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Modeling, Optimization and Nonlinear Parameter Estimation for Scientists and Engineers

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Computer-Aided Modeling of Reactive Systems


Warren E. Stewart, Sc.D.  and  Michael Caracotsios, Ph.D.



Chapter 1:   Overview

Chapter 2:   Chemical Reaction Models

Chapter 3:   Chemical Reactor Models

Chapter 4:   Introduction to Probability and Statistics

Chapter 5:   Introduction to Bayesian Estimation

Chapter 6:   Process Modeling with Single-Response Data

Chapter 7:   Process Modeling with Multi-Response Data


Appendix A:  Solution of Linear Algebraic Equations

Appendix B:  DDAPLUS Documentation

Appendix C:  GREGPLUS Documentation



Scientific learning is an iterative process that employs experimentation, mathematical modeling and nonlinear parameter estimation, model criticism and discrimination.  The mathematical modeling task encapsulates our knowledge in a well defined set of user postulated functions. Estimation is applied to estimate adjustable  parameters and their posterior probability density conditional on the model's truth. Model criticism and discrimination induces enhancement and further modification.


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