CONTENTS

Front Matter

Title Page, Preface and Acknowledgements
About the Author
Status, History, Issues and Updates
Complementary Textbooks
Teaching Notes and Resources
A Note about Numerical Solutions

Course Units

I. Chemical Reactions
1. Stoichiometry and Reaction Progress
2. Reaction Thermochemistry
3. Reaction Equilibrium
II. Chemical Reaction Kinetics
A. Rate Expressions
4. Reaction Rates and Temperature Effects
5. Empirical and Theoretical Rate Expressions
6. Reaction Mechanisms
7. The Steady State Approximation
8. Rate-Determining Step
9. Homogeneous and Enzymatic Catalysis
10. Heterogeneous Catalysis
B. Kinetics Experiments
11. Laboratory Reactors
12. Performing Kinetics Experiments
C. Analysis of Kinetics Data
13. CSTR Data Analysis
14. Differential Data Analysis
15. Integral Data Analysis
16. Numerical Data Analysis
III. Chemical Reaction Engineering
A. Ideal Reactors
17. Reactor Models and Reaction Types
B. Perfectly Mixed Batch Reactors
18. Reaction Engineering of Batch Reactors
19. Analysis of Batch Reactors
20. Optimization of Batch Reactor Processes
C. Continuous Flow Stirred Tank Reactors
21. Reaction Engineering of CSTRs
22. Analysis of Steady State CSTRs
23. Analysis of Transient CSTRs
24. Multiple Steady States in CSTRs
D. Plug Flow Reactors
25. Reaction Engineering of PFRs
26. Analysis of Steady State PFRs
27. Analysis of Transient PFRs
E. Matching Reactors to Reactions
28. Choosing a Reactor Type
29. Multiple Reactor Networks
30. Thermal Back-Mixing in a PFR
31. Back-Mixing in a PFR via Recycle
32. Ideal Semi-Batch Reactors
IV. Non-Ideal Reactions and Reactors
A. Alternatives to the Ideal Reactor Models
33. Axial Dispersion Model
34. 2-D and 3-D Tubular Reactor Models
35. Zoned Reactor Models
36. Segregated Flow Models
37. Overview of Multi-Phase Reactors
B. Coupled Chemical and Physical Kinetics
38. Heterogeneous Catalytic Reactions
39. Gas-Liquid Reactions
40. Gas-Solid Reactions

Supplemental Units

S1. Identifying Independent Reactions
S2. Solving Non-differential Equations
S3. Fitting Linear Models to Data
S4. Numerically Fitting Models to Data
S5. Solving Initial Value Differential Equations
S6. Solving Boundary Value Differential Equations

Supplemental Unit S3. Fitting Linear Models to Data

Kinetics and reaction engineering involve solving several different kinds of mathematics problems. In very simple cases these problems can be solved analytically, that is, using paper, pencil and hand-held calculator. In many cases, however, even very simple problems cannot be solved analytically. Instead they must be solved numerically using a computer. This supplement to “A First Course on Kinetics and Reaction Engineering” presents a very brief overview of numerical methods that are commonly used to solve kinetics and reaction engineering problems. The intent is not to provide comprehensive coverage of the methods; for that you should consult a book on numerical methods or take a course on the topic.

Fortunately, there are several software packages that make it very easy to use numerical methods to solve these kinds of problems. However, each software package has its own unique user interface, data structure and coding format. These supplemental units do not try to describe how to use every available software package. No matter which specific software package one chooses to use, solving a particular type of problem will require that you provide certain information and input data. These supplemental units each begin with a concise statement of the problem followed by a listing of the information and data one will need to provide when solving such a problem, irrespective of what software is actually used. The form of the result that will be returned by the software is also described. That is followed by a general explanation of what the software does in order to obtain that result. Finally, for those with access to MATLAB and who choose to use it, generic template files that can be adapted to specific problems are provided. Examples from the main body of “A First Course on Kinetics and Reaction Engineering” are used to illustrate the use of the MATLAB template files.

This supplemental unit describes how to fit a linear model to single response data. Most generally, each data point (x1, x2, ..., xn, ŷ) consists of a number of set variables, xi, and a single response variable, ŷ. The corresponding model is a linear equation, y = p1x1 + p2x2 + ... + pnxn + pn+1, where the pi's are constant parameters. Two commonly encountered simplifications of the general case are also considered. In both simplified situations, each data point (x,ŷ) consists of a single set variable, x, and a single response variable, ŷ. The simplified situations differ in the mathematical form of the model. In one case the model, y = mx + b, has two constant parameters: a slope, m, and an intercept, b. In the other case, the model, y = mx, has only one constant parameter: a slope, m.

Learning Resources

Teaching Resources