Açık Akademik Arşiv Sistemi

Artificial neural networks modeling for the prediction of Pb(II) adsorption

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dc.contributor.authors Kiraz, A; Canpolat, O; Erkan, EF; Ozer, C;
dc.date.accessioned 2020-02-26T08:57:16Z
dc.date.available 2020-02-26T08:57:16Z
dc.date.issued 2019
dc.identifier.citation Kiraz, A; Canpolat, O; Erkan, EF; Ozer, C; (2019). Artificial neural networks modeling for the prediction of Pb(II) adsorption. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 16, 5086-5079
dc.identifier.issn 1735-1472
dc.identifier.uri https://doi.org/10.1007/s13762-018-1798-4
dc.identifier.uri https://hdl.handle.net/20.500.12619/50241
dc.description.abstract The work presents an artificial neural network (ANN) model predicting the efficiency of Pb(II) adsorption on polyamine-polyurea polymer modified with pyromellitic dianhydride. Adsorption percentages of Pb(II) ions, as calculated using the results of batch experiments, are used as data inputs for the ANN model. In the developed model, the contact time (5-240 min.), pH (1-7), the initial Pb(II) concentration (50-300 mg/L), amount of adsorbent (20-75 mg) and temperature (25-55 degrees C) values constitute the input layer, while adsorption percentage values constitute the output layer. Simulation-based development of ANN models was carried out with eight values for neurons in the hidden layer (2, 3, 5, 10, 20, 30, 50 and 100). The best results were obtained with 10 neurons. The prediction data of ANN models were statistically compared to experimental data. With the developed model's trial period and cost savings, the adsorption ratio was estimated with an error rate of about 2%. The results show that the multilayer perception ANN model (R-2 = 0.9858) justified the prediction of adsorption percentage.
dc.language English
dc.publisher SPRINGER
dc.subject Environmental Sciences & Ecology
dc.title Artificial neural networks modeling for the prediction of Pb(II) adsorption
dc.type Proceedings Paper
dc.identifier.volume 16
dc.identifier.startpage 5079
dc.identifier.endpage 5086
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü
dc.contributor.saüauthor Kiraz, Alper
dc.contributor.saüauthor Canpolat, Onur
dc.contributor.saüauthor Özer, Çiğdem
dc.relation.journal INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
dc.identifier.wos WOS:000478894500014
dc.identifier.doi 10.1007/s13762-018-1798-4
dc.identifier.eissn 1735-2630
dc.contributor.author Kiraz, Alper
dc.contributor.author Canpolat, Onur
dc.contributor.author E. F. Erkan
dc.contributor.author Özer, Çiğdem


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