大学化学 >> 2026, Vol. 41 >> Issue (1): 85-94.doi: 10.12461/PKU.DXHX202506021

所属专题： 化学实验数字化设计竞赛获奖作品

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智能可视化重铬酸钾回流法测定化学需氧量

周跃明1, 邱新2, 周馨1, 万潇天1, 张末凡2, 李丰2, 邵鑫鑫1, 丁鹏2, 梁喜珍1   

  1. 1 东华理工大学化学与材料学院, 南昌 330013;
    2 东华理工大学信息工程学院, 南昌 330013
  • 收稿日期:2025-06-05 录用日期:2025-10-20 发布日期:2025-12-30
  • 通讯作者: 梁喜珍 E-mail:xzhliang@ecut.edu.cn Xizhen Liang
  • 基金资助:
    东华理工大学实验技术开发项目(DHSY-202313, DHSY-202511)；东华理工大学教学改革研究课题(DHJG-23-33)

Intelligent Visualization of Potassium Dichromate Reflux Method for Determination of Chemical Oxygen Demand

Yueming Zhou1, Xin Qiu2, Xin Zhou1, Xiaotian Wan1, Mofan Zhang2, Feng Li2, Xinxin Shao1, Peng Ding2, Xizhen Liang1   

  1. 1 School of Chemistry and Materials Science, East China University of Technology, Nanchang 330013, China;
    2 School of Information Engineering, East China University of Technology, Nanchang 330013, China
  • Received:2025-06-05 Accepted:2025-10-20 Published:2025-12-30
  • Contact: Xizhen Liang E-mail:xzhliang@ecut.edu.cn

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摘要： 重铬酸钾回流法测定化学需氧量是水环境质量监测标准方法(HJ 828-2017)。该方法危险性强、成本高且排污严重，限制了其在化学基础实验教学中的推广应用。用色敏摄像机采集溶液反应图像，Python中的OpenCV库获取RGB数据，结合机器学习聚类分析，实现加热回流与滴定过程的自动化监测。将数字孪生可视化技术创新性地融入重铬酸钾回流法测化学需氧量实验中，交互式操作提升了仿真实验教学效果。

关键词: 数字孪生, 重铬酸钾回流法, 化学需氧量, 机器学习, 自动化监测

Abstract: The potassium dichromate reflux method, as specified in the water quality monitoring standard (HJ 828-2017), serves as a conventional approach for chemical oxygen demand (COD) determination. However, its application in fundamental chemistry laboratory education has been constrained due to inherent safety hazards, high operational costs, and significant pollutant discharge. This study presents an innovative approach employing a color-sensitive camera to capture solution reaction images, with subsequent RGB data extraction through Python’s OpenCV library. By integrating machine learning-based cluster analysis, we achieved automated monitoring of both the heating reflux and titration processes. The incorporation of digital twin visualization technology into the potassium dichromate reflux method for COD measurement represents a novel advancement, with interactive operations significantly enhancing the effectiveness of simulated experimental instruction.

Key words: Digital twin, Potassium dichromate reflux method, Chemical oxygen demand, Machine learning, Automatic monitoring