Computer-Aided Modeling of Reactive Systems

Warren E. Stewart and Michael Caracotsios

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:◄Take a pick inside this Chapter

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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