Machine Learning-Based Prediction of Effluent Quality in Industrial Wastewater Treatment Plants Using Process Sensor Data

Abstract

Accurate prediction of effluent quality is essential for maintaining regulatory compliance, optimizing treatment efficiency, and ensuring sustainable operation of industrial wastewater treatment plants. Conventional monitoring approaches often struggle to respond effectively to dynamic variations in influent characteristics and process conditions, leading to operational inefficiencies and inconsistent treatment performance. This study presents a machine learning-based framework for predicting effluent quality in industrial wastewater treatment plants using real-time process sensor data. The proposed system integrates advanced data analytics, intelligent predictive modeling, and continuous sensor monitoring to estimate critical effluent quality parameters under varying operational conditions. Process variables including pH, temperature, dissolved oxygen, flow rate, chemical oxygen demand, biochemical oxygen demand, turbidity, conductivity, and suspended solids concentration were continuously collected and utilized for model training and validation. Multiple machine learning algorithms were employed to identify nonlinear relationships between treatment process conditions and effluent quality indicators, enabling accurate prediction of plant performance and contaminant removal efficiency. Experimental and simulation analyses demonstrated that the proposed predictive models achieved high forecasting accuracy and improved adaptability compared to conventional empirical monitoring approaches. The machine learning framework also enhanced operational decision-making by enabling early detection of treatment disturbances, process anomalies, and potential compliance violations.