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Citation: Yueming Zhou, Xin Qiu, Xin Zhou, Xiaotian Wan, Mofan Zhang, Feng Li, Xinxin Shao, Peng Ding, Xizhen Liang. Intelligent Visualization of Potassium Dichromate Reflux Method for Determination of Chemical Oxygen Demand[J]. University Chemistry, ;2026, 41(1): 85-94. doi: 10.12461/PKU.DXHX202506021 shu

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

Yueming Zhou ¹ ,
Xin Qiu ² ,
Xin Zhou ¹ ,
Xiaotian Wan ¹ ,
Mofan Zhang ² ,
Feng Li ² ,
Xinxin Shao ¹ ,
Peng Ding ² ,
Xizhen Liang ^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

Corresponding author: Xizhen Liang, xzhliang@ecut.edu.cn
Received Date: 5 June 2025
Revised Date: 20 October 2025

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.
- Digital twin,
- Potassium dichromate reflux method,
- Chemical oxygen demand,
- Machine learning,
- Automatic monitoring
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