大学化学 >> 2026, Vol. 41 >> Issue (1): 227-243.doi: 10.12461/PKU.DXHX202503107

所属专题： 化学实验数字化设计竞赛获奖作品

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液体饱和蒸气压的测定数字化实验

叶凯, 张力中, 张明宇, 吴秦雄, 王魁, 王琪   

  1. 合肥工业大学化学与化工学院, 合肥 230009
  • 收稿日期:2025-03-26 录用日期:2025-09-16 发布日期:2025-12-30
  • 通讯作者: 王魁, 王琪 E-mail:kuiwang@hfut.edu.cn;wangqi@hfut.edu.cn Kui Wang, Qi Wang
  • 基金资助:
    安徽省工科专业化学教学创新团队(2023cxtd005)

Digital Experiment for the Determination of Liquid Saturated Vapor Pressure

Kai Ye, Lizhong Zhang, Mingyu Zhang, Qinxiong Wu, Kui Wang, Qi Wang   

  1. College of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei 230009, China
  • Received:2025-03-26 Accepted:2025-09-16 Published:2025-12-30
  • Contact: Kui Wang, Qi Wang E-mail:kuiwang@hfut.edu.cn;wangqi@hfut.edu.cn

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摘要： 液体饱和蒸气压的测定是大学物理化学基础实验的重要内容，有助于学生理解物质性质及相态变化规律。然而，传统的动态法存在操作复杂、实验误差大、开放性不足和安全性问题。本数字化实验方案引入基于比例-积分-微分(PID)控制的自动化系统，并结合Web虚拟实验室平台，利用机器学习对实验数据进行深入分析，从而提供一个安全、高效、精确的实验教学环境，实现测量平均相对误差从传统的8.7%降低至0.5%，教学周期从原先的一周以上缩短至一周以内，温度测量误差控制在±0.5℃，气压误差控制在±0.2 kPa。这一方案优化了传统教学方式，有效弥补了传统实验教学的不足。

关键词: 饱和蒸气压, 数字化, 自动控制, Web实验室, 机器学习

Abstract: The determination of liquid saturated vapor pressure is a key component of undergraduate physical chemistry laboratory instruction, facilitating students’ systematic understanding of material properties and phase-transition behavior. However, the conventional dynamic method suffers from cumbersome operation, substantial experimental error, limited openness, and safety concerns. The proposed digital laboratory scheme integrates a proportional-integral-derivative (PID)-based automated control system with a web-based virtual laboratory platform and applies machine learning for in-depth analysis of experimental data, thereby providing a safe, efficient, and precise instructional environment. Empirical results show that the mean relative measurement error is reduced from the traditional 8.7% to 0.5%, the instructional cycle is shortened from more than one week to within a week, the temperature measurement error is controlled within ±0.5 °C, and the pressure error within ±0.2 kPa. This approach optimizes conventional teaching practices and effectively remedies deficiencies in current experimental instruction.

Key words: Saturated vapor pressure, Digitalization, Automatic control, Web laboratory, Machine learning