Exploring Teaching Reform of Chemical Process Simulation with AI Integration: Optimization Practice Based on Python and Aspen Plus

With the rapid development of artificial intelligence (AI) technologies, the field of chemical process simulation has ushered in new opportunities for growth. However, traditional chemical process simulation teaching focuses mainly on the static simulation of operational units and process flows, neglecting the cultivation of optimization skills and dynamic analysis capabilities. To address this issue, this paper proposes integrating AI technologies with traditional chemical process simulation teaching. By using Python to interact with Aspen Plus for optimization analysis, this approach not only compensates for Aspen Plus' limitations in optimization but also innovates teaching methodologies. Combining case studies on coal-to-ethylene glycol processes, students are engaged in hands-on simulation, data optimization analysis, and the integration of political education points to enhance their practical engineering skills and green development awareness. Furthermore, the inclusion of AI-driven educational analysis tools offers personalized feedback and learning support for students while promoting new directions in teacher professional development. This paper discusses teaching design, technical implementation, and case studies in detail, and outlines future directions for teaching reform.