Chen Zhichi, Cheng Hong, Wang Xinge, Chen Bowen, Chen Yao, Cai Ran, Zhang Gongliang, Song Chenxin, He Qiang
Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China.
Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China.
Water Res. 2024 Nov 15;266:122337. doi: 10.1016/j.watres.2024.122337. Epub 2024 Aug 30.
Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4-6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.
优化氮去除对于确保污水处理厂（WWTPs）的高效运行至关重要，但它易受进水条件变化和运行参数约束的影响，并且与节能和碳排放减少目标存在冲突。为了解决这些问题，本研究提出了一个集成过程模拟、机器学习和多目标遗传算法的混合框架，用于氮去除诊断和优化，旨在预测出水总氮、诊断氮超标风险并优化控制策略。以一座全尺寸污水处理厂为例，开发了一种过程时滞模拟增强型机器学习模型（PTLS-ML），训练集和测试集的R值分别达到0.94和0.79。所提出的模型成功识别了不同进水条件和运行参数下氮超标风险的潜在原因，并据此提供了优化建议。此外，多目标优化（MOO）算法分析进一步表明，通过调整过程运行参数将出水总氮浓度维持在4-6mg/L可以有效平衡多个目标（即出水水质、运行成本和温室气体排放），实现协同优化。该框架可为污水处理厂氮去除的稳定运行、节能和减排提供参考。
