Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant

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dc.contributor.authorBoztoprak, Halime
dc.contributor.authorOzbay, Yuksel
dc.contributor.authorGuclu, Dunyamin
dc.contributor.authorKucukhemek, Murat
dc.date.accessioned2020-03-26T19:25:43Z
dc.date.available2020-03-26T19:25:43Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSludge volume index parameter should be monitored daily for the performance of wastewater treatment plants. It was aimed to estimate this parameter using image processing and artificial intelligence techniques for full-scale wastewater treatment plant. The activated sludge samples were collected from the aeration tank of the activated sludge process in Konya Domestic Wastewater Treatment Plant. Sludge characteristics and settling properties were observed microscopically via the measurements of flocs and filaments. The 49 images per slide were taken by an image-analysis system developed for automated image acquisition. A total of 120 samples were examined over a period of year. The floc and filament structures were analyzed using Cellular Neural Networks (CNN). Iteration value of the CNN was modified according to the image. Then, a number of morphological operations were applied for an accurate identification of the floc and filaments separately. Textural, shape, and statistical approaches were utilized for creating a set of data for each sample. After preparing the training and test data by shuffling the data randomly, a fivefold cross-validation method was applied. And, these training and test data were applied to an artificial neural network. The weights of the neural network were trained using the Levenberg-Marquardt, Genetic, and Artificial Bee Colony algorithms.en_US
dc.description.sponsorshipCoordinator Ship of Selcuk UniversitySelcuk University [11201043]en_US
dc.description.sponsorshipThis work was supported by the Coordinator Ship of Selcuk University's research projects under Project No: 11201043. The authors would like to thank the personnel of the Konya municipal wastewater treatment unit for their support in collecting and analyzing the samples.en_US
dc.identifier.doi10.1080/19443994.2015.1085909en_US
dc.identifier.endpage17205en_US
dc.identifier.issn1944-3994en_US
dc.identifier.issn1944-3986en_US
dc.identifier.issue37en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage17195en_US
dc.identifier.urihttps://dx.doi.org/10.1080/19443994.2015.1085909
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33895
dc.identifier.volume57en_US
dc.identifier.wosWOS:000378616600004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherDESALINATION PUBLen_US
dc.relation.ispartofDESALINATION AND WATER TREATMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial bee colony algorithmen_US
dc.subjectArtificial neural networken_US
dc.subjectGenetic algorithmen_US
dc.subjectSludge bulkingen_US
dc.subjectSludge volume indexen_US
dc.subjectWastewater treatmenten_US
dc.titlePrediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge planten_US
dc.typeArticleen_US

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