Abstract：
Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.
摘要：
精确预测污水处理厂（WWTP）中氮基污染物的浓度对于优化WWTP的运营调整仍然是一项具有挑战性但至关重要的任务。在这项研究中,采用因子分析（FA）和机器学习（ML）模型的集成方法来准确预测污水处理厂的总氮（Ntoteff）和硝酸盐氮（NO3-Neff）浓度。通过FA磨练ML模型的输入值，以优化因素，从而显著提高了ML预测精度。预测模型实现了97.43%(Ntoteff)和99.38%(NO3-Neff)的最高决定系数(R2),对提前三天的预测表现出令人满意的泛化能力(R2>80%)。此外，可解释性分析确定了反硝化因子，污染物负荷系数，气象因子显著。本研究提出的模型框架为优化污水处理运行管理提供了有价值的参考。
