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 R2 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.

中文翻译：

---

污水处理厂智能脱氮诊断与优化框架的开发与应用：低碳稳定运行


优化脱氮对于确保污水处理厂 （WWTP） 的高效运行至关重要，但它容易受到进水条件和运行参数限制的变化，并与节能和碳减排目标相冲突。针对这些问题，本研究提出了一种融合过程仿真、机器学习和多目标遗传算法的混合框架，用于脱氮诊断和优化，旨在预测出水中的总氮，诊断氮超限风险，并优化控制策略。以全尺寸污水处理厂为例，开发了过程时滞仿真增强机器学习模型 （PTLS-ML），训练集和测试集的 R2 值分别为 0.94 和 0.79。所提模型成功识别了不同进水条件和运行参数下氮气超限风险的潜在原因，并据此提供优化建议。此外，多目标优化 （MOO） 算法分析进一步表明，通过调整工艺运行参数来维持 4–6 mg/L 的污水总氮浓度可以有效平衡多个目标（即污水水质、运营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脱氮的稳定运行、节能减排提供参考。