Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass
- Narayana, P. L.; Maurya, A. K.; Wang, Xiao-Song; Harsha, M. R.; Srikanth, O.; Alnuaim, Abeer Ali; Hatamleh, Wesam Atef; Hatamleh, Ashraf Atef; Cho, K. K.; Paturi, Uma Maheshwera Reddy; Reddy, N. S.
- Issue Date
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Adsorption; Artificial neural networks; Lead removal; Sensitivity analysis; Quantitative estimation
- ENVIRONMENTAL RESEARCH, v.199
- Journal Title
- ENVIRONMENTAL RESEARCH
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
- 공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.