Açık Akademik Arşiv Sistemi

Development of artificial neural network for prediction of salt recovery by nanofiltration from textile industry wastewaters

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dc.contributor.authors Eren, B; Ileri, R; Dogan, E; Caglar, N; Koyuncu, I;
dc.date.accessioned 2020-02-26T08:56:42Z
dc.date.available 2020-02-26T08:56:42Z
dc.date.issued 2012
dc.identifier.citation Eren, B; Ileri, R; Dogan, E; Caglar, N; Koyuncu, I; (2012). Development of artificial neural network for prediction of salt recovery by nanofiltration from textile industry wastewaters. DESALINATION AND WATER TREATMENT, 50, 328-317
dc.identifier.issn 1944-3994
dc.identifier.uri https://doi.org/10.1080/19443994.2012.719743
dc.identifier.uri https://hdl.handle.net/20.500.12619/50114
dc.description.abstract This paper presents the use of artificial neural network (ANN) to develop a model for predicting rejection rate (R-o) of single salt (NaCl) by nanofiltration based on experimental datasets. The rejection rates of NaCl were obtained when operating conditions, such as feed pressure (Delta P) and cross flow velocity (V), varied along with different physicochemical properties of feed water like salt and dye concentrations, and pH. In the modeling work, sensitivity analyses were performed to identify relative impact of each parameter and to find the best combination of input parameters in the ANN model. The optimal network architecture was developed through trial and error approach. Model predictions in each trial were compared with experimental results based on statistical evaluation such as root mean square error, mean absolute error, and coefficient of determination (R-2). Optimal network architecture was determined as one hidden layer with 25 neurons using Levenberg-Marquardt (trainlm) back-propagation algorithm. In this architecture, tangent sigmoid (tansig) in hidden layer and linear (purelin) in output layer was also used as transfer functions. The results showed that the developed ANN model predictions and experimental data matched well and the model can be employed successfully for the prediction of the R-o.
dc.language English
dc.publisher TAYLOR & FRANCIS INC
dc.subject Water Resources
dc.title Development of artificial neural network for prediction of salt recovery by nanofiltration from textile industry wastewaters
dc.type Article
dc.identifier.volume 50
dc.identifier.startpage 317
dc.identifier.endpage 328
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü
dc.contributor.saüauthor Eren, Beytullah
dc.contributor.saüauthor İleri, Recep
dc.contributor.saüauthor Doğan, Emrah
dc.contributor.saüauthor Çağlar, Naci
dc.relation.journal DESALINATION AND WATER TREATMENT
dc.identifier.wos WOS:000312458600035
dc.identifier.doi 10.1080/19443994.2012.719743
dc.contributor.author Eren, Beytullah
dc.contributor.author İleri, Recep
dc.contributor.author Doğan, Emrah
dc.contributor.author Çağlar, Naci


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