Lv Jiaqiang, Du Lili, Lin Hongyong, Wang Baogui, Yin Wanxin, Song Yunpeng, Chen Jiaji, Yang Jixian, Wang Aijie, Wang Hongcheng
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
Central Plains Environmental Protection Co., LCD., Zhengzhou 450000, China.
Bioresour Technol. 2024 Feb;393:130008. doi: 10.1016/j.biortech.2023.130008. Epub 2023 Nov 18.
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 (Ntot) and nitrate nitrogen (NO-N) 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 (R) of 97.43 % (Ntot) and 99.38 % (NO-N), demonstrating satisfactory generalization ability for predictions up to three days ahead (R >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.
精确预测污水处理厂（WWTPs）中氮基污染物的浓度，对于优化污水处理厂的运行调整而言，仍然是一项具有挑战性但至关重要的任务。在本研究中，采用了一种结合因子分析（FA）和机器学习（ML）模型的综合方法，以准确预测污水处理厂的出水总氮（Ntot）和硝酸盐氮（NO-N）浓度。通过因子分析对机器学习模型的输入值进行优化，从而显著提高了机器学习的预测准确性。该预测模型的最高决定系数（R）分别达到了97.43%（Ntot）和99.38%（NO-N），表明其对提前三天的预测具有令人满意的泛化能力（R>80%）。此外，可解释性分析表明，反硝化因子、污染物负荷因子和气象因子具有重要意义。本研究提出的数据模型框架为优化污水处理的运行和管理提供了有价值的参考。
