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

Erdei, L; Dackermann, U; Ball, JE

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

Erdei, L Dackermann, U Ball, JE

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Publisher:: Chemical Industry Press
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 9th International Conference on Hydroinformatics 2010, 2010, pp. 1175 - 1182
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Erdei, L	en_US
dc.contributor.author	Dackermann, U	en_US
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.contributor.editor	N/A	en_US
dc.date	2010-09-07	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Proceedings of the 9th International Conference on Hydroinformatics 2010, 2010, pp. 1175 - 1182	en_US
dc.identifier.isbn	978-7-89472-324-6	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 flocculationâsubmerged 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.publisher	Chemical Industry Press	en_US
dc.relation.ispartof	Proceedings of the 9th International Conference on Hydroinformatics 2010	en_US
dc.relation.ispartof	International Conference on Hydroinformatics	en_US
dc.title	Data-Driven Modelling Of Low-Pressure Hybrid Membrane Filtration Using Multivariate Polynomial Regression	en_US
dc.type	Conference Proceeding
utslib.location	China	en_US
utslib.location.activity	Tianjin, CHINA	en_US
utslib.for	170205 Neurocognitive Patterns and Neural Networks	en_US
dc.location.activity	Tianjin, CHINA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	China	en_US
pubs.start-date	2010-09-07	en_US

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 flocculationâsubmerged 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.

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http://hdl.handle.net/10453/16716