Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in mas...

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Main Authors: Almalawi, A., Khan, A.I., Alqurashi, F., Abushark, Y.B., Alam, M.M., Qaiyum, S.
Format: Article
Published: Elsevier Ltd 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131544504&doi=10.1016%2fj.chemosphere.2022.135065&partnerID=40&md5=c604604de48a3606ebd9932004c68685
http://eprints.utp.edu.my/33519/
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spelling my.utp.eprints.335192022-09-07T07:42:21Z Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar Almalawi, A. Khan, A.I. Alqurashi, F. Abushark, Y.B. Alam, M.M. Qaiyum, S. Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods. © 2022 Elsevier Ltd Elsevier Ltd 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131544504&doi=10.1016%2fj.chemosphere.2022.135065&partnerID=40&md5=c604604de48a3606ebd9932004c68685 Almalawi, A. and Khan, A.I. and Alqurashi, F. and Abushark, Y.B. and Alam, M.M. and Qaiyum, S. (2022) Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar. Chemosphere, 303 . http://eprints.utp.edu.my/33519/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods. © 2022 Elsevier Ltd
format Article
author Almalawi, A.
Khan, A.I.
Alqurashi, F.
Abushark, Y.B.
Alam, M.M.
Qaiyum, S.
spellingShingle Almalawi, A.
Khan, A.I.
Alqurashi, F.
Abushark, Y.B.
Alam, M.M.
Qaiyum, S.
Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar
author_facet Almalawi, A.
Khan, A.I.
Alqurashi, F.
Abushark, Y.B.
Alam, M.M.
Qaiyum, S.
author_sort Almalawi, A.
title Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar
title_short Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar
title_full Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar
title_fullStr Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar
title_full_unstemmed Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar
title_sort modeling of remora optimization with deep learning enabled heavy metal sorption efficiency prediction onto biochar
publisher Elsevier Ltd
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131544504&doi=10.1016%2fj.chemosphere.2022.135065&partnerID=40&md5=c604604de48a3606ebd9932004c68685
http://eprints.utp.edu.my/33519/
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