污泥厌氧消化领域机器学习的主要应用：从过程优化到智能决策

宋鑫; 曹依航; 张驰; 罗景阳

doi:10.13205/j.hjgc.202607022

污泥厌氧消化领域机器学习的主要应用：从过程优化到智能决策

doi: 10.13205/j.hjgc.202607022

宋鑫^1,2,
曹依航^1,2,
张驰^1,2,
罗景阳^1,2, ,

1. 河海大学浅水湖泊综合治理与资源开发教育部重点实验室, 南京 210098;
2. 河海大学环境学院, 南京 210098

基金项目:

江苏省基础研究计划（BK20250189）

详细信息

作者简介:
宋鑫（2005—），女，主要研究方向为有机废物的资源化利用。2314040103@hhu.edu.cn

通讯作者:
罗景阳（1989—），男，博士，教授，博士生导师，主要研究方向为有毒有害污染物控制理论与技术及有机废物资源化利用。luojy2016@hhu.edu.cn

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出版历程
- 收稿日期: 2026-03-31
- 网络出版日期: 2026-09-01

Main applications of machine learning in sludge anaerobic digestion： from process optimization to intelligent decision-making

SONG Xin^1,2,
CAO Yihang^1,2,
ZHANG Chi^1,2,
LUO Jingyang^{1,2
, ,}

1. Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of the Ministry of Education, Hohai University, Nanjing 210098, China;
2. College of Environment, Hohai University, Nanjing 210098, China

摘要

摘要: 污泥厌氧消化是实现污水厂能源回收与污泥减量化的核心工艺，但其复杂的生物反应机制和多变量耦合特性给过程优化与稳定控制带来了持续挑战。传统机理模型虽机理清晰，却在处理动态工况和非线性关系时存在参数标定困难、适应性不足等局限。近年来，机器学习因其对复杂数据非线性关系的建模能力，在污泥消化领域受到广泛关注。系统梳理了机器学习在污泥厌氧消化产气性能预测、过程监测与预警、工艺参数优化等方面的主要应用。在产气预测领域，混合模型与深度学习方法实现了对甲烷产量的高精度预测；在过程监测方面，基于易测参数的软测量模型可实现对挥发性脂肪酸、总氨氮等关键指标的实时感知；在工艺优化层面，代理模型与优化算法的耦合为共消化配比和预处理条件提供了动态调控策略。此外，可解释性方法的引入为解决模型“黑箱”问题，增强模型工程可接受性提供了技术支撑，其与工艺参数动态优化深度融合，进而构建智能决策框架具有重要的工程价值。然而，已有研究从实验室向工程应用的转化仍面临数据质量、模型泛化及工程落地等多重制约因素。该文章旨在为污泥处理工艺的优化调控提供兼具预测能力与工程可信度的分析框架。
- 机器学习 /
- 厌氧消化 /
- 污泥处理 /
- 过程优化 /
- 可解释性分析
Abstract: Anaerobic sludge digestion is the core process for achieving energy recovery and sludge reduction in wastewater treatment plants. However， its complex biological reaction mechanisms and multivariable coupling characteristics pose persistent challenges to process optimization and stable control. Although traditional mechanistic models have clear theoretical foundations， they have limitations such as the difficulty in parameter calibration and insufficient adaptability when addressing dynamic conditions and nonlinear relationships. In recent years， machine learning has attracted extensive attention in the field of sludge digestion due to its powerful data modeling capabilities. This paper systematically reviews the main applications of machine learning in the performance prediction， process monitoring and early warning， and process parameter optimization of sludge anaerobic digestion. For gas production prediction， hybrid models and deep learning methods have achieved high-precision methane prediction. For process monitoring， soft-sensing models using easy-to-measure parameters facilitate real-time estimation of key indicators such as volatile fatty acids and total ammonia nitrogen. At the level of process optimization， the integration of the surrogate models with optimization algorithms offers dynamic regulation strategies for co-digestion ratios and pretreatment conditions. In addition， the incorporation of interpretable methods provides a technical path for addressing the model "black-box" problem and enhances operators' acceptability of applying models in engineering. The deep integration of these methods with dynamic optimization of process parameters enables the construction of an intelligent decision-making framework， which has significant engineering value. However， the transformation of these research findings from laboratory to engineering application is still hindered by multiple constraints， including data quality， model generalization， and engineering implementation. This paper provides an analytical framework for the optimization and regulation of sludge treatment processes that combines predictive capability with engineering reliability.
- machine learning /
- anaerobic digestion /
- sludge treatment /
- process optimization /
- interpretable artificial intelligence

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参考文献(49)

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污泥厌氧消化领域机器学习的主要应用：从过程优化到智能决策

doi: 10.13205/j.hjgc.202607022

作者简介:
宋鑫（2005—），女，主要研究方向为有机废物的资源化利用。2314040103@hhu.edu.cn

通讯作者:
罗景阳（1989—），男，博士，教授，博士生导师，主要研究方向为有毒有害污染物控制理论与技术及有机废物资源化利用。luojy2016@hhu.edu.cn

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Main applications of machine learning in sludge anaerobic digestion： from process optimization to intelligent decision-making

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污泥厌氧消化领域机器学习的主要应用：从过程优化到智能决策

doi: 10.13205/j.hjgc.202607022

作者简介: 宋鑫（2005—），女，主要研究方向为有机废物的资源化利用。2314040103@hhu.edu.cn

通讯作者: 罗景阳（1989—），男，博士，教授，博士生导师，主要研究方向为有毒有害污染物控制理论与技术及有机废物资源化利用。luojy2016@hhu.edu.cn

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Main applications of machine learning in sludge anaerobic digestion： from process optimization to intelligent decision-making

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出版历程

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期刊在线

作者中心

友情链接

作者简介:
宋鑫（2005—），女，主要研究方向为有机废物的资源化利用。2314040103@hhu.edu.cn

通讯作者:
罗景阳（1989—），男，博士，教授，博士生导师，主要研究方向为有毒有害污染物控制理论与技术及有机废物资源化利用。luojy2016@hhu.edu.cn