AI-Assisted Prediction of Environmental Pollutant Removal Using Advanced Materials

This study presented an AI-assisted framework for predicting environmental pollutant-removal efficiency using advanced materials. The framework integrates material properties, pollutant characteristics, and operating parameters with machine-learning models to capture the complex nonlinear behaviour of pollutant-removal processes. The analysis indicates that parameters such as specific surface area, material dosage, contact time, initial pollutant concentration, pore characteristics, and pH are important for predicting treatment performance. Explainable-AI approaches further improve model transparency by identifying the relative influence of these parameters [1][2]. The proposed approach can support advanced-material screening, treatment-condition optimization, and reduction of experimental trial-and-error. Integration with real-time monitoring, IoT sensors, and larger experimental datasets could further extend the framework toward intelligent and sustainable wastewater-treatment systems [3][4]. Overall, AI-assisted prediction provides a promising data-driven approach for improving the efficiency, optimization, and sustainability of environmental pollutant-removal processes.  

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