(743d) Safety Analysis with MODEL-Based Dynamic Simulation on Mobile Devices | AIChE

(743d) Safety Analysis with MODEL-Based Dynamic Simulation on Mobile Devices


Shacham, M. - Presenter, Ben Gurion University of the Negev
Cutlip, M. B., University of Connecticut
Elly, M., Ben-Gurion University of the Negev

Microsoft Word - SafetyEdu_12_4_14


Mordechai Shacham, Ben-Gurion University, Beer-Sheva, Israel Michael B. Cutlip, University of Connecticut, Storrs, CT 06269, USA Michael Elly, Ben-Gurion University, Beer-Sheva, Israel
The prediction and prevention of chemical process hazards are essential parts of the chemical engineer’s education. It is widely recognized (see, for example, Behm et al, 2014) that hazards and risks must be minimized early in the design process, and engineering students must acquire the "safe design" thinking throughout their studies. Model-based dynamic simulation, MBDS, is a frequently used tool for hazard identification in chemical processes (Labovska et al, 2014, Koulainen et al., 2012, Eizenberg et al., 2006) both in the design and the operation stages. MBDS can be very beneficial in teaching process safety, as well. This approach enables identification of predictable and even unpredictable hazards by numerical experimentation and concurrent quantitative HAZOP (hazard and operability analysis) studies.
MBDS of chemical processes has been typically carried out using commercial process
simulation software (see for example Komulainen et al., 2012) and mathematical software packages (such as MATLAB1), where the process model is preferably accessed via a GUI (Eizenberg et al, 2006). Most educational use of commercial simulators and large mathematical software packages must be accessed at specific locations (i. e. at the university)
because of the required computing capabilities and software license restrictions. However, the educational use of MBDS for safety analysis can be extended considerably by making use of mobile devices (smart-phones and tablets) with computational applications. An example is the PolyMathLite (PML) application2 that has been developed for Android-based devices. This
application is a slightly simplified version of the PolyMath software package (Polymath is a product of PolyMath Software: www.polymath-software.com). The Android operating system was selected because it is predicted that by 2017 it will be the dominant operating system for all computing devices (Wingfield, 2013). PolyMathLite enables users to obtain numerical solutions to a wide range of problems including systems of linear and nonlinear algebraic equations (NLE), systems of ordinary differential equations (ODE, stiff and non-stiff) and carry out linear, multiple–linear, polynomial and non-linear regressions (REG).
Several Android demonstration programs have been prepared for model-based safety analysis of chemical processes using PolyMathLite. The analysis of a process involving the
oxidation of 2-octanol to 2-octanone in a semi-batch reactor (Eizenberg et al., 2006) is used to
demonstrate the proposed approach. For this reactor, small deviations from the appropriate operating conditions may cause a sudden reaction of accumulated product 2-octanone, followed by reaction rate and temperature runaway. Students are required to carry out safety analysis of this dynamic process. The models of the reactor and its cooling jacket are prepared, their operation in normal conditions is verified and the model is saved in the mobile device. The safety related parameters are put on the top of the model in order to allow easy access (Figure 1). Their roles and units are clearly identified (in comments marked by the # sign) to allow changes of these parameters.

1 MATLAB is a trademark of The Math Works, Inc. (http://www.mathworks.com).

2 PolyMathLite is an Android app produced by PolyMath Software. (http://www.polymathlite.com).

Figure 1- Safety related parameters section of the 2-octanone production simulation
After introduction to the process details, students are asked to carry out HAZOP analysis related to the coolant flow rate, coolant inlet temperature, dosing time, dosing volume and nitric acid concentration (the solvent with autocatalytic effect). The results can be presented in tabular and graphical forms to enable identification of the limiting values of the parameters which cause temperature runaway.
The extended abstract and the presentation will describe the model-based safety analysis applications in more depth, and their use in process safety education and practice will be discussed.


1. Michael Behm, M., John Culvenor, J. and G. Dixon, Development of Safe Design Thinking Among

Engineering Students, Safety Science 63, 1-7 (2014)

2. Eizenberg, 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)

3. Komulainen, T. M., Enemark-Rasmussen, R, Sin, G., Fletcher, J. P. and D. Cameron, Experiences on Dynamic Simulation Software in Chemical Engineering Education, Education for Chemical Engineers 7, e153–e162 (2012)

4. Labovská, Z., Labovský, J., Jelemenský, L., Dudás, J. and J. Markos, Model-based Hazard

Identification In Multiphase Chemical Reactors, Journal of Loss Prevention in the Process Industries

29, 155-162 (2014)

5. Wingfield, N., "PC Sales Still in a Slump, Despite New Offerings", The New York Times, April 10,




This paper has an Extended Abstract file available; you must purchase the conference proceedings to access it.


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