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dc.contributor.authorMujtaba, Iqbal M.*
dc.contributor.authorAziz, Norashid*
dc.contributor.authorHussain, M.A.*
dc.date.accessioned2009-10-13T10:33:06Z
dc.date.available2009-10-13T10:33:06Z
dc.date.issued2006
dc.identifier.citationMujtaba, I.M., Aziz, N. and Hussain, M.A. (2006). Neural network based modelling and control of batch reactor. Chemical Engineering Research and Design. Vol. 84, No. 8, pp. 635-644.en
dc.identifier.urihttp://hdl.handle.net/10454/3653
dc.descriptionNoen
dc.description.abstractThe use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better.en
dc.language.isoenen
dc.subjectBatch reactoren
dc.subjectDynamic optimizationen
dc.subjectControl;en
dc.subjectNeural networksen
dc.subjectInverse modellingen
dc.titleNeural network based modelling and control of batch reactor.en
dc.status.refereedYesen
dc.typeArticleen
dc.type.versionNo full-text available in the repositoryen
dc.identifier.doihttps://doi.org/10.1205/cherd.05096


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