耗氯量机器学习预测模型对污水厂余氯监测频率的适应性OA

Currently,a number of wastewater treatment plants(WWTPs)in China still rely on manual residual chlorine monitoring.Due to the low monitoring frequency,precise control of disinfectant dosage remains challenging.This study initially selected WWTP A in the southwestern region,which is equipped with an online residual chlorine analyzer,as the research object.The effluent indicators(including water temperature,flow rate,NH3-N,CODCr,TP,and TN)along with the chlorine dosage were used as inputs to predict chlorine demand.These inputs were derived from high-frequency online monitoring data from WWTP A.A comparative analysis was conducted to evaluate the predictive performance of four machine learning models(i.e.,Backpropagation(BP)Neural Network,Long Short-Term Memory(LSTM),Random Forest(RF),and Support Vector Regression(SVR))in predicting chlorine demand at non-monitoring time points under different residual chlorine monitoring frequencies of once per 1,2,4,6,and 8 hours.The results indicated that the LSTM model achieved the highest prediction accuracy at a monitoring frequency of once per hour;the RF model exhibited superior performance at monitoring frequencies of once per 2 to 4 hours The BP model outperformed other models when the monitoring frequency dropped below once per 6 hours,and the SVR model consistently demonstrated the lowest accuracy across all monitoring frequencies.To ensure the robustness of the findings,additional validation was conducted using datasets from WWTP B and WWTP C,which relied on manual residual chlorine monitoring at frequencies of once per 6 hours and once per 8 hours,respectively.The results demonstrated that the BP model exhibited sustained optimal prediction performance under low-frequency residual chlorine monitoring conditions,and its prediction accuracy can be significantly improved via the optimization by the Particle Swarm Optimization(PSO)algorithm.This study provides a reference for selecting suitable machine learning models to predict chlorine demand under different residual chlorine monitoring frequencies,particularly in scenarios with low-frequency manual monitoring,thereby supporting the precise control of disinfectant dosing in WWTPs.