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Narayana PL, Maurya AK, Wang XS, Harsha MR, Srikanth O, Alnuaim AA, Hatamleh WA, Hatamleh AA, Cho KK, Paturi UMR, Reddy NS. Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass. Environ Res 2021; 199:111370. [PMID: 34043971 DOI: 10.1016/j.envres.2021.111370] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 06/12/2023]
Abstract
Heavy metal ions in aqueous solutions are taken into account as one of the most harmful environmental issues that ominously affect human health. Pb(II) is a common pollutant among heavy metals found in industrial wastewater, and various methods were developed to remove the Pb(II). The adsorption method was more efficient, cheap, and eco-friendly to remove the Pb(II) from aqueous solutions. The removal efficiency depends on the process parameters (initial concentration, the adsorbent dosage of T-Fe3O4 nanocomposites, residence time, and adsorbent pH). The relationship between the process parameters and output is non-linear and complex. The purpose of the present study is to develop an artificial neural networks (ANN) model to estimate and analyze the relationship between Pb(II) removal and adsorption process parameters. The model was trained with the backpropagation algorithm. The model was validated with the unseen datasets. The correlation coefficient adj.R2 values for total datasets is 0.991. The relationship between the parameters and Pb(II) removal was analyzed by sensitivity analysis and creating a virtual adsorption process. The study determined that the ANN modeling was a reliable tool for predicting and optimizing adsorption process parameters for maximum lead removal from aqueous solutions.
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Affiliation(s)
- P L Narayana
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea
| | - A K Maurya
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea
| | - Xiao-Song Wang
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea
| | - M R Harsha
- Machine Learning and Artificial Intelligence, International Institute of Information Technology, Banglore, India
| | - O Srikanth
- Department of Mechanical Engineering, Dhanekula Institute of Engineering & Technology, Ganguru, Vijayawada, 521139, India
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Ashraf Atef Hatamleh
- Department of Botany and Microbiology, College of science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - K K Cho
- Department of Materials Engineering and Convergence Technology & RIGET, Gyeongsang National University, Jinju, South Korea
| | | | - N S Reddy
- School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea.
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