edu with
a request for this video from their university e-mail
address.
END-OF-SEMESTER PROJECT: PUZZLING
QUESTIONS IN EVERYDAY FLUIDS
Description. The last week of the semester is typically
reserved for course review, since the introduction of new
material the week prior to final exams is challenging at best.
In this variation on that theme, small groups of students an116
swer unique questions related to a puzzling fluid mechanical
phenomena seen in everyday life, the answers to which draw
on the course content throughout the semester: buoyancy, turbulence, drag force, hydrostatics, surface tension, mechanical
energy balance, dimensionless numbers, surface forces, etc.
The questions are assigned several weeks prior to the end of
the semester. During the final week of class, each group turns
in a short report on their findings, and gives a 6-10 minute
presentation to the entire class, in which illustrative calculations, demonstrations, and videos are encouraged.
Table 1 contains a listing of the project questions, along
with the general topic area. Before the questions are revealed
to the class, a sheet is passed around for students to sign up in
self-selected groups, with each group having a unique group
number. The project questions assigned to each group are then
read aloud, generating a considerable amount of enthusiasm
given the perplexing and often humorous nature of the questions. Because an aim of the presentation is to “teach” the class
a variety of topics, students are asked to relate their content
to the material presented previously during the course. Also,
because of the varying degree of difficulty associated with the
project questions, students are asked to make their own decision as to whether a full analysis with example calculations is
possible, or whether the bulk of the material will be presented
in a qualitative manner. Finally, students are encouraged to
be creative in their presentations, using videos and in-class
demonstrations where appropriate.
The presentations are intentionally brief. First, practical
time constraints exist. Most recently, this project has been
used with a class of 100 students forming 18 groups (five to
six students / group). This breakdown allowed for 6-minute
presentations (1 minute per student) and two additional
minutes for questions and transition, which consume nearly
the entire 150 minutes (for a three-credit course) during the
last week of class. Because keeping to the schedule is critical, students are asked to treat this like a timed conference
presentation and are encouraged to rehearse ahead of time. To
further aid in keeping to the schedule, (i) the instructor stands
with a minute left on the clock, (ii) an alarm goes off at the
end of the allotted time, and (iii) a portion of the grade goes
toward keeping under the time limit. Second, and perhaps
more importantly, since fluid mechanics is typically required
early in the chemical engineering curriculum (sophomore
year), many students have not yet had an opportunity to orally
present technical results to their peers. As such, nerves can be
high, so keeping the presentations short and the environment
both encouraging and informal helps to build confidence for
future presentations.
The short report by each group on the puzzling question
allows for more detailed technical feedback on the approach
and corresponding calculations. Because these questions are
intentionally open-ended and do not take the form of a typical
homework problem where there is a single correct numeriChemical Engineering Education
cal answer (since different assumptions may be made in the
analysis), students are encouraged to come to office hours to
discuss their topic well in advance. As a result, the reported
findings are generally scientifically sound, but regardless the
instructor has the opportunity to give feedback at this stage.
On a final note, past students have more than risen to the
occasion with a plethora of entertaining and effective demonstrations, like watching an egg sink in tap water but float
in salt water to demonstrate the principles behind floating in
the Dead Sea, making hourglasses of both sand and water to
demonstrate the linear nature of timekeeping by the former
but not the latter, etc. Furthermore, students are encouraged to
add to the list of puzzling questions for use in future courses,
and indeed several of the questions appearing in Table 1 have
been put forth by former students. Additional suggestions are
welcome (send to hrenya@colorado.edu), and will be shared
with the community via inclusion on the website indicated
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.
“Balloon Boy”) could be supported by his parents’ homemade contraption, would he/she have recommended to continue the all-day, costly chase or search for the boy on the ground?*
Buoyancy
5
Why can a sailboat travel faster than the wind?
Drag force
6
Why can a water bug walk on water when I 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
home runs hit in Coors Field, and thus high ERA’s. In 2002, the Rockies started storing their baseballs
in humidors, leading to a dramatic decrease in home runs. Why was the number of home runs in Denver so high prior to 2002? What caused the reduction?
Fluid properties (density) /
drag force
10
Why is it that I get more snow on my windshield when my car is stopped at a light than when it’s
moving, but I get more rain on my windshield when it’s moving than when it’s stopped?
Dimensionless numbers
(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
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?
Fluid properties (density)
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
does the weight of the wheel remain the same when boats enter or exit? Why does it consume so little
power given the huge weight being moved?
Buoyancy
18
What are the effects of some “dirty tricks” in baseball: (i) lubrication of ball and (ii) roughening/polishing ball surface?
Surface forces
19
How does a hot air balloon work?
Buoyancy
20
What is the “magic” behind the trick in which a piece of cardboard is put on top a glass of water, and
then the cardboard/water stays in place when the glass is flipped?
Surface tension
21
Why does a curve ball curve?
Surface forces
22
Does the distance a discus is thrown depend more on drag or lift or both?
Surface forces
23
How do self-righting and self-bailing boats work?
Buoyancy / stability
24
Why does a boomerang return to the thrower?
Force balance
*Some useful assumptions: (i) balloon was constructed with tarps (typically made from HDPE) and duct tape and then filled with helium, (ii) authorities said the silver balloon, 20 feet long and 5 feet high, at times reached 7,000 feet above the ground while adrift (<http://www.cnn.com/2009/
US/10/15/colorado.boy.balloon/index.html>), and (iii) balloon can be estimated to be an oblate spheroid.
Vol. 45, No. 2, Spring 2011
117
Benefits. The benefits of this project include: (i) course
material presented throughout semester is reinforced via peer
instruction, including creative, student-generated demonstrations; (ii) students are exposed to a wide range of everyday
applications of fluid mechanics, including current news
stories; (iii) students work in self-selected group on question
with open-ended nature; and (iv) students gain early experience in written and oral communication, with feedback from
the instructor.
Course Materials. All materials listed below are available at the website <http://hrenya.colorado.edu/Hrenya.
php?page=teaching>:
The survey also contained a section for open-ended comments addressing the best and worst aspects of each activity.
Representative comments are included below.
Tube Flow Experiments—Best Aspects
• “Cannot overstate the benefit of actually observing
how the equations we learn in class can be used in a
real-time experiment.”
• “the fact that we were able to see how the simplifying
assumptions we made in class in order to solve the
mechanical energy equation, as well as others (e.g.,
Navier-Stokes), are actually applicable and pertinent, and not just things we do to make the problems
easier.”
• “allowed the student to become engaged in the solution, fusing academia with enthusiasm and a competitive spirit that promoted comprehension of the
subject.”
1) list of puzzling questions, including those in Table 1
and to be updated with future suggestions;
2) sample signup sheet;
3) sample project description; and
4) sample grading sheet with point breakdown.
Tube Flow Experiments—Worst Aspects
• “I wish that we had been informed there would be a
(competition) because I would have read and known
better how to do the problem.”
• “too little time”
• “slightly random nature of the answers—because the
results were taken experimentally, somebody could
have done the calculations exactly correct and yet not
“won” the prize.
EVALUATION
An anonymous, voluntary (online) survey was given at the
end of the semester to get feedback from the students on their
experiences with these active-learning exercises. Of the 97
students enrolled in the class, 46 students responded to the
survey (~50%). The items surveyed are listed in Table 2, with
results displayed in Figure 2a for the tube flow experiment
and in Figure 2b for project on puzzling fluids questions.
Overall, the student responses are quite positive, highlighting
the learning value of these exercises relative to the traditional
(non-active-learning) format and the added benefits of gaining
experience with group work and the oral communication of
technical material.
50
40
35
30
70
(a)
Q1
Q2
Q3
25
20
15
10
60
50
40
30
(b)
Q4
Q5
Q6
Q7
Q8
20
10
5
0
• “learning of how fluid mechanics affects our everyday
lives without even knowing it”
• “It was great putting engineering minds together, and
Percentage of Students
Percentage of Students
45
Puzzling Fluids Questions—Best Aspects
strongly
disagree
disagree undecided
agree
strongly
agree
0
strongly
disagree
disagree undecided
agree
strongly
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
problem. Groups can have a great deal of creativity
with a cumulative effect from each individual.”
• “learning about not only our project but other
projects”
• “The projects were just plain fun.”
Puzzling Fluids Questions—Worst Aspects
•
“trying to get everyone to agree on ideas.”
• “difficult to try to explain the concept and for everyone
TABLE 2
#
Items Used in Student Survey
See Figure 2 for responses.
Item
Tube Flow Demonstration
Q1
This class period was a more valuable learning experience than a lecture with example problems.
Q2
The contest format (i.e., prizes for winners) provided
more focus and energy on the task than would have
been present otherwise.
Q3
This class period was the most fun of the semester.
Q4
Attending these presentations and working on my
own project illustrated the everyday relevance of fluid
mechanics better than other means used during the semester (examples during lecture, homework problems,
etc.).
Puzzling Questions in Fluid Mechanics
Q5
Attending these presentations strengthened my understanding of basic fluid mechanical principles.
Q6
This project provided a good learning experience about
working in teams.
Q7
This project provided a good learning experience for
the oral communication of scientific ideas to peers.
Q8
This project provided a good learning experience in
written communication of scientific ideas.
Vol. 45, No. 2, Spring 2011
•
to talk and still keep it under 6 minutes”
“having to talk in front of our peers (it was scary!)”
• “not all of the groups had applied fluids equations in
an understandable manner”
CONCLUDING REMARKS
In this work, two active-learning exercises appropriate for
an undergraduate course in fluid mechanics are presented.
Based on firsthand experience using these exercises with
hundreds of students, it is found that the exercises effectively promote student interaction, give rise to thoughtful
student questions, serve as good learning tools, and last but
not least, add quite a bit of enjoyment to the class period for
all involved.
ACKNOWLEDGMENTS
The author would like to express thanks to Will Brewer,
who prepared the sample calculations and YouTube video.
The author is also indebted to the students, teaching assistants,
and colleagues who have contributed to the list of puzzling
fluid-mechanics questions over the years. Funding support for
this work was provided by the National Science Foundation
(CBET-0658903).
REFERENCES
1. Prince, M., “Does Active Learning Work? A Review of the Research,”
J. Eng. Ed., 93, 223-231 (2004)
2. Smith, K.A., S.D. Sheppard, D. W. Johnson, and R.T. Johnson, “Pedagogies of Engagement: Classroom-Based Practices,” J. Eng. Ed., 94,
87-101 (2005)
3. Felder, R., D. Woods, J. Stice, and A. Rugarcia, “The Future of Engineering Education: II. Teaching Methods That Work,” Chem. Eng.
Ed., 34, 26-39 (2000)
4. Ford, L.P. , “Water Day: An Experiential Lecture for Fluid Mechanics,”
Chem. Eng. Ed., 37, (2003)
5. Munson, B.R., D.F. Young, T.H. Okiishi, and W.W. Huebsch, Fundamentals of Fluid Mechanics, Wiley, New York (2009) p
119
ChE laboratory
A SEMI-BATCH REACTOR EXPERIMENT
for the Undergraduate Laboratory
Mario Derevjanik, Solmaz Badri, and Robert Barat
T
New Jersey Institute of Technology • Newark, NJ 0102
he advantages of the semi-batch reactor (SBR) are
exploited in several industrial reactor applications. For
example, in the reaction of a gas with a liquid (e.g.,
ozonation of industrial wastewater to remove dyes[1]), the gas
is continuously bubbled through the batch liquid. Conversely,
a gaseous product can be continuously removed from a liquid
system (e.g., CO2 in fermentation). The slow addition of one
reactant into another assists in the control of a strong exotherm
in the SBR, such as in polymerization reactors (e.g., nylon[2]
and polypropylene[3]). Polymer molecular weight distributions
can be controlled by careful addition of the monomer (e.g.,
styrene-butadiene rubber[4]). The SBR can be used to maximize selectivity, especially where byproducts or competing
reactions are an issue (e.g., substituted alkyl phenols[5]).
In spite of its industrial use, the SBR is often ignored in
undergraduate reactor engineering classes. Still, the SBR
offers a useful opportunity to combine both batch and flow
concepts. Haji and Erkey[6] present an SBR experiment with
the exothermic hydrolysis of acetic anhydride. In-situ Fourier
transfer infrared spectroscopy is used for monitoring species of interest vs. time. Kinetic analyses are subsequently
performed.
In this paper, an SBR is used to process the simple reaction between sodium hypochlorite and hydrogen peroxide.
Inexpensive household bleach and pharmaceutical hydrogen
peroxide solution serve as the convenient reactants. Product
molecular oxygen is monitored through a rotameter. The overall change in solution conductivity is metered with a conductivity probe. The reaction exothermicity is monitored through
a reactor thermocouple. The elegant model analyses combine
reaction kinetics with species and energy balances.
REACTION AND KINETICS
The reaction used in this experiment is inspired by Shams
El Din and Mohammed,[7] who studied the kinetics of this
reaction as a means to remove residual bleach from water
purification equipment.
H2O2(aq) + NaOCl(aq) → H2O(l) + NaCl(aq) + O2(g)
A+B → R+S+T
120
The letters representing the species are shown in corresponding order. The reported rate expression for the disappearance of A is second order:
−rA = kCA C B = −rB = rS = rT
(1)
where ri = reaction rate of species i, k = reaction rate constant, and Ci = molar concentration of i. Because the reaction
evolves gaseous O2 rather rapidly, it is preferable to run it in a
semi-batch reactor. To start, a batch vessel contains hydrogen
peroxide (H2O2 – species A) in a water solution. The aqueous
solution of sodium hypochlorite (NaOCl – species B) is fed
slowly over time at a constant rate. As shown above, species
S and T are NaCl and O2, respectively.
Mario Derevjanik graduated from NJIT
with a B.S. in chemical engineering in
2008. During his undergraduate career,
Mario assisted Dr. Barat in developing new
student experiments. Mario is working as a
chemical engineer for ConSerTech, a small
environmental consulting company. His current responsibilities, including VOC monitoring, are at the Conoco-Phillips refinery in
Linden, NJ.
Solmaz Badri
was born in Tehran, Iran, and came to the United States after
completing high school. She joined NJIT and
graduated in 2009, majoring in chemical
engineering. She is now living in New York
City, married to a physician, and working
as an individual contractor. She dedicates
her work and research to her newborn son,
Amin Zamanian.
Robert Barat is currently a professor of
chemical engineering at NJIT, where he has
been a member of the faculty since 1990. He
completed his Ph.D. in chemical engineering
at MIT in 1990. His research has been in combustion, reactor engineering, environmental
monitoring, applied optics, and is currently
in applied catalysis. He is also the faculty
coordinator for the chemical engineering
laboratories at NJIT.
© Copyright ChE Division of ASEE 2011
Chemical Engineering Education
The Ideal Gas Law can be used to convert FT to a volumetric rate.
REACTOR SPECIES BALANCES
A semi-batch design equation applies for B:
FB + rB V =
dN B
dt
where FB = molar flow rate inlet to the batch, V = batch liquid
volume, and Ni = moles of i in the batch. A simple batch design
equation applies for A:
rA V =
dN A
dt
(3)
The inlet molar flow rate of B can be written in more convenient volumetric terms:
FB =
v Bρ B f B
WB
(4)
where vB = volumetric feed rate of B, ρ B = bleach mass density, fB = mass fraction of species B in the feed bleach solution,
and WB = molecular weight of B. Since Ni = CiV, and V = f(t)
in the most general semi-batch case, Eq. (3) becomes:
rA −
CA dV dCA
=
V dt
dt
(5)
and Eq. (2) becomes:
FB
C dV dC B
+ rB − B
=
V
V dt
dt
(6)
The rate of change of the volume is accounted for with a
transient mass balance:
v Bρ B =
d ( ρV)
dt
rT VRTs
≈ vT
Ps
(2)
(7 )
where ρ =mass density of batch solution. It can be reasonably assumed ρ is constant; then, Eq. (7) reduces to:
ρ
dV
vB B =
(8)
ρ
dt
The volumetric feed rate of B is set at a constant value by the
user in the experiment.
Eqs. (1), (4)-(6), and (8) form a partial system that will be
solved simultaneously. The system is integrated from t = 0
(when the reactant B solution flow begins) to whenever the
peroxide feed is ended by the user.
(10)
where Ts, Ps represent standard temperature and pressure
conditions (298 K, 1 atm), respectively, and R = ideal gas
constant (0.0821 liter-atm/mole-K). Eq. (10) can be added
to the set of equations to be solved. The volumetric rate of
evolved O2 is one of three possible sources of data in this
experiment. The rate rT is obtained from Eq. (1).
CONDUCTIVITY CHANGE AND CHLORIDE ION
The conductivity of the solution is a weighted sum of the
contributions of the ionic species, including NaOCl as the
active ingredient, a small amount of NaOH to help prevent
degradation of NaOCl to release Cl2, and residual NaCl from
the bleach manufacturing process. We assume that CNaOCl in
the SBR is very small, and insignificantly contributes to the
solution conductivity. Subsequent SBR modeling supports this
claim. The batch solution conductivity can be estimated as:
C ≈ ∑ ci Ci = cNaCl C NaCl + cNaOH C NaOH
i
≡ Cond NaCl + Cond NaOH
(11)
where C = solution conductivity, ci = effective molar conductivity of species i, Ci = molar concentration of i, and
Condi = contribution of i to the total conductivity.
Accounting for the contribution of NaCl to the solution
conductivity requires a species balance, including the presence of NaCl in the bleach feed:
C dV dCs
Fs
+ rs − s
=
V
V dt
dt
(12)
The inlet molar flow rate of S can be written in more convenient volumetric terms:
FS =
v B ρ B fS
WS
(13)
where fS = mass fraction of NaCl in the feed bleach solution.
Molar conductivity data for NaCl aqueous solutions are
available[8] over the temperature range of interest to yield a
relationship valid up to 0.85 molar concentration:
ΛSO = 0.0117 Tc2 + 1.3737 Tc + 51.665
EVOLUTION OF O2
(ΛSO in mS/cm/m
molar, temperature Tc in C)
Assuming that the bleach solution mixes thoroughly into
the peroxide solution, the reaction mixture will likely saturate
with O2 very rapidly. We can assume that the O2 evolution
rate is approximately the same as the reaction rate, and is
given by:
The contribution of the NaOH to the solution conductivity is:
(9)
Accounting for the contribution of NaOH to the solution
FT ≈ rT V
Vol. 45, No. 2, Spring 2011
Cond S = ΛSO CS
(14)
121
conductivity requires a non-reactive species balance. Representing NaOH as the inert I, the balance is:
FI C I dV dC I
−
=
V V dt
dt
(15)
The inlet molar flow rate of I can be written in more convenient volumetric terms:
v ρ f
FI = B B I
WI
(16)
where fI = mass fraction of NaOH in the feed bleach solution.
Molar conductivity ΛOI data for NaOH aqueous solutions are
available[11] over the temperature range of interest to yield a
relationship valid up to 0.3 molar concentration:
species j, Cj = molar concentration of j inside the reactor, Fjo
= molar feed rate of j, Tf = feed temperature, ΔHrA = heat of
reaction per mole of A, and P = system pressure. The final
term in the numerator is included since the fluid volume is not
constant. It is small compared to the other terms, however, and
can be neglected. Selected terms are now examined.
∑c
j
T
∑F ∫ c
(Λ in mS/cm//molar, temperature Tc in C)
j
The contribution of the NaOH to the solution conductivity is:
(17)
Cond I = ΛOI C I
ENERGY BALANCE
The energy balance should reflect the configuration of the
reactor vessel. In a typical experiment, the liquid is in contact
with stainless steel walls and internal components (e.g., agitator, probes). An air-filled jacket surrounds the walls. Heat
losses to this metal must be considered. A simple heat loss
calibration was performed wherein an electric immersion
heater of known wattage was placed into the vessel filled
with water covering the metal parts. A simple heat balance
of this calibration is:
Qh
dT
=
dt m w c pw + m m c pm
(18a )
where Qh = electrical heating rate; mw and mm = masses of
water and metal parts, respectively; and cpw and cpm = massbased specific heats of water and metal, respectively. A successful 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:


 −
dT 
=
dt 



T
∑i Fj ∫ cp dT + V (−rA )(−∆H r )
o
Tf
j
A
m m c pm + V∑ c p j C j
j

dV 

+P
dt 
 18b
 ( )




where T = reactor temperature, cpj = molar heat capacity of
122
Cj =
cp ρ
(19)
M
where cp and M are the mean molar heat capacity and molecular weight, respectively, of the solution. As an approximation
due to the high degree of dilution, the properties of the solvent
water can be used. If the mass-based value is used for cp ,
M is not needed.
ΛOI = −0.0241Tc2 + 5.0658Tc + 111.13
O
I
pj
jo
Tf
pj
dT ≈
v Bρ Bc p
MB
B
(T − TB )
(20)
where TB, MB, and cpB = temperature, average molecular
weight, and mean heat capacity (mole-based), respectively,
of the feed bleach. If the mass-based value is used for cpB,
MB is not needed.
The standard heat of reaction (-37.2 kcal/mole at 25 ˚C for
the reaction as written earlier) is assumed to be independent
of temperature, especially in consideration of the limited
temperature range of the experiment.
The energy balance in the form used for data modeling is
now written as:
(

dT  −v Bρ Bc pB (T − TB ) + V (−rA ) −∆H rA
=
dt 
m m c pm + Vcp ρ

)



(21)
EXPERIMENTAL CONSIDERATIONS
Figure 1 illustrates the basic configuration of the current
experimental system. An agitated reactor vessel is used. The
O 2 product
conductivity
probe
Bleach
thermocouple
Data PC
Temp
Figure 1. Schematic of the semi-batch reactor experiment.
Chemical Engineering Education
bleach solution, held in an external reservoir, is pumped through
a calibrated flow meter, and into the reactor. A bypass is used
since the pump capacity is too large. A magnetic-drive centrifugal pump is useful since all wetted parts are plastic-coated to
avoid corrosion. The vessel has access ports for a stainless steel
thermocouple and a conductivity probe. The probe is inserted
through a side port to ensure immersion. The vessel is sealed
since product O2 gas is vented through a calibrated flow meter.
A differential pressure gauge (not shown in the figure) is used
in the current system to measure the pressure in the vapor space
in the vessel during a run. Variations on this setup should be
considered depending on available equipment.
In the present system, a Vernier® conductivity probe with
GoLink® interface and Logger Lite® data collection and plotting software are used. A data collection PC is accessed via the
USB interface. The probe is calibrated with two conductivity
standard solutions available from Vernier®.
A Vernier® chloride ion specific electrode (ISE) is an alternative to the conductivity probe. Its membrane requires
more care, however, making the ISE not as robust as the
all-metal conductivity probe. Hence, the ISE was limited to
determination of the chloride content of the bleach, and not
inserted into the reactor.
Finally, the most likely experimental parameter to vary is
the bleach feed rate. Alternative experiments include dilution
of either the peroxide or bleach solutions. In either case, care
should be taken such that the O2 evolution rate remains within
the useful range of the flow meter.
In a typical run of the present system, a 5 liter agitated (200
rpm) vessel is filled with 3 liters of over-the-counter hydrogen
peroxide solution (3%) that had been stored in a laboratory
refrigerator to improve shelf life. The initial conductivity
reading is ≈ 0 and the initial temperature is ≈ 13 ˚C. About
one liter of laundry bleach is stored in the reservoir. At time
t = 0, the bleach is flowed into the batch at a constant 4 gallons/hour rate. The O2 evolution begins almost immediately,
and continues until the available bleach is exhausted (~ 350
seconds). The batch solution conductivity rises monotonically
until the maximum value measurable by the probe (~ 28,000
μS/cm) is reached. A larger or second bleach reservoir can
be used to feed more bleach so as to exhaust the remaining
peroxide. Consuming ≈ 1 liter of bleach in this system causes
the batch temperature to rise to ≈ 8 centigrade degrees. Current runs show reactor pressures of only a few inches of
water above atmospheric. The data from this run are shown
in Figures 2 and 3 (page 124).
The Clorox® bleach contains ~ 6 wt. % NaOCl as the active
ingredient. In addition,[10] it contains NaOH added to prevent
degradation of the NaOCl to Cl2. The MSDS also quotes a
specific gravity of 1.1 and pH of ~ 11.4 for the bleach. A
sample of the bleach revealed a pH of 12, corresponding to
an NaOH concentration of 0.01 molar or 0.36 wt. %. It also
Vol. 45, No. 2, Spring 2011
contains residual NaCl from the manufacturing process.[11]
The NaCl concentration in the bleach, determined from an
ISE measurement, is 32 grams/liter or 2.9 wt. %. For bleach,
ρ B = 1.1 g/cm3, and cpB = 0.9 cal/g- ˚C (estimated).
DATA, ANALYSIS, AND DISCUSSION
The rate constant used in Eq. (1) is estimated from the data
of Shams and Mohammed.[7]
 −11800 
 liter / mole − seec
k ≈ 2 ⋅1012 exp
 RT 
(22)
where R = 1.987 cal/mole-K, and T = absolute temperature
(K).
The analysis approaches the simulation of the experiment
as a design problem. In this approach, the model defined by
Eqs. (1), (4)-(6), (8), (10)-(17), (21), and (22) is solved with
a numerical ordinary differential equation solver package.
Figures 2 and 3 show experimental and corresponding
model results for batch solution conductivity, batch temperature, and evolved O2 rate. The uncertainty bars are based on
estimated precisions of the measuring devices. Relative fits
are reasonable for temperature and O2. In fact, the heat loss
term in the energy balance accounts for ~ 2-3 degree reduction
in the observed temperature rise. The model under-prediction
of the conductivity suggests that the bleach might contain an
additional inert ionic species not accounted for. In addition,
modeling results are most sensitive to the bleach rate. An accurate measure of the bleach flow rate is critical.
As a point of discussion, and lacking direct concentration
measurements, the model profiles for CA and CB are shown
in Figure 4 (page 125). The peroxide concentration drops
monotonically as the bleach is added. The batch concentration
of the bleach jumps initially as the bleach is first added, and
then rises slowly, but all at a very low value. These values
are consistent with the
C H O >>C NaOCl
2
2
assumption made earlier. It also is consistent with the claim
that NaOCl does not appreciably contribute to the batch
conductivity.
CONCLUSIONS
The reaction H2O2(aq) + NaOCl(aq) → H2O(l) + NaCl(aq) + O2(g)
is a useful system to study in a semi-batch reactor. Generation
of a gaseous product offers an opportunity for additional data
beyond that of probes. The availability of published conductivity data provides a direct means to convert data to concentration of a product. Therefore, unlike most experiments,
products are monitored instead of reactants. The multiple
species balances required for modeling will challenge the
student, but not be out of the realm of undergraduate reactor
engineering. This is especially true with the inclusion of an
energy balance.
123
30000
25000
Conductivity (uS/cm)
Figure 2. (right)
Observed and
predicted batch
solution conductivity for bleach /
hydrogen peroxide
semi-batch run.
Figure 3. (below)
Observed and
predicted batch
solution temperature and evolved
oxygen rate.
20000
15000
10000
Exper
5000
Model
0
0
25
50
75
100
125
150
175
200
Time (seconds)
Model Temp
Exp Temp
Model O2
Exp O2
26
6
24
5
4
20
3
18
16
2
Evolved O2 rate (slm)
Temperature (oC)
22
14
1
12
10
0
50
100
150
200
250
300
0
350
Time (seconds)
124
Chemical Engineering Education
REFERENCES
Ind. Eng. Chem. Res., 36(12), 5196 (1997)
6. Haji, S., and C. Erkey, “Kinetics of Hydrolysis of Acetic Anhydride
by In-Situ FTIR Spectroscopy: An Experiment for the Undergraduate
Laboratory,” Chem. Eng. Ed., 39(1), 56 (2005)
7. Shams El Din, A.M., and R.A. Mohammed,, “Kinetics of the Reaction
Between hydrogen Peroxide and Hypochlorite,” Desalination, 115,
145-153 (1998)
8. Landolt, H., and R. Bornstein, Zahlenwerte und Funktionen aus Naturwissenschaften und Technik., K.H. Hellwege (ed.), Volume 2, Part.
Volume 6, Springer-Verlag, Berlin (1987)—obtained via Honeywell
Sensing and Control, Freeport, IL
9. Fogler, H.S., and N.M. Gurmen, Elements of Chemical Reaction
Engineering, 4th Ed., Prentice-Hall (2006)
10. <http://www.powellfab.com/technical_information/preview/general_info_about_so dium_hypo.asp>
11. Clorox® MSDS: <http://www.thecloroxcompany.com/products/msds/> p
1. Gharbani, P., S.M. Tabatabaii, and A. Mehrizad, “Removal of Congo
Red from Textile Wastewater by Ozonation,” Int. J. of Environmental
Science and Technology, 5(4), 495 (2008)
2. Wakabayashi, C., M. Embiruçu, C. Fontes, and R. Kalid, “Fuzzy
Control of a Nylon Polymerization Semi-Batch Reactor,” Fuzzy Sets
and Systems, 160(4), 537 (2009)
3. Seki, H., M. Ogawa, and M. Ohshima, “Industrial Application of a
Nonlinear Predictive Control to a Semi-Batch Polymerization Reactor,”
in Advance Control of Chemical Processes, L.T. Biegler, A. Brambilla,
and G. Marchetti (eds.), Proceedings of the IFAC Symposium, Pisa,
Italy 2000, 2, 539-544 (2001)
4. Yabuki, Y., and J.F. MacGregor, “Product Quality Control in Semibatch Reactors Using Midcourse Correction Policies,” Industrial &
Engineering Chemistry Research, 36(4), 1268 (1997)
5. Lehtonen, J., T. Salmi, A. Vuori, and H. Haario, “Optimization of the
Reaction Conditions for Complex Kinetics in a Semibatch Reactor,”
0.9
4
3.5
3
0.7
2.5
0.6
2
1.5
0.5
CA (H2O2)
1
CB (NaOCl) x 10^11
Batch Concentration of NaOCl x 10^11 (mole/liter)
Batch Concentration of H2O2 (moles/liter)
0.8
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
C
University of Minnesota, Duluth • Duluth, MN
onservation of energy (COE) and conservation of mass
(COM)—both are fundamental principles that apply
to all aspects of chemical engineering design, analysis,
and education. In most cases, we cannot apply one without
consideration of the other. Yet, a third fundamental principle
exists that is too often not recognized as on the same level of
importance as COE and COM: prevention of serious human
injury, major property damage, and environmental harm,
which is a primary focus of industrial chemical engineering
practice. We choose to call this third principle “conservation
of life” (COL), reflecting the need for fundamental awareness
and application of process safety and product sustainability
concepts in chemical engineering education. COL was first
introduced to our knowledge by Lewis DeBlois,[1-3] who was
DuPont’s first corporate safety manager and later president of
the National Safety Council, when he wrote in 1918:
… safety engineering, with its interests in design, equipment, organization, supervision, and education … bears
as well a very definite and important relation to all other
branches of engineering. This relation is so close, and its
need so urgent, that I am convinced that some instruction
in the fundamentals of safety engineering should be given a
place in the training of every young engineer. He should be
taught to think in terms of safety as he now thinks in terms
of efficiency. Conservation of life should surely not be rated
below the conservation of energy. Yet, few of our technical
schools and universities offer instruction in this subject,
and the graduates go out to their profession with only vague
surmises on “what all this talk on safety is about.”[4]
126
Much of what DeBlois observed and recommended remains
true today, over 90 years later, as identified by the U.S. Chemical Safety Board (CSB) in their report on the T2 Laboratories
incident in 2009:
In 2006, the Mary Kay O’Connor Process Safety Center
surveyed 180 chemical engineering departments at U.S.
universities to determine whether process safety was part
of their chemical engineering curricula. Of the universities
surveyed, only 11 percent required process safety education in the core baccalaureate curriculum. An additional 13
percent offered an elective process safety course.[5]
CSB recommended that the American Institute of Chemical
Engineers and the Accreditation Board for Engineering and
Technology (ABET) work together to improve requirements
for chemical engineering education to include greater emJames Klein is a Sr. PSM Competency Consultant, North America PSM
Co-lead, at DuPont. He has more than 30 years experience in process
engineering, research, operations, and safety. He received his chemical
engineering degrees from MIT (B.S.) and Drexel (M.S.) and also has an
M.S. in management of technology from the University of Minnesota.
Richard Davis is a professor of chemical engineering at the University
of Minnesota, Duluth, where he teaches computational methods, heat
and mass transfer, green engineering, and separations. His current
research interests include process modeling and simulation applied to
energy conversion, pollution control, and environmental management
in mineral processing. He received his chemical engineering degrees
from Brigham Young University (B.S.) and the University of California,
Santa Barbara (Ph.D.).
© Copyright ChE Division of ASEE 2011
Chemical Engineering Education
phasis on process safety, in particular awareness of chemical
reactivity hazards. In response, the following additional
program outcome has been proposed for the general ABET
criterion for accrediting undergraduate chemical engineering
programs:
Engineering programs must demonstrate that their students
attain the following outcomes: (l) an awareness of the need
to identify, analyze, and mitigate hazards in all aspects
of engineering practice, for example design, operational
procedures and use policies, hazards detection and response
systems, fail-safe systems, life-cycle analyses, etc.[6]
COL can be used by universities as a concept and unifying
theme for increasing awareness, application, and integration
of safety throughout the chemical engineering curriculum
and for meeting the revised ABET accreditation criteria.
Students need to think of COE, COM, and COL as equally
important fundamental principles in engineering design,
analysis, and practice. By providing students appropriate
tools for evaluating and implementing COL principles, we
can help them to better understand “what all this safety talk
is about,” and what their role is in contributing to safety in
chemical engineering.
ture, high pressure, and mechanical energy. Hazards
assessment can be defined as the detailed evaluation and
development of information about a chemical, material,
mixing, or interaction of chemicals/materials and about
any operating conditions that can create process hazards.
Hazards assessment therefore provides the basic understanding and data for conducting further process hazards
and risk analysis and management. The starting point for
hazards assessment is often the Material Safety Data Sheet
(MSDS), but the MSDS should be considered only for
initial information, which should be verified and expanded
on through additional literature and experimental data.
2. Evaluate hazardous events
Multiple hazardous events, such as loss of containment,
fires, explosions, runaway reactions, etc., can be described
for most chemical processes, based on the material and
process hazards and intended or accidental processing
steps. Consequence analysis and modeling consist of
identifying and evaluating the direct, undesirable impacts
of potentially hazardous events, resulting from failure of
engineering and/or administrative controls for the process.
The purpose of consequence analysis is to help estimate
the type, severity, and number of potential injuries,
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 smallfor 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 position. Catastrophic failure events are those where there is a
1. Assess material/process hazards
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 containengineering de1. 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
2.
Evaluate
hazardous
events
can be defined
events are rare, the
–
Apply methodologies to estimate potential hazardous impacts
as a physical or
consequences of such
chemical con3. Manage process risks
an event could be sigdition that has
–
Evaluate risk vs. acceptable risk criteria
nificant and should be
the potential for
carefully evaluated.[9]
–
Apply inherently safer approaches
causing harm to
3. Manage process
–
Design and evaluate multiple layers of protection
people, property,
risks
or the environ4. Consider real-world operations
[7]
ment. ExamProcess hazards/risk
–
Implement comprehensive PSM systems
ples of material
analysis consists of the
–
Recognize importance of human factors
hazards include
detailed, methodical
–
Learn from experience – Case Histories
flammability,
evaluation of process
toxicity, and reequipment, materials,
5. Ensure product sustainability
activity. Examconditions, and op–
Implement product safety / stewardship practices
ples of process
erating steps in order
–
Apply life cycle management
hazards include
to control and reduce
high temperaFigure 1. COL Principles.
process risks. Specific
Vol. 45, No. 2, Spring 2011
127
failures of process equipment, operating procedures, or related systems that can lead to potentially hazardous events
must be identified and evaluated to ensure that appropriate
and reliable safeguards (layers of protection) are provided
to achieve acceptable risk levels. Typical hazards evaluation
Assessing Hazards and Risks
Process
Hazards
Analysis
Process
Technology
methods include hazard and operability analysis (HAZOP),
what-if/checklist analysis, failure modes and effects analysis
(FMEA), and fault tree analysis (FTA).[7] Risk analysis can
range from qualitative to semi-quantitative (e.g., Layer of
Protection Analysis)[10] to quantitative,[11] depending on the
potential risks associated with the process.
The initial process design and risk analysis
activities also provide the greatest opportunities for consideration and implementation

of inherently safer process concepts[12,13] to

significantly reduce process risks.
Managing Operations
Operating
Procedures
Personnel
Training
Managing Equipment and Facilities
Quality
Assurance
Mechanical
Integrity
Contractor
Safety
Managing Change
MOC-T,S
PSSR
MOC-P
Managing Incidents
Emergency
Incident
Planning &
Investigation
Response
Figure 2. Elements of a process safety management program.
4. Consider real-world operations
Process hazard identification, evaluation,
and management is essential to chemical
engineering design, but consists of only
the initial elements of a sound industrial
process safety management program, as
shown in Figure 2. Real-world chemical
operations must develop and implement
systems for operating procedures, training,
management of change, equipment maintenance and reliability, etc.,[14,15] in order to
obtain desired results. In addition, humans
make mistakes, so human factors [16-18]
must be considered during the initial risk
analysis, management of day-to-day operations, and emergency response. Incidents
and case studies[19,20] also provide opportunities for learning from previous problems
to help prevent their re-occurrence.
5. Ensure product sustainability
Chemical products must be designed and managed for human
health and safety throughout the
product life cycle from manufacture to intended use to ultimate
disposal without the potential
for significant environmental
impact. Comprehensive product
stewardship programs should
include environmental risk assessment and management, regulatory
compliance, life cycle analysis,
and stakeholder engagement.[21]
Student awareness and understanding of the social, environmental,
and economic impact of chemical
engineering design and analysis
is essential for ensuring optimal
product sustainability practices.
Figure 3. Application of COL in undergraduate chemical engineering curriculum.
128
Application of COL principles
is intended to help achieve “the
Chemical Engineering Education
SACHE Modules by COL Principle
SACHE Modules by ChemE Course
1. Assess material/process hazards
–
–
–
–
–
–
–
–
Reaction Engineering Course
Chemical reactivity hazards (2005)
Dust explosion prevention / control (2006)
–
Chemical reactivity hazards (2005)
–
Reactive and explosive materials (2009)
–
Runaway reactions: Experimental
characterization and vent sizing (2005)
–
Explosions (2009)
Properties of materials (2007)
–
Reactive and explosive materials (2009)
Runaway reactions (2003)
–
Seminar on fire (2009)
–
Etc.
goal is zero” with respect to injuries, incidents, and environmental/social impact associated with chemical engineering
practices and products. Awareness and use of these principles
by students should help them understand their important roles
as engineers in helping make achievement of this goal a reality. Students may simply wish to think of these concepts as
“people in = people out.”
A practical method for measuring the impact of COL in
either process or product safety is to consider risk reduction,
such as shown in Eq. (1):
∆R = log ( R o R p )
(1)
∆R is the order of magnitude improvement in risk for the
event being evaluated, where Rp is the risk level (e.g., fatalities
per year) when COL principles have been applied, and Ro is
the inherent risk associated with the handling, processing, or
use of potentially hazardous materials or products. Cost-effective risk reduction improvements should be identified and
considered for implementation, based on application of COL
principles. ∆R measures the collective risk improvement, and
risk criteria[22] are typically used to determine if an overall
acceptable level of risk has been achieved.
APPLICATION OF COL TO
CHEMICAL ENGINEERING CURRICULA
There are three main reasons for use of COL as a unifying
concept and •theme in undergraduate chemical engineering
education:
• Emphasize importance of safety to students as a funda-
mental principle that must be considered and evaluated
in all aspects of engineering practice equivalent to
COE and COM
• Consistent application and reinforcement of safety
integrated throughout the curriculum
• Meet ABET accreditation changes related to safety.
Use of COL will help develop a process safety culture in the
curriculum, where students see connections and applications
related to COL in most courses. Students will not be able to
Vol. 45, No. 2, Spring 2011
Hydroxylamine explosion case (2003)
Runaway reactions (2003)
Figure 4.
SACHE Modules for COL
Principles
(examples).
Rupture of a nitroaniline reactor (2007)
Etc.
easily compartmentalize COL as a separate, unrelated activity, but will see it as an activity that is inherent to all courses
and engineering activities. Using a spiral learning model,
COL will build up awareness, understanding, and capability
related to safety as students gain experience by revisiting the
COL principles at increasing levels of depth and breadth. Ultimately, students will demonstrate knowledge and application
of COL principles in the capstone design course reports and
presentations[22-24] by addressing subjects such as:
• Process hazards
• Hazardous events
• Hazard/risk analysis
• Layers of protection
• Human factors issues
• Product safety and life-cycle considerations.
An example of where COL principles could be applied in
the undergraduate chemical engineering curriculum is shown
in Figure 3.
Additional resource materials for both engineering instructors and students for use in applying COL in undergraduate
chemical engineering education are planned. Excellent training materials currently exist that can be used to get started
with COL immediately, including:
• SACHE modules[26,27]
• Engineering texts[28-31]
• Incident compilations[19,20]
•
US Chemical Safety Board investigations[32]
•
Process safety literature (e.g., Process Safety
Progress).
• Process Safety Beacon[33,34]
A SACHE module introducing COL has been prepared,
and materials have been tested in presentations at several
universities. Many SACHE modules are currently available,[27]
which can be sorted for application of the COL principles. An
example is shown in Figure 4.
129
EXAMPLE
A simple example of a classroom active-learning exercise
that reinforces the principles of COL in a separations course
was adapted from the April 2003 Process Safety Beacon.[33,34]
The article describes an incident involving a fire and explosion originating in an activated carbon drum used to control
hydrocarbon emissions from a flammable liquids storage terminal. Starting with COL principle four—consider real-world
operations—the class is presented with a basic description of
the incident, and then asked to work through the first three
COL principles of assessment, evaluation, and management
of process hazards applied to this case study. The class is
divided into small teams of two or three students and allowed a short time to work on the problem. Students typically
reference the table of Failure Scenarios for Mass Transfer
Equipment.[7] An instructor-led classroom discussion solicits
student input and may include the following observations and
recommendations:
1. Assess Hazards: Flammable materials exist in the carbon
bed and hydrocarbon vapor, and low thermal conductivity in
the carbon bed reduces heat transfer rates with a potential
for exceeding the auto-ignition temperature.
2. Evaluate Hazards: Reference the fire triangle,
as shown in Figure 5,
and identify sources for
fuel (organic materials),
oxygen (air in the tank
space) and heat (exothermic heat of adsorption
reaction).
FUEL
OXYGEN
HEAT
Figure 5. Fire triangle.
3. Manage Risk: Apply
LOPA[10] to recommend passive and active design solutions that include: proper flow distribution in the bed,
minimizing the bed cross sectional area, continuous
monitoring of bed temperature, flooding/inerting, flame
arresters, foam fire protection, interlock to isolate feed
on detection of high temperature, etc.
SUMMARY
COL is a fundamental principle equivalent to COE and
COM in terms of application to all aspects of chemical engineering design, analysis, and practice. COL can be used as a
concept and unifying theme integrated into the undergraduate
chemical engineering curriculum to emphasize and reinforce
consistent application of COL principles, increase student
awareness and capabilities, and help meet revised ABET accreditation requirements. One author’s university—University
of Minnesota, Duluth—has officially adopted COL for use
in its undergraduate chemical engineering program. Other
universities may benefit from a similar approach.
REFERENCES
1. DeBlois, L.A., Industrial Safety Organization for Executive and En130
gineer, McGraw Hill (1926)
2. Petersen, P.B., Lewis A. DeBlois and the Inception of Modern Safety
Management at DuPont, 1907-1926, submitted to Academy ofManagement, Management History, Division, Hagley Museum and Library,
Wilmington, DE, ca 1987
3. Klein, J.A., “Two Centuries of Process Safety at DuPont,” Process
Safety Progress, 28(24) (2009)
4. DeBlois, L.A., “The Safety Engineer,” American Society of Mechanical
Engineers, Hagley Museum and Library, Wilmington, DE (1918)
5. Chemical Safety Board, Investigation Report, T2 Laboratories, Inc.,
Runaway Reaction, Report No. 2008-3-I-FL, Sept. 2009
6. AICHE, Letter to Mr. John Bresland from H. Scott Fogler and June
Wispelwey, Dec. 7, 2009
7. Center for Chemical Process Safety, Guidelines for Hazard Evaluation
Procedures, 3rd Ed., John Wiley & Sons (2008)
8. Center for Chemical Process Safety, Guidelines for Consequence
Analysis of Chemical Releases, AICHE (1999)
9. Dharmavaram, S., and J.A. Klein, “Using Hazards Assessment to Prevent Loss of Containment,” Process Safety Progress, 29(4) (2010)
10. Center for Chemical Process Safety, Layer of Protection Analysis:
Simplified Process Risk Assessment, John Wiley & Sons (2001)
11. Center for Chemical Process Safety, Guidelines for Chemical Process
Quantitative Risk Analysis, AICHE (1999)
12. Center for Chemical Process Safety, Inherently Safer Chemical Processes: A Life Cycle Approach, 2nd Ed., John Wiley & Sons (2008)
13. Seay, J.R., and M.R. Eden, “Incorporating Risk Assessment and Inherently Safer Design Practices into Chemical Engineering Education,”
Chem. Eng. Ed., 42(3) (2008)
14. Center for Chemical Process Safety, Guidelines for Implementing
Process Safety Management Systems, AICHE (1993)
15. Center for Chemical Process Safety, Guidelines for Risk Based Process
Safety, John Wiley & Sons (2007)
16. Center for Chemical Process Safety, Human Factors Methods for Improving Performance in the Process Industries, John Wiley & Sons (2007)
17. Kletz, T., An Engineer’s View of Human Error, 3rd Ed., IChemE, Rugby,
UK (2001)
18. Klein, J.A., and B.K. Vaughen, “A Revised Model for Operational
Discipline,” Process Safety Progress, 27(1) (2008)
19. Kletz, T., What Went Wrong? Case Histories of Process Plant Disasters
and How They Could Have Been Avoided, 5th Ed., Elsevier (2009)
20. Atherton, J., and F. Gil, Incidents That Define Process Safety, Center
for Chemical Process Safety, John Wiley & Sons (2008)
21. <www2.dupont.com/sustainability/en_US/>
22. Center for Chemical Process Safety, Guidelines for Developing Quantitative Safety Risk Criteria, John Wiley & Sons (2009)
23. Kletz, T., Process Plants: A Handbook for Inherently Safer Design,
Taylor & Francis, Philadelphia (1998)
24. Ulrich, G.D., and T.V. Palligarnai, “Predesign with Safety in Mind,”
Chem. Eng. Progress, July, 2006
25. Turton, R., R.C. Bailie, W.B. Whiting, and J.A. Shaeiwitz, Analysis,
Synthesis, and Design of Chemical Processes, 3rd Ed., Prentice Hall,
Upper Saddle River, NJ (2009)
26. Louvar, J.F., “Safety and Chemical Engineering Education—History
and Results,” Process Safety Progress, 28(2) (2009)
27. <www.sache.org>
28. Crowl, D.A., and J.F. Louvar, Chemical Process Safety: Fundamentals
and Applications, 2nd Ed., Prentice Hall (2001)
29. Center for Chemical Process Safety, Guidelines for Design Solutions
for Process Equipment Failures, AIChE (1998)
30. National Safety Council, Product Safety Management Guidelines, 2nd
Ed., NSC (1997)
31. Horne, R., T. Grant, and K. Verghese, Life Cycle Assessment: Principles,
Practice, and Prospects, CSIRO (2009)
32. <www.csb.gov>
33. <www.sache.org/beacon/products.asp>
34. Luper, D., “Create Effective Process Safety Moments,” Chem. Eng.
Progress (2010) p
Chemical Engineering Education
Random Thoughts . . .
HANG IN THERE!
Dealing with Student Resistance
to Learner-Centered Teaching
Richard M. Felder
Dear Dr. Felder,
What can I do about low teaching evaluations from students
I teach actively when what they clearly want is much more
traditional (passive ride, smooth highway please)? I’m about
ready to give up and return to just lecturing, as I am sure
students will evaluate my courses higher if I do. Thank you
for your time and consideration.
Sincerely, _____________
Dear ____________,
***
Before I respond to your question, let me assure you that
I get it. Learner-centered teaching methods like active and
cooperative and problem-based learning make students take
more responsibility for their learning than traditional teachercentered methods do, and the students are not necessarily
thrilled about it. All college instructors who have tried the
former methods have experienced student resistance—and
if they were getting high evaluations when they taught traditionally, their ratings may have dropped when they made the
switch. As you’ve discovered, it doesn’t feel good when that
happens, so it will be understandable if you decide to go back
to teaching classes where you just lecture and the students
just listen (or text or surf or daydream or sleep).
Please think about a couple of things before you make your
decision, however. An important part of our job as teachers is
equipping as many of our students as possible with high-level
problem-solving and thinking skills, including critical and
creative thinking. If there’s broad agreement about anything
in educational research, it’s that well-implemented learnercentered instruction is much more effective than traditional
lecture-based instruction at promoting those skills. (If you’d
Vol. 45, No. 2, Spring 2011
like to check the research for yourself, the attached bibliography suggests some good starting points.) It’s true that
many students want us to simply tell them up front in our
lectures everything they need to know for the exam rather
than challenging them to figure any of it out for themselves.
If we give them that, though, we are failing those who have
an aptitude for high-level thinking and problem solving but
might not develop those skills without the guidance, practice,
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 commonly see their ratings increase when they adopt a more
learner-centered approach.
I don’t know what your institution is like, but here’s the
way things go at the universities and colleges I’ve visited.
Most instructors teach traditionally but there are quite a few
who use active learning and other learner-centered methods,
including some of the best teachers on the campus—the ones
who routinely get excellent performance and high ratings from
their students, teaching awards, and wedding invitations and
birth announcements from their former students. At some
point another faculty member may decide to try, say, active
Richard M. Felder is Hoechst Celanese
Professor Emeritus of Chemical Engineering
at North Carolina State University. He is coauthor of Elementary Principles of Chemical
Processes (Wiley, 2005) and numerous
articles on chemical process engineering
and engineering and science education,
and regularly presents workshops on effective college teaching at campuses and
conferences around the world. Many of his
publications can be seen at <www.ncsu.
edu/effective_teaching>.
© Copyright ChE Division of ASEE 2011
131
learning, perhaps after attending a workshop or reading a
paper or constantly hearing about the superb student responses
their gifted colleague always enjoys. He or she tries it and
it doesn’t go well—the evaluations are mediocre and some
students grumble that their professor made them do all the
work instead of teaching them.* Instructors in this situation
can easily conclude that the nontraditional methods caused
their poor ratings. What that conclusion doesn’t explain,
however, is how that talented colleague of theirs can use the
same methods on the same students and get good performance
and glowing reviews.
Whenever I’ve explored this issue with instructors distressed by it, I have invariably found that the teaching method
they were trying was not the real problem. It was either that
they were making one or more mistakes in implementing
the method, or something else was troubling the students
and the method was a convenient scapegoat. So, if you’ve
used a learner-centered method, didn’t like the outcomes,
and would like to do some exploring, you might start with
these questions:
•
•
•
•
In your student evaluations, were complaints limited
to the method, or did they also relate to other things
such as the length of your assignments and exams, the
clarity of your lecturing, or your lack of availability
and/or respect for students? If they did, consider
addressing those complaints before abandoning the
method.
Did you explain to the students why you were using
the method? If you tell them you’re doing it because
research has shown that it leads to improved learning,
greater acquisition of skills that potential employers
consider valuable, and higher grades, most will set aside
their objections long enough to find that you’re telling
the truth. (See Reference 2 in the bibliography.)
Did you use the new method long enough to overcome
the learning curve associated with it? It can take most
of a semester to become comfortable with and adept
at active learning, and if you’re using a more complex technique such as cooperative or problem-based
learning and you’re not being mentored by an expert,
it might take several years.
If you got unsatisfactory student ratings, did you check
references on the method to see if you were doing
something wrong? For example, did you assign smallgroup activities in class that lasted for more than 2–3
minutes or call for volunteers to respond every time?
(See Reference 4 to find out how both practices can kill
the effectiveness of active learning.) The bibliography
suggests references you might consult for each of the
most common learner-centered methods.
•
In your midterm evaluations, did you specifically ask
the students whether they thought active learning (or
whatever you were doing) was (a) helping their learning, (b) hindering their learning, or (c) neither helping
nor hindering? If you do this, you may find that the
students objecting vigorously to the method are only
a small minority of the class. If that’s so, announce
the survey results in the next class session. Students
who complain about learner-centered methods often
imagine that they are speaking for most of their classmates. Once they find out that very few others feel
the way they do, the grumbling tends to disappear
immediately.
If your answers to any of those questions suggest that
making some changes in your approach to the method and
trying again might be worthwhile, consider doing it. If you
conclude, however, that you’ve done all you can and going
back to traditional teaching is your only viable course of action, then so be it. I hope you choose the first option, but it’s
totally your call.
Best regards, and good luck,
Richard Felder
BIBLIOGRAPHY
1. Bullard L.G., and R.M. Felder, “A Learner-centered Approach to Teaching Material and Energy Balances. 1. Course Design,” Chem. Eng. Ed.,
41(2), 93 <http://www.ncsu.edu/felder-public/Papers/StoichPap-pt1.
pdf>; “2. Course Instruction and Assessment,” Chem. Eng. Ed., 41(3),
167 <http://www.ncsu.edu/felder-public/Papers/StoichPap-pt2.pdf>
(2007)
2. Felder, R.M., Sermons for Grumpy Campers,” Chem. Eng. Ed., 41(3),
183, <http://www.ncsu.edu/felder-public/Columns/Sermons.pdf>
(2007)
3. Felder, R.M., and R. Brent, “Cooperative Learning,” in P.A. Mabrouk,
ed., Active Learning: Models from the Analytical Sciences, ACS
Symposium Series 970, Chapter 4, 34–53, Washington, DC: American
Chemical Society, <http://www.ncsu.edu/felder-public/Papers/CLChapter.pdf> (2007)
4. Felder, R.M., and R. Brent, “Active Learning: An Introduction,” ASQ
Higher Education Brief, 2(4), <http://www.ncsu.edu/felder-public/Papers/ALpaper(ASQ).pdf> (2009)
5. Prince, M.J., “Does Active Learning Work? A Review of the Research,”
J. Eng. Ed., 93(3), 223, <http://www.ncsu.edu/felder-public/Papers/
Prince_AL.pdf> (2004)
6. Prince, M.J., and R.M. Felder, “Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases,” J. Eng. Ed., 95(2),
123, <http://www.ncsu.edu/felder-public/Papers/InductiveTeaching.
pdf> (Inductive methods include inquiry-based, problem-based, and
project-based learning.) (2006) p
* My favorite student evaluation came from someone who wrote “Felder
really makes us think!” It was on his list of the three things he disliked
most about the course.
All of the Random Thoughts columns are now available on the World Wide Web at
http://www.ncsu.edu/effective_teaching
and at
http://che.ufl.edu/~cee/
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
O
Worcester Polytechnic Institute, Worcester MA 01609
ne educational goal of the unit operations laboratory
is to help students understand fundamental principles
by connecting theory and equations in their textbooks
to real-world applications. We have found, however, that
collecting data and analyzing it with empirical correlations
does not always translate into a good understanding of what
is happening inside the pipes.[1] One problem is that the
theoretical development behind the labs is often comprised
of approximate methods using lumped parameters that describe the results but not the details of the physical process.
For example, when a mass transfer coefficient is obtained
from an absorption experiment, some students struggle to
explain what the mass transfer coefficient represents and
why it increases with increasing absorbent flow rate. To
address this problem, we are using computer simulations to
solidify the link between experiment and theory and provide
improved learning.[1,2]
Commercial software packages like COMSOL MultiphysicsTM allow students to set up and solve the partial differential
equations that describe momentum, energy, and mass balances
and also to visualize the velocity, pressure, temperature, and
concentration profiles within the equipment. Visualization
of the processes may not only help reinforce concepts and
clarify the underlying physics but it may also help “bring to
life” the mathematics as well as the experiments. With this
software, students don’t necessarily need to know the details
of how to solve complex equations, but they need to know
Vol. 45, No. 2, Spring 2011
which equations to solve and how to validate the results.[3]
This type of simulation can also extend the range of experience beyond what is possible in the lab by allowing studies
that would otherwise be prohibited by time, financial, or
safety constraints.
In this paper we present experiments and computer models for studying the environmentally important problem of
removing CO2 from air. Simple models are shown to provide
straightforward analysis of the experimental data even when
the system is not dilute. In addition, we present more detailed
models that illustrate the two-film theory and provide insight
William Clark is an associate professor of chemical engineering at
Worcester Polytechnic Institute. He received a B.S. degree from Clemson
University and a Ph.D. degree from Rice University, both in chemical
engineering. He has more than 20 years of experience teaching thermodynamics and unit operations laboratory at WPI. In addition to research
efforts in teaching and learning, he has conducted disciplinary research
in separation processes.
Yaminah Jackson graduated from the WPI Chemical Engineering Department in Spring 2008. She is currently attending graduate school at
the University of Southern California.
Michael Morin graduated from the WPI Chemical Engineering Department in Spring 2009. He is currently a Ph.D. candidate in mechanical
engineering at WPI.
Giacomo Ferraro is the laboratory manager in the Chemical Engineering
Department at WPI. He is a master machinist and has facilitated equipment design, fabrication, and use for teaching and research at WPI for
more than 30 years.
© Copyright ChE Division of ASEE 2011
133
into the absorption process. These models help explain the
absorbent flow rate dependence of the mass transfer coefficient and how the process is liquid phase resistance controlled
when using water and dependent on the gas phase resistance
when using dilute NaOH solution as absorbent. Finally, we
provide some discussion of how the simulations have been
received by students.
LABORATORY EXPERIMENT
End caps for the acrylic column were made with rubber stoppers fitted with liquid and gas inlet and outlets.
We describe here the analysis of representative sets of experimental runs using the two columns. Our students use the
larger column to determine the effect of water flow rate on
the mass transfer process. Experimental data are presented in
Table 1 for four different water flow rates at fixed gas phase
inlet conditions and room temperature. At present we don’t
have our students working with NaOH in the lab for safety
reasons. Instead, we give them data obtained on the smaller
column by a student working on his senior thesis. Table 2
shows the data collected for both water and 1 N NaOH solution at five different liquid rates and a fixed gas phase inlet
condition at room temperature. It can be seen that very little
CO2 is removed in the small column at these conditions with
water as absorbent. On the other hand, most of the CO2 is
removed from the gas stream when NaOH is used, even in
the small column.
A few years ago our old 30-foot-tall, 6-inch-diameter, steel
absorption tower became clogged with rust and residue from
years of use with sodium carbonate solution as absorbent for
removing CO2 from air. Since concerns over global warming
are a political reality even if the causes and effects are not
clear, we wanted to continue to offer a CO2 absorption experiment because of its appeal to student interest as well as its
ability to illustrate mass transfer fundamentals. To reduce cost
and avoid column fouling in the future, we chose to use pure
water as absorbent in our new 6-foot-tall, 3-inch-diameter,
TABLE 1
glass column packed with 54 inches of ¼-inch glass Raschig
Large Column Data and Results for CO2 Absorption
rings that we purchased from Hampden Engineering Corporafrom Air Using Water at Room Temperature
tion[4] and modified to suit our needs. Although using water as
Air Rate, A = 1.42 L/min; Inlet CO2, yb = 0.185
absorbent focuses the lab on mass transfer concepts without
Water Rate, W
Outlet CO2, yt
Kya
the added complexity of reactions, the limited solubility of
L/min
mole fraction
mol/m3s
CO2 in water makes it necessary to have accurate analysis of
0.53
0.143
0.333
the gas phase and to work with concentrated gas streams to
1.06
0.099
0.558
get good results. A Rosemount Analytical, Inc.,[5] model 880a
infrared analyzer provides accurate and reliable measure1.58
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
or 1 N NaOH
is high. Having a low gas rate also provides
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
yb = 0.175 (water)
yb = 0.178 (NaOH)
both the exiting gas and water streams.
To illustrate the advantage of combining
a chemical reaction with the absorption
process, we also built a small-scale column
for use with NaOH solution as absorbent.
A 1.75-in-diameter, 15-in-long acrylic tube
was filled to a height of 12.75 in with the
same glass rings used in our larger column.
Liquid Rate, W
L/min
Outlet CO2 , yt
mole fraction
K ya
mol/m3s
Outlet CO2, yt
mole fraction
Kya
mol/m3s
0.14
0.168
0.237
0.062
2.96
0.23
0.165
0.285
0.050
3.69
0.28
0.164
0.312
0.037
4.09
0.35
0.162
0.349
0.031
4.66
0.40
0.161
0.375
0.027
5.07
TABLE 3
Heights of Transfer Units and Mass Transfer Coefficients for Large Column
134
Water Rate
L/min
Hx
m
Hy
m
mGHx/L
m
HOy
m
kya
mol/m3s
kxa
mol/m3s
kxa/m
mol/m3s
K ya
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
Chemical Engineering Education
TRADITIONAL ANALYSIS
If we neglect temperature and pressure effects and assume
that CO2 only is experiencing mass transfer between the gas
and the liquid phases, traditional analysis leads to a design
equation for our absorber given by[6]:
Z
Z = ∫ dz =
0
yt
G0
dy
= H Oy N Oy
∫
2
y
K y a b (1− y) ( y − y )
e
(1)
where t and b represent top and bottom of the column, respectively, 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
overall mass transfer coefficient based on the gas phase driving force, G0 is the solute free gas flux, HOy is called the height
of a transfer unit, and NOy is the number of transfer units.
Neglecting details of reactions between CO2 and water and
any impurities we can describe the vapor liquid equilibrium
with Henry’s law using Henry’s constant, H = 1420 atm at
20 ˚C.[7] Since the height of the laboratory column is known,
experimental gas phase composition data can be used in
Eq. (1) to solve for the mass transfer coefficient at various
operating conditions.
Integrating Eq. (1) is tedious since a mass balance in the
form of an operating line equation must first be used to
determine x at every value of y before Henry’s law can be
used to find ye at each x that corresponds to each y. This has
traditionally been done by plotting the operating line and the
equilibrium line and then graphically integrating Eq. (1). Modern computing environments like MATLABTM can be used to
integrate this equation and back out mass transfer coefficients
from laboratory data as shown in Appendix 1. Results for Kya
obtained by this method are given in Table 1 and these can
be seen to increase with increasing water rate.
The traditional analysis doesn’t give much insight into the
details of the mass transfer process or the physical reason the
mass transfer improves with increasing water rate. To obtain
that insight, students are directed to textbooks for an explanation of the two-film theory of Whitman[8] where they learn
that the overall resistance to mass transfer can be considered
to be made of a gas phase film resistance and a liquid phase
film resistance:
G
H Oy = H y + m H x
L
( 2)
1
1
m
=
+
K ya k ya k x a
(3)
or equivalently,
where m is the slope of the equilibrium line, equal to the Henry’s constant here. Geankoplis[7] gives correlations for Hx and
Hy and the results of these correlations are given in Table 3.
Vol. 45, No. 2, Spring 2011
A few years ago our old 30-foot-tall,
6-inch-diameter, steel absorption
tower became clogged with rust and
residue from years of use with sodium carbonate solution as absorbent
for removing CO2 from air.
Although these correlations are not generally expected to give
accurate quantitative predictions, the correlated results for Kya
are in reasonably good agreement with the experimentally
obtained results.
HOy, Hx, and Hy are often thought of as the overall, liquid
side, and gas side resistance to mass transfer, respectively.
Confusion can result, however, when using these to explain
the water rate dependence of the mass transfer coefficient,
because while Hx is larger than Hy, Hx is observed to increase
rather than decrease with increasing water rate. Apparently
the term mGHx/L is the controlling factor here, but this still
doesn’t provide a clear physical explanation.
SIMPLE MODEL
Our simple absorber model uses COMSOL Multiphysics to
solve two instances of the convection and diffusion equation
simultaneously with appropriate boundary conditions in a
cylinder with the dimensions of our column:
∇i(−D∇c) = R − u i∇c
( 4)
R represents a reaction or source term and u is the velocity
vector in the convection term. One instance of Eq. (4) evaluates the concentration of solute in the gas phase, cg, and the
other instance evaluates the concentration of solute in the
liquid phase, cl. In the simple model, we included a mass
transfer term as a “reaction” and consider that solute leaving
the gas phase by this “reaction” enters the liquid phase by a
similar mass transfer “reaction.” For the gas phase, the mass
transfer “reaction” was written as
R = −K y a (1− y)( y − ye )
(5)
The quantity (1-y) accounts for part of the (1-y)2 term in
Eq. (1) while the other part is accounted for by setting the
gas velocity in the z-direction to vg = vg0 / (1-y). Thus, the
changing gas velocity along the length of the column is easily taken into account. This treatment was not needed for the
liquid phase because the small amount of solute dissolved in
the liquid had a negligible effect on the liquid velocity.
The absorber can be modeled equally well in 1-D, 2-D,
or 3-D, but we prefer the 2-D axial symmetric implementa-
135
tion because it gives the best visual representation of our
process. One of the important advantages of the powerful
modern computing environments is that there is usually
no need for transformation or scaling of variables; we
can work with the actual dimensions of the equipment
and with SI dimensioned variables. This what-you-seeis-what-you-get philosophy is aimed at making a strong
connection between the equations and the physical process
and appealing to visual learners.
The model results can be presented in a variety of ways
including a colorful surface plot of y within the column
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
information readily obtained from the model, it is of interest
to note that only three of the four experimentally obtained
Kya results in Table 1 follow the expected trend of a linear
function of water rate raised to the 0.7 power.[6] At first, we
rationalized that the reason the first point, at the lowest water
rate, did not follow the expected trend may have been channeling or poor wetting of the packing at this water rate. When
we observed the liquid phase mole fraction, x, as a function
of column height in our model for this run, however, we saw
that the liquid was essentially saturated before reaching the
column outlet. Thus, the experimental outlet results can be
modeled using a wide range of Kya values including the value
of 0.333 mol/m3s that we obtained earlier but also the value
of 0.480 mol/m3s that would fall in line with our other results
in a correlation of Kya vs. (W)0.7.
Here we have used our model to calculate the outlet concentrations that will occur in the column given an overall
mass transfer coefficient. We could just as easily have used
the built-in Parametric Solver capability of COMSOL to
find the values of the mass transfer coefficients that fit our
experimental data. Our model could be easily modified to
include variable mass transfer coefficients, multiple solutes,
temperature and pressure effects, and even time dependence,
but these modifications were not needed here. We have included the effect of the chemical reaction between NaOH
and CO2, however.
MODEL WITH REACTION
The chemical reaction between CO2 and NaOH is well
studied and according to the literature[10] the rate limiting
step in this reaction is:
CO 2 + OH − → HCO−3
(6)
and the rate of reaction can be expressed as:
r = k B CCO 2 COH−
(7 )
with second order rate constant given as a function of ionic
strength by
log (k B ) = 11.875 − 2382 / T + 0.221 I − 0.016 I 2
(8)
where kB is in m3/kmol s, T is in K, and I is in kmol/m3. The
ionic strength is calculated as
I = 0.5(C Na+ + COH− + 4 C HCO 3− )
(9)
Our absorber model was easily modified to account for this
chemical reaction by writing the “reaction” term for CO2 in
the liquid phase as
R = K y a ( y − ye ) − k BCCO 2 COH−
(10)
indicating that CO2 arrives at the liquid phase from the gas
phase by mass transfer and disappears from the liquid phase
by reaction. This model also keeps track of the ions, Na+,
OH–, and HCO3–, by solving Eq. (4) for each species in the
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
Kya = 0.480, lower (a) curve is for Kya = 0.333 mol/m3s.
136
Chemical Engineering Education
The Parametric Solver in COMSOL was used to find the
values of Kya needed to make the outlet y results of the model
match the experimental y results. The resulting Kya values
are shown in Table 2. The dramatic improvement in the mass
transfer process due to the reaction is reflected in the increase
in Kya with reaction compared to without.
QUALITATIVE FALLING FILM MODEL
Although our simple absorber model is easier to use than
the traditional analysis and has the added benefit of showing
a colorful representation of the composition in the column, it
doesn’t given much insight into the details of the process or
help explain why the mass transfer coefficients increase with
increasing water flow rate. The physical process that actually occurs inside the column is that solute diffuses through
a flowing gas phase to the gas-liquid interface, crosses the
interface to maintain equilibrium there, and diffuses into a
flowing liquid phase. To model this process more directly we
should solve Eq. (4) with R = 0 and use the actual diffusion
coefficients in the gas and liquid phases and an appropriate
boundary condition at the interface. We describe here a qualitative diffusion-based falling film model aimed at addressing
these concerns and providing a basis for understanding an
explicit two-film model presented below.
Inside our packed column are glass rings that have a thin
layer of water flowing down over them surrounded by gas
flowing upward. Although it can be done, it is complicated
and expensive in computer time to model the exact details of
the fluid flow and mass transfer that takes place around these
rings randomly packed inside the column. As an illustration,
however, it was reasonable to approximate the process with a
number of identical glass rods each extending the full height of
the column. The water layer around each rod was considered
to flow downward in laminar flow and the gas layer around
that was considered to flow upward in plug flow. The thickness and velocities in these flowing layers were selected to
give approximate results that illustrate our points. It was only
necessary to model one rod with its surrounding layers axially
symmetrically as shown in Figure 2.
As before, two instances of the convection and diffusion
equation, one for the gas phase and one for the liquid phase,
were solved simultaneously. The inlet and outlet boundary
conditions are shown in Figure 2. The so-called “stiff-spring”
equilibrium boundary condition[11] was used at the gas-liquid
interface according to Henry’s law. That is, the boundary
condition on the gas side of the interface was set to
Flux = −M ( y − ye )
(11)
and the boundary condition on the liquid side was set to
Flux = M ( y − ye )
(12)
where M is an arbitrary large number; e.g., M = 10000. This
assures a continuous flux across the interface and enforces
the equilibrium condition ye = H x. Mass transfer coefficients
were not used in this diffusion-based model. Instead, carbon
dioxide diffuses through the gas phase, crosses the interface,
and diffuses into the liquid phase according to molecular
diffusion using diffusivities for CO2 in air and water of 1.6
3 10-5 m2/s and 1.8 3 10-9 m2/s, respectively. The velocity
profile in the liquid phase was given by the solution to the
built-in Incompressible Navier-Stokes mode of COMSOL.
The velocity in the gas phase was considered uniform in the
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
the r-direction at a height equal to Z/10 for two different water
velocities. Curve a is for a relatively low water rate and the
curve b is for a relatively high one. More CO2 is removed from
the gas phase at the high water rate as expected. In both cases,
the gas phase concentration is nearly uniform in the r-direc-
tion. On the other hand, the liquid phase concentration varies
in the r-direction and can be characterized as having a rapidly
changing region close to the interface and a nearly constant region in the bulk. The region where the concentration changes
is often called the concentration boundary layer.[12] Figure 3
shows that the thickness of this boundary layer decreases with
increasing water rate due to increased convection. In
reality, a change in water rate would probably affect
the interfacial area as well as the boundary layer thickness, 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 indicates 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
Figure 3. Concentration in the r-direction at z/Z = 0.1 for qualitative
falling film model (a) low water rate, (b) high water rate, (c) NaOH
solution rate equal to water rate in (a). Note that the x-axis begins at
r = 0.005 m to show only the flowing layers in this figure.
Our falling film model illustrates the diffusion and
convection process but does not give accurate predictions for outlet compositions because it does not
take into account all the details of the non-uniform
packing and flow patterns in the column. We describe
here an explicit two-film model that gives accurate
outlet compositions, illustrates the two-film theory,
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 resistance to mass transfer throughout
the column.[6] As shown above,
this approach describes absorption 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 complex 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:
Flux =
Figure 4. Model geometry showing two-film theory.
138
D
∆c
l
(13)
where l is the film thickness and
Chemical Engineering Education
Δc is the concentration difference across the film. The mass
transfer coefficient was defined to give a similar simple equation for the flux for more complex situations:
Flux = k c ∆c
(14)
One way to understand what the mass transfer coefficient
represents is to compare Eqs. (13) and (14) and let
kc =
D
δ
(15)
where δ is some equivalent stagnant film (or concentration
boundary layer) thickness that can be viewed as controlling
(providing resistance to) the mass transfer in a complex situation. Note that kc has units of m/s.
To introduce the two-film concept into our diffusion-based
model we could incorporate a stagnant film (with vg or vl = 0)
of the appropriate thickness on each side of the interface and
use Eq. (4) (with R = 0 and D = Dg or Dl) over those films.
Alternatively, and equivalently, we have used an effective diffusivity acting over an arbitrarily established film thickness,
tfilm, instead of the actual diffusivity over a film thickness, δ,
that would need to be adjusted to fit each data point:
D Deff
=
δ
t film
values of the individual mass transfer coefficients, kya and kxa,
accounting for the interfacial area per volume, a, as a separate
component of kya and kxa, and some unit conversions.
From Table 3 it can be observed that 1/kya is a minor contributor to 1/Kya in Eq. (3), for this system. We have, therefore,
chosen to assume that the correlated values of kya shown in
Table 3 are correct, knowing that uncertainties in these values
will not have a strong effect on our subsequent results and
interpretations. With this assumption, kxa could be calculated
from Eq. (3) using the previously obtained experimentally
derived values of Kya at each liquid flow rate. The resulting
values for kxa are given in Table 4 (next page). For our model,
the interfacial area per volume is 2πRiZNR/V = 667 m2/m3.
Ri is the radius of the model at the interface and NR is the
number of glass rods. Taking into account unit conversions
between cg and y and cl and x yields the following equations
for effective diffusivities in the gas and liquid films.
Dg =
eff
Dl =
eff
(16)
Figure 4 shows the geometry and boundary conditions for
our two-film model based on this effective diffusivity approach. The appropriate resistance to mass transfer in each
film has been established by setting the effective diffusivity
in the r-direction of the film to be equal to the individual mass
transfer coefficient times the film thickness. Obtaining appropriate values for the effective diffusivities requires estimating
k y a ( t film )(8.314m 3 Pa / mol K )
a (101325 Pa )
k x a ( t film )(1000 cm 3 / L)
3
a (55.556 mol / L)(100 cm / m)
(17 )
(18)
where tfilm is the thickness of the stagnant gas and liquid films
used in the model (arbitrarily set to 0.001 m).
Note that although we have used mass transfer coefficients
in defining our effective diffusivities, our two-film model does
not use the mass transfer coefficient approach but instead describes mass transfer as governed only by molecular diffusion
through stagnant films, equilibrium at the interface, and convection 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 correct 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 conFigure 5. Concentration in the r-direction 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
Rate, W
L/min
Kya
mol/m3s
Large
Column
No
Reaction
0.53
0.480*
1.06
0.558
1.58
2.11
Small
Column
No
Reaction
0.14
0.23
Hy
m
kya
mol/m3s
kxa
mol/m3s
kx
mol/m2s
δl
m 3 105
δg
m 3 102
kcl
m/s 3 104
kcg
m/s 3 104
0.065
3.55
789
1.18
8.45
13.41
0.213
1.19
0.046
5.02
891
1.34
7.48
9.49
0.241
1.69
0.634
0.038
6.13
1004
1.51
6.64
7.77
0.271
2.06
0.712
0.033
7.08
1123
1.68
5.93
6.73
0.303
2.38
0.237
0.110
6.53
350
0.525
19.1
7.3
0.095
2.19
0.285
0.086
8.37
419
0.629
15.9
5.7
0.113
2.81
0.28
0.312
0.078
9.23
458
0.687
14.6
5.1
0.124
3.10
0.35
0.349
0.070
10.32
512
0.768
13.0
4.6
0.138
3.47
0.40
0.375
0.065
11.04
551
0.827
12.1
4.3
0.149
3.71
Small
Column
With
Reaction
0.14
2.96
0.110
6.53
7667
11.50
0.870
7.3
2.07
2.19
0.23
3.69
0.086
8.37
9354
14.03
0.713
5.7
2.52
2.81
0.28
4.09
0.078
9.23
10438
15.66
0.639
5.1
2.82
3.10
0.35
4.66
0.070
10.32
12066
18.10
0.552
4.6
3.26
3.47
0.40
5.07
0.065
11.04
13295
19.94
0.501
4.3
3.59
3.71
centration across the various layers at various heights in the
column a student can observe the resistance to mass transfer
in each of the films as well as the concentration difference
imposed by equilibrium at the interface. More resistance is
indicated by a larger concentration change. In this system, it
can be seen that the liquid phase offers considerably more
resistance than the gas phase.
From Table 4 we see that kxa increases with increasing
water rate. This could be due to either kx increasing or the
interfacial area, a, increasing or both. The interfacial area
probably does increase with increasing water rate because
more of the packing is wetted and the flowing liquid layer may
also be thicker. If we assume, however, that a is constant as
we have done in our model, we can see that kx increases with
increasing water rate. What physical process can account for
this? As shown above, kc (and with unit conversions kx) can
be assumed to be equal to the molecular diffusivity divided
by the stagnant film thickness. Since we used an arbitrary
film thickness, tfilm, for convenience in our model, an estimate
of the stagnant liquid film thickness in our absorber can be
obtained by solving Eq. (16) for δl.
Results for this stagnant film (or concentration boundary
layer) thickness estimated by this approach are given in Table
4 at each of the absorbent flow rates studied. Even though the
140
stagnant film thicknesses are fictitious constructs of the film
theory and subject to the assumptions in our model, the estimated film thicknesses can be seen to decrease with increasing water rate, thus providing a physical explanation for the
observed dependence of mass transfer on water flow rate.
Our explicit two-film model can also be used to provide
more insight into the difference between absorption with and
without reaction. To include the chemical reaction, we initially
used Eq. (7) in the flowing liquid layer only. The resulting
concentration profiles at various heights in the small column
with and without reaction are shown in Figure 6. For the case
with no reaction, in Figure 6a, it can be seen that the liquid
side resistance dominates the process. For the reaction case,
shown in Figure 6b, the concentration in the flowing liquid
is essentially zero everywhere providing a consistently high
driving force for mass transfer and preventing saturation of
the liquid even at low liquid rates. It can also be seen that the
resistance in the gas phase is comparable to the resistance in
the liquid phase when reaction is present.
Estimates of the effective film thicknesses in the small
column obtained from Eq. (16) are given in Table 4. In accordance with our qualitative falling film model, it can be
seen that the chemical reaction has the effect of dramatically
reducing the liquid film thickness. The fact that the gas film
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 thickall the resistance to mass
nesses can be explained by
transfer would be in the gas
the fact that the gas phase diffilm 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 resisequal to the Kya values shown
tance offered by each phase
in Table 3 and setting the efit is instructive to compare
fective diffusivity in the r-dithe kc values. These values
rection in our liquid film to an
have been calculated from
artificially large number. The
Eq. (15) using film thickresulting 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
to those in Figure 6b. It is
Figure 7. Concentration profile for W = 0.35 in the small
kya and kxa values using appossible that Figure 7 is more
column at z/Z = 0, 0.25, 0.5, 0.75, 1.0 with reaction in the
propriate unit conversions. liquid and all mass transfer resistance in the gas film. Note representative of reality than
that the x-axis begins at r = 0.005 m to show only the fluid
The resulting values of kcl
Figure 6b because the k ya
layers in this figure.
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
film to account for resistance due to diffusion into the liquid phase separately from the reaction taking place almost
instantaneously in the flowing liquid layer. Another way to
analyze this type of fast reaction process is to consider that
there is no liquid film (or no resistance in the liquid film)
since the reaction can take place as soon as the solute crosses
Vol. 45, No. 2, Spring 2011
In our unit operations lab, students spend about two weeks
on each experiment. Groups of three or four students first
collaborate on writing a pre-lab report describing the relevant
theory and their plans to conduct the experiment. For the
absorber lab, the groups then spend two days of lab work
collecting data that they analyze and include in a final report.
It was disappointing, but revealing, that very few students
141
bothered to use the simulations the first year they
were offered as a completely optional resource. In the
second offering, we required each student to complete
an interactive tutorial containing the simulations and
an associated online quiz that asked questions about
them. At the end of the course that year, the students
completed a survey regarding their perception of the
benefits of using the simulations.
Students in the course did not build the simulations
from scratch but instead re-ran previously developed
simulations with different operating conditions. The
tutorial walked the students through the pre-built
simulations and included several multiple-choice
questions requiring simulation results to obtain correct answers. For example, one question asked for
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
the value that would be obtained if the process were
considered dilute with straight equilibrium and operating lines. In addition to answering these questions,
students were encouraged to experiment with changing operating conditions to see the effect on column
performance. Students were invited to study the
simulations and answer the multiple choice questions
on their own time and at their own pace. They were
encouraged to study the simulations before completing their pre-lab reports but were required to submit
the answers to the multiple choice questions on-line
after the pre-lab was completed and before the final
report was due. It should be noted that these students
were not necessarily COMSOL model builders but did
have some familiarity with COMSOL from previous
homework assignments using pre-built simulations
via tutorials and online questions.
TABLE 5
Results for Three Survey Questions
The percentage of students giving each response is indicated in brackets.
(1) The learning tool helped me to understand mass transfer, in general:
(a) not at all [13%], (b) just a little [13%], (c) somewhat [40%], (d) much [27%],
(e) very much [7%].
(2) It helped me understand how the mass transfer coefficient varies with water
flow rate:
(a) not at all [7%], (b) just a little [7%], (c) somewhat [20%], (d) much [53%],
(e) very much [13%].
(3) The best time to use this learning tool would be: (a) as a homework before
the pre-lab and in addition to a written pre-lab report [47%],
(b) at the pre-lab stage instead of a written pre-lab report [27%],
(c) after a written pre-lab and the lab itself are complete, as an aid to writing a
good final report [13%],
(d) after a written pre-lab and the lab itself are complete, to be used instead of a
final report [0%],
(e) not necessary for the average student to spend time on this at any point [13%]
TABLE 6
Example Student Comments About the Absorber Simulation
• “it allowed me to visualize the diffusion of gas into the liquid”
• “it allowed me to see the connection between the theoretical equation and how
they relate to the physical world”
• “being able to adjust the values and quickly observing changes in the system
makes for a nice learning tool”
• “I would not have remembered as much about mass transfer if I didn’t have it”
• “really helped me visualize what is occurring and then linking the theoretical
values to what is found experimentally, and why it may vary”
• “It allowed me to understand how changing variables could affect the final
resistance to mass transfer. By doing this as a simulation, it was easier to see
relationships compared to just looking at equations.”
• “the ability to change variables and investigate their effects on mass transfer
helped provide a greater understanding of mass transfer principles”
• “it basically showed me what the lab would be like … and prepared me for the
experiment in an excellent way”
• “It helps you visualize the process and makes it easier for you to make a mistake and rectify it without wasting much time in the lab. And you can also change
constants to see the effect of each on mass transfer.”
The end of course survey revealed that most, but not
all, of the students found the simulations to be useful,
particularly for illustrating the resistance to mass transfer and
providing a physical feel for why the mass transfer coefficient
increases with increasing water rate. Table 5 shows example
questions and the percent of students responding to each of the
multiple choice answers for each question. Table 6 provides
examples of student comments on the absorber simulations.
CONCLUSION
Our new absorption experiment provides an effective way
of teaching mass transfer fundamentals while using relatively
small amounts of CO2, air, and water. Experiments presented
with NaOH as absorbent provide a good demonstration of the
dramatic improvement in absorption due to reaction. A simple
model made with COMSOL Multiphysics gives accurate
calculations, is easier to use than the traditional analysis,
and provides a visual representation of the absorption pro142
cess. More detailed models that illustrate the concentration
boundary layer and the two-film theory provide a physical
feel for the observed increase in the mass transfer coefficient
with an increase in water rate. These models also make it
clear that the improved mass transfer with reaction is due to
reduced resistance in the liquid phase as well as maintaining
a high driving force and preventing saturation of the liquid.
The straightforward and relatively easily obtained solutions
together with the richness of information afforded by post
processing capabilities in COMSOL can make the details of
complex process calculations “come alive” in comparison to
the rare, static, printed examples in text books. Combining
the experiments with computer simulations that show the
concentration profile within the equipment appears to benefit
the learning process and help students gain a more complete
understanding of mass transfer in an absorber.
Chemical Engineering Education
ACKNOWLEDGMENTS
This material is based on work supported by the National
Science Foundation under grant no. DUE-0536342.
REFERENCES
1. Clark, W.M., and D. DiBiasio, “Computer Simulation of Laboratory
Experiments for Enhanced Learning,” Proceedings of the ASEE Annual
Conference, Honolulu, Hawaii, June 24-27, (2007)
2. Clark, W.M., “COMSOL Multiphysics Models for Teaching Chemical
Engineering Fundamentals: Absorption Column Models and Illustration of the Two-Film Theory of Mass Transfer,” COMSOL Conference
2008 Proceedings, Boston, October (2008)
3. Finlayson, B.E., Introduction to Chemical Engineering Computing,
Wiley-Interscience, Hoboken, NJ, (2006)
4. <http://www.hampden.com>
5. <http://www2.emersonprocess.com/en-US/Pages/Home.aspx>
6. Cussler, E.L., Diffusion: Mass Transfer in Fluid Systems, 3rd Ed.,
Cambridge University Press, New York, (2009)
7. Geankoplis, C.J., Transport Processes and Separation Process Principles (Includes Unit Operations), 4th Ed., Prentice Hall, Upper Saddle
River, NJ (2003)
8. Whitman, W.G., “The Two-Film Theory of Gas Absorption,” Che.
Metal. Eng., 29, 146-150 (1923)
9. <http://www.comsol.com>
10. Pohorecki, R., and W. Moniuk, “Kinetics of Reaction Between Carbon
Dioxide and Hydroxyl Ions in Aqueous Electrolyte Solutions,” Chem.
Eng. Sci., 43(7), 1677 (1988)
11. COMSOL Multiphysics, Chemical Engineering Module User’s Guide,
Separation Through Dialysis Example.
12. Seader, J.D., and E.J. Henley, Separation Process Principles, 2nd Ed.,
Wiley, Hoboken, NJ (2006)
APPENDIX
1. Matlab m-files for absorber analysis.
The function quadv is a built-in Matlab function that performs numerical integration of a complex function between
finite limits.
% run_absorber.m
% this is the driver file to calculate the overall
Vol. 45, No. 2, Spring 2011
gas phase
% mass transfer coefficient, Kya, and the HTU and
NTU for an absorber
% input is Z, packing height (m); S, cross sectional area (m^2);
% L0, liquid flux, (mol/m2s), G0, non absorbing
gas flux (mol/m2s);
% yb, inlet gas mole fraction solute; yt, outlet
gas mole fraction solute.
% inlet liquid is assumed pure solvent
% outlet liquid xb is obtained from mass balance
% ye = ystar = H*x
% Kya is in mol/m^3h
global L0 G0 xb yb yt H
H=1420;
Z = 1.372;
S = 0.00456;
L0 = 1.06*1000/60/18/S;
G0 = 1.42*1000/(100^3*60*0.022415)/S;
yb = 0.185;
yt = 0.099;
xb = G0/L0*(yb/(1-yb)-yt/(1-yt))/(1+G0/L0*(yb/(1yb)-yt/(1-yt)))
NTU = quadv(@funynew,yt,yb)
HTU = Z/NTU
Kya = G0/HTU*3600
% funy.m
% function to integrate to get NTU
function f = funy(y)
global L0 G0 xb yb yt H
OPTIONS=[];
x = G0/L0*(y/(1-y)-yt/(1-yt))/(1+G0/L0*(y/(1-y)yt/(1-yt)));
ye=H*x;
f = 1/((1-y)^2*(y-ye));
>> run_absorber
xb = 1.2597e-004
NTU = 3.3846
HTU = 0.4054
Kya = 2.0563e+003 p
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 demonstrate 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:
briedis@egr.msu.edu), 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
O
West Virginia University • Morgantown, WV 26506-6102
ptimization is often considered to be an advanced,
highly mathematical, and sometimes a somewhat
obscure discipline. While it is true that many advanced optimization techniques exist, optimization problems
can be developed that are suitable for undergraduates at all
levels. Two of these problems will be described in this paper,
and many others are available on the web.[1] A pedagogy is
described that requires students to identify the trends of the
components of the objective function and to understand how
trade-offs between these components lead to the existence
of the optimum.
The ability to solve “routine” optimization problems has
been simplified by advances in computing power over the
last generation. Earlier editions of current design textbooks[2]
presented a sequence of optimization techniques aimed at
minimizing the number of cases that had to be considered
to close in on the optimum. Now, it is possible to perform
optimization calculations involving numerous cases with a
few clicks of a mouse, and an entire chemical process can be
simulated and results exported to a spreadsheet in a matter
of minutes.
Several optimization examples are routinely discussed
in undergraduate textbooks; however, the objective function does not usually involve economics. These examples
include optimum interstage compressor pressure,[3] optimum
insulation thickness,[4] and identifying conditions for the
optimum selectivity.[5] Qualitative representations of the
economic optimum pipe diameter[6] and reflux ratio[7] are
also available. Other examples of optimization problems
are available, but these do not involve an economic objective function.[8-10] The problems presented here all involve
an economic objective function.
TYPES OF PROBLEMS
Three types of optimization problems are available, and
they are summarized in Table 1. The ones highlighted in italics are discussed in this paper, and the others are available on
the web.[1] The numbers in parenthesis indicate the number
of different versions available for each problem. All of these
have been used successfully in a freshman class designed to
develop computing skills appropriate for an undergraduate
chemical engineering student. Most of these problems would
also be suitable for assignments or projects in unit operations
TABLE 1
Single
Variable
Available Optimization Problems
Multi-variable
Projects
Pipe diameter (2)
Absorber
Generic chemical
process (2)
Reactor/
preheater
(2)
Batch reactor/preheater
Geothermal energy
(2)
Reflux ratio
Staged compressors
Fuel production
from biomass (4)
Brian J. Anderson is the Verl Purdy Faculty Fellow and an assistant professor in
the Department of Chemical Engineering at West Virginia University. His research
experience includes sustainable energy and development, economic modeling
of energy systems, and geothermal energy development as well as molecular
and reservoir modeling.
Joseph A. Shaeiwitz received his B.S. degree from the University of Delaware
and his M.S. and Ph.D. degrees from Carnegie Mellon University. His professional interests are in design, design education, and outcomes assessment. Joe
is a co-author of the text Analysis, Synthesis, and Design of Chemical Processes
(3rd Ed.), published by Prentice Hall in 2009.
Robin S. Hissam received her B.S. and M.S. degrees in materials science and
engineering from Virginia Tech and her Ph.D. in materials science and engineering from the University of Delaware. After a post-doctoral fellowship in chemical
engineering and applied chemistry at the University of Toronto, Robin joined the
Chemical Engineering Department at West Virginia University. Her research is
in production of protein polymers for application in tissue engineering, biomineralization, and biosensors.
Richard Turton, P.E., has taught the senior design course at West Virginia
University for the past 24 years. Prior to this, he spent five years in the design
and construction industry. His main interests are in design education, particulate
processing, and modeling of advanced energy processes. Richard is a co-author
of the text Analysis, Synthesis, and Design of Chemical Processes (3rd Ed.),
published by Prentice Hall in 2009.
144
© Copyright ChE Division of ASEE 2011
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 industry. The reaction occurs in a large, well-stirred, isothermal
bioreactor, such that the reactor temperature is identical to
the inlet temperature. Because this chemical is temperature
sensitive, the maximum operating temperature in the reactor
is limited to 65 ˚C by using a heating medium available at this
maximum temperature. The feed material is fed to the reactor
through a heat exchanger that can increase the temperature of
the reactants (contents of the reactor), which in turn increases
the rate of the reaction. This is illustrated in Figure 1. The
time spent in the bioreactor (known as the space time) must
be adjusted to obtain the desired conversion of reactant. As the
temperature in the reactor increases so does the reaction rate,
thereby decreasing the size (and cost) of the reactor required
to give the desired conversion. The problem to be solved is
to determine the optimal value for the single independent
variable; namely, the temperature (Tc,2) at which to maintain
the reactor (preheat the feed). The costs to be considered are
the purchase costs of the reactor and heat exchanger and the
operating cost for the energy to heat the feed.
Problem Statement
It is desired to optimize the preheat temperature for a reactant feed flow of 5,000 gal/h. 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 available
at a temperature of 65 ˚C and must leave the heat exchanger
at 30 ˚C. Therefore, the desired reactor inlet temperature is
adjusted by changing the flowrate of the heating medium.
The physical properties of the heating medium are ρ = 920
kg/m3, Cp = 2.2 kJ/kg ˚C.
The reaction rate for this reaction, –rA, is given in terms of the
concentration of reactant A (CA) by the following equation:
−rA = kCA
(1)
where


3, 500 
k s−1  = 2.5 exp −

 
 T  K  
vo XA
k (1− X A )
( 2)
Vol. 45, No. 2, Spring 2011
Q = M c C p,c (Tc ,2 − Tc ,1 ) = M h C p,h (Th ,1 − Th ,2 ) = UAF∆Tlm ( 4)
where
∆Tlm =
(T
h ,2
− Tc ,1 ) − (Th ,1 − Tc ,2 )
(T
ln
(T
h ,2
h ,1
− Tc ,1 )
(5)
− Tc ,2 )
and
F = log-mean temperature correction factor = 0.8 (assume
that this is constant for all cases)
U = overall heat transfer coefficient = 400 W/m2K
The optimum reactor inlet temperature is the one that
minimizes the equivalent annual operating cost (EAOC). The
EAOC is given by
2
EAOC $ / y = ∑ PCi $ (A / P, i, n) 1 / y + UC $ / y
(6)
i=1
where PCi are the purchase equipment costs for the heat
exchanger and reactor, UC is the operating (utility) cost for
the heating medium, and (A/P, i, n) is the capital recovery
factor given by
n
(A / P, i, n) =
i(1 + i)
n
(1+ i)
(7 )
−1
For this problem, use i = 7% and n = 12 years.
PCreactor = $17, 000 V 0.85
(8)
where V is the volume of the reactor in m3. The cost of the
heat exchanger is:
(3)
where V is the reactor volume (m ), vo is the volumetric flowrate of fluid into the reactor (m3/s), and XA is the conversion
(assumed to be 80% or 0.8 for this reaction).
3
The design equation for the heat exchanger is given by:
The purchase cost of the reactor is given by:
The design equation for the reactor is given by:
V=
Figure 1. Process flow diagram of the feed preheater
and bioreactor.
{
PCexchanger = $12, 000 A  m 2 
 
}
0.57
(9)
where A is the area of the heat exchanger in m2. The cost of
the heating medium is:
UC $ / h  = $5×106 Q  kJ / h 
(10)
145
The results should be presented as two plots. The first should
show how each term in Eq. (6) changes with Tc,2, and the
second plot should show the EAOC (y-axis) as a function of
Tc,2 (x-axis). The report should contain a physical explanation
of the reason for the trends on these plots.
Problem 2: Batch Bioreactor
Background
A liquid-phase, biological reaction is used to produce an
intermediate chemical for use in the biotech industry. The
reaction occurs in a large, well-stirred, isothermal bioreactor, such that the reactor temperature is identical to the inlet
temperature. Because this chemical is temperature sensitive,
the maximum operating temperature in the reactor is set to
55 ˚C. The feed material is fed to the reactor through a heat
exchanger that increases the temperature of the reactants
(contents of the reactor), which in turn increases the rate of
the reaction. This is illustrated in Figure 2.
The reactor runs as a batch operation in which the contents
remain in the equipment for a given period of time. The time
spent in the bioreactor must be adjusted to obtain the optimal
conversion of reactant. Because of the fear of contamination
by pathogens and parasitic fungi, the reactor must be cleaned
thoroughly between batch operations. The cleaning time
per batch (tclean) and the cost of cleaning both vary based
on the size of the reactor used.
by the following equation:
−rA = kCA
(11)


3, 500 
k s−1  = 2.5 exp −

 
 T  K  
(12)
where
The design equation for the reactor is given by:
t s =
1
1
ln
−1 

−
1
XA
k s 
 
(13)
where t is the time spent in the reactor and XA is the fractional
conversion of reactants to products. The amount of product
formed in time t is given as NXA, where N is the number of
moles of reactant fed to the reactor.
The energy balance equation for the heat exchanger is
given by:
Q = M c C p,c (Tc ,2 − Tc ,1 ) = M h C p,h (Th ,1 − Th ,2 )
(14)
where
M is the mass of fluid to be heated or cooled (kg)
Cp is the specific heat capacity of the fluid (kJ/kg ˚C)
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
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.
Figure 2. Process flow diagram of feed preheater and bioreactor.
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 available 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
kg/m3, Cp = 2.2 kJ/kg ˚C.
The reaction rate for this reaction, -rA, is given
in terms of the concentration of reactant A (CA)
146
Figure 3. Optimization plot for Example 1.
Chemical Engineering Education
T is the temperature (˚C)
1 and 2 refer to inlet and outlet conditions, respectively.
h and c refer to the hot and cold stream, respectively.
The optimal reactor configuration is the one that minimizes the equivalent
annual operating cost (EAOC). The EAOC is given by:
2
3
i=1
i=1
EAOC $ / y = ∑ PCi $ (A / P, i, n) 1 / y + ∑ UCi $ / y − R $ / y
(15)
where PCi are the purchase equipment costs for the heat exchanger and reactor;
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
product. For this problem, use i = 0.07 and n = 12 years.
The purchase cost of the reactor is given by
PCreactor = $17, 000 V 0.85
(16)
where V is the volume of the reactor in m3. The cost of the heat exchanger may
be taken to be equal to 20% of the cost of the reactor from Eq. (16).
The cost of the heating medium is given by:
UC heating $ / h  = 5×10−6 Q  kJ / h 
(17 )
where Q is the heat duty obtained from Eq. (14).
The price of the feed is $2/mol, the value of the product is $10/mol, and the
molar density (concentration) of both feed and product is 100 mol/m3. The
cost of cleaning the reactor is given by

Vreactor  gal 


UCclean $ / cleaning  = 1, 000 $ / cleaning  1 + 0.5
(18)

1, 000  gal 

and the time to clean a reactor is

Vreactor  gal 


t clean  h  = 4  h  1 + 0.5

1, 000  gal 

(19)
Figure 4. Component optimization trends in Problem 1.
Vol. 45, No. 2, Spring 2011
The ability to solve “routine”
optimization problems
has been simplified by
advances in computing power
over the last generation.
The final results should be presented as two
plots. The first plot should show how each
term in Eq. (15) changes with the batch time,
t, and the second plot should show the EAOC
(y- axis) as a function of t (x-axis). The report
should contain a physical explanation of the
reason for the trends on these plots.
OPTIMIZATION PROBLEMS
In Problem 1, the optimum reactor feed
temperature is to be determined. There is
a trade-off, which is necessary to obtain
an absolute maximum or minimum in the
objective function (EAOC) as the decision
variable (reactor feed temperature) varies.
In this case, at higher temperatures, it costs
more to heat the reactor feed, but, since the
reaction rate increases with temperature, the
reactor cost is lower because a smaller reactor is needed. Additionally, at higher reactor
feed temperatures, a larger heat exchanger is
needed. Students can develop a spreadsheet
that varies the reactor inlet temperature and
plot the EAOC vs. the reactor inlet temperature. 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 approaches 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 understand the trends. It is possible to
solve this entire problem on Excel
using the Solver tool; however,
much of the understanding/synthesis
147
Since these problems have been used
successfully in a freshman class for
several years, we believe they can be
used anywhere in the curriculum.
of the problem is lost. We believe that optimization is more
than finding an answer. An understanding of the underlying
trends is essential.
It is also possible to illustrate how changes in operating conditions change the optimum. In a problem similar to Problem
1,[11] if the reaction kinetics are increased (pre-exponential
factor increased to 7.0 and the activation energy reduced to
3300), the optimum temperature shifts down to about 35 ˚C.
Many different versions of this and other problems can be
created by changing some parameters or by changing the
economics. We use different versions of these for different
groups in the same class. During oral presentations, we ask
them to explain why the optima differ.
In Problem 2, there are two decision variables (bivariate
optimization) due to the batch processing. Therefore, this
problem in slightly more complex than Problem 1, and it illustrates that there may be more than one decision variable.
One decision variable is the reactor volume, which in this
case is limited to three standard sizes (an arbitrary number),
and the other decision variable is the processing time. The
trade-off is that for longer processing times, more product is
made, but fewer batches can be made per year. For a larger
reactor, more product can be made per batch, but fewer
batches can be made per year due to the longer cleaning
time. Although this problem does not include it, the reactor
feed temperature could also be varied, as in Problem 1, to
create a three-variable optimization. In this problem, it turns
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 bivariate optimization plot, with the x-axis containing
one decision variable with several curves indicating
the second decision variable.
components lead to the existence of the optimum. That is why
methods, such as using the Excel Solver, are not emphasized,
and making plots to investigate trends is emphasized. Once
the trends are understood, Excel Solver can be used to obtain
a more exact value of the optimum.
We have used these problems as part of a freshman class
taken by students who know that they are interested in chemical engineering. Other students take a college-wide programming class. In our class, students are taught computer skills
applicable to chemical engineering, mostly using the advanced
features of Excel in addition to some elementary programming techniques and algorithms. All assignments are based on
industrially relevant chemical engineering problems. Some of
these problems also appear in the optimization chapter of our
textbook.[11] Since these problems have been used successfully
in a freshman class for several years, we believe they can be
used anywhere in the curriculum.
Since all students in chemical engineering do not take the
class in which these problems are assigned, assessment of
their long-term impact is difficult. The freshmen do a good
job on these problems, and they seem to appreciate the actual
chemical engineering application compared to their peers in
the programming class.
Additional optimization problems are available on the
web.[1] It is observed that virtually an infinite source of these
problems could be obtained by manipulating some of the
values given in these problems.
CONCLUSION
Two example optimization problems that are believed to be
suitable for all levels of chemical engineering students have
been presented. These problems do not require advanced
mathematical techniques; they can be solved using typical
software used by students and practitioners, such as Excel.
These problems involve an economic objective function with
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
148
Figure 5. Optimization plot for Example 2.
Chemical Engineering Education
component capital and operating cost terms. An important
part of the pedagogy of these problems is an understanding
of how the trends of the components terms in the objective
function contribute to the trade-off involved in most optimization problems.
REFERENCES
1. <http://www.che.cemr.wvu.edu/publications/projects/index.
php#opt>
2. Peters, M.S., and K.D. Timmerhaus, Plant Design and Economics for
Chemical Engineers, (3rd Ed.), McGraw Hill, New York, 1980, Chapter
10
3. Sandler, S.I., Chemical, Biochemical, and Engineering Thermodynamics (4th Ed.), Wiley, New York, 2006, Chapter 4, Problem 4.21b
4. Geankoplis, C., Transport Processes and Separation Principles (4th
Vol. 45, No. 2, Spring 2011
Ed.), Prentice Hall PTR, Upper Saddle River, NJ, 2003, Chapter 4.3F
5. Fogler, H.S., Elements of Chemical Reaction Engineering (4th Ed.),
Prentice Hall PTR, Upper Saddle River, NJ, 2006, Chapter 6
6. de Nevers, N., Fluid Mechanics for Chemical Engineers (3rd Ed.),
McGraw Hill, New York, 2005, Chapter 6
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
8. Barolo, M., “Batch Distillation Optimization Made Easy,” Chem. Eng.
Ed., 32(4), 280 (1998)
9. Smart, J., “Using the Evolutionary Method to Optimize Gas Absorber
Operation,” Chem. Eng. Ed., 38(3), 204 (2004)
10. Mitsos, A., “Design Course for Micropower Generation Devices,”
Chem. Eng. Ed., 43(3), 201 (2009)
11. Turton, R., R.C. Bailie, W.B. Whiting, and J.A. Shaeiwitz, Analysis,
Synthesis, and Design of Chemical Processes (3rd Ed.), Prentice Hall
PTR, Upper Saddle River, NJ, 2009, Chapter 14 p
149
ChE department
ChE at...
The University of Houston
Michael P. Harold and
Ramanan Krishnamoorti
C
hemical engineering at the University of Houston has reflected
the growth and diversification of
the field: from traditional petrochemicals
to advanced materials to energy and sustainability 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 university 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 national recognition when it established KUHT, the world’s
first educational television station. Today, the University of
Houston is the flagship of the University of Houston System
and is considered one of the most ethnically diverse campuses
among U.S. universities.
The Department of Chemical & Biomolecular Engineering
(ChBE) at the University of Houston started as a program
during the late 1940s and by the 1952/’53 academic year, a
full-time faculty of chemical engineering was formed. During
the next three years, under the leadership of Joseph Crump,
a vision emerged with three short-term
goals: (i) establishment of a graduate
program comprising M.S. and Ph.D.
degrees supported by an internationally
recognized research program, (ii) establishment of an accredited undergraduate
program with strong industrial ties, and
(iii) growth of a department supported
by university administration. During
the next 15 years, under the leadership
Joseph Crump
of Frank Tiller (Dean of Engineering,
1955 to 1963) and Abe Dukler (Chair), UH Chemical Engineering emerged as the young upstart department. Under the
150
leadership of Dan Luss from the mid ’70s, through the ’80s,
UH Chemical Engineering became one of the top departments
in the United States (ranked 8th by the National Research
Council in 1982). The leadership was passed to Jim Richardson, who chaired the department from 1996-1998. After a
challenging period of budget pressures in the mid 1990s, UH
attracted one of its former faculty members, Ray Flumerfelt,
to serve as dean of the Cullen College of Engineering. One
of Flumerfelt’s primary goals was to invest in the Chemical Engineering Department to re-establish its prominence.
In 2000 Flumerfelt hired one of UH’s own, Mike Harold
(Ph.D., 1985) who chaired the department from 2000 to 2008
when it underwent the name change to include Biomolecular.
The injection of resources has led to a new period of growth
and resurgence of the department, now under the leadership
of Ramanan Krishnamoorti—transforming itself from its
unit operations and transport focus to sustained excellence
in reaction engineering, and new strengths in materials and
biomolecular engineering. The full-time faculty is now approaching 20 in number while enhancing its reputation and
impact. The most recent 2010 NRC review has the department
ranked 18th (based on the more objective “S” ranking).
© Copyright ChE Division of ASEE 2011
Chemical Engineering Education
MISSION AND DEGREE PROGRAMS
It is this strong foundation and standard that the UH Chemical & 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 science, mathematics, engineering science, and engineering 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 students, procure support for this research on a continuous
basis, and contribute to the development of fundamental
knowledge in the field of chemical engineering. The
department’s varied and aggressively pursued research
ensures that our faculty members remain at the technological forefront of their respective areas of specialization.
3. To be of service to the community at large and, in
particular, to the City of Houston and the State of Texas,
and to provide the local engineering community opportunities for advanced and continuing education.
The department currently confers the following degrees:
• Bachelor of Science in Chemical Engineering (B.S.
ChE)
• Master of Chemical Engineering (non-thesis; MChE)
• Master of Science in Chemical Engineering (thesis and
non-thesis M.S. ChE)
• Doctorate in Chemical Engineering (Ph.D. ChE).
In addition, the department has administrative responsibility for a Petroleum Engineering program that confers the
following degrees:
• Bachelor of Science in Petroleum Engineering (B.S. PE)
• Master of Science in Petroleum Engineering (M.S. PE)
• Master of Petroleum Engineering (non-thesis, MPE).
The department has traditionally attracted excellent undergraduate students who are among the best at UH. Reflecting
the diversity of the UH student body as a whole, our undergrads are a very diverse group, with under-represented stu-
Areas of graduate employment.
dents (African-American, Hispanic, Asian) making up about
60% of the total. Moreover, the department does very well
in attracting female students and provides a flexible program
for working part-time students. Currently there are about 400
students in the program with recent graduation rates of about
35-45 per calendar year. The graduate program numbers approximately 100 students, about 25 of whom are part-time
students (most have full-time employment and are MChE
students). Current enrollment in the Petroleum Engineering
program numbers about 130 students, equally divided among
undergraduate and Master’s students, the majority of whom
are part-time working professionals.
At the undergraduate level, the department has been effective in educating students for productive careers in the
chemical process industry, process design firms, and the energy industry, particularly the upstream sector in recent years.
Feedback obtained from local employers reveals that the UH
ChBE students are top-performing, typically more mature
students from the start. This is testimony to the fundamental
focus of the curriculum, the standards of the instructors, and
the diversity—including age—of the student population.
Undergraduate enrollments in the program generally follow
national trends influenced by the hiring dynamics in the
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
of high-quality undergraduate students. Recent changes to
include biomolecular engineering principles and materials
science and engineering in the core undergraduate training
along with development of minor options in petroleum engineering and nanomaterials engineering have diversified the
education and training of the students.
THE EARLY YEARS
The department was founded in the late 1940s when the
University of Houston was at that time a small, private undergraduate university principally attended by white students
from more affluent families of the greater Houston area.
Crump, the first department chair, recruited several key faculty members who were, as Jim Richardson refers to them,
“the instigators.” These were William Prengle, Dukler, and
Frank Worley. Prengle and Dukler were hired from Shell
Oil Company and at first were part-time lecturers and became
full time faculty in 1952.
Dr. Larry Witte, Professor of Mechanical Engineering at
UH, recalls the important impact that Crump, Dukler, and
Prengle had on the department. “These three scholars were
role models for the rest of the college,” says Witte. “They
showed us how to transform an undergraduate program into a
successful graduate research program. In the 1960s they won
a National Science Foundation (NSF) matching excellence
grant that enabled them to expand and bring in more research.
Other departments wanted to emulate their success.”
An important step for the department and college occurred
in 1955 when Frank Tiller was hired from Lamar University as
the first dean of the College of Engineering. Dean Tiller set out
to expand the college, enhance the quality of the faculty, and
gain accreditation for the college programs. On arrival only
14% of the engineering faculty had doctoral degrees. Tiller
actually sent some of them back to school to earn their Ph.D.’s.
By 1963, 40% of the college faculty had doctorates.
As critical of a leadership role as Dean Tiller provided to the
young college, he also became one of the stalwart researchers
in the university. Tiller established himself as one of the leading academicians who used mathematical methods to solve
chemical engineering problems.[1] His primary interest was
in advancing the understanding of solid-liquid systems with
application to separations, notably filtration. A long string of
doctoral students would study with Tiller and were coveted
by industry to improve the many processes involving solids
and their purification. Tiller helped to establish and grow
the American Filtration and Separations Society (AFS) as
evidenced by the AFS Tiller Award which annually honors a
top engineer in the field.
Complementing Tiller was Dukler, who established himself
as the leading expert in multiphase flow. Dukler advanced the
high-speed laser Doppler velocimetry method for flow of gas
152
and liquid in vertical pipes. Dukler was elected to the National
Academy of Engineering in 1977 for his pioneering advances
in high Reynolds number multiphase flow.
The department hired Ernest Henley from Columbia University in 1961. Henley has distinguished himself for decades
as being an innovator in his research, teaching, and extramural
business pursuits. For a period of over two decades and ending a few years ago upon his retirement, Henley taught the
two-course capstone design course to UH senior undergraduates. This was one of the main reasons why UH graduates
were coveted by industry: UH graduates knew chemical
engineering design and process economics. Henley’s book
with J.D. Seader and D. Keith Roper, Separations Process
Principles, is in its third edition and has established itself
as the text of choice for unit operations and separations at
chemical engineering departments in the United States and
internationally.[2]
During this period, the strong industrial ties to the department’s research and educational activities were established.
As department chair from 1966-1974 and dean of the college
from 1976 to 1982, Dukler accelerated the department towards
becoming an upstart among chemical engineering departments in the United States. In 1968 Dukler landed a $600,000
“Center of Excellence Departmental Development Grant”
from the National Science Foundation, a highly competitive
program. These monies were used to hire faculty members
and build world-class research laboratories. Prof. Osman I.
Ghazzaly, a faculty member in the Department of Civil and
Environmental Engineering since 1966, points out that: “The
real quantum jump in the direction of research came when
Dukler took over. He wanted us to really show a change in
direction, and he emphasized that research was the number
one pursuit.”
Says Stuart Long, Professor of Electrical and Computer
Engineering and currently Interim Vice President for Research
at UH, “Dukler was willing to take the heat for making this
transition. He was willing to sacrifice his popularity to do
the right thing.” Around 1975 the department recruited Alkis
Payatakes, an expert in transport phenomena, from Syracuse.
Payatakes would join forces with Flumerfelt to start a center
in enhanced oil recovery, which used theory of low Reynolds
fluid dynamics to understand the movement and recovery of
oil ganglia in porous media. Their approach changed the way
the oil industry looked at petroleum recovery and helped to
forge closer ties between the upstream energy industry and
the department. The department’s tradition in multiphase
transport would receive a boost with the hiring of two junior
faculty in the early 1980s, Vemuri Balakotaiah in 1983 and
Hsieh Chia Chang in 1984. Balakotaiah was one of UH’s
own, a student of Dan Luss, while Chia was recruited away
from UC Santa Barbara. While Bala and Chia had roots in
chemical reaction engineering and nonlinear analysis, both
applied their skills to the inherent nonlinearities of wavy flows
Chemical Engineering Education
and flows in porous media, among other systems. During the
1990s the department recruited Kishore Mohanty away from
the oil industry. Mohanty would further solidify the ties with
the upstream energy industry with his fundamental focus on
transport in porous media applied to oil and gas recovery.
Complementing Mohanty’s efforts was Michael Economides,
hired from Texas A&M in 1998, who brought more practical
aspects of petroleum engineering to the program.
THE REACTION ENGINEERING COMPETENCY
The hiring of Luss in 1967 was arguably the most important
hire in the department’s 60+ years. Luss, a highly accomplished student of Neal Amundson at Minnesota, was an
expert in chemical reaction engineering. In the same period
the department attracted Richardson, an accomplished expert
in heterogeneous catalysis, from Exxon. Together their hiring
ushered the emergence of chemical reaction engineering as
the area in which UH chemical engineering would become
the recognized national leader. In 1971, UH attracted Jay
Bailey as an assistant professor with primary research interest in reaction engineering, and broadened the impact of the
pioneering research. Bailey applied the principles of chemical
reaction engineering and mathematical methods developed
in chemical engineering first to enzyme catalyzed reactions
and later to biochemical engineering, becoming one of the
pre-eminent biochemical engineers.[3,4]
Luss became chair of the department in 1975, a position that
he held until 1996. It was during Luss’ tenure as chair that the
department would ascend dramatically, thanks to the seeds
planted by Dukler, strategic hires by Luss, and a sustained
focus on research excellence in chemical engineering science.
Indeed, it was Luss who stunned chemical engineering academe in 1976 when he attracted his former Ph.D. advisor, Neal
Amundson, “The Chief,” to Houston. Amundson brought his
expertise in applied mathematics and reaction engineering
to the department, and proceeded to graduate about 10 more
doctoral students during his second career at UH.
Collectively, the department trained a new generation of
students who would primarily join industrial research organizations and help to change the way that chemical reactors
in particular would be analyzed, modeled, and designed. In
the late ’80s the department hired Demetre Economou, an
expert in electronic materials processing. Economou helped
to bridge the gap between reaction engineering and materials,
and has become one of the leading researchers in gas-solid reactions in plasma processes. In 2000 another of Luss’ students,
Mike Harold, was recruited to become the sixth department
chair. Harold had established a strong reputation first as an
academic at the University of Massachusetts at Amherst, then
as a researcher, then a manager at the DuPont Company’s
Engineering Research labs at the Experimental Station. Additional hires included Roy Jackson from Rice in 1977.
Vol. 45, No. 2, Spring 2011
In recent years the department has emerged as a leading
center for environmental reaction engineering and catalysis.
Balakotaiah focused on transport and reaction in catalytic
monoliths used in emission aftertreatment systems such as
three-way catalytic converters. Harold founded a clean diesel
testing and research facility in the early 2000s, now called
the Texas Diesel Testing and Research Center and managed
by Dr. Charles Rooks who was recruited from industry by
Harold. The creation of the diesel center was in response to the
regional need to reduce emissions of NOx (NO + NO2) from
the exhaust of diesel vehicles and equipment. The Houston
area had the dubious distinction of being one of the worst
offenders of the Clean Air Act’s ozone standard. Harold attracted a City of Houston grant of $4 million to create a diesel
vehicle testing facility and a few years later a $12 million
grant to expand the operation.
Today Harold and Rooks lead a team of 15 engineers and
staff and collaborate with other faculty members in the ChBE
and Mechanical Engineering on basic research and technology development focused on clean diesel. The center has
capabilities spanning bench-scale development of emerging
technologies to full-scale testing of diesel vehicles. The
main focus of the testing activities is on retrofit technologies to decrease NOx and particulate matter emissions from
on-road and off-road vehicles and equipment. More recently
the department has attracted Jeff Rimer from the University
of Delaware, an expert in the synthesis of shape-selective
crystalline materials such as zeolites. Rimer and Harold
are joining forces to discover new zeolitic materials with
enhanced activity and selectivity for the aforementioned
lean NOx reduction. Joining the faculty in 2011 will be Bill
Epling as an associate professor and Lars Grabow as an
assistant professor. Epling, with earlier industrial experience
from Cummins, Inc., and an established academic from the
University of Waterloo, will be a perfect fit in the department’s
efforts in environmental reaction engineering. Grabow brings
his expertise in molecular modeling of catalysts to apply to
a wide range of problems including environmental reaction
engineering, biofuels, electrochemistry and development of
a new generation of catalyst materials.
MATERIALS AND BIOTECHNOLOGY
Materials-related research in colloidal, polymeric, and nano
materials along with biotechnology and biomolecular engineering have become significant strengths of the department
over the last three decades and in part reflect the changing
nature of the discipline.
The discovery of high-temperature superconductivity at
the University of Houston sparked a materials revolution
on campus and the department became a leader in the area
of oxide materials. The significant investments in materials
characterization, developed in part as a result of the NSF
Materials Research Science and Engineering Center, led
153
to the growth of not only inorganic materials but also to
the growth of polymeric and nanoscale materials. In 2002,
Vince Donnelly, a leading plasma physics expert, joined the
department after two decades at AT&T’s Bell Labs. Since then
Donnelly and Economou have established the pre-eminent
plasma physics and processing laboratory, with both of them
receiving the highest honors from the American Vacuum Society. Michael Nikolaou, an expert in process control, works
closely with them to provide robust control for industrial
plasma processes.
The proximity of the petrochemical industry and the growth
of advanced materials during the last quarter of the 20th century
were reflected in the UH Chemical Engineering department’s
focus. Starting with Raj Rajagopalan, an expert in colloids
recruited from Syracuse in the mid ’80s, and Jay Scheiber,
a theoretician working on polymer dynamics, the efforts in
soft materials were strengthened by the addition of Ramanan
Krishnamoorti and most recently of Manolis Doxastakis, Gila
Stein, Jacinta Conrad, and Megan Robertson. These faculty
have also led the advancement of nanotechnology research
at UH with Krishnamoorti becoming a pioneer in the area of
polymer nanocomposites. Doxastakis has developed expertise
in applying molecular and multiscale modeling to understand
entangled polymers, nanocomposites, and lipid-protein interactions. Stein is an expert in polymer thin films, working on
developing materials for optoelectronics, advanced optical
lithography, and organic photovoltaics. Conrad is studying
the interaction between complex fluids such as polymers and
colloids and the surfaces that confine or support them with
potential applications in petroleum engineering, environmental
engineering, and materials engineering. Robertson’s research
combines novel synthetic polymer chemistry and elucidation of
polymer physics to design nanostructured materials to develop
a new generation of materials based on renewable resources
and in some cases with biomedical applications. Additionally,
the development of a ~ 4000 ft2 class 10/100 nanofabrication
facility at UH has enabled the rapid growth of nanoscale soft
materials research. Ongoing research to develop advanced
materials for energy applications including improved hydrocarbon recovery, solar energy capture, and wind energy—along
with a focus on sustainability by using natural biodegradable
alternatives to petroleum-based materials—is representative
of the efforts of the department to address many of the grand
challenges facing humanity.
The growth of the Texas Medical Center over the last 30
years, starting from the pioneering efforts to produce the first
artificial heart to the latest innovations in treating cancer, has
triggered growth of biomolecular and biochemical engineering
in the department. Jay Bailey’s evolution from chemical reaction
engineer to the pre-eminent biochemical engineer by the time
he left UH in 1980, was the precursor for the current growth in
biomolecular research in the department. Richard Willson, an
154
expert in biomolecular recognition and nucleic acid purification, joined the department in the late ’80s and is currently the
theme leader for the diagnostics thrust of the NIH Western
Regional Center of Excellence. Along with Mike Nikolaou
(drug delivery), Peter Vekilov (an expert in phase transitions
that occur in protein solutions with implications for deadly
diseases including sickle cell anemia and Alzheimer’s and for
pharmaceutical drug preparation), and most recently Navin
Varadarajan (quantifying functional human immune responses by integrated single cell analysis and developing new
cancer therapeutics and vaccines) and Patrick Cirino (protein
and metabolic engineering and biocatalysis toward cost-effective “green” chemistry and renewable fuels, bioremediation,
and “next-generation” therapeutics), the department is wellpositioned to grow biomolecular research and find solutions
to challenging issues involving human health.
FEATURES AND OUTLOOK
The unique location of the University of Houston and the
close relationship between the petroleum, petro-chemical,
and materials industry along with the relation with NASA
and, more recently, advanced materials companies and the
Texas Medical Center, have positioned the department to be
at the forefront of chemical engineering. The department has
a unique relationship with the chemical industry and medical
center through the graduate and research programs as well as
the industrial advisory board. The continued vitality of the
short course on heterogeneous catalysis and the significant interest in the short course on polymers, along with the renewed
interest in the MChE program for working professionals and
the significant interest in the part-time Ph.D. program (with
doctoral candidates working in the numerous research and
development centers in the greater Houston area), demonstrate
the close relationship.
These strategic partnerships will continue to drive the success of the students and faculty of the Department of Chemical
and Biomolecular Engineering at the University of Houston.
The analytical, quantitative, and systems-based approach that
was pioneered by Tiller, Dukler, Amundson, and Luss will
continue to be the hallmark of the research performed at UH
and will be integrated into the developments in cutting-edge
applications in materials, human health, and energy. These
will also help shape our evolving undergraduate and graduate
curricula and maintain excellence in our teaching, service,
and research missions.
REFERENCES
1. Yelshin, A., Filtration & Separation, 29, 37
2. Seader, J.D., K. Henley, and D. Roper, Separation Process Principles,
3rd Ed., Wiley (2011)
3. Bailey, J.E., and D.F. Olis, Biochemical Engineering Fundamentals,
McGraw-Hill, Inc., New York (1986)
4. Reardon, K.F., K.H. Lee, K.D. Wittrup, and V. Hatzimanikatis, Biotechnology and Bioengineering 2002, 79, 484 p
Chemical Engineering Education
ChE book reviews
An Introduction to Granular Flow
by K. Rao and P. Nott
Cambridge (2009) $155.00
Reviewed by
Kimberly H. Henthorn
Rose-Hulman Institute of Technology
Granular flows are ubiquitous in nature and industry,
particularly in systems involving food, pharmaceutical, and
chemical processes. Although it is extremely important to be
able to characterize and model these systems, granular flow
behavior is still not well-understood. A number of theoretical and empirical models have been proposed to describe the
behavior of particulate systems, but there is still much room
for refinement. This book gives a solid discussion of a broad
range of topics related to granular flow, with much emphasis
on theoretical modeling. The authors focus on continuum
models, although there is some attention to discrete models as
well. Overall, the book is well-written and provides a thorough
overview of the current state of granular flow research.
The book begins with an introduction that previews a large
number of areas including interparticle forces, packing, granular statics and flow, and modeling, with most of these topics
covered in more detail in subsequent chapters. The authors do
a good job of briefly describing each of these topics, and offer
a lot of external references for further consideration. In my
opinion, this chapter could easily be broken into two chapters,
with the modeling sections discussed separately, in order to
better organize the material. Some portions are a bit choppy
and incomplete because too much information is presented at
once. Dividing the material and adding more detail in certain
places would definitely help with this.
The rest of the book delves into a detailed theoretical
discussion of slow plane and three-dimensional flows, flows
through hoppers and bunkers, and rapid flows. The material
seemed a little unorganized and incomplete in places, and I
was disappointed with the quality and placement of many of
the figures and tables. I think the authors did an especially
good job with Chapter 6 (Flow through Axisymmetric Hoppers and Bunkers), however. They provided a good mix of
theory and experimental data, and I thought their figures in
this section were interesting and useful.
Since most of the material is based on complex theories, the
authors offer several appendices that provide a basic mathematics review. Operations with vectors and tensors, a brief
analysis of the stress tensor, and methods to evaluate common
integrals are a few topics covered here. I was very happy to
see these appendices, because the authors assume the readers
have a good understanding of advanced mathematics when
discussing the material in the main portion of the text.
Vol. 45, No. 2, Spring 2011
Each chapter ends with a set of practice problems. These
problems were challenging but appropriate for the material in
each section. It was interesting to note that many of the problems were adapted from other sources. I especially appreciated
that each problem was labeled with a heading that described
what concept was being tested. I am not sure if the authors offer
a solutions manual for this textbook, but it would certainly be
useful for instructors adopting the book for a course.
I disagree with the authors when they state that this book is
appropriate for advanced undergraduates or beginning graduate students, at least in the chemical engineering discipline.
The material is presented at a much higher level than what I
would expect an undergraduate chemical engineering student
to be able to handle. The amount of mathematics and modeling background required to understand the material and the
authors’ use of specialized vocabulary makes this book more
appropriate for graduate students concentrating in particle
technology related fields. I would recommend students first
take an introductory particle technology course using an intermediate text such as Rhodes1 so that they are better prepared
for the material presented in this book.
My comments about the incompleteness of certain topics
stem from the overwhelming amount of information available
on granular flows. It would be impossible to cover everything
without developing a series of texts about the topic. My overall
impression of An Introduction to Granular Flow, however,
is very positive, and I commend the authors for providing a
solid reference for those interested in granular flows. They
do a nice job of summarizing peripheral topics while going
into the appropriate detail in their focus areas.
1
Rhodes, Introduction to Particle Technology, John Wiley & Sons, 2nd
ed., 2008
Good Mentoring: Fostering Excellent
Practice in Higher Education
by Jeanne Nakamura and David J Shernoff
with Charles H. Hooker
Josey-Bass, 303 pages, $40 (2009)
Reviewed by
Joseph H. Holles
University of Wyoming
Is good mentoring in the genes? Can successful mentors
automatically transmit their knowledge, skills, and values
to the next generation of students? If so, how can these attributes be transmitted in a way that is most useful to their
academic offspring? In an effort to better understand “how to
keep what has been learned from being lost” Good Mentoring
examines three lineages of scientists and the ability of their
skills, values, and practices to be transmitted to their students
and successive generations.
The general question that the authors are seeking to address
is: “Can one generation’s ‘good workers’ nurture similar com155
mitments in members of the next generation even as changing
sociocultural conditions pose new challenges to the pursuit
of excellence and ethics in a field?” Included in this question
was a particular emphasis on the transmission of orienting
values and principles uniting excellence with responsible
practice. The authors postulate that “the best chance for their
cultivation is likely to lie with teachers who embody these
values and practices and the learning environments that the
teachers create.” Since graduate science education has a strong
reliance on learning by apprenticeship, it is an ideal situation
for examining mentoring of future generations.
While the subtitle is “Fostering Excellent Practice in Higher
Education,” the book is most relevant to a smaller subset of
higher ed. In particular, the focus of the book is effective
mentoring for supervisors in research. While the case studies
focus on mentoring of graduate students and post-doctoral researchers in academia, the same outcomes are also applicable
in any research mentoring situation including undergraduate
researchers, government, or industrially sponsored research
laboratories. Finally, both mentors and their students can gain
insight into successful relationships from this work.
Good Mentoring is divided into three distinct parts. Part
One presents case studies of each of the three lineages. Part
Two summarizes the transmission of knowledge, practices,
and values across the mentoring generations. Part Three then
summarizes the key lessons learned and draws out implications for practitioners and researchers.
In Part One, the authors examine three scientists and their
lineages through the second and third generation of academic
offspring. In perhaps a bit of irony, all three of these academic
lineages are in the field of genetics. The goal of these chapters
is to provide a qualitative view of the approaches of each scientist towards successful research and mentoring. Subsequent
discussion of second and third generations then provides
insight into what knowledge was successfully passed down.
From these second- and third-generation profiles, we also
obtain some insight into how individual scientists affected the
overall memes (building blocks of culture) of the lineage.
In Part Two, the authors take a quantitative approach to
complement the previous profiles. Values and practices specific to each lineage are identified and the successful transition of these memes through three generations is quantified.
Categories of memes common to all three lineages were also
investigated.
From their quantitative analysis, the authors found that even the
most widely inherited memes are inherited less from generation
to generation. However, this is compensated for by the larger
number of offspring in each generation and thus the absolute
effect remains high. The mentors in this study transmitted memes
“through two intertwined aspects: mentor’s direct impact on
the student through verbal exchanges and the mentor’s indirect
impact through student participation in the lab community.”
Contrary to the author’s expectation, the influence on students by
example and shaping the culture of the lab was just as important
156
as intense personal interactions. The defining characteristic of
positive mentoring was supportiveness. Supportiveness included:
consistent availability and involvement, balance between freedom and guidance, frequent and specific positive feedback, treatment as respected colleagues, and individualized interest in the
student. Good mentoring does not appear to include hectoring,
guilt trips, yelling, insults, or subtle jabs.
In Part Three, the most important results are discussed and
then concrete suggestions for mentor, mentees, and institutions are presented. For mentors, the most commonly cited
resource was to facilitate students’ building of social capital.
For mentees, the authors recommend seeking out multiple
influences since many of the worst cases of mentoring occur
when a single person has significant control over the student.
Finally, for institutions, good mentoring is a sound investment
for the future and the reward structure should reflect this. There
also need to be places in the institution for advisees to evaluate
mentoring experiences similar to the way teaching evaluations
provide feedback to classroom instructors.
All of the examples and conclusions are drawn from mentoring relationships between graduate students and their advisor
(a faculty member). There is a significant amount of mentoring
that goes on in higher education outside of what is investigated
and discussed in this book, such as advisor/undergraduate
researcher relationships and teacher/student classroom relationships. There are even mentoring relationships between established faculty members and new faculty members. While the
authors don’t investigate all of the higher education mentoring
relationships, the conclusions from this study can help in all.
In fact, one of the ripest areas for application of these conclusions would appear to be in the opportunities for institutions to
improve the mentoring of new faculty by senior faculty.
How can this book best be used by faculty members today?
Clearly, the most direct place is in the laboratory when mentoring students. The main results from the study indicate that
simply being there for the students, showing a strong work
ethic, and being flexible will result in a positive experience
for the student and transmit desired good work practices on to
the next generation of researchers. However, the ideas from
this book can also be applied in the classroom. In addition,
simply providing a welcoming, open, and safe environment
for all can have positive results.
Since the authors examine the ability of effective mentoring
memes to be passed down from advisor to academic offspring,
the work becomes very mentor focused. Only in the last chapter
do the authors discuss how a mentee should use the results of
their study. Again, as a result of their premise, the authors tend
to focus on academic offspring who have done well in academia.
The applicability of mentoring on non-academic offspring does
not appear to be addressed. Finally, while the point of this work
was to investigate “stars” since they were capable of doing good
academic work in parallel with performing good mentoring, the
ability and effectiveness of “non-star” researchers to instill responsible practice in their academic offsprings is still unknown. p
© Copyright ChE Division of ASEE 2011
Chemical Engineering Education