Dynamic Model Development: Methods, Theory and ApplicationsEdited by
- S. Macchietto
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.
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.
Computer Aided Chemical Engineering
Hardbound, 266 Pages
Published: August 2003
- Methodological Aspects in the Modelling of Novel Unit OperationsDynamic Modelling, Nonlinear Parameter Fitting and Sensitivity Analysis of a Living Free-radical Polymerisation ReactorAn Investigation of Some Tools for Process Model Identification for PredictionMultivariate Weighted Least Squares as an Alternative to the Determinant Criterion for Multiresponse Parameter EstimationModel Selection: An Overview of Practices in Chemical EngineeringStatistical Dynamic Model Building: Applications of Semi-infinite ProgrammingNon-constant Variance and the Design of Experiments for Chemical Kinetic ModelsA Continuous-Time Hammerstein Approach Working with Statistical Experimental DesignProcess Design Under Uncertainty: Robustness Criteria and value of informationA Modelling Tool for Different Stages of the Process Life