Using process simulators in Chemical Engineering education: Is it possible to minimize the “black box” effect?

Abstract

Simulators are instrumental to engineers and scientists, in general, and to chemical engineers,more specifically, which enable the optimization and analysis of alternative solutions in a time‐efficient and cost‐effective way. Thus, their integration into Chemical Engineering studies has gained traction over the years in supporting traditional lectures or as the sole tool for knowledge transmission. However, despite their proven benefits in Higher Education, limitations exist, as the application of the tool may result in deficient learning. In fact, the algorithms and design equations that are used to transform inputs into results are not identifiable by the students, thereby instilling a “superficial understanding” of the problem. Thus, the objective of this paper is to provide some guidelines through three simple case studies, which may help minimize the so‐called “black box” effect. Two different state‐of‐the‐art computational tools were used in parallel. Aspen One simulations provided the reference point, which was used by the students to verify the process models built by them with Mathcad Prime. A survey taken by the students then allowed conclusions to be made, which concurred that the experience had been very successful.

Using in silico process simulation tools in pharmacy education: Considerations for pivoting to online learning

“…The value and challenges of such approaches, have been broadly reported in clinical education literature (Croft et al, 2019;Sarfati et al, 2019;Ferguson et al, 2020). However, the use of in silico, or computer-based process simulations tends to be reported more in educational literature relating to physical sciences, for example, chemical engineering (Li & Huang, 2017;Rasteiro & Urbano, 2017;de Lucas-Consuegra, Serrano, & Llanos, 2018;Golman & Yermukhambetova, 2019;Moodley, 2020;Nachtigalova et al, 2020;Roman et al, 2020).…”

“…While not engaging in programming the code, this insight can be considered valuable to the user in terms of understanding the fundamentals of an in-silico simulation approach. The need to engage students with the simulation code/basic equations and therefore the fundamental principles underpinning the process simulation, in order to mitigate concerns relating to superficial learning using process simulation tools, has been recognised (Roman et al, 2020).…”

“…Students should have an understanding of the underpinning equations being simulated; what assumptions are in the model applications; what is not included in the simulation and what might result in erroneous output. In summary, efforts should be made to minimise the 'black box' effect in using process simulations in education (Roman, Delgado, & García-Morales, 2020). Care should be taken with reduced-order models (ROM), which aim to approximate physical phenomena from their complete three-dimensional behaviour into one-or zero-dimensional elements.…”

Simulation of a Depropanizer Distillation Column and Validation of Results by Industrial Data

“…Simulation of distillation columns by an equilibrium model was first studied in 1899 by Sorel for steady-state systems [1][2][3]. In 1922, Ponchon and Savarit modified the equilibrium equations by involving the interactions between components in binary systems [4,5]. In 1925, Murphree showed that the concept of equilibrium trays could not simply be used to accurately predict the separation patterns of the distillation column in more complex multicomponent systems [6][7][8].…”

A Mathcad‐based educational experience to address the design of nonisothermal plug flow reactors

“…The School of Engineering at the University of Huelva, thanks to various Teaching Innovation Projects, is currently implementing the use of Mathcad in some courses corresponding to a Bachelor's Degree in Industrial Chemical Engineering and a Master's Degree in Chemical Engineering [5,6,21,22]. As for the CRE area, we used Mathcad for the design of isothermal reactors involving multiple reactions (series and parallel reactions) [6].…”