Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

Ahmed H. Sadek, Omar M. Fahmy, Mahmoud Nasr () and Mohamed K. Mostafa
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Ahmed H. Sadek: Environmental Engineering Program, Zewail City of Science, Technology and Innovation, 6th October City 12578, Egypt
Omar M. Fahmy: Faculty of Engineering and Technology, Badr University in Cairo (BUC), Cairo 11829, Egypt
Mahmoud Nasr: Environmental Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), New Borg El-Arab City 21934, Egypt
Mohamed K. Mostafa: Faculty of Engineering and Technology, Badr University in Cairo (BUC), Cairo 11829, Egypt

Sustainability, 2023, vol. 15, issue 3, 1-21

Abstract: Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (C o ) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model ( R 2 : 0.925) and pseudo-second-order (PSO) kinetic model ( R 2 : 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10 −5 , suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.

Keywords: adsorption optimization; aluminum-based nanoparticles; Cu(II); linear regression; neural network; support vector regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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