(349g) Modeling in Chemical Engineering Practice: Math, Science, and Something Different | AIChE

(349g) Modeling in Chemical Engineering Practice: Math, Science, and Something Different

The historical development of modeling in chemical engineering provides evidence of its importance. For instance, Levenspiel (2002) documents the development and importance of modeling in chemical engineering (ChE), “[I]n its 90 year life …ChE has changed our accepted concepts and our ways of thinking in science and technology. Here modeling stands out as the primary development.” However, while the role of modeling in the natural sciences has been extensively studied by historians, philosophers, cognitive scientists, and educators, relatively few studies have been conducted explicating the role modeling plays in engineering practice or the education of engineering students.

Our objective for this presentation is to consider the differences in the role of modeling in the natural sciences and engineering and consider whether these differences make a difference to chemical engineering educators. We discuss some vital features of models related to idealizations, engineering knowledge, incomplete knowledge and learning. We then illustrate these features with respect to an authentic senior level engineering project that we have been studying. In the project, senior students work in teams to develop a process “recipe” (i.e. choice process parameters) for a set of Chemical Vapor Deposition (CVD) reactors at as low a cost as possible. Their task is enabled by experimentation in a virtual fabrication facility where they can grow material and measure properties. The teams iteratively adjust their approach as they incorporate the data they collect into their developing models to build understanding.

We can consider our investigation of models and modeling in the virtual CVD process development task from this context. The virtual CVD task is designed, to some extent, to contain elements of a specific messy, open-ended context that engineering work is done in practice (as opposed to a back of the chapter problem). We are interested in how both student and expert teams approach this task outside the context of a specific topic in a core engineering science course, and how models in this task compare with physical laboratory projects. We ask, “What general knowledge do teams recognize and apply to complete the task? How do they modify their models (or not) when facing incomplete knowledge? In what way do the models help them learn about the process and about the foundational principles and methods in the discipline?” We are interested both in learning the value of this type of task in the context of the engineering curriculum, and to provide a specific test bed to explore the use of modeling in engineering. By looking at the models the teams generate, we gain understanding of how they apply general knowledge in the specific context and can identify the naïve conceptions in the mental models they bring to the task.