Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater

Heavy metal pollution is becoming an increasingly serious environmental and public health concern. As a result, developing efficient, cost-effective, and sustainable wastewater treatment technologies has become a pressing challenge. Adsorption technology has emerged as a promising solution due to its flexibility, simplicity, and economic advantages. However, conventional adsorbent design methods rely heavily on empirical approaches and trial-and-error experimentation, leading to inefficiencies and limitations. In recent years, machine learning (ML) has emerged as a powerful tool for accelerating material discovery and optimizing adsorption processes. This review provides a comprehensive analysis of ML applications in adsorbent material design, focusing on key algorithms, data-driven methodologies, and their role in improving adsorption performance. It explores innovative ML applications in developing high-performance adsorbents, emphasizing relevant data sources, feature engineering techniques, and preprocessing strategies critical for model accuracy. Additionally, we discuss the major challenges and limitations of integrating ML into adsorption studies, including data scarcity, model interpretability, and computational costs. Finally, we outline future research directions to advance the tailored design of adsorbent materials and foster the sustainable development of wastewater treatment technologies. • ML enables efficient adsorbent design for heavy metal removal from wastewater. • Feature engineering and preprocessing improve ML accuracy in adsorption studies. • Hybrid experimental-simulation datasets address data scarcity in material discovery. • ML-driven optimization enhances cost-efficiency in adsorption-based water treatment. • Interpretable ML models and open data promote sustainable wastewater solutions.