Dynamic Model Development: Methods, Theory and Applications - 1st Edition - ISBN: 9780444514653, 9780080530574

Dynamic Model Development: Methods, Theory and Applications, Volume 16

1st Edition

Editors: S. Macchietto
eBook ISBN: 9780080530574
Hardcover ISBN: 9780444514653
Imprint: Elsevier Science
Published Date: 4th August 2003
Page Count: 266
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Table of Contents

Methodological Aspects in the Modelling of Novel Unit Operations 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


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.

Key Features

  • Contains wide coverage of statistical methods applied to process modelling
  • Serves as a recent compilation of dynamic model building tools
  • Presents several examples of applying advanced statistical and modelling methods to real process systems problems


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.


No. of pages:
© Elsevier Science 2003
Elsevier Science
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Hardcover ISBN:

About the Editors

S. Macchietto Editor

Affiliations and Expertise

Imperial College, London, U.K.