Abstract
At Rowan University we decided to address a perennial student complaint that the computer science programming class material was never used in later chemical engineering classes. In 2022, we replaced the required programming course with a required chemical engineering course called ChE Modeling. This course introduces students to the modeling of chemical processes using practical simulation tools; the same ones used in industry. Students learn how to build models of complex chemical processes, evaluate the accuracy of models, and use models for process optimization and design decisions. We start this course using the Begin Python with TCLAB[i] modules. This is a unique module in which they learn a programming language to control an Arduino that has 2 heaters and thermistors. This immediately addresses a common complaint that they never used the programming language taught by computer science in a chemical engineering class; they now use python immediately. John Hedengren, the developer of this program, estimates that students can complete the 12 modules in 2 - 3 hours. In this course they learn how to control a system with 2 heaters and 2 thermistors. In this module they use on/off control, similar to a home heating/cooling system. The TCLAB can be used in several other courses in ChE. The material in the ChE modeling course then focuses on ChE computational tools solving problems introduced in the previous years (1st and 2nd). Based on the Felder, Rousseau and Bullard book they use python jupyter notebook templates, Excel Templates, and Aspen Plus Tutorials to perform rigorous multicomponent flash drum problems using the Rachford-Rice Equation[ii], Adiabatic Flame Temperature, and Transient Material Balances. The energy balances are based on thermodynamic properties from DIPPR. We continue with fluid mechanics and use a solver to solve for the flowrate in pipes and complex pipe networks. Next are transport problems in momentum, mass, and heat transfer ODE's and finite difference approximations. Next are the linear and non-linear regressions with confidence intervals, root finding methods, and plug flow models. Finally of interest to this conference are the Machine Learning Basics that are introduced and the changes required to incorporate AI in teaching programming.