Modular neural network modelling for long-range prediction of an evaporator

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dc.citation.issue1
dc.citation.volume8
dc.contributor.authorRussell NT
dc.contributor.authorBakker HH
dc.contributor.authorChaplin RI
dc.date.available2000-01
dc.date.issued2000
dc.description.abstractThis paper presents the development of a modular neural network model of a three-effect, falling-film evaporator. The model comprises a number of sub-networks each modelling a specific element of the overall system. The modular structure was employed in order to provide benefits in terms of improved model training and performance. The performance of the modular neural model is demonstrated for long-range prediction by comparing it with process data, an analytical simulation and a linear ARX model. The results show that the modular neural model can satisfactorily predict over a horizon of arbitrary length and is suited for implementation within a predictive control scheme. Benefits in terms of model flexibility and interpretability are also discussed. (C) 2000 Elsevier Science Ltd. All rights reserved.
dc.description.publication-statusPublished
dc.format.extent49 - 59
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000085091100006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationCONTROL ENGINEERING PRACTICE, 2000, 8 (1), pp. 49 - 59
dc.identifier.doi10.1016/S0967-0661(99)00123-9
dc.identifier.elements-id4201
dc.identifier.harvestedMassey_Dark
dc.identifier.issn0967-0661
dc.identifier.urihttps://hdl.handle.net/10179/14812
dc.relation.isPartOfCONTROL ENGINEERING PRACTICE
dc.subjectneural networks
dc.subjectsimulation
dc.subjectprediction
dc.subjectmodular modelling
dc.subjectevaporators
dc.subjectmodel-based predictive control
dc.subject.anzsrc0102 Applied Mathematics
dc.subject.anzsrc0906 Electrical and Electronic Engineering
dc.subject.anzsrc0913 Mechanical Engineering
dc.titleModular neural network modelling for long-range prediction of an evaporator
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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