BAIYu-xin,TIANLi,WANGQing-jiao,et al.Early Warning and Risk Control for Total Nitrogen Exceeding Discharge Standard in Wastewater Treatment Plants Based on Machine Learning[J].China Water & Wastewater,2025,41(19):109-115.
基于机器学习的污水厂总氮超标预警及风险控制
- Title:
- Early Warning and Risk Control for Total Nitrogen Exceeding Discharge Standard in Wastewater Treatment Plants Based on Machine Learning
- Keywords:
- wastewater treatment plant; grey correlation analysis; machine learning; critical value exceeding the discharge standard; prediction of effluent total nitrogen
- 摘要:
- 总氮是衡量河流水质的关键指标之一，控制污水处理厂出水总氮对确保河道水质具有重要意义。为准确预测污水厂出水总氮，首先收集了深圳市某污水厂5 108条在线监测运行数据，采用灰色关联度分析法选取7项关联度≥0.5的指标；然后利用支持向量机、XGBoost和LightGBM 三种机器学习模型预测出水总氮，其中LightGBM为拟合优度最好、预测精度最高的算法；最后通过分析生产数据，找出影响总氮超标关键指标预警值，即进水C/N≤6、DO≤1.6 mg/L或DO>2.2 mg/L、MLSS≤4 500 mg/L，将其与预测模型结合，助力出水总氮稳定达标。
- Abstract:
- Total nitrogen (TN) is a critical parameter for assessing river water quality. Regulating the TN in the effluent from wastewater treatment plants is of significant importance for effective river water quality management. This study collected a dataset comprising 5 108 instances of online monitoring operational data from a wastewater treatment plant in Shenzhen, and selected seven indicators with a correlation degree equal or greater than 0.5 using grey correlation analysis, to accurately predict the TN in the effluent of a wastewater treatment plant. Subsequently, three machine learning models—specifically, the support vector machine (SVM), XGBoost, and LightGBM—were employed to predict the TN in the effluent. Among these models, LightGBM demonstrated the best goodness of fit and achieved the highest prediction accuracy. Finally, key indicator warning values affecting the TN exceeding the discharge standard were identified through the analysis of production data. These included an influent C/N ratio equal or lesser than 6, dissolved oxygen equal or lesser than 1.6 mg/L or DO greater 2.2 mg/L, and mixed liquor suspended solids (MLSS) equal or lesser than 4 500 mg/L. The effluent TN was effectively controlledand maintained within the limit specified in the discharge standard by integrating these parameters with the prediction model.
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