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In process modelling, knowledge of the process under consideration is typically partial with significant disturbances to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification takes advantage of two sources of process information that may be available: any invariant prior knowledge and response data from experiments.
Practical Grey-box Process Identification is in three parts: The first part is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa, a MATLAB -compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends.
List of contents
Part I: Theory of Grey-box Process Identification.- Prospects and Problems.- The MoCaVa Solution.- Part II: Tutorial on MoCaVa.- Preprocessing.- Calibration.- Some Modelling Support.- Part III: Case Studies.- Case 1: Rinsing of the Steel Strip in a Rolling Mill.- Case 2: Quality Prediction in a Cardboard Making Process.- Appendices.- Mathematics and Algorithms; Glossary.
About the author
Professor (emeritus) Torsten Bohlin has been employed in the following capacities:
1963 - 1971 at the IBM Nordic Laboratories as Research Engeneer working with computerized industrial process ontrol.
1971 appointed (by the king) Professor of the chair of Automatic Control at Linkœping Technical Institute.
1972 - 1996 Professor in Automatic Control at the Royal Institute of Tecknology (KTH) in Stockholm.
1972 - 1988 Head of the Department of Automatic Control,
Member of the board of the school of Technical Physics, and Member of the faculty of KTH.
Member of the Swedish IFAC comittee, TFF (national), and IEEE
Reviewer 66 times
Summary
In process modelling, knowledge of the process under consideration is typically partial with significant disturbances to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification takes advantage of two sources of process information that may be available: any invariant prior knowledge and response data from experiments.
"Practical Grey-box Process Identification" is in three parts: The first part is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa, a MATLAB®-compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends.
