Data Driven Optimization of Industrial Runoff Treatment Systems

Abstract

Industrial runoff contains a complex mixture of pollutants, including heavy metals, nutrients, hydrocarbons, and suspended solids, which pose serious environmental and public health risks. Conventional treatment systems often operate under fixed design parameters and fail to adapt to dynamic variations in runoff quantity and quality. This study presents a data-driven optimization framework for improving the performance of industrial runoff treatment systems using advanced analytics and machine learning techniques. Models such as Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Networks (ANN) were applied to predict treatment efficiency and optimize operational parameters. Key variables, including flow rate, pollutant concentration, hydraulic retention time (HRT), and media composition, were analyzed. Results indicate that data driven optimization significantly enhances pollutant removal efficiency (up to 20–30%), reduces operational costs, and improves system adaptability. The integration of real-time monitoring with predictive analytics enables dynamic control and efficient resource utilization. The study highlights the potential of intelligent optimization frameworks for sustainable and resilient industrial water management.