Trends and Future Directions on Machine Learning for Enhancing Optimal Methods of Heavy Metal Ion Removal from Industrial Wastewater

Gomaa, Ahmed M.; Gaber, Gaber A.; El Shafie, Merna; Hussein, Ahmed H.; Hamoud, Ahmed S.; Elgyar, Mohamed N.; Omran, Mahmoud S.; Hagag, Moaaz A.; Eltohamy, Zyad A.; El-Majeed, Salma A.

doi:10.21608/ijeaid.2025.396650.1002

	Trends and Future Directions on Machine Learning for Enhancing Optimal Methods of Heavy Metal Ion Removal from Industrial Wastewater
International Journal of Engineering & Artificial Intelligence Art Design
Volume 1, Issue 1, August 2025, Page 41-63 PDF (518.4 K)
Document Type: Review article
DOI: 10.21608/ijeaid.2025.396650.1002
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Authors
Ahmed M. Gomaa ¹; Gaber A. Gaber²; Merna El Shafie²; Ahmed H. Hussein²; Ahmed S. Hamoud²; Mohamed N. Elgyar²; Mahmoud S. Omran²; Moaaz A. Hagag²; Zyad A. Eltohamy²; Salma A. El-Majeed²
¹Assistant Professor at Department of Construction and Building Engineering, Faculty of Engineering and Technology, Egyptian Chinese University, Cairo, Egypt
²Department of Civil Engineering, The Higher Institute of Engineering and Technology Fifth Settlement, Egypt
Abstract
Heavy metal contamination in industrial wastewater is a pressing environmental and public health issue, necessitating the development of innovative and efficient remediation methods. Machine learning (ML) has emerged as a promising approach for optimizing existing treatment techniques, predicting system performance, and enhancing decision-making in the removal of heavy metal ions. This study employs a bibliometric analysis to investigate research trends, influential contributions, and future directions in the application of ML for optimizing heavy metal removal methods. By analyzing scholarly publications, citation networks, and keyword trends, we identify key advancements, leading researchers, and prominent institutions shaping this interdisciplinary field. The findings underscore the integration of ML algorithms, such as artificial neural networks (ANNs), support vector machines (SVMs), and deep learning, into various remediation methods, including adsorption, membrane filtration, and bioremediation. Additionally, this study highlights challenges such as data scarcity, model generalizability, and practical implementation, while exploring opportunities for hybrid approaches, big data analytics, and interdisciplinary collaboration. The insights from this bibliometric analysis provide a comprehensive understanding of the current research landscape and offer strategic guidance for advancing ML-driven solutions for the efficient and sustainable removal of heavy metal ions from industrial wastewater
Keywords
Machine Learning; Heavy metal ions; Bibliometric Analysis; Removal methods; Adsorption


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