Descripción del título
Practical Grey-box Process ...
In process modelling, knowledge of the process under consideration is typically partial with significant unknown inputs (disturbances) to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification can assist, in these circumstances, by taking advantage of the two sources of information that may be available: any invariant prior knowledge and response data from experiments. Practical Grey-box Process Identification is a three-stranded response to the following questions which are frequently raised in connection with grey-box methods: \2022 How much of my prior knowledge is useful and even correct in this environment? \2022 Are my experimental data sufficient and relevant? \2022 What do I do about the disturbances that I can\2019t get rid of? \2022 How do I know when my model is good enough? The first part of the book 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. Practical Grey-box Process Identification will be of great interest and help to process control engineers and researchers and the software show-cased here will be of much practical assistance to students doing project work in this field. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control
Monografía
monografia Rebiun12524527 https://catalogo.rebiun.org/rebiun/record/Rebiun12524527 cr nn 008mamaa 100301s2006 xxk| s |||| 0|eng d 9781846284038 10.1007/1-84628-403-1 doi UPVA 996883874103706 UAM 991007781853804211 CBUC 991004877494706711 CBUC 991010403510506709 UCAR 991007933674004213 CBUC 991010403510506709 UMO 69349 UPCT u326547 Bohlin, Torsten Practical Grey-box Process Identification Recurso electrónico-En línea] Theory and Applications by Torsten Bohlin London Springer London 2006 London London Springer London XIX, 351 p. 186 illus. Also available online. digital XIX, 351 p. 186 illus. Also available online. Advances in Industrial Control 1430-9491 Engineering (Springer-11647) 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 Accesible sólo para usuarios de la UPV Recurso a texto completo In process modelling, knowledge of the process under consideration is typically partial with significant unknown inputs (disturbances) to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification can assist, in these circumstances, by taking advantage of the two sources of information that may be available: any invariant prior knowledge and response data from experiments. Practical Grey-box Process Identification is a three-stranded response to the following questions which are frequently raised in connection with grey-box methods: \2022 How much of my prior knowledge is useful and even correct in this environment? \2022 Are my experimental data sufficient and relevant? \2022 What do I do about the disturbances that I can\2019t get rid of? \2022 How do I know when my model is good enough? The first part of the book 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. Practical Grey-box Process Identification will be of great interest and help to process control engineers and researchers and the software show-cased here will be of much practical assistance to students doing project work in this field. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control Reproducción electrónica Forma de acceso: Web Engineering Computer simulation Computer aided design Engineering Control Engineering Simulation and Modeling Mathematical Modeling and Industrial Mathematics Computer-Aided Engineering (CAD, CAE) and Design SpringerLink (Servicio en línea) Springer eBooks Springer eBooks Printed edition 9781846284021 Advances in Industrial Control 1430-9491