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

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 degrees 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.

Automated summary

Predicting the removal efficiency of Cu(II) ions using adsorption factors, this research tested nano zero-valent aluminum (nZVAl) for removing Cu(II) from contaminated water. The study found that the optimal conditions for Cu(II) removal were an initial Cu(II) concentration of 50 mg/L, nZVAl dose of 1.0 g/L, pH 5, mixing speed of 150 rpm, and 30 degrees C, resulting in a removal efficiency of 53.2 +/- 2.4% within 10 minutes. The adsorption data fit well with the Langmuir isotherm model (R-2: 0.925) and pseudo-second-order kinetic model (R-2: 0.9957). Artificial neural network (ANN) was the best modeling technique for predicting removal efficiency.