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020 _a9781846284038
_99781846284038
024 7 _a10.1007/1846284031
_2doi
035 _avtls000343832
039 9 _a201509030752
_bVLOAD
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aBohlin, Torsten.
_eautor
_9323206
245 1 0 _aPractical Grey-box Process Identification :
_bTheory and Applications /
_cby Torsten Bohlin.
264 1 _aLondon :
_bSpringer London,
_c2006.
300 _axIx, 351 páginas 186 ilustraciones Also available online.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aAdvances in Industrial Control,
_x1430-9491
500 _aSpringer eBooks
505 0 _aTheory of Grey-box Process Identification -- Prospects and Problems -- The MoCaVa Solution -- Tutorial on MoCaVa -- Preparations -- Calibration -- Some Modelling Support -- Case Studies -- Rinsing of the Steel Strip in a Rolling Mill -- Quality Prediction in a Cardboard Making Process.
520 _aIn 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: • How much of my prior knowledge is useful and even correct in this environment? • Are my experimental data sufficient and relevant? • What do I do about the disturbances that I can’t get rid of? • 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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781846284021
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-403-1
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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