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电敏感区法结合机器学习实现水处理中纯颗粒物种类识别

蒲艺涛,  肖康,  王晓东,  杨茹月,  王宇轩,  柯水洲,  高静思

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文章导航 > 环境工程  > 2025 >  43(11): 1-10
蒲艺涛, 肖康, 王晓东, 杨茹月, 王宇轩, 柯水洲, 高静思. 电敏感区法结合机器学习实现水处理中纯颗粒物种类识别[J]. 环境工程, 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001
引用本文: 蒲艺涛, 肖康, 王晓东, 杨茹月, 王宇轩, 柯水洲, 高静思. 电敏感区法结合机器学习实现水处理中纯颗粒物种类识别[J]. 环境工程, 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001
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PU Yitao, XIAO Kang, WANG Xiaodong, YANG Ruyue, WANG Yuxuan, KE Shuizhou, GAO Jingsi. Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001
Citation: PU Yitao, XIAO Kang, WANG Xiaodong, YANG Ruyue, WANG Yuxuan, KE Shuizhou, GAO Jingsi. Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001
shu

电敏感区法结合机器学习实现水处理中纯颗粒物种类识别

doi: 10.13205/j.hjgc.202511001

  • 1. 湖南大学 土木工程学院, 长沙 410082;

  • 2. 深圳职业技术大学 材料与环境工程学院, 广东 深圳 518055;

  • 3. 中国科学院大学 资源与环境学院, 北京 101408;

  • 4. 北京燕山地球关键带国家野外科学观测研究站, 北京 101408;

  • 5. 中国科学院大学 材料科学与光电技术学院, 北京 101408;

  • 6. 中国科学院大学 中丹学院, 北京 101408

基金项目: 

深圳职业技术学院重点研发项目（6022310008K）；中央高校基本科研业务费专项资金资助（E2EG0502X2）

详细信息
    作者简介:

    蒲艺涛（1998—），男，硕士研究生，主要研究方向为粒径监测和机器学习。3492648685@qq.com

    通讯作者:

    柯水洲，ksz@hnu.edu.cn；高静思，gaojingsi@szpt.edu.cn

    柯水洲，ksz@hnu.edu.cn；高静思，gaojingsi@szpt.edu.cn

Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning

  • 1. College of Civil Engineering, Hunan University, Changsha 410082, China;

  • 2. College of Materials and Environmental Engineering, Shenzhen Polytechnic, Shenzhen 518055, China;

  • 3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China;

  • 4. Beijing Yanshan Earth Critical Zone National Research Station, Beijing 101408, China;

  • 5. College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101408, China;

  • 6. Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China

    摘要
  • 摘要: 水处理过程中存在多类颗粒物，准确检测其浓度和粒径分布对保障水厂运行和环境监测至关重要。然而，实际环境中各类颗粒物常相互混杂，现有检测方法难以实现混合颗粒体系中的精准检测。为实现水厂智能化监测需求，研究提出一种结合电敏感区法（ESZ）与机器学习（ML）的方法，首次实现了水处理中典型纯物质颗粒物的高精度分类识别。通过优化ESZ采集条件，确定3 mL/min抽吸速度和300 r/min搅拌速度为最佳参数，并从理论上分析了ESZ信号的形成机制，发现其波形受流体速度、颗粒直径、密度及形状等多因素影响。基于ESZ构建了纯物质颗粒数据库，通过对比多种机器学习分类算法，最终采用支持向量机（SVM）模型。该模型对气泡、石英砂、聚对苯二甲酸乙二醇酯（PET）和草履虫4类纯物质颗粒的识别准确率达95.3%。其中，气泡与石英砂识别效果优异，但PET与草履虫存在一定混淆，需进一步优化算法。该研究为水处理工艺中颗粒物识别提供了新思路，未来可通过混合体系验证拓展应用，为水处理监测的精准化与智能化提供技术支持。
    Abstract: The accurate detection of particle concentration and size distribution is crucial for ensuring the efficient operation of water treatment plants and environmental monitoring. However， in practical environments， various particles are often mixed， making it challenging for existing detection methods to achieve precise measurements in complex particle systems. To address the need for intelligent monitoring in water treatment plants， this study proposed an approach that integrated the electrical sensing zone （ESZ） method with machine learning （ML）， achieving high-precision classification and identification of typical pure-substance particles in water treatment for the first time. By optimizing ESZ acquisition conditions， the optimal parameters were determined as a suction speed of 3 mL/min and a stirring speed of 300 r/min. The formation mechanism of ESZ signals was theoretically analyzed， revealing that multiple factors exist， including fluid velocity， particle diameter， density， and shape， influence waveforms. A pure-substance particle database was constructed based on ESZ， and various machine learning classification algorithms were compared. The support vector machine （SVM） model demonstrated the best performance， achieving an identification accuracy of 95.3% for four pure-substance particles： bubbles， quartz sand， polyethylene terephthalate （PET）， and paramecium. While bubbles and quartz sand were distinguished with excellent accuracy， some confusion remained between PET and paramecium， indicating the need for further algorithm optimization. This study provides a new approach for particle identification， and future work will involve validation in mixed-particle systems， offering technical support for the precision and intelligence of water treatment monitoring.
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出版历程
  • 收稿日期:  2024-07-17
  • 录用日期:  2024-08-31
  • 修回日期:  2024-08-21
  • 网络出版日期:  2026-01-09

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