Unveiling the role of artificial neural network in heavy metal detection and remediation

Heavy metals (HMs) are regarded as a significant environmental concern and are increasingly recognized as one of the most pressing environmental issues. These metals influence the air, soil, and groundwater and pose significant risks to living organisms particularly humans once they enter the food chain. Chemical precipitation, ion exchange, membrane separation, and electrochemical processes are a few conventional treatment methods that frequently have issues with cost, energy consumption, secondary pollution, and process inefficiency under challenging operating conditions. Because of its ease of use, affordability, and high removal efficiency, adsorption has become one of the most promising remediation techniques. Artificial intelligence (AI), especially artificial neural networks (ANNs), has drawn a lot of interest lately as a potent modeling and optimization tool for forecasting and improving heavy metal removal procedures. The removal of heavy metals from aquatic systems using ANN-based modeling techniques is thoroughly and critically evaluated in this paper. Training methods, performance evaluation measures, and the foundations of AI and ANN structures are all methodically covered. The use of ANN models to forecast the principal hazardous metals’ adsorption behavior with different adsorbents is examined critically and compared with conventional statistical and mechanistic models.