Machine learning in Industrial Wastewater Treatment: A Bibliometric Analysis of Research Trends and Future Prospects

El Shafie, Merna; El Shahawy, Abeer; Shaltout, Amina; Mubarak, Mahmoud F.; Nasr, Mahmoud

doi:10.21608/sceee.2025.374216.1072

Machine learning in Industrial Wastewater Treatment: A Bibliometric Analysis of Research Trends and Future Prospects

Document Type : Original Article

Authors

¹ civil engineering department ,faculty of engineering ,suez canal university, ismailia

² Suez canal university, faculty of engineering, department of civil engineering

³ Department of Civil Engineering, Faculty of Engineering, Suez Canal University, PO Box 41522, Ismailia, Egypt

⁴ Petroleum Applications Department, Egyptian Petroleum Research Institute (EPRI),1 Ahmed El Zomor St. Nasr City, Cairo, 11727, Egypt,

⁵ Sanitary Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt,

10.21608/sceee.2025.374216.1072

Abstract

Industrial wastewater treatment is a critical area of environmental research and engineering, with increasing attention on advanced technologies to enhance efficiency and sustainability. Among these technologies, machine learning (ML) has emerged as a powerful tool for optimizing treatment processes, predicting contaminant behavior, and improving decision-making in wastewater management. This study conducts a bibliometric analysis to examine research trends, key contributions, and future directions in the application of machine learning for industrial wastewater treatment. By analyzing scholarly publications, citation patterns, and keyword occurrences, we identify the most influential studies, prominent authors, and leading research institutions driving innovation in this field. The findings highlight the growing integration of ML techniques, such as artificial neural networks (ANNs), support vector machines (SVMs), and deep learning, in contaminant removal, process optimization, and real-time monitoring. Furthermore, this study discusses the challenges and future opportunities for expanding the role of ML in industrial wastewater treatment, emphasizing the need for data-driven models, hybrid approaches, and real-world applications. The insights derived from this bibliometric analysis provide a comprehensive understanding of the current research landscape and pave the way for future advancements in AI-driven wastewater treatment solutions.

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