| 引用本文: | 吴知静,江锦琦,毕鑫祺,王宗平,郭刚.基于机器学习的SANI工艺除碳脱氮性能预测与优化[J].哈尔滨工业大学学报,2026,58(6):120.DOI:10.11918/202510089 |
| WU Zhijing,JIANG Jinqi,BI Xinqi,WANG Zongping,GUO Gang.Prediction and optimization of carbon and nitrogen removal performance in SANI process based on machine learning[J].Journal of Harbin Institute of Technology,2026,58(6):120.DOI:10.11918/202510089 |
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| 基于机器学习的SANI工艺除碳脱氮性能预测与优化 |
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吴知静1,江锦琦1,2,毕鑫祺3,王宗平1,郭刚1
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(1.长江流域多介质污染协同控制湖北省重点实验室(华中科技大学),武汉 430074; 2.华中科技大学 人工智能研究院,武汉 430074;3.北京市城市规划设计研究院,北京 100045)
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| 摘要: |
| 硫酸盐还原硫自养反硝化硝化（SANI）工艺是一种具有污泥产量低、耐高温、耐盐等优势的新型污水生物脱氮处理技术。然而,该工艺内部的复杂微生物群落对运行参数极为敏感,导致系统性能易波动,亟需可靠的建模工具优化。传统机理模型构建繁琐且通用性有限,而机器学习方法又常受到小样本、数据不均衡及可解释性不足等限制。为此,提出一种融合生成对抗网络（GAN）与分阶段机器学习建模（包括极端梯度提升、人工神经网络和支持向量机算法）的数据驱动新方法,依据工艺生化机理构建厌氧硫酸盐还原和缺氧硫自养反硝化两阶段预测模型,并综合驯化与运行数据以拓展模型适用性。结果表明,该模型对COD与氮去除率的预测均方根误差（RMSE）分别低至6.8%与5.6%,展现出优异预测精度。可解释性分析进一步明确了工艺的关键环境因子及最优调控区间:进水COD 340～500 mg/L、进水硫酸盐（以S计）300～450 mg/L、厌氧出水硫化物（以S计）50～100 mg/L、厌氧工作容积5～6.5 L和水力停留时间6～8 h。本研究为SANI工艺的精准调控提供了可靠工具,更构建了一套可迁移、可扩展的“数据增强—分阶段建模—机理解释”融合范式,为复杂污水生物处理过程的智慧优化提供了全新的方法论基础。 |
| 关键词: 硫酸盐还原 硫自养反硝化 机器学习 生成对抗网络 可解释性 |
| DOI：10.11918/202510089 |
| 分类号:X703 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2023YFC3207203) |
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| Prediction and optimization of carbon and nitrogen removal performance in SANI process based on machine learning |
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WU Zhijing1,JIANG Jinqi1,2,BI Xinqi3,WANG Zongping1,GUO Gang1
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(1.Hubei Key Laboratory of Multi-Media Pollution Control in Yangtze Basin (Huazhong University of Science and Technology), Wuhan 430074, China; 2.Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China; 3.Beijing Municipal Institute of City Planning & Design, Beijing 100045, China)
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| Abstract: |
| The sulfate reduction-sulfur autotrophic denitrification-nitrification (SANI) process is a novel biological wastewater treatment technology for nitrogen removal, which has advantages such as low sludge yield, high temperature tolerance, and salt tolerance. However, the complex microbial communities within this process are extremely sensitive to operational parameters, leading to easy fluctuation of system performance, which urgently requires reliable modeling tools for optimization. Traditional mechanistic models are cumbersome to construct and have limited universality, while machine learning methods are often limited by small sample size, data imbalance, and insufficient interpretability. To this end, this paper proposed a new data-driven method integrating generative adversarial network (GAN) and staged machine learning modeling (including XGBoost, Artificial Neural Network, and Support Vector Machine algorithms), constructed two-stage prediction models for anaerobic sulfate reduction and anoxic sulfur autotrophic denitrification according to the biochemical mechanism of the process, and integrated acclimation and operation data to expand the applicability of the models. The results show that the root mean square error (RMSE) of the model for the prediction of COD and nitrogen removal rates are as low as 6.8% and 5.6%, respectively, exhibiting excellent prediction accuracy. The interpretability analysis further clarifies the key environmental factors and optimal control ranges of the process: influent COD of 340-500 mg/L, influent sulfate (as S) of 300-450 mg/L, anaerobic effluent sulfide (as S) of 50-100 mg/L, anaerobic working volume of 5-6.5 L, and hydraulic retention time of 6-8 h. This paper provides a reliable tool for the precise regulation of the SANI process and further constructs a transferable and scalable integration paradigm of "data augmentation, staged modeling, and mechanism interpretation", which provides a brand-new methodological foundation for the intelligent optimization of complex biological wastewater treatment processes. |
| Key words: sulfate reduction sulfur autotrophic denitrification machine learning generative adversarial network interpretability |
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