Detailed mathematical models are increasingly being used by companies to gain competitive advantage through such applications as model-based process design, control and optimization. Thus, building various types of high quality models for processing systems has become a key activity in Process Engineering. This activity involves the use of several methods and techniques including model solution techniques, nonlinear systems identification, model verification and validation, and optimal design of experiments just to name a few. In turn, several issues and open-ended problems arise within these methods, including, for instance, use of higher-order information in establishing parameter estimates, establishing metrics for model credibility, and extending experiment design to the dynamic situation.
The material covered in this book is aimed at allowing easier development and full use of detailed and high fidelity models. Potential applications of these techniques in all engineering disciplines are abundant, including applications in chemical kinetics and reaction mechanism elucidation, polymer reaction engineering, and physical properties estimation. On the academic side, the book will serve to generate research ideas.
Dynamic Modelling, Nonlinear Parameter Fitting and Sensitivity Analysis of a Living Free-radical Polymerisation Reactor
An Investigation of Some Tools for Process Model Identification for Prediction
Multivariate Weighted Least Squares as an Alternative to the Determinant Criterion for Multiresponse Parameter Estimation
Model Selection: An Overview of Practices in Chemical Engineering
Statistical Dynamic Model Building: Applications of Semi-infinite Programming
Non-constant Variance and the Design of Experiments for Chemical Kinetic Models
A Continuous-Time Hammerstein Approach Working with Statistical Experimental Design
Process Design Under Uncertainty: Robustness Criteria and value of information
A Modelling Tool for Different Stages of the Process Life