Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor

Mullai, P; Arulselvi, S; Ngo, HH; Sabarathinam, PL

Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor

Mullai, P Arulselvi, S Ngo, HH

Sabarathinam, PL

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Publication Type:: Journal Article
Citation:: Bioresource Technology, 2011, 102 (9), pp. 5492 - 5497
Issue Date:: 2011-05-01

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Field	Value	Language
dc.contributor.author	Mullai, P	en_US
dc.contributor.author	Arulselvi, S	en_US
dc.contributor.author	Ngo, HH https://orcid.org/0000-0002-0296-2866	en_US
dc.contributor.author	Sabarathinam, PL	en_US
dc.date.available	2011-01-31	en_US
dc.date.issued	2011-05-01	en_US
dc.identifier.citation	Bioresource Technology, 2011, 102 (9), pp. 5492 - 5497	en_US
dc.identifier.issn	0960-8524	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14904
dc.description.abstract	The performance of an anaerobic hybrid reactor (AHR) for treating penicillin-G wastewater was investigated at the ambient temperatures of 30-35°C for 245days in three phases. The experimental data were analysed by adopting an adaptive network-based fuzzy inference system (ANFIS) model, which combines the merits of both fuzzy systems and neural network technology. The statistical quality of the ANFIS model was significant due to its high correlation coefficient R2 between experimental and simulated COD values. The R2 was found to be 0.9718, 0.9268 and 0.9796 for the I, II and III phases, respectively. Furthermore, one to one correlation among the simulated and observed values was also observed. The results showed the proposed ANFIS model was well performed in predicting the performance of AHR. © 2011 Elsevier Ltd.	en_US
dc.relation.ispartof	Bioresource Technology	en_US
dc.relation.isbasedon	10.1016/j.biortech.2011.01.085	en_US
dc.subject.classification	Biotechnology	en_US
dc.subject.mesh	Penicillin G	en_US
dc.subject.mesh	Bioreactors	en_US
dc.subject.mesh	Waste Disposal, Fluid	en_US
dc.subject.mesh	Water Purification	en_US
dc.subject.mesh	Anaerobiosis	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Models, Chemical	en_US
dc.subject.mesh	Time Factors	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Biodegradation, Environmental	en_US
dc.subject.mesh	Biological Oxygen Demand Analysis	en_US
dc.title	Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	102	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	0907 Environmental Engineering	en_US
dc.location.activity	WOS:000290009300035	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 - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	102	en_US

Abstract:

The performance of an anaerobic hybrid reactor (AHR) for treating penicillin-G wastewater was investigated at the ambient temperatures of 30-35°C for 245days in three phases. The experimental data were analysed by adopting an adaptive network-based fuzzy inference system (ANFIS) model, which combines the merits of both fuzzy systems and neural network technology. The statistical quality of the ANFIS model was significant due to its high correlation coefficient R2 between experimental and simulated COD values. The R2 was found to be 0.9718, 0.9268 and 0.9796 for the I, II and III phases, respectively. Furthermore, one to one correlation among the simulated and observed values was also observed. The results showed the proposed ANFIS model was well performed in predicting the performance of AHR. © 2011 Elsevier Ltd.

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http://hdl.handle.net/10453/14904