(414d) Design of Core Courses on Computing and Data Analytics in Our Chemical Engineering Program

Skills for computing and data analytics are in high industrial demand and becoming critical to our students’ future career success. As our industrial advisor boards recommend, we continue enhancing our students’ learning of computational methods, modeling, simulation of chemical engineering processes, and data-driven process optimization. It is crucial in a systematic way to design core courses on computing and data analytics in our chemical engineering program to bridge the skills gap.

We first designed the CHE 3031 Computational Methods course in 2010 as recommended by our industrial advisor boards. The original topics include Excel for engineering analysis, Pivot Tables and Charts, VBA programming, Linear equations, Derivative approximations, Roots of nonlinear equations, Optimization, Uncertainty, Least-square regression, Interpolation, Integrals, Initial-value problems and ODEs, Boundary-value problems, and PDEs. We added Python for scientific computing and data analytics in the course 2019 as recommended by our industrial advisor boards.

Secondly, the CHE 3032 Process Modeling course was designed as a computer lab-based course to train students in using commercial simulation and modeling tools such as Aspen HYSYS and SolidWorks to prepare them for our unit operations and design courses.

Thirdly, CHE5031 Chemical Engineering Analysis was designed to teach topics including Introduction to modeling in chemical engineering, MATLAB skill preparation, Model fitting, spline functions, Optimization toolbox, Enhanced optimization, Global optimization, Optimal experimental design, Data statistics and analytics, Complex equation systems, Process integration, and optimization. MATLAB was selected as the computational language for this course in 2020.

Computational Methods and Process Modeling are required core courses in our undergraduate program. Our graduate program requires the Analysis course, and undergraduate students may take this course to fulfill a technical elective requirement. As shown in Figure 1, these courses aim to train our chemical engineering students to higher proficiency with modeling and simulation methods and computer software tools. It can be seen from student evaluations that they enjoyed those courses and realize the importance of computing and data analytics through those courses. These courses also enable them to use the “data science” techniques, skills, and modern engineering tools necessary for engineering practice.