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Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen Demand

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dc.contributor.authors Dogan, E; Ates, A; Yilmaz, EC; Eren, B;
dc.date.accessioned 2020-02-26T08:57:46Z
dc.date.available 2020-02-26T08:57:46Z
dc.date.issued 2008
dc.identifier.citation Dogan, E; Ates, A; Yilmaz, EC; Eren, B; (2008). Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen Demand. ENVIRONMENTAL PROGRESS, 27, 446-439
dc.identifier.issn 0278-4491
dc.identifier.uri https://doi.org/10.1002/ep.10295
dc.identifier.uri https://hdl.handle.net/20.500.12619/50309
dc.description.abstract Biochemical oxygen demand (BOD) has been shown to be an important variable in water quality management and planning. However, BOD is difficult to measure and needs longer time periods (5 days) to get results. Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resource variables. The objective of this research was to develop an ANNs model to estimate daily BOD in the inlet of wastewater biochemical treatment plants. The plantscale data set (364 daily records of the year 2005) was obtained from a local wastewater treatment plant. Various combinations of daily water quality data, namely chemical oxygen demand (COD), water discharge (Q,,), suspended solid (SS), total nitrogen (N), and total phosphorus (P) are used as inputs into the ANN so as to evaluate the degree of effect of each of these variables on the daily inlet BOD. The results of the ANN model are compared with the multiple linear regression model (MLR). Mean square error, average absolute relative error, and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performance. The ANN technique whose inputs are COD, Q,v, SS, A, and P gave mean square errors of 708.01, average absolute relative errors of 10.03%, and a coefficient of determination 0.919, respectively. On the basis of the comparisons, it was found that the ANN model could be employed successfully in estimating the daily BOD in the inlet of wastewater biochemical treatment plants. (C) 2008 American Institute of Chemical Engineers Environ Prog, 27: 439-446, 2008
dc.language English
dc.publisher WILEY
dc.subject Environmental Sciences & Ecology
dc.title Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen Demand
dc.type Article
dc.identifier.volume 27
dc.identifier.startpage 439
dc.identifier.endpage 446
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü
dc.contributor.saüauthor Doğan, Emrah
dc.contributor.saüauthor Ateş, Asude
dc.contributor.saüauthor Eren, Beytullah
dc.relation.journal ENVIRONMENTAL PROGRESS
dc.identifier.wos WOS:000261457900002
dc.identifier.doi 10.1002/ep.10295
dc.contributor.author Doğan, Emrah
dc.contributor.author Ateş, Asude
dc.contributor.author Eren, Beytullah


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