Digitized and Intelligent Upgrade of Binary Gas-Liquid Equilibrium Phase Diagram Determination Experiments
ZHANG Xin1,2**, PU Peng-Xin1,2, ZHANG Jia-Xuan1, CUI Meng1, HUANG Yong-Lin1, LIU Jun-Jie1, FENG Hai-Song1,2, TONG Xiao-Mei1, WANG Feng-Mei1, XU Xiang-Yu1
1. College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China; 2. State Key Laboratory of Chemical Resource Engineering, Beijing 100029, China
Abstract The gas-liquid equilibrium phase diagram determination experiment of ethanol-cyclohexane two-component system is an important undergraduate physical chemistry experiment.However,the traditional teaching program has limitations due to the simple conclusions and content.In this study,machine learning approaches were used to upgrade this experiment with digitalization and intelligence.The random forest model trained on 9124 different components of the gas-liquid equilibrium dataset achieves accurate prediction of the gas-liquid equilibrium phase diagram of ethanol-cyclohexane and its derivatives.SHAP analysis reveals that the key factor affecting the morphology of the phase diagram is the boiling point difference of the two components.A round of teaching practice shows that the digitized and intelligent design scheme not only deepened students’ understanding of gas-liquid equilibrium phase diagram,but also provided an interactive platform for students to explore different forms of phase diagram through data analysis and model interpretation,and enhances the attractiveness of the course.This research promoted the deep integration of data intelligence technology and chemical experimental teaching,and was expected to play a demonstration role in the field of experimental teaching.
ZHANG Xin, PU Peng-Xin, ZHANG Jia-Xuan, CUI Meng, HUANG Yong-Lin, LIU Jun-Jie, FENG Hai-Song, TONG Xiao-Mei, WANG Feng-Mei, XU Xiang-Yu. Digitized and Intelligent Upgrade of Binary Gas-Liquid Equilibrium Phase Diagram Determination Experiments[J]. Chinese Journal of Chemical Education, 2026, 47(8): 65-73.
