Dynamic Model Development: Methods, Theory and Applications
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 o…
Read More ↓Title Statements
- Full title :Dynamic Model Development : Methods, Theory and Applications.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2003.
Publication Statements
- Publisher(s):Elsevier Science
- Place of Publication:Burlington
- Language:eng
- Publication Date:2003.
The Physical Object
- Description:1 online resource (267 pages).
Edition Identifiers
- ISBN:0444514651, 9780444514653
- oclc:ocn990413185
- LC Classification:TP155.2.M35 W67 2000
- Dewey Decimal:660/.01/5118 22
Table of Contents
Front Cover; DYNAMIC MODEL DEVELOPMENT; Copyright Page; Foreword; Contents; Chapter 1. Methodological Aspects in the Modelling of Novel Unit Operations; Chapter 2. Dynamic Modelling, Nonlinear Parameter Fitting and Sensitivity Analysis of a Living Free-Radical Polymerisation Reactor; Chapter 3. An Investigation of Some Tools for Process Model Identification for Prediction; Chapter 4. Multivariate Weighted Least Squares as an Alternative to the Determinant Criterion for Multiresponse Parameter Estimation; Chapter 5. Model Selection: An Overview of Practices in Chemical Engineering.
Chapter 6. Statistical Dynamic Model Building: Applications of Semi-infinite ProgrammingChapter 7. Non-constant Variance and the Design of Experiments for Chemical Kinetic Models; Chapter 8. A Continuous-Time Hammerstein Approach Working with Statistical Experimental Design; Chapter 9. Process Design Under Uncertainty: Robustness Criteria and Value of Information; Chapter 10. A Modelling Tool for Different Stages of the Process Life; Appendix; Other Papers Presented at the Workshop; Author Index; Subject Index.
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