Edited by
S. Macchietto, Imperial College, London, U.K.
Description
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.
Included in series
Computer Aided Chemical Engineering
Audience:
Researchers and practitioners within the process industries and academia. Postgraduate and research students concerned with modeling principles
in Chemical Engineering and/or Process Systems Engineering.
