Molecular simulation and ANN modelling for Cadmium (Cd) and Lead (Pb) adsorption from water using zeolites

Noor, e Hira and Serene Lock, Sow Mun and Lim, Lam Ghai and Ushtar, Arshad and Mehtab, Ali Darban and Abid Salam, Farooqi and Suhaib Umer, Ilyas and Yiin, Chung Loong (2025) Molecular simulation and ANN modelling for Cadmium (Cd) and Lead (Pb) adsorption from water using zeolites. Results in Engineering, 25. pp. 1-23. ISSN 2590-1230

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Abstract

Heavy metal contamination in water threatens health and ecosystems, driving the search for effective, eco-friendly removal techniques. Zeolites show great potential for extracting heavy metals from water. Understanding the structure and chemistry of zeolites at the atomistic level is crucial for designing water treatment processes. In this work, 24 zeolites were evaluated using computational chemistry approach to find the most efficient adsorbents, thereby avoiding experimental hurdles. In this context, a computational framework for molecular simulation employing Monte Carlo and Molecular Dynamics had been utilized to study Cadmium (Cd) and Lead (Pb) adsorption capacities of zeolites. Simulation calculation was performed at pH 6, temperature 24.85 °C, and 101.3 kPa pressure. The most effective adsorbents for Cd removal were LTA and FAU with adsorption capacities of 212.5 and 199.9 mg/g. On the other hand, CLO and FAU were most efficient for Pb removal with adsorption capacities of 489.5 and 420.6 mg/g, respectively. The effects of pH (1 to 14), temperature (8 to 68 °C), and pressure (100 to 350 kPa) were examined for Cd removal using LTA and for Pb removal using CLO zeolite. Moreover, an artificial neural network (ANN) model was developed for CLO and LTA with R2 values during training, validation, and test phases 0.9978, 0.9842, 0.9854 for Pb adsorption (for CLO) and 0.9797, 0.9424, 0.9876 (for LTA), respectively. Incorporating molecular simulation and ANN in the field of water treatment can serve industries without delay and less expenses, while supporting the potential integration of machine learning into water treatment applications.

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