The removal of residual free chlorine ions from industrial wastewater is a critical step toward achieving sustainable and environmentally compliant water reuse. Excess chlorine in sludge collector water causes corrosion of process equipment, inhibits biological treatment, and leads to toxic discharge effects. In this study, an intelligent control strategy was developed for an ion-exchange-based dechlorination process to dynamically regulate chlorine concentration in the effluent stream. A pilot-scale ion-exchange filtration unit, designed with a nominal capacity of 500 L h⁻¹, was constructed using a strong-base anion-exchange resin to selectively adsorb chloride and free chlorine ions. A total of 200 experimental observations were obtained to characterize the nonlinear relationship between inlet flow rate and outlet chlorine concentration under varying operational conditions. Based on these experimental data, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed in MATLABR2025 to simulate and control the ion-exchange process. Two model-optimization techniques, Grid Partition + Hybrid and Subtractive Clustering + Hybrid, were applied. The subtractive clustering approach demonstrated faster convergence and superior accuracy, achieving RMSE = 0.147 mg L⁻¹, MAE = 0.101 mg L⁻¹, and R² = 0.993, outperforming the grid-partition model (RMSE ≈ 0.29, R² ≈ 0.97). The resulting ANFIS model was subsequently integrated into a MATLAB/Simulink-based intelligent control system for real-time regulation of chlorine concentration. A comparative dynamic simulation was performed between the proposed ANFIS controller and a conventional PID (Proportional-Differential-Integral) controller. The results revealed that the ANFIS controller achieved a faster response (rise time ≈ 28 s), lower overshoot (≈6%), and shorter settling time (≈90 s) compared to the PID controller (rise time ≈ 35 s, overshoot ≈ 18%, settling time ≈ 120 s). These improvements demonstrate the ability of the proposed model to adapt to nonlinear process behavior and to maintain stable operation under varying flow conditions.

Ion-exchange-based wastewater treatment processes exhibit nonlinear and time-varying dynamics, making the control of total dissolved solids (TDS) and water hardness a complex task. Conventional Proportional–Integral–Derivative (PID) controllers often show limited performance under such conditions due to fixed tuning parameters and linear assumptions. To address these limitations, this study presents a comparative evaluation of traditional and intelligent control strategies for regulating TDS and water hardness through influent flow control. A classical PID controller is compared with fuzzy logic and Adaptive neuro-fuzzy inference system (ANFIS) controllers using a unified MATLAB/Simulink simulation framework. The control performance is evaluated based on dynamic response characteristics, including rise time, settling time, and overshoot. For TDS control, the PID controller exhibits a rise time of 15.9 s and a settling time of 50.9 s, while the fuzzy logic controller improves the response with a rise time of 13.6 s and settling time of 44.1 s. The ANFIS controller achieves the fastest response, with a rise time of 8.31 s and a settling time of 27.1 s. Similar trends are observed for water hardness control, where the PID controller shows a rise time of 17.0 s and settling time of 55.8 s, the fuzzy logic controller reduces these values to 12.3 s and 40.4 s, respectively, and the ANFIS controller further improves performance with a rise time of 9.23 s and settling time of 30.3 s. The overshoot values for all controllers remain comparable, within the range of approximately 4.4–5.0%. The results clearly demonstrate that intelligent control strategies, particularly ANFIS, provide significantly faster convergence and improved dynamic performance compared to conventional PID control. The reduced settling time implies lower control effort and decreased energy consumption, highlighting the potential of intelligent controllers for efficient and reliable industrial wastewater treatment applications.

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.

The effective removal of free chlorine ions from industrial wastewater is critical to ensure process safety, minimize equipment corrosion, and protect aquatic environments. In this study, a laboratory-scale activated carbon filtration system was developed and tested to treat chlorine-contaminated water collected from sludge collectors at industrial facilities. A total of 200 experimental trials were conducted under varied operational conditions, including changes in flow rate, initial chlorine concentration, pressure, pH, temperature, and activated carbon dosage. The resulting dataset was used to train a predictive model based on a feedforward backpropagation artificial neural network (ANN) implemented in MATLAB. The ANN model demonstrated excellent performance, achieving a mean squared error (MSE) of 9.63 × 10⁻⁵ and a high regression coefficient (R = 0.9665) across all data. The model’s predictive capability was validated through detailed performance plots, error histograms, and regression analysis. The results confirm that the ANN can reliably estimate residual chlorine concentrations based on key process variables, making it a valuable tool for optimizing activated carbon filter operations and enhancing intelligent control in industrial wastewater treatment systems. This research highlights the potential of integrating AI-based modeling with traditional water purification technologies to improve process efficiency and environmental sustainability.