Nanomaterial-Enhanced Machine Learning Framework for Wastewater Treatment Optimization: A Deep Hybrid CNN-LSTM Approach with Explainable AI

Abstract

Nanomaterials for water/wastewater treatment are among the most important applications in environmental science, but traditional monitoring and optimization methods have substantial limitations in predicting treatment efficiency under different pollutant loads and dynamic environmental conditions. This paper proposes a new real-time optimization framework based oneXplainable Artificial Intelligence (XAI) with the integration of Deep Hybrid Convolutional Neural Network–LongShort-Term Memory (CNN-LSTM) for nanomaterial-based wastewater treatment systems. The model that we propose in this research utilizes multi-sensor temporal data such as pH, turbidity, chemical oxygen demand (COD), biological oxygen demand (BOD), heavy metal concentrations and parameters regarding the dosage of nanomaterials to predict pollutant removal efficiency with an unprecedented level of specificity.
The model includes a newly integrated Adaptive Nanomaterial Dosage Controller (ANDC) mechanism, which adjusts TiO2 and ZnO nanoparticle concentrations dynamically through real-time predictions to achieve 97.8% COD removal efficiency and 98.3% accuracy in heavy metal sequestration. The experimental validation results obtained using three real industrial wastewater datasets showed that the proposed hybrid framework outperforms all existing methods leading to RMSE of 0.021 and R2=0.991. SHAP analysis shows that nanomaterial concentration and hydraulic retention time are the major factors. The system offers actionable intelligence for plant operators and is a step toward intelligent, autonomous wastewater treatment infrastructure.