Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model

Abujazar, MSS; Fatihah, S; Ibrahim, IA; Kabeel, AE; Sharil, S

Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model

Abujazar, MSS Fatihah, S Ibrahim, IA Kabeel, AE Sharil, S

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Journal of Cleaner Production, 2018, 170, pp. 147-159
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Abujazar, MSS
dc.contributor.author	Fatihah, S
dc.contributor.author	Ibrahim, IA
dc.contributor.author	Kabeel, AE
dc.contributor.author	Sharil, S
dc.date.accessioned	2022-10-29T20:27:17Z
dc.date.available	2022-10-29T20:27:17Z
dc.date.issued	2018-01-01
dc.identifier.citation	Journal of Cleaner Production, 2018, 170, pp. 147-159
dc.identifier.issn	0959-6526
dc.identifier.issn	1879-1786
dc.identifier.uri	http://hdl.handle.net/10453/162864
dc.description.abstract	This paper presents a cascaded forward neural network model for predicting the productivity of a developed inclined stepped solar still system. The actual recorded data of the developed inclined stepped solar still system is used to develop the proposed model. The results of the predicted productivity are compared with that obtained from regression and linear models. In this study, three statistical error terms are used to evaluate the proposed model: root mean square error (RMSE), mean absolute percentage error (MAPE) and mean bias error (MBE). The results show that the proposedcascaded forward neural network (CFNN) model more accurately predicts the productivity of the system than the other modelsmentioned. The RMSE, MAPE and MBE values of the proposed model are 22.48%, 18.51% and −26.46%, respectively. Therefore, the CFNN model provides benefits for modelling the solar still.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Journal of Cleaner Production
dc.relation.isbasedon	10.1016/j.jclepro.2017.09.092
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0907 Environmental Engineering, 0910 Manufacturing Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Environmental Sciences
dc.title	Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model
dc.type	Journal Article
utslib.citation.volume	170
utslib.for	0907 Environmental Engineering
utslib.for	0910 Manufacturing Engineering
utslib.for	0915 Interdisciplinary Engineering
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-29T20:27:15Z
pubs.publication-status	Published
pubs.volume	170

Abstract:

This paper presents a cascaded forward neural network model for predicting the productivity of a developed inclined stepped solar still system. The actual recorded data of the developed inclined stepped solar still system is used to develop the proposed model. The results of the predicted productivity are compared with that obtained from regression and linear models. In this study, three statistical error terms are used to evaluate the proposed model: root mean square error (RMSE), mean absolute percentage error (MAPE) and mean bias error (MBE). The results show that the proposedcascaded forward neural network (CFNN) model more accurately predicts the productivity of the system than the other modelsmentioned. The RMSE, MAPE and MBE values of the proposed model are 22.48%, 18.51% and −26.46%, respectively. Therefore, the CFNN model provides benefits for modelling the solar still.

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http://hdl.handle.net/10453/162864