WANGChenbo,WANGYue,YUHuarong,et al.Machine Learning-based Optimization of Electrocoagulation for Fluoride Removal from Groundwater[J].China Water & Wastewater,2026,42(11):16-25.
Machine Learning-based Optimization of Electrocoagulation for Fluoride Removal from Groundwater
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第42卷
Number:
第11期
Page:
16-25
Column:
Date of publication:
2026-06-01
- Keywords:
- groundwater; electrocoagulation; fluoride removal; machine learning; parameter optimization
- Abstract:
- This study presented a machine learning?based framework for optimizing the electrocoagulation (EC) process parameters to efficiently remove fluoride in groundwater. By integrating 204 experimental data points collected from the literature, a multidimensional dataset encompassing reaction conditions and treatment results was constructed. The performance of six typical machine learning algorithms was systematically compared. The results showed that the XGBoost model performed the best in the dual prediction task of residual fluoride concentration and energy consumption (R2test=0.76-0.89, MSEtest≤0.004 7), outperforming KNN, SVR, RF, LightGBM, and MLP. Feature importance analysis based on SHAP revealed that initial fluoride concentration, electrolysis time, and current density were the key factors determining fluoride removal efficiency, while electrolysis time,current density, and anode area dominated energy consumption levels. Furthermore, the well-trained XGBoost model was integrated into a non-dominated sorting genetic algorithm (NSGA-Ⅱ) for multi-objective optimization of removal rate maximization-energy consumption minimization,yielding multiple Pareto front solutions. Experimental validation showed that the relative error between model predictions and actual measurements ranged from 10% to 25%. This framework effectively demonstrates the potential of data-driven reverse design, providing new insights for the intelligent design of electrochemical water treatment processes, improving fluoride removal efficiency.
Last Update:
2026-06-01