Computational and Statistical Methods for Chemical Engineering

I. Preliminaries. 1. What to expect in this book? 2. Calculus and Linear Algebra Essentials. 2.1. Scalars, Vectors and Matrices. 2.2. Sequences and Series. 2.3. Functions. 2.4. Differentiation. 2.5. Maxima and Minima. 2.6. Integration. 2.7. Differential Equations. 2.8. Complex Numbers and Functions. 2.9. Exercises. 3. Probability Essentials. 3.1. Probability of Events. 3.2. Random Variables. 3.3. Pseudo Random Number Generation. 3.4. Notes and Comments. 3.5. Notes on Using R. 3.6. Exercises. II. Numerics and Error Propagation. 4. Introduction to Numerical Methods. 4.1. Fixed Point Problems. 4.2. Numerical Methods for Solving Differential Equations. 4.3. Differential Algebraic Equations. 4.4. Notes and Comments. 4.5. Notes on Using R. 4.6. Exercises. 5. Laws on propagation of Error. 5.1. Absolute and Relative Error of Measurement. 5.2. Mean and Variance. 5.3. Functions that Depend on One Variable. 5.4. Functions that Depend on Two Variables. 5.5. Notes and Comments. 5.6. Notes on Using R. 5.7. Exercises. III. Various Types of Models and their Estimation. 6. Measurement Models for a Chemical Quantity. 6.1. Measurement Model. 6.2. Law of Large Numbers. 6.3. Constructing Confidence Intervals. 6.4. Testing Chemical Hypotheses related to Measurement Models. 6.5. General Inference Paradigm. 6.6. Notes and Comments. 6.7. Notes on Using R. 6.8. Exercises. 7. Linear Models. 7.1. Linear Model. 7.2. Estimation and Prediction. 7.3. Model Diagnostics. 7.4. Model Selection. 7.5. Specific Linear Models. 7.6. Notes and Comments. 7.7. Notes on Using R. 7.8. Exercises. 8. Non-linear Models. 8.1. Some non-linear Functions Modeling chemical Processes. 8.2. Non-linear Regression. 8.3. Inverse Regression. 8.4. Generalized Linear Models. 9. Chemodynamics and Stoichiometry. 9.1. Stoichiometry of Systems of Reactions. 9.2. Stochastic Models for Particle Dynamics. 9.3. Estimating Reaction Rates. 9.4. Mean-field Approximation of Reaction System. 10. Multivariate Exploration. 10.1. Data Visualisation. 10.2. Matrix Decomposition. 10.3. Principal Components Analysis. 10.4. Regression using a Subspace. 10.5. Notes and Comments. 10.6. Notes on Using R. 10.7. Exercises. IV. Analysis of Designed Experiments. 11. Analysis of Data from Designed Experiments. 11.1. Concepts of Factorial Designs. 11.2. Analysis of Variance. 11.3. Analysis of the Response Surface. 11.4. Mixed Effects Models. 11.5. Notes and Comments. 11.6. Notes on Using R. 11.7. Exercises. 12. Robust Analysis of Models. 12.1. Outlying Data Points. 12.2. Robust Estimation. 12.3. Robust Linear Regression. 12.4. Robust Nonlinear Regression. 12.5. Dealing with Heterogeneity. 12.6. Appendix: Scale Tau Estimator. 12.7. Notes and Comments. 12.8. Notes on Using R. 12.9. Exercises. V. Appendix. 13. Basics of R Computing Environment. 13.1. R Basics. 13.2. Useful Functions. 13.3. Model Notation.13.4. Finding Help. 13.5. Exercises

Biography

Wim P. Krijnen is a lecturer at the Faculty of Science and Engineering at the University of Groningen. He has been teaching a course called "Computational and Statistical Methods" for several years to undergraduate students in Chemical Engineering. In addition, he has taught courses on linear algebra, probability theory, mathematical statistics, statistical modeling, statistical consulting to bachelor’s and master’s students in various fields. He is a professor of Applied Statistical Research Methods at the Hanze University of Applied Sciences in Groningen.

Ernst C. Wit is the Fondazione Leonardo Professor of Data Science at the Universita della Svizzera italiana in Switzerland. He has 30 years of experience in teaching statistics, applied mathematics and data science courses to students from many fields, including chemical engineering. His course in philopsophy is about combining theoretical insights with practical skills, as he believes that the former without the latter is pointless whereas the latter without the former aimless.