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dc.contributor.authorYAMUNA RANI, Ken_US
dc.contributor.authorPATWARDHAN, SCen_US
dc.date.accessioned2009-03-19T05:11:24Zen_US
dc.date.accessioned2011-11-25T19:30:19Zen_US
dc.date.accessioned2011-12-26T13:07:32Zen_US
dc.date.accessioned2011-12-27T05:55:34Z
dc.date.available2009-03-19T05:11:24Zen_US
dc.date.available2011-11-25T19:30:19Zen_US
dc.date.available2011-12-26T13:07:32Zen_US
dc.date.available2011-12-27T05:55:34Z
dc.date.issued2007en_US
dc.identifier.citationChemical Engineering Research and Design 85(10), 1397-1406en_US
dc.identifier.issn0263-8762en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0263-8762(07)73180-5en_US
dc.identifier.urihttp://hdl.handle.net/10054/988en_US
dc.identifier.urihttp://dspace.library.iitb.ac.in/xmlui/handle/10054/988en_US
dc.description.abstractGeneric model control (GMC) has been successfully used for achieving tight control of batch/semi-batch processes. As the requirement to developing a mechanistic model can prove to be a bottle-neck while implementing GMC, many researchers have recently proposed GMC formulations based on black box models developed using artificial neural networks (ANN). The applicability of most of these formulations is limited to continuously operated systems with relative degree one. In addition, these formulations cannot handle constraints on inputs systematically. In the present study, ANN based GMC (ANNGMC) approach is extended to semi-batch processes with relative order higher than one. The nonlinear time-varying behaviour of batch/semi-batch processes is approximated using ANN model developed in the desired operating region. The ANN model is further used to formulate a nonlinear controller using GMC framework for solving trajectory-tracking problems associated with semi-batch reactors. The control problem at each sampling instant is formulated as a constrained optimization problem whereby the constraints on manipulated inputs can be handled systematically. The proposed controller formulation is used for solving trajectory-tracking problems associated with semi-batch reactors. The performance of the proposed control algorithm is evaluated by simulating the challenge problem proposed by Chylla and Haase (1993), which involves temperature control of a multi-product semi-batch polymerization reactor under widely varying operating conditions. The simulation exercise reveals that the performance of proposed ANNGMC formulation is comparable to the performance of the GMC formulation based on the exact mechanistic model, and is much better than PID controller performance.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectTemperature Controlen_US
dc.subjectNeural Networksen_US
dc.subjectPolymerizationen_US
dc.subjectOptimizationen_US
dc.titleData-driven model based control of a multi-product semi-batch polymerization reactoren_US
dc.typeArticleen_US
dc.description.copyright© Elsevieren_US


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