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 aqueous solutions, modeling predicting removal efficiency (R%) using factors. The prepared nZVAl characterized for elemental composition surface morphology texture. It depicted that, at an initial level (Co) 50 mg/L, dose 1.0 g/L, pH 5, mixing speed 150 rpm, 30 °C, R% 53.2 ± 2.4% within 10 min. data were well defined by Langmuir isotherm model (R2: 0.925) pseudo-second-order (PSO) kinetic 0.9957). best technique used predict neural network (ANN), followed support vector regression (SVR) linear (LR). high accuracy of ANN, with MSE < 10−5, suggested its applicability maximize removing large scale under operational conditions.