Ann-Assisted Forecasting of Adsorption Efficiency To Remove Heavy Metals

dc.authorid Buaisha, Dr.Magdi/0000-0001-9879-968X
dc.authorid Ozalp Yaman, Seniz/0000-0002-4166-0529
dc.authorscopusid 57211402383
dc.authorscopusid 11139496600
dc.authorscopusid 56054555600
dc.authorwosid Yaman, Şeniz Özalp/AAK-1854-2021
dc.contributor.author Buaısha, Magdi
dc.contributor.author Balku, Şaziye
dc.contributor.author Yaman, Şeniz Özalp
dc.contributor.other Energy Systems Engineering
dc.contributor.other Chemical Engineering
dc.date.accessioned 2024-07-05T15:28:15Z
dc.date.available 2024-07-05T15:28:15Z
dc.date.issued 2019
dc.department Atılım University en_US
dc.department-temp ATILIM ÜNİVERSİTESİ,ATILIM ÜNİVERSİTESİ,ATILIM ÜNİVERSİTESİ en_US
dc.description Buaisha, Dr.Magdi/0000-0001-9879-968X; Ozalp Yaman, Seniz/0000-0002-4166-0529 en_US
dc.description.abstract In wastewater treatment, scientific and practical models utilizing numerical computational techniques suchas artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems.In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R2). According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transferfunction (purelin) in the output layer. Furthermore, the Levenberg–Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R2 . In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.3906/kim-1902-28
dc.identifier.endpage 1424 en_US
dc.identifier.issn 1300-0527
dc.identifier.issn 1303-6130
dc.identifier.issue 5 en_US
dc.identifier.scopus 2-s2.0-85073800712
dc.identifier.scopusquality Q3
dc.identifier.startpage 1407 en_US
dc.identifier.trdizinid 336065
dc.identifier.uri https://doi.org/10.3906/kim-1902-28
dc.identifier.uri https://search.trdizin.gov.tr/tr/yayin/detay/336065/ann-assisted-forecasting-of-adsorption-efficiency-to-remove-heavy-metals
dc.identifier.volume 43 en_US
dc.identifier.wos WOS:000489111500014
dc.identifier.wosquality Q4
dc.institutionauthor Balku, Şaziye
dc.institutionauthor Özalp Yaman, Şeniz
dc.language.iso en en_US
dc.publisher Tubitak Scientific & Technological Research Council Turkey en_US
dc.relation.ispartof Turkish Journal of Chemistry en_US
dc.relation.publicationcategory Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 6
dc.subject Kimya en_US
dc.subject Analitik en_US
dc.subject Kimya en_US
dc.subject Uygulamalı en_US
dc.subject Kimya en_US
dc.subject Organik en_US
dc.subject Kimya en_US
dc.subject Tıbbi en_US
dc.subject Mühendislik en_US
dc.subject Kimya en_US
dc.subject Kimya en_US
dc.subject İnorganik ve Nükleer en_US
dc.title Ann-Assisted Forecasting of Adsorption Efficiency To Remove Heavy Metals en_US
dc.type Article en_US
dc.wos.citedbyCount 5
dspace.entity.type Publication
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