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, Mohamed K. Mostafa
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 (Co) 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 (R2: 0.925) and pseudo-second-order (PSO) kinetic model (R2: 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.