From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator

Jerome Frutiger; Mark Jones; Nevin  Gerek Inceb; Gürkan Sin

doi:10.1016/B978-0-444-64241-7.50243-3

From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator

Jerome Frutiger, Mark Jones, Nevin Gerek Inceb, Gürkan Sin

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

This study presents a methodology to apply Monte Carlo methods for property uncertainty propagation in the process simulation software SimSci PRO/II. The aim of this work is to integrate advanced uncertainty and sensitivity analysis tools into commercial process simulators. The property uncertainty and sensitivity analysis tools were applied to a heat pump system with cyclopentane as a working fluid. Monte Carlo sampling technique was used to generate property samples of the SRK equation of state parameters critical temperature, critical pressure and acentric factor. The samples were subsequently evaluated in the heat pump flowsheet built in SimSci PRO/II. This allowed describing the process model output uncertainty in a distribution and with the 95% confidence interval. Furthermore, Monte Carlo based standard regression could be used to analyse the sensitivity of the respective fluid properties. The results showed that property uncertainty propagation strongly depends on the correlation between the property parameters. The sensitivity analysis showed that the acentric factor is the most sensitive SRK parameter. This works demonstrates that Monte Carlo methods are a simple and useful tool, which can be used in commercial process simulators by industrial users.

Original language	English
Title of host publication	Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018
Editors	Mario R. Eden, Marianthi G. Ierapetritou, Gavin P. Towler
Publisher	Elsevier
Publication date	2018
Pages	1489-1494
DOIs	https://doi.org/10.1016/B978-0-444-64241-7.50243-3
Publication status	Published - 2018
Event	13th International Symposium on Process Systems Engineering (PSE 2018) - San DIego, United States Duration: 1 Jul 2018 → 5 Jul 2018

Conference

Conference	13th International Symposium on Process Systems Engineering (PSE 2018)
Country	United States
City	San DIego
Period	01/07/2018 → 05/07/2018

Series	Computer Aided Chemical Engineering
ISSN	1570-7946

Keywords

Monte Carlo method
Process simulator
Uncertainty

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Frutiger, J., Jones, M., Gerek Inceb, N., & Sin, G. (2018). From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator. In M. R. Eden, M. G. Ierapetritou, & G. P. Towler (Eds.), Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018 (pp. 1489-1494). Elsevier. Computer Aided Chemical Engineering https://doi.org/10.1016/B978-0-444-64241-7.50243-3

Frutiger, Jerome ; Jones, Mark ; Gerek Inceb, Nevin ; Sin, Gürkan. / From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator. Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. editor / Mario R. Eden ; Marianthi G. Ierapetritou ; Gavin P. Towler. Elsevier, 2018. pp. 1489-1494 (Computer Aided Chemical Engineering).

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abstract = "This study presents a methodology to apply Monte Carlo methods for property uncertainty propagation in the process simulation software SimSci PRO/II. The aim of this work is to integrate advanced uncertainty and sensitivity analysis tools into commercial process simulators. The property uncertainty and sensitivity analysis tools were applied to a heat pump system with cyclopentane as a working fluid. Monte Carlo sampling technique was used to generate property samples of the SRK equation of state parameters critical temperature, critical pressure and acentric factor. The samples were subsequently evaluated in the heat pump flowsheet built in SimSci PRO/II. This allowed describing the process model output uncertainty in a distribution and with the 95% confidence interval. Furthermore, Monte Carlo based standard regression could be used to analyse the sensitivity of the respective fluid properties. The results showed that property uncertainty propagation strongly depends on the correlation between the property parameters. The sensitivity analysis showed that the acentric factor is the most sensitive SRK parameter. This works demonstrates that Monte Carlo methods are a simple and useful tool, which can be used in commercial process simulators by industrial users.",

keywords = "Monte Carlo method, Process simulator, Uncertainty",

author = "Jerome Frutiger and Mark Jones and {Gerek Inceb}, Nevin and G{\"u}rkan Sin",

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Frutiger, J, Jones, M, Gerek Inceb, N & Sin, G 2018, From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator. in MR Eden, MG Ierapetritou & G P. Towler (eds), Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. Elsevier, Computer Aided Chemical Engineering, pp. 1489-1494, 13th International Symposium on Process Systems Engineering (PSE 2018), San DIego, United States, 01/07/2018. https://doi.org/10.1016/B978-0-444-64241-7.50243-3

From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator. / Frutiger, Jerome; Jones, Mark; Gerek Inceb, Nevin ; Sin, Gürkan.

Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. ed. / Mario R. Eden; Marianthi G. Ierapetritou; Gavin P. Towler. Elsevier, 2018. p. 1489-1494 (Computer Aided Chemical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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N2 - This study presents a methodology to apply Monte Carlo methods for property uncertainty propagation in the process simulation software SimSci PRO/II. The aim of this work is to integrate advanced uncertainty and sensitivity analysis tools into commercial process simulators. The property uncertainty and sensitivity analysis tools were applied to a heat pump system with cyclopentane as a working fluid. Monte Carlo sampling technique was used to generate property samples of the SRK equation of state parameters critical temperature, critical pressure and acentric factor. The samples were subsequently evaluated in the heat pump flowsheet built in SimSci PRO/II. This allowed describing the process model output uncertainty in a distribution and with the 95% confidence interval. Furthermore, Monte Carlo based standard regression could be used to analyse the sensitivity of the respective fluid properties. The results showed that property uncertainty propagation strongly depends on the correlation between the property parameters. The sensitivity analysis showed that the acentric factor is the most sensitive SRK parameter. This works demonstrates that Monte Carlo methods are a simple and useful tool, which can be used in commercial process simulators by industrial users.

AB - This study presents a methodology to apply Monte Carlo methods for property uncertainty propagation in the process simulation software SimSci PRO/II. The aim of this work is to integrate advanced uncertainty and sensitivity analysis tools into commercial process simulators. The property uncertainty and sensitivity analysis tools were applied to a heat pump system with cyclopentane as a working fluid. Monte Carlo sampling technique was used to generate property samples of the SRK equation of state parameters critical temperature, critical pressure and acentric factor. The samples were subsequently evaluated in the heat pump flowsheet built in SimSci PRO/II. This allowed describing the process model output uncertainty in a distribution and with the 95% confidence interval. Furthermore, Monte Carlo based standard regression could be used to analyse the sensitivity of the respective fluid properties. The results showed that property uncertainty propagation strongly depends on the correlation between the property parameters. The sensitivity analysis showed that the acentric factor is the most sensitive SRK parameter. This works demonstrates that Monte Carlo methods are a simple and useful tool, which can be used in commercial process simulators by industrial users.

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BT - Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018

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Frutiger J, Jones M, Gerek Inceb N, Sin G. From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator. In Eden MR, Ierapetritou MG, P. Towler G, editors, Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. Elsevier. 2018. p. 1489-1494. (Computer Aided Chemical Engineering). https://doi.org/10.1016/B978-0-444-64241-7.50243-3