The Application of Artificial Intelligence (AI) in Adsorption Process of Heavy Metals: A Systematic Review

Abstract

The application of Artificial Intelligence (AI) has shown significant promise in optimizing adsorption processes for heavy metal removal, an essential component of water treatment plant (WTP) operations. This systematic review presents a comprehensive analysis of AI techniques applied to improve adsorption performance, focusing on machine learning (ML) and metaheuristic algorithms. AI models, such as neural networks and support vector machines, have been leveraged to analyze large datasets related to adsorption parameters, enhancing prediction accuracy and optimizing operational efficiency. Additionally, metaheuristic algorithms like Genetic Algorithms and Simulated Annealing contribute to efficient solution exploration, identifying optimal parameter configurations for the adsorption process. The integration of AI enables real-time monitoring, predictive maintenance, and dynamic adjustment of process parameters, thus ensuring the continuous improvement of adsorption efficiency. AI-based approaches also facilitate the identification of key adsorption features, allowing for precise control and improved resource utilization. Moreover, by combining AI with traditional adsorption models, such as Langmuir and Freundlich isotherms, this review explores new methods for improving adsorption kinetics and thermodynamics. The structured implementation of AI is demonstrated as a path forward in achieving sustainable, adaptive, and reliable solutions for water quality control. Future studies should prioritize the development of more advanced AI-driven predictive systems, enhancing the applicability of these methods across different adsorption contexts and pollutant types. This review underscores the transformative potential of AI in advancing adsorption technology, paving the way for smarter water treatment solutions that enhance environmental sustainability.