Data-Driven Modelling Of Low-Pressure Hybrid Membrane Filtration Using Multivariate Polynomial Regression

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dc.contributor.author Erdei, Laszlo en_US
dc.contributor.author Dackermann, Ulrike en_US
dc.contributor.author Ball, James en_US
dc.contributor.editor N/A en_US
dc.date.accessioned 2012-02-02T11:12:01Z
dc.date.available 2012-02-02T11:12:01Z
dc.date.issued 2010 en_US
dc.identifier 2009007244 en_US
dc.identifier.citation Erdei Laszlo, Dackermann Ulrike, and Ball James 2010, 'Data-Driven Modelling Of Low-Pressure Hybrid Membrane Filtration Using Multivariate Polynomial Regression', , Chemical Industry Press, China, , pp. 1175-1182. en_US
dc.identifier.issn 978-7-89472-324-6 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16716
dc.description.abstract Hybrid membrane filtration processes involve complex physical, chemical, and biological phenomena, thus their mechanistic modelling is overly challenging. In this study we use multivariate polynomials to model the fouling of an in-line flocculationa??submerged membrane filtration system. The performance of obtained models is comparable to that of artificial neural network (ANN) models, to suit the needs of process optimisation and plant control. Their additional advantages are rapid model construction, easy presentation, inspection, and use. en_US
dc.language English en_US
dc.publisher Chemical Industry Press en_US
dc.relation.isbasedon NA en_US
dc.title Data-Driven Modelling Of Low-Pressure Hybrid Membrane Filtration Using Multivariate Polynomial Regression en_US
dc.parent Proceedings of the 9th International Conference on Hydroinformatics 2010 en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation China en_US
dc.identifier.startpage 1175 en_US
dc.identifier.endpage 1182 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 170205 en_US
dc.personcode 10265066 en_US
dc.personcode 995216 en_US
dc.personcode 997686 en_US
dc.percentage 100 en_US
dc.classification.name Neurocognitive Patterns and Neural Networks en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Hydroinformatics en_US
dc.date.activity 20100907 en_US
dc.location.activity Tianjin, CHINA en_US
dc.description.keywords NA en_US
dc.staffid 997686 en_US


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