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Problem Solving in CBE

CBE602 Problem Solving in CBE

Spring Semester 2016

 Instructor: Shin-Hyun Kim

 TA: Kwang Hwi Je (Room: 1111 –Ext.:3951,

  Class will meet at 9:00 a.m. #2122 (W1-3) on Mondays and Wednesdays.

 Lecture notes and notices


·         Morton M. Denn: Process modeling, Longman, New York & London (1986).


·         Warren L. McCabe, Julian C. Smith, Peter Harriott: Unit Operation of Chemical Engineering

·         William L. Luyben: Process Modeling, Simulation, and Control for Chemical Engineers, McGraw-Hill, New York (1996).

·         Octave Levenspiel: Chemical Reaction Engineering, John Wiley & Sons, New York (1999).

·         R. Byron Bird, Warren E. Stewart, Edwin N. Lightfoot: Transport Phenomena, John Wiley & Sons, New York (2002)

·         J. M. Smith, H. C. Van Ness, M. M. Abbott: Introduction to Chemical Engineering Thermodynamics, McGraw-Hill, New York (1996)


        There will be problem sets, a midterm exam, and a final exam. All exams will be in-class and will have time limits in the range of 2 hours. A probable weighting scheme for calculating the final grade is as follows:


Homework                                       20 %

Midterm                                           40 %

Final                                                40 %

General description for course: In this course, we study the method to deal with problems in chemical and biomolecular engineering, based on understanding of transport phenomena, thermodynamics and reaction engineering. To do this, we model representative chemical and biological engineering processes. The scope of this course is outlined below.

Course Outline

1. Introduction to Models (Chapter 1-3)

      A. Definition of a model

      B. Three methodologies for equations for a model

      C. Variables

D. Constitutive equation and control volume


2. Conservation of mass (Chapter 4)

A.   The principle of conservation of mass

B.   A continuous flow stirred-tank reactor

C.   A design example

D.   Transients and time constants

E.    Catalytic oxidation: Multiplicity and instability

F.    Autocatalytic systems

G.   Batch and tubular reactors


3. Conservation of energy (Chapter 5)

      A. The principle of conservation of energy

      B. Thermodynamic variables

      C. A well-stirred tank reactor

      D. Batch and tubular reactors

      E. Order reduction

      F. Multiplicity

      G. Transient behavior

      H. Fluid catalytic cracker model

1)       The problem statement

2)       The model equations

3)       Validation

4)       Application


4. Case study: Distillation

      A. Flash distillation

      B. Continuous distillation with reflux

1)       Material balance

2)       Operating lines and feed conditions

3)       The number of ideal plates

4)       Energy balance


5. Scaling (Chapter 6)

      A. Vectors and tensors

B. The energy balance and energy equation

      C. Slab example

           1) The heat equation

           2) Homogeneous boundary conditions

           3) Separation of variables

      D. Scaling the heat equation

E. Transport of mass, momentum, and energy

F. Example: A fluid which flows past a sharp-edged plate.

           1) Fluid flow: Navier-Stokes equation and boundary layer equations

           2) Heat transfer: Thermal boundary layer

           3) Dimensionless numbers: Reynolds number, Prandtl number, and Nussult number.


6. Case study: Microfluidic H-filter

      A. The diffusion equation

      B. Solution for constant planar-source diffusion

      C. Analysis of H-filter


7. From discrete to continuous (Chapter 11)

      A. Distillation

      B. Batch polymerization


8. Case study: Fiber spinline (Chapter 12)

      A. Problem definition

      B. Derivation of balance equations

           1) Conservation of mass

           2) Conservation of momentum

           3) Conservation of energy

      C. Constitutive equation

D. Phenomenological coefficients and boundary conditions

E. Asymptotic solutions

F. Steady-state simulation

           1) Pilot plant results

           2) Parameter sensitivity