大学化学 >> 2026, Vol. 41 >> Issue (1): 363-372.doi: 10.12461/PKU.DXHX202506055

所属专题： 化学实验数字化设计竞赛获奖作品

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“数”制烯烃——数据驱动的CO2氧化乙烷脱氢制乙烯催化剂的数字化设计

马雯雯, 孔莲, 褚金洋, 马丽, 马子晴, 程鹤雨, 李鑫源, 于湛, 赵震   

  1. 沈阳师范大学化学化工学院, 能源与环境催化研究所, 沈阳 110034
  • 收稿日期:2025-06-13 录用日期:2025-09-09 发布日期:2025-12-30
  • 通讯作者: 于湛, 赵震 E-mail:yuzhan@synu.edu.cn;zhaozhen@synu.edu.cn Zhan Yu, Zhen Zhao
  • 基金资助:
    辽宁省高等学校基本科研项目(LJ232410166032, LJ202410166037)；沈阳师范大学教育教改项目(JGY202405)；辽宁省研究生教改项目(LNYJG2022400, LNYJG2023280)

Digitalization-Driven Olefin Production: Digital Design of Catalysts for CO2-Assisted Oxidation Dehydrogenation of Ethane to Ethylene

Wenwen Ma, Lian Kong, Jinyang Chu, Li Ma, Ziqing Ma, Heyu Cheng, Xinyuan Li, Zhan Yu, Zhen Zhao   

  1. Institute of Energy and Environmental Catalysis, College of Chemistry and Chemical Engineering, Shenyang Normal University, Shenyang 110034, China
  • Received:2025-06-13 Accepted:2025-09-09 Published:2025-12-30
  • Contact: Zhan Yu, Zhen Zhao E-mail:yuzhan@synu.edu.cn;zhaozhen@synu.edu.cn

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摘要： 针对能源化学工程专业的培养目标，为了提高本科生科研兴趣，培养其科研思维和方法论，我们将教师的科研成果融入本科生专业实验教学，并借助数字化手段辅助实验教学过程，以构建一门具有综合性和创新性的本科生专业实验课程。该数字化创新实验以CO2氧化乙烷脱氢制乙烯为探针反应，涵盖催化剂制备和表征、催化性能评价以及实验数据处理等完整流程，深入加强学生对能源转化催化过程的理解。在实验预习和数据处理过程引入数字化教学手段不仅可以促进学生自主学习，还可快速对实验数据进行分类和整理。此外，基于我们的实验数据库，具有最优树深度的决策树模型可以识别关键的输入变量，并为催化剂设计提供有针对性的指导。该实验巧妙融合了无机化学、物理化学与仪器分析的核心要素，构建了一个富含创新元素且全面发展的实验平台。它不仅能够培养学生基础实验技能，更激发了他们对化学实验的热情，并全方位提升了同学们在操作、思维、创新和团队合作等方面的能力。

关键词: 数字化, 决策树, CO2氧化乙烷脱氢, 基础实验技能, 能力培养

Abstract: To align with the educational objectives of Energy Chemical Engineering and enhance undergraduates’ research interest while cultivating their scientific thinking and methodology, we have incorporated faculty research achievements into specialized experimental teaching. By employing digital tools to support the experimental learning process, we have developed a comprehensive and innovative undergraduate laboratory course. This digitally-enhanced experiment employs CO2-assisted oxidative dehydrogenation of ethane to ethylene as a probe reaction, encompassing the complete workflow of catalyst preparation, characterization, performance evaluation, and data analysis. This approach significantly deepens students’ understanding of catalytic processes in energy conversion. The integration of digital teaching methods during experimental preparation and data processing not only promotes autonomous learning but also enables efficient data classification and organization. Furthermore, utilizing our experimental database, decision tree models with optimized depth can identify critical input variables and provide targeted guidance for catalyst design. The experiment skillfully combines core elements from inorganic chemistry, physical chemistry, and instrumental analysis, creating an innovative and well-rounded experimental platform. This methodology not only develops students’ fundamental laboratory skills but also stimulates their enthusiasm for chemical experimentation while comprehensively enhancing their operational, analytical, innovative, and collaborative competencies.

Key words: Digitization, Decision tree, Oxidative dehydrogenation of ethane with CO2, Fundamental experimental skills, Ability cultivation