A Modular Modelling Environment for Computer-Aided Process Design

Mark Nicholas Jones*, Simon Anthony Jones, Gürkan Sin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We present a modelling approach which allows to formulate and solve steady-state and dynamic process systems engineering problems in the programming languages Fortran and Python. Additionally, we have developed a property prediction software which can be installed with a container on a local machine or on servers rented by the user, research institution or industrial entity. The prediction tool comes with a web-interface and an application programming interface (API) which one can connect to and request predictions of fixed physical and thermo-physical properties directly into their routines. Further we used Pyomo to solve superstructure optimization problems with surrogate models to find the optimal process structure and point of operation to the given global constraints. On the superstructure level we connect a graphical user interface (GUI) with Pyomo to make it easier for the general user to work with an equation-based environment where initial estimates for all variables can be defined through the spreadsheet-style table of the GUI. This modular, multi-level approach also allowed us to connect commercial process simulators with our developed Python-COM interface to perform sensitivity analysis or retrieve surrogate models in e.g. Aspen or PRO/II. The process design, process flow-sheeting and superstructure optimization layer can individually be wrapped with lifecycle analysis tools. The modelling environment is platform independent (except the information retrieval from Aspen or PRO/II). All the necessary code examples in this environment is hosted and further developed in several different repositories freely accessible to scientific and industrial users. The code is published under a free license.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Foundations of Computer-Aided Process Design
EditorsSalvador Garcia Muñoz , Carl D. Laird , Matthew J. Realff
Number of pages6
Volume47
PublisherElsevier
Publication date2019
Pages23-28
ISBN (Electronic)978-0-12-818597-1
DOIs
Publication statusPublished - 2019
Event9th International Conference on Foundations of Computer-Aided Process Design - Copper Mountain, United States
Duration: 14 Jul 201918 Jul 2019

Conference

Conference9th International Conference on Foundations of Computer-Aided Process Design
CountryUnited States
CityCopper Mountain
Period14/07/201918/07/2019
SeriesComputer Aided Chemical Engineering
ISSN1570-7946

Keywords

  • Derivative-free optimization
  • Graphical flowsheeting tool
  • Multi-level Framework
  • Process Design
  • Property Prediction
  • Superstructure Optimization

Cite this

Jones, M. N., Jones, S. A., & Sin, G. (2019). A Modular Modelling Environment for Computer-Aided Process Design. In S. G. M., C. D. L., & M. J. R. (Eds.), Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design (Vol. 47, pp. 23-28). Elsevier. Computer Aided Chemical Engineering https://doi.org/10.1016/B978-0-12-818597-1.50004-7
Jones, Mark Nicholas ; Jones, Simon Anthony ; Sin, Gürkan. / A Modular Modelling Environment for Computer-Aided Process Design. Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design. editor / Salvador Garcia Muñoz ; Carl D. Laird ; Matthew J. Realff. Vol. 47 Elsevier, 2019. pp. 23-28 (Computer Aided Chemical Engineering).
@inproceedings{bafc09c0136d4bf0987b27c9a21e8f4f,
title = "A Modular Modelling Environment for Computer-Aided Process Design",
abstract = "We present a modelling approach which allows to formulate and solve steady-state and dynamic process systems engineering problems in the programming languages Fortran and Python. Additionally, we have developed a property prediction software which can be installed with a container on a local machine or on servers rented by the user, research institution or industrial entity. The prediction tool comes with a web-interface and an application programming interface (API) which one can connect to and request predictions of fixed physical and thermo-physical properties directly into their routines. Further we used Pyomo to solve superstructure optimization problems with surrogate models to find the optimal process structure and point of operation to the given global constraints. On the superstructure level we connect a graphical user interface (GUI) with Pyomo to make it easier for the general user to work with an equation-based environment where initial estimates for all variables can be defined through the spreadsheet-style table of the GUI. This modular, multi-level approach also allowed us to connect commercial process simulators with our developed Python-COM interface to perform sensitivity analysis or retrieve surrogate models in e.g. Aspen or PRO/II. The process design, process flow-sheeting and superstructure optimization layer can individually be wrapped with lifecycle analysis tools. The modelling environment is platform independent (except the information retrieval from Aspen or PRO/II). All the necessary code examples in this environment is hosted and further developed in several different repositories freely accessible to scientific and industrial users. The code is published under a free license.",
keywords = "Derivative-free optimization, Graphical flowsheeting tool, Multi-level Framework, Process Design, Property Prediction, Superstructure Optimization",
author = "Jones, {Mark Nicholas} and Jones, {Simon Anthony} and G{\"u}rkan Sin",
year = "2019",
doi = "10.1016/B978-0-12-818597-1.50004-7",
language = "English",
volume = "47",
pages = "23--28",
editor = "{Salvador Garcia Mu{\~n}oz} and {Carl D. Laird} and {Matthew J. Realff}",
booktitle = "Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design",
publisher = "Elsevier",
address = "United Kingdom",

}

Jones, MN, Jones, SA & Sin, G 2019, A Modular Modelling Environment for Computer-Aided Process Design. in SGM, CDL & MJR (eds), Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design. vol. 47, Elsevier, Computer Aided Chemical Engineering, pp. 23-28, 9th International Conference on Foundations of Computer-Aided Process Design, Copper Mountain, United States, 14/07/2019. https://doi.org/10.1016/B978-0-12-818597-1.50004-7

A Modular Modelling Environment for Computer-Aided Process Design. / Jones, Mark Nicholas; Jones, Simon Anthony; Sin, Gürkan.

Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design. ed. / Salvador Garcia Muñoz; Carl D. Laird; Matthew J. Realff. Vol. 47 Elsevier, 2019. p. 23-28 (Computer Aided Chemical Engineering).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AU - Jones, Mark Nicholas

AU - Jones, Simon Anthony

AU - Sin, Gürkan

PY - 2019

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N2 - We present a modelling approach which allows to formulate and solve steady-state and dynamic process systems engineering problems in the programming languages Fortran and Python. Additionally, we have developed a property prediction software which can be installed with a container on a local machine or on servers rented by the user, research institution or industrial entity. The prediction tool comes with a web-interface and an application programming interface (API) which one can connect to and request predictions of fixed physical and thermo-physical properties directly into their routines. Further we used Pyomo to solve superstructure optimization problems with surrogate models to find the optimal process structure and point of operation to the given global constraints. On the superstructure level we connect a graphical user interface (GUI) with Pyomo to make it easier for the general user to work with an equation-based environment where initial estimates for all variables can be defined through the spreadsheet-style table of the GUI. This modular, multi-level approach also allowed us to connect commercial process simulators with our developed Python-COM interface to perform sensitivity analysis or retrieve surrogate models in e.g. Aspen or PRO/II. The process design, process flow-sheeting and superstructure optimization layer can individually be wrapped with lifecycle analysis tools. The modelling environment is platform independent (except the information retrieval from Aspen or PRO/II). All the necessary code examples in this environment is hosted and further developed in several different repositories freely accessible to scientific and industrial users. The code is published under a free license.

AB - We present a modelling approach which allows to formulate and solve steady-state and dynamic process systems engineering problems in the programming languages Fortran and Python. Additionally, we have developed a property prediction software which can be installed with a container on a local machine or on servers rented by the user, research institution or industrial entity. The prediction tool comes with a web-interface and an application programming interface (API) which one can connect to and request predictions of fixed physical and thermo-physical properties directly into their routines. Further we used Pyomo to solve superstructure optimization problems with surrogate models to find the optimal process structure and point of operation to the given global constraints. On the superstructure level we connect a graphical user interface (GUI) with Pyomo to make it easier for the general user to work with an equation-based environment where initial estimates for all variables can be defined through the spreadsheet-style table of the GUI. This modular, multi-level approach also allowed us to connect commercial process simulators with our developed Python-COM interface to perform sensitivity analysis or retrieve surrogate models in e.g. Aspen or PRO/II. The process design, process flow-sheeting and superstructure optimization layer can individually be wrapped with lifecycle analysis tools. The modelling environment is platform independent (except the information retrieval from Aspen or PRO/II). All the necessary code examples in this environment is hosted and further developed in several different repositories freely accessible to scientific and industrial users. The code is published under a free license.

KW - Derivative-free optimization

KW - Graphical flowsheeting tool

KW - Multi-level Framework

KW - Process Design

KW - Property Prediction

KW - Superstructure Optimization

U2 - 10.1016/B978-0-12-818597-1.50004-7

DO - 10.1016/B978-0-12-818597-1.50004-7

M3 - Article in proceedings

VL - 47

SP - 23

EP - 28

BT - Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design

A2 - , Salvador Garcia Muñoz

A2 - , Carl D. Laird

A2 - , Matthew J. Realff

PB - Elsevier

ER -

Jones MN, Jones SA, Sin G. A Modular Modelling Environment for Computer-Aided Process Design. In SGM, CDL, MJR, editors, Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design. Vol. 47. Elsevier. 2019. p. 23-28. (Computer Aided Chemical Engineering). https://doi.org/10.1016/B978-0-12-818597-1.50004-7