An Intelligent Prediction–Optimization Framework for Free Chlorine Removal from Industrial Wastewater Using Activated Carbon Filtration

Free chlorine removal from industrial wastewater using activated carbon filtration requires accurate modeling and optimal control to balance treatment efficiency and adsorbent consumption. In this study, a combined experimental–machine learning–optimization framework was developed to predict and optimize residual chlorine concentration in a pilot-scale activated carbon filtration unit. A total of 200 experimental runs were collected using a pilot activated carbon filtration system by varying flow rate, initial chlorine concentration, pressure, pH, temperature, and carbon dose. Two ensemble learning models, Random Forest (RF) and Gradient Boosting (GB), were trained and validated using five-fold cross-validation. Both models exhibited high predictive accuracy, with GB outperforming RF on the full dataset (R2 = 0.9995, Root Mean Square Error (RMSE) = 0.0355 mg·L−1, Mean Absolute Error (MAE) = 0.0276 mg·L−1) and on the independent test set (R2 = 0.9417). Feature importance and partial dependence analyses revealed that the initial chlorine concentration and activated carbon dose were the dominant controlling variables, while increasing flow rate led to higher residual chlorine levels. A multi-objective optimization strategy based on Pareto dominance was implemented using the trained GB model as a surrogate to simultaneously minimize residual chlorine and carbon consumption. The optimal compromise solution corresponded to an activated carbon dose of approximately 51.5 kg and a residual chlorine concentration of 0.156 mg·L−1 at a flow rate of 43.1 m3·h−1. The proposed framework demonstrates a reliable and cost-effective approach for predictive control and sustainable optimization of dechlorination processes in industrial wastewater treatment.

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