吸附
水溶液
废水
过程(计算)
水溶液中的金属离子
人工神经网络
化学
金属
材料科学
环境科学
计算机科学
环境工程
机器学习
有机化学
操作系统
作者
P.L. Narayana,A.K. Maurya,Xiaosong Wang,M.R. Harsha,Ommi Srikanth,Abeer Ali Alnuaim,Wesam Atef Hatamleh,Ashraf Atef Hatamleh,K.K. Cho,Uma Maheshwera Reddy Paturi,N.S. Reddy
标识
DOI:10.1016/j.envres.2021.111370
摘要
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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