Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality

dc.contributor.authorErdirencelebi, Dilek
dc.contributor.authorYalpir, Sukran
dc.date.accessioned2020-03-26T18:13:42Z
dc.date.available2020-03-26T18:13:42Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents the development and evaluation of three adaptive network fuzzy inference system (ANFIS) models for a laboratory scale anaerobic digestion system outputs with varied input selection approaches. The aim was the investigation of feasibility of the approach-based-control system for the prediction of effluent quality from a sequential upflow anaerobic sludge bed reactor (UASBR) system that produced a strong nonlinearship between its inputs and outputs. As ANFIS demonstrated its ability to construct any nonlinear function with multiple inputs and outputs in many applications, its estimating performance was investigated for a complex wastewater treatment process at increasing organic loading rates from 1.1 to 5.5 g COD/L d. Approximation of the ANFIS models was validated using correlation coefficient. MAPE and RMSE. ANFIS was successful to model unsteady data for pH and acceptable for COD within anaerobic digestion limits with multiple input structure. The prediction performance showed a high feasibility of the model-based-control system on the anaerobic digester system to produce an effluent amenable for a consecutive aerobic treatment unit. (C) 2011 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipTubitakTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThis study was carried out at the Environmental Engineering Department of Selcuk University with the data obtained in the scope of the Project No.107Y245 "Determination of the Anaerobic Treatability, Kinetics and Performance of Cheese and Yoghurt Production Wastewaters" supported by Tubitak, The Scientific and Technological Research Council of Turkey which has no involvement in the mathematical modeling study presented here.en_US
dc.identifier.doi10.1016/j.apm.2011.02.015en_US
dc.identifier.endpage3832en_US
dc.identifier.issn0307-904Xen_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3821en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.apm.2011.02.015
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26091
dc.identifier.volume35en_US
dc.identifier.wosWOS:000291081800015en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofAPPLIED MATHEMATICAL MODELLINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectModelingen_US
dc.subjectAnaerobic treatmenten_US
dc.subjectDairy wastewateren_US
dc.subjectANFISen_US
dc.subjectUASBRen_US
dc.titleAdaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent qualityen_US
dc.typeArticleen_US

Dosyalar