Using Stochastic Approaches for Teaching Mass Transfer Unit Operations: The Monte Carlo Method

Abstract

Mass transfer separations are of extreme importance to professionals working in the chemical industry. For this reason, chemical engineering and industrial chemistry students are both taught these unit operations at the final years of their study programs. The traditional way of carrying out their design is based on the use of deterministic models, most often involving ordinary differential equations. However, the macroscopic result of many existing chemical processes is due to the effect of statistical molecular motion. Hence, the use of stochastic or random approaches enables the issue to be addressed with just basic probability concepts aided by computer software. Based on these ideas, this article explores the implementation of Monte Carlo simulations for addressing two case studies related to mass transfer processes (batch distillation and gas membrane separation) and presents a comparative evaluation with respect to traditional deterministic methods. The easiness and accuracy of the method allow the conclusion that Monte Carlo simulations may facilitate understanding and add another dimension to students' computational skills without detriment to the result's quality. The case studies presented may also enrich the instructors' educational competence, providing them with an alternative view of the matter.