大学化学2026，Vol.41Issue(1)：85-94,10.DOI:10.12461/PKU.DXHX202506021
智能可视化重铬酸钾回流法测定化学需氧量
Intelligent Visualization of Potassium Dichromate Reflux Method for Determination of Chemical Oxygen Demand
摘要
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.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Key words
Digital twin/Potassium dichromate reflux method/Chemical oxygen demand/Machine learning/Automatic monitoring分类
社会科学引用本文复制引用
周跃明,邱新,周馨,万潇天,张末凡,李丰,邵鑫鑫,丁鹏,梁喜珍..智能可视化重铬酸钾回流法测定化学需氧量[J].大学化学,2026,41(1):85-94,10.基金项目
东华理工大学实验技术开发项目(DHSY-202313,DHSY-202511) （DHSY-202313,DHSY-202511）
东华理工大学教学改革研究课题(DHJG-23-33) （DHJG-23-33）
