Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal

Kennedy, MJ; Gandomi, AH; Miller, CM

Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal

Kennedy, MJ Gandomi, AH Miller, CM

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Environmental Chemical Engineering, 2015, 3, (4), pp. 2829-2838
Issue Date:: 2015-12-01

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Field	Value	Language
dc.contributor.author	Kennedy, MJ
dc.contributor.author	Gandomi, AH
dc.contributor.author	Miller, CM
dc.date.accessioned	2023-03-28T04:35:27Z
dc.date.available	2023-03-28T04:35:27Z
dc.date.issued	2015-12-01
dc.identifier.citation	Journal of Environmental Chemical Engineering, 2015, 3, (4), pp. 2829-2838
dc.identifier.issn	2213-2929
dc.identifier.issn	2213-3437
dc.identifier.uri	http://hdl.handle.net/10453/168731
dc.description.abstract	In this study, four different neural network models were evaluated for predicting both turbidity and dissolved organic matter (DOM) removal during the coagulation process at the Akron Water Treatment Plant (Akron, Ohio, USA). DOM was monitored and characterized using fluorescence spectroscopy and parallel factor (PARAFAC) analysis, building upon previous research which identified three unique fluorescence components (C1, C2, and C3). Neural network models were built using operational data to predict each of the fluorescence components and turbidity after coagulation based on variable raw water quality and chemical doses. Correlation coefficients between measured and model predicted values for the final turbidity, C1, C2, and C3 models on an unseen test data set were 0.91, 0.95, 0.97, and 0.51, respectively. The predictive capability of the top performing model for each parameter was evaluated using parametric analysis, external validation criteria, and the absolute relative error distribution. Results suggest that the models for settled turbidity and the three settled component scores are valid and can be used to predict the removal of individual fractions of DOM (as measured by PARAFAC components) as a function of chemical dose and raw water quality, providing the water plant the ability to simultaneously manage two key water quality treatment objectives.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Environmental Chemical Engineering
dc.relation.isbasedon	10.1016/j.jece.2015.10.010
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0306 Physical Chemistry (incl. Structural), 0904 Chemical Engineering, 0907 Environmental Engineering
dc.title	Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal
dc.type	Journal Article
utslib.citation.volume	3
utslib.for	0306 Physical Chemistry (incl. Structural)
utslib.for	0904 Chemical Engineering
utslib.for	0907 Environmental Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-28T04:35:25Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	3
utslib.citation.issue	4

Abstract:

In this study, four different neural network models were evaluated for predicting both turbidity and dissolved organic matter (DOM) removal during the coagulation process at the Akron Water Treatment Plant (Akron, Ohio, USA). DOM was monitored and characterized using fluorescence spectroscopy and parallel factor (PARAFAC) analysis, building upon previous research which identified three unique fluorescence components (C1, C2, and C3). Neural network models were built using operational data to predict each of the fluorescence components and turbidity after coagulation based on variable raw water quality and chemical doses. Correlation coefficients between measured and model predicted values for the final turbidity, C1, C2, and C3 models on an unseen test data set were 0.91, 0.95, 0.97, and 0.51, respectively. The predictive capability of the top performing model for each parameter was evaluated using parametric analysis, external validation criteria, and the absolute relative error distribution. Results suggest that the models for settled turbidity and the three settled component scores are valid and can be used to predict the removal of individual fractions of DOM (as measured by PARAFAC components) as a function of chemical dose and raw water quality, providing the water plant the ability to simultaneously manage two key water quality treatment objectives.

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http://hdl.handle.net/10453/168731