基于机器学习的全规模生态组合池城市污水处理厂强化脱氮优化

IF 12.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-06-14 DOI:10.1016/j.watres.2025.123976

Jinhu Yun , Yang Yu , Chenliang Tao , Mudi Zhai , Hongliang Zhang , Ying Chen , Hongjing Li , Bao Zhang , Jun Ma

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引用次数: 0

摘要

中国小城镇采用生态组合池（ECPs）的城市污水处理厂（WWTPs）在波动的进水水质条件下难以动态调整运行参数，导致曝气过度和外部碳源过量，以满足严格的总氮（TN）排放标准。为了解决这些问题，有必要建立一个适当的增强TN去除模型，用于具有ECPs的污水处理厂。在这项研究中，我们利用ECPs收集了一个全规模城市污水处理厂三年的运行数据，并实施了一种可解释的机器学习方法来预测和优化污水TN浓度。XGBoost模型的训练集和测试集的R2分别为0.997和0.911，RMSE分别为0.196和1.283。Shapley加性解释分析和部分依赖图确定了在平衡能量消耗和化学需氧量（COD）减少量的同时提高TN去除率的最佳操作参数。开发了一个图形用户界面，以促进对工艺操作参数的持续预测和协调优化，同时减少出水TN，能源消耗和外部碳源使用。出水TN浓度年均下降17.50%，COD投加量年均下降33.29%。因此，采用ECPs的污水处理厂显示出巨大的碳减排潜力。仅基于增强的TN去除，以及能耗和COD用量的降低，就可以计算出年总碳排放量减少（788.40 t CO2/y）。我们的研究结果提供了在不同水质压力下利用ECPs增强城市污水处理厂TN去除的最佳模型，从而满足严格的TN排放标准，并有助于实现节能和碳减排目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine learning-based optimization of enhanced nitrogen removal in a full-scale urban wastewater treatment plant with ecological combination ponds

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Machine learning-based optimization of enhanced nitrogen removal in a full-scale urban wastewater treatment plant with ecological combination ponds

Urban wastewater treatment plants (WWTPs) employing ecological combination ponds (ECPs) in small towns across China are struggling to dynamically adjust operating parameters under fluctuating influent quality conditions, resulting in excessive aeration and external carbon source overdosing to meet the stringent total nitrogen (TN) discharge standards. To address these issues, it is essential to establish an appropriate enhanced TN removal model for WWTPs with ECPs. In this study, we collected three years of operational data from a full-scale urban WWTP utilizing ECPs and implemented an interpretable machine learning approach to predict and optimize effluent TN concentration. The XGBoost model attained R² values of 0.997 and 0.911, RMSE values of 0.196 and 1.283 for the training and testing sets, respectively. The Shapley additive explanation analysis and partial dependence plots identified optimal operating parameters to improve TN removal while balancing energy consumption and chemical oxygen demand (COD) dosage reduction. A graphical user interface was developed to facilitate ongoing prediction and coordinated optimization of process operational parameters, achieving simultaneous reductions in effluent TN, energy consumption, and external carbon source usage. Notably, the effluent TN concentration decreased by 17.50 %, while COD dosage was reduced by 33.29 % annually. Consequently, WWTP with ECPs demonstrated substantial potential for carbon emission reduction. Total annual carbon emission reductions (788.40 t CO₂/y) were calculated solely based on enhanced TN removal, along with reductions in energy consumption and COD dosage. Our findings provide the optimal model for enhanced TN removal in urban WWTPs utilizing ECPs under variable influent quality pressure, thereby meeting stringent TN discharge standards and contributing to energy savings and carbon emission reduction goals.

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.