ShenHao,XinKunlun.Advances and Prospects of Artificial Intelligence Applications in Biochemical Reaction Simulation for Municipal Wastewater Treatment Plants[J].China Water & Wastewater,2026,42(14):26-34.
面向智能化运行的污水处理厂生化反应模拟进展与挑战
- Title:
- Advances and Prospects of Artificial Intelligence Applications in Biochemical Reaction Simulation for Municipal Wastewater Treatment Plants
- Keywords:
- artificial intelligence; machine learning; deep learning; wastewater treatment plants; biological reaction simulation
- 摘要:
- 城镇污水处理厂智能化运行是水污染控制领域的关键发展方向。基于数据驱动的人工智能（AI）模型为污水处理生化反应模拟提供了新途径，突破了传统机理模型结构复杂、参数校准困难的限制。本文系统综述了AI方法在污水处理厂生化反应模拟中的进展，重点分析了传统机器学习与深度学习模型的性能特点与适用场景。结果表明，传统机器学习在小样本和连续流工艺中表现稳健，而深度学习凭借其强大的时序建模与特征提取能力，在动态性强、机理复杂的工艺中预测精度更高。当前研究仍面临模型可解释性不足、关键控制参数输入缺失、数据质量依赖性强等挑战。未来应推动机理与数据融合、构建面向控制的智能模型，以实现从预测到优化决策的跨越，为污水处理厂智能化、低碳化运行提供关键技术支撑。
- Abstract:
- The intelligent operation of municipal wastewater treatment plants (WWTPs) is a crucial development direction in the field of water pollution control. Data-driven artificial intelligence (AI) models offer a new approach for simulating biological reactions in wastewater treatment, overcoming the limitations of traditional mechanistic models, such as structural complexity and difficult parameter calibration. This paper systematically reviews the progress of AI methods in biological reaction simulation for WWTPs, with a focus on analyzing the performance characteristics and applicable scenarios of traditional machine learning and deep learning models. The results indicate that traditional machine learning performs robustly with small datasets and in continuous-flow processes, while deep learning, leveraging its powerful capabilities in temporal modelling and feature extraction, achieves higher prediction accuracy in processes with strong dynamics or complex mechanisms. Current research still faces challenges including poor model interpretability, lack of input of key control parameters, and strong dependence on data quality. Future efforts should promote the integration of mechanism and data, construct control-oriented intelligent models, and achieve the leap from prediction to optimized decision- making, thereby providing key technological support for the intelligent and low-carbon operation of wastewater treatment plants.
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