The status of the incorporation of computational tools in the undergraduate Chemical Engineering curriculum has been recently reviewed by Shacham et al. (2009). This review, as well as some additional references (Dahm et al., 2002, Rockstraw, 2005, Dias et al., 2010), reveal that process simulation is being used to some extent in various courses. Most often commercial simulators (such as HYSIS, AspenPlus and PRO II) are used to model the steady state or dynamic operations of processes. The benefit of such use of a process simulator (as stated by Dahm et al., 2002) is that it "provides a time-efficient and effective way for students to examine cause-effect relationships" among various parameters of the process. A pedagogical drawback to the use of such packages is that "it is possible for students to successfully construct and use models without really understanding the physical phenomena within each unit operation" (Dahm et al., 2002). Furthermore, Streicher et al. (2005) have found that "the majority of students see simulations merely as sophisticated calculators that save time.”
In order to promote and emphasize the full educational benefits of process simulation, we have developed a process simulation course where the students need to prepare process models, in most cases, that are ultimately simulated with MATLAB. The course content begins with the steady state operation of simple recycle processes that contain mixers, simple splitters and conversion type reactors. Material balances on such systems can be represented by linear models. For such systems, a direct solution (of a system of linear equations) can be obtained and the performance of various iterative algorithms can be examined. The students learn to establish the computational sequence in the flow-sheet using various partitioning and tearing algorithms. Then, students solve the problems directly and iteratively using the successive substitution, dominant eigenvalue, Wegstein's and Broyden's methods. After understanding the basic principles of the operation of the steady state simulators, models of unit operations with increasing level of complexity (such as isothermal, adiabatic and three-phase flash) are prepared. The models are implemented as MATLAB functions where the input parameters are the vectors of flow-rates and enthalpy of the inlet streams and the design parameters of the process unit. The output parameters are the vectors of flow-rates and enthalpies of the outlet streams. Physical property data and correlations that required for the modeling the unit operation are taken from the DIPPR thermophysical database (http://dippr.byu.edu/). Binary equilibrium data, needed for calculations of activity coefficients, are retrieved from the Dorthmund Data Bank (http://www.ddbst.com/en/ddbst/index.php) and regressed with the Polymath program. The systems of nonlinear algebraic equations that represent typically the steady state models of the various units operations are solved using the constrained version of the Newton-Raphson method (Shacham, 1988).
In dynamic simulation, the emphasis is on the solution of Multiple-Model, Multiple-Algorithm (MMMA, see Cutlip et al., 2009) problems. An example is a process unit that can operate in different modes (such as a reactor with heating and cooling periods) and requires different integration algorithms (stiff and non-stiff) in each period. For example, the students can model the operation of a runaway chemical reactor which is described in detail by Eisenberg et al., 2006.
The process simulation course has been given as an elective course for fourth year undergraduate students and new graduate students. Success in this course requires that students must review, enhance, update and make practical use of their knowledge of programming, material and energy balances, thermodynamics, numerical methods and reaction engineering. With the integration of such content, the course can be considered as a culmination of core chemical engineering coursework.
In the extended abstract and in the presentation, the syllabus of the "Process Simulation" course will be described in detail. In addition, some example problems will be presented and the student evaluation of the course will be discussed.
1. Cutlip, M. B., N. Brauner, and M. Shacham, "Biokinetic Modeling of Imperfect Mixing in a Chemostat – an Example of Multiscale Modeling", Chem. Eng. Ed., 43, 243 (2009)
2. Dahm, K. D., R. P. Hesketh, and M. J. Savelski, "Is process simulation used effectively in ChE courses?" Chem. Eng. Ed., 36, 192 (2002)
3. Dias, R. S., Silva, L. C., and A. J. De Assis, Plant wide simulation using the free chemical process simulator Sim42: Natural gas separation and reforming, Published online in Wiley InterScience; DOI 10.1002/cae.20200
4. Eisenberg, S., M. Shacham and N. Brauner, "Combining HAZOP with Dynamic Simulation - Applications for Safety Education", Journal of Loss Prevention in the Process Industries 19, 754–761 (2006)
5. Rockstraw, D. A., "ASPEN Plus in the ChE curriculum. Suitable course content and teaching methodology," Chem. Eng. Ed., 39, 68 (2005)
6. Shacham, M., "Numerical Solution of Constrained Non-Linear Algebraic Equations", International Journal of Numerical Methods in Engineering, 23, 1455-1481 (1986).
7. Shacham, M., Cutlip, M. B. and N. Brauner, "From Numerical Problem Solving to Model Based Experimentation – Incorporating Computer Based Tools of Various Scales into the ChE Curriculum", Chem. Eng. Ed., 43, 299 (2009)
8. Streicher, S. J., K. West, D. M. Fraser, J. M. Case, and C. Linder, "Learning through simulation, student engagement," Chem. Eng. Ed., 39, 288 (2005)
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