Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques
| Autor: | Mahmoud Nasr, Mohamed Mostafa, Ahmed H. Sadek, Omar Fahmy |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Sustainability Volume 15 Issue 3 Pages: 2081 |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su15032081 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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