Machine Learning-Based Optimization of Energy Consumption in Ion-Exchange Wastewater Treatment Systems

Improving the energy efficiency of industrial wastewater treatment processes is a critical challenge driven by increasing operational costs and sustainability requirements. In ion-exchange-based wastewater treatment systems, the specific final energy use is primarily associated with pumping operations and hydraulic losses, which are strongly influenced by flow regulation strategies. Conventional control approaches typically operate under fixed or conservatively selected flow conditions to ensure treatment quality, often leading to excessive final energy use, especially under varying influent water quality. This study investigates the application of machine learning (ML) methods to support energy-efficient operation of an ion-exchange wastewater treatment system by identifying adaptive flow control strategies that balance treatment performance and energy use. A laboratory-scale experimental setup was developed, and a dataset of 300 operating samples was collected under varying conditions of water hardness, total dissolved solids (TDSs), and valve opening degree. The specific final energy use (kWh/m³), estimated from hydraulic operating parameters, was used as a key performance indicator. The dataset was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using R², RMSE, and MAE metrics. Extreme Gradient Boosting (XGBoost) was employed as the primary predictive model due to its robustness in handling nonlinear relationships and small-to-medium datasets, while Random Forest (RF) was used as a baseline for comparison. Hyperparameters of both models were tuned using cross-validation to improve generalization performance. The results demonstrate that ML models can accurately approximate the nonlinear relationship between water quality parameters, control actions, and energy use. XGBoost achieved higher predictive accuracy and stability compared to RF. Model-based analysis identified operating regions where treatment requirements are satisfied with reduced final energy use. Under representative operating scenarios, the proposed ML-assisted control strategy indicates a potential reduction of specific final energy use by approximately 10–15% without compromising treatment performance. These findings confirm the feasibility of integrating machine learning as a decision-support tool for energy-aware control in ion-exchange wastewater treatment systems and provide a basis for future implementation of real-time intelligent control frameworks.