Application of neural network prediction model to full-scale anaerobic sludge digestion

Basit Öğe Kaydı
dc.contributor.authorGuclu, Dunyamin
dc.contributor.authorYilmaz, Nihat
dc.contributor.authorOzkan-Yucel, Umay G.
dc.date.accessioned2020-03-26T18:13:48Z
dc.date.available2020-03-26T18:13:48Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBACKGROUND: Process modeling is a useful tool for description and prediction of the performance of anaerobic digestion systems under varying operation conditions. The objective of this study was to implement a model to simulate the dynamic behavior of a large-scale anaerobic sewage sludge digestion system. Artificial neural network (ANN) models using algorithms best suited to environmental problems (the Levenberg-Marquardt algorithm and the 'gradient descent with adaptive learning rate' back propagation algorithms) were used to model the anaerobic sludge digester of the Ankara Central Wastewater Treatment Plant (ACWTP) using dynamic data. RESULTS: Based on the relatively low mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) and very high r values, ANN models predicted effluent volatile solid (VS) concentration and methane yield satisfactorily. Effluent VS and methane yields were predicted by the ANN using only conventional parameters such as pH, temperature, flow rate, volatile fatty acids, alkalinity, dry matter and organic matter. The best back propagation algorithm was the gradient descent with adaptive learning rate algorithm in both models. In the training of the neural network, four-fold cross-validation was used for validation of the model for better reliability. CONCLUSION: The proposed ANN models were shown to be capable of dynamically predicting the VS and CH(4) production rates for real system behavior. Only relatively simple monitoring parameters are needed to build the model for this complex anaerobic digestion process. (C) 2011 Society of Chemical Industryen_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis study was supported by the Selcuk University Research Fund (BAP). The authors would like to thank ASKI, Ankara, Turkey, for their help during the study and for providing ACWTP process data.en_US
dc.identifier.doi10.1002/jctb.2569en_US
dc.identifier.endpage698en_US
dc.identifier.issn0268-2575en_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage691en_US
dc.identifier.urihttps://dx.doi.org/10.1002/jctb.2569
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26161
dc.identifier.volume86en_US
dc.identifier.wosWOS:000289363600010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWILEY-BLACKWELLen_US
dc.relation.ispartofJOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectanaerobic sludge digestion modelingen_US
dc.subjectartificial neural networken_US
dc.subjectK-fold cross-validationen_US
dc.subjectlarge-scale wastewater treatment planten_US
dc.titleApplication of neural network prediction model to full-scale anaerobic sludge digestionen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu