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

 

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