Process Simulation Failure As A Learning Tool

Abstract

Chemical process simulators have become ubiquitous in chemical engineering education. Students frequently view them as a black box of wonder that will make their engineering life much easier, when in fact the simulator may actually more closely resemble Pandora’s Box. When given a choice amongst an analytical solution to model equations, a numerical solution to model equations, or a simulator solution, students often trust the simulator results over their model, not realizing the simulator uses the same (or possibly an inappropriate) model. This paper discusses an approach of “learning through failure”, where students develop simulations for systems that produce results not matching reality. The goal is to make students aware of the need to critically validate any results obtained from a process simulator.

Process simulation has become a core element of chemical engineering education. Recent surveys have indicated that simulators are used in most chemical engineering curricula, with an increasing use outside the capstone design course. Equilibrium staged separations, process control, and thermodynamics courses are the three most common settings for incorporating simulation into the curriculum outside the capstone design sequence.1

The increased use of process simulation has coincided with an increased dependency on computers in the lives of students. Students have grown accustomed to using computers for entertainment as well as engineering, and tend to trust software to give them accurate answers. With process simulators, this attitude is potentially dangerous. The adage “garbage in, garbage out” applies to process simulation, a fact that may be lost as fewer programs require computer programming as part of the core curriculum.1 For the purposes of this paper, process simulation failure is defined as simulations that run as expected, but yield results inconsistent with the processes they are intended to simulate.

Motivation

One of the topics covered during the Spring 2001 and Spring 2002 offerings of a course in Process Modeling at the University of Kentucky (UK) extended campus in Paducah was the need to validate computer generated results through various methods. As part of this discussion, the role of numerical methods in process simulation was covered along