(209e) Bridging Simulation and Data Science in the Modern Chemical Engineering Classroom

As data science and digital technologies increasingly shape the future of Chemical Engineering, there is a growing need to thoughtfully integrate computational and simulation tools into both undergraduate and graduate curricular [1]–[4]. This presentation will outline a comprehensive instructional approach that incorporates platforms such as MATLAB, Python, Excel, R, and Aspen HYSYS to enhance students’ data literacy, modeling competence, and problem-solving abilities. Drawing from classroom experiences, we demonstrate how these tools can strategically embed across a variety of course topics to strengthen both physical and statistical modeling capabilities.

In our approach, simulation tools are integrated not simply as technical skills to be learned in isolation, but as instrumental frameworks that deepen conceptual understanding across various chemical engineering topics – including material and energy balances, thermodynamics, process simulation, and data analysis. Homework assignments are designed to help students draw clear connections between experimental data, model development, and engineering decision-making. For example, students can use Python and R to conduct exploratory data analysis and regression [5], Excel to interpret experimental results [6], and Aspen HYSYS to simulate real-world chemical processes under varying design and operating conditions [7].

A core component of the pedagogy is its emphasis on scaffolded learning – starting with guided simulations and progressing to open-ended design and analysis projects that foster independence and critical thinking. The use of multiple platforms allows students to gain both flexibility and familiarity with industry-relevant tools, while also reinforcing foundational principles of data-driven reasoning. Additionally, students are introduced to concepts such as uncertainty quantification and decision-making under uncertainty, which can encourage them to move beyond deterministic assumptions and better understand the complexity of real-world systems.

This presentation will share best practices for integrating simulation and data science into chemical engineering curriculum, including practical implementation strategies, sample assignments, and student feedback. Special attention will be given to address common challenges such as varying student backgrounds in coding, balancing conceptual depth with tool frequency, and ensuring meaningful assessment. Attendees will gain actionable insights into how a tool-integrated, data-centered teaching approach can enhance students’ engagement, support deeper learning, and better prepare the next-generation chemical engineers for a data-rich professional landscape.

Reference

[1] Beck, D. A., Carothers, J. M., Subramanian, V. R., & Pfaendtner, J. (2016). Data science: Accelerating innovation and discovery in chemical engineering. AIChE Journal, 62(5), 1402-1416.
[2] Ashraf, C., Joshi, N., Beck, D. A., & Pfaendtner, J. (2021). Data science in chemical engineering: applications to molecular science. Annual Review of Chemical and Biomolecular Engineering, 12(1), 15-37.
[3] Chiang, L., Lu, B., & Castillo, I. (2017). Big data analytics in chemical engineering. Annual review of chemical and biomolecular engineering, 8(1), 63-85.
[4] Duever, T. A. (2019). Data science in the chemical engineering curriculum. Processes, 7(11), 830.
[5] Caccavale, F., Gargalo, C. L., Gernaey, K. V., & Krühne, U. (2023). Integrating Python in the (bio) chemical engineering curriculum: challenges and opportunities. Computer Aided Chemical Engineering, 52, 3471-3476.
[6] Wong, K. W., & Barford, J. P. (2010). Teaching Excel VBA as a problem solving tool for chemical engineering core courses. Education for Chemical Engineers, 5(4), e72-e77.
[7] De Tommaso, J., Rossi, F., Moradi, N., Pirola, C., Patience, G. S., & Galli, F. (2020). Experimental methods in chemical engineering: Process simulation. The Canadian Journal of Chemical Engineering, 98(11), 2301-2320.