| 引用本文: | 马珍,叶程,魏卿,罗嘉程,王品,钟士发,楚文海,徐祖信.基于机器学习的加载絮凝效能优化与机理解析[J].哈尔滨工业大学学报,2026,58(6):1.DOI:10.11918/202509041 |
| MA Zhen,YE Cheng,WEI Qing,LUO Jiacheng,WANG Pin,ZHONG Shifa,CHU Wenhai,XU Zuxin.Machine learning-based efficiency optimization and mechanistic analysis of ballasted flocculation[J].Journal of Harbin Institute of Technology,2026,58(6):1.DOI:10.11918/202509041 |
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| 基于机器学习的加载絮凝效能优化与机理解析 |
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马珍1,2,叶程1,2,魏卿1,2,罗嘉程1,2,王品1,2,钟士发1,2,楚文海1,2,徐祖信1,2
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(1.同济大学 环境科学与工程学院,上海 200092; 2.水污染控制与资源绿色循环全国重点实验室(同济大学),上海 200092)
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| 摘要: |
| 利用加载絮凝技术强化溢流污水颗粒物去除,是控制雨天排水污染的关键手段。然而,传统优化方法依赖经验与单因素试验,难以系统揭示多参数间的复杂作用,导致工艺设计缺乏理论指导,运行效能不稳定。为此,引入机器学习方法,构建加载絮凝工艺的数据驱动优化与机制解析框架,以实现关键参数识别与澄清性能提升。通过整合580组试验数据,涵盖药剂特性、操作条件和介质特性等14个特征参数,构建随机森林（Random Forest,RF）、极端梯度提升（eXtreme Gradient Boosting,XGBoost）及轻量级梯度提升机（Light Gradient Boosting Machine,LightGBM）等多种机器学习模型预测加载絮凝的澄清性能。结果表明,LightGBM模型的预测准确性和泛化性能最佳（均方根误差ERMS为7.31%,R2=0.74）。可解释的SHAP分析揭示,运行参数对加载絮凝澄清效果的影响占主导作用（贡献度占38.6%）,显著高于介质参数和药剂参数,其中,沉淀时间与混凝强度为关键驱动因子。同时,结合水处理化学与动力学原理,阐明初始浊度与混凝强度之间的动态耦合效应,并揭示沉淀时间与介质相对密度之间存在显著的协同效应。溢流污水实验验证了经优化的参数组合可实现95%以上的颗粒物去除率,预测值与实测值高度吻合。本研究构建的“数据驱动建模—参数解释与推荐—实验验证”的框架,为加载絮凝工艺的精细化调控与智能决策提供了理论依据与方法支撑。 |
| 关键词: 加载絮凝 机器学习 溢流污水 轻量级梯度提升机 合流制系统 |
| DOI：10.11918/202509041 |
| 分类号:X703 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2021YFC0,1YFC3200702) |
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| Machine learning-based efficiency optimization and mechanistic analysis of ballasted flocculation |
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MA Zhen1,2,YE Cheng1,2,WEI Qing1,2,LUO Jiacheng1,2,WANG Pin1,2,ZHONG Shifa1,2,CHU Wenhai1,2,XU Zuxin1,2
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(1.College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; 2.State Key Laboratory of Water Pollution Control and Green Resource Recycling (Tongji University), Shanghai 200092, China)
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| Abstract: |
| Enhancing particle removal from combined sewer overflows using ballasted flocculation is a critical strategy for drainage pollution on rainy days. However, traditional optimization methods, which rely on empirical knowledge and one-factor-at-a-time experiments, struggle to systematically unravel the complex interactions among multiple parameters, resulting in a lack of theoretical guidance for process design and inconsistent operational performance. This study established a machine learning-based method for data-driven optimization and mechanistic interpretation of the ballasted flocculation process, aiming to identify key parameters and improve clarification efficiency. It integrated a comprehensive dataset of 580 experimental records, encompassing 14 feature parameters across reagent properties, operational conditions, and medium characteristics. Multiple machine learning models, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were developed to predict clarification efficiency. Results demonstrate that the LightGBM model achieves the best predictive accuracy and generalization capability, with a root mean square error (ERMS) of 7.31% and a coefficient of determination (R2) of 0.74. Interpretable shapley additive explanations analysis reveals that operational parameters contribute most significantly to clarification performance (contribution rate of 38.6%), substantially outperforming medium and reagent parameters. Settling time and coagulation velocity gradient are identified as the key drivers. Furthermore, by integrating water treatment chemistry and kinetic principles, the analysis elucidates the dynamic coupling effect between initial turbidity and coagulation velocity gradient and reveals a significant synergistic effect between settling time and specific gravity of media. Experimental verification using actual combined sewer overflows confirms that the optimized parameter combination achieves over 95% particulate removal, with strong agreement between predicted and actual values. This study establishes a research framework comprising "data-driven modeling, parameter interpretation and recommendation, and experimental validation", providing a theoretical basis and methodological support for the precise regulation and intelligent decision-making in ballasted flocculation. |
| Key words: ballasted flocculation Machine Learning combined sewer overflow Light Gradient Boosting Machine combined sewer system |
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