Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model

Mikael Yamanee-Nolin; Anton Löfgren; Niklas Andersson; Bernt Nilsson; Mark Max-Hansen; Oleg Pajalic

doi:10.1016/B978-0-12-818634-3.50121-1

Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model

Mikael Yamanee-Nolin, Anton Löfgren, Niklas Andersson, Bernt Nilsson, Mark Max-Hansen, Oleg Pajalic

Division of Chemical Engineering

Perstorp AB

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

Abstract

The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.

Original language	English
Title of host publication	Computer Aided Chemical Engineering
Publisher	Elsevier Science Publishers B.V.
Pages	721-726
Number of pages	6
ISBN (Print)	978-0-12-818634-3
DOIs	https://doi.org/10.1016/B978-0-12-818634-3.50121-1
Publication status	Published - 2019 Jun 16
Event	29th European Symposium on Computer Aided Process Engineering - Evoluon, Eindhoven, Netherlands Duration: 2019 Jun 16 → 2019 Jun 19 Conference number: 29

Publication series

Name	Computer Aided Chemical Engineering
Volume	46
ISSN (Print)	1570-7946

Conference

Conference	29th European Symposium on Computer Aided Process Engineering
Abbreviated title	ESCAPE
Country/Territory	Netherlands
City	Eindhoven
Period	2019/06/16 → 2019/06/19

Subject classification (UKÄ)

Chemical Engineering

Free keywords

Aspen Plus Dynamics
co-simulation
optimization
PyMoC
Python

Access to Document

10.1016/B978-0-12-818634-3.50121-1

1 Doctoral Thesis (compilation)

Computer-aided optimization of complex processes in production systems and urban environments
Yamanee-Nolin, M., 2020 Apr 14, Lund: Department of Chemical Engineering, Lund University. 209 p.
Research output: Thesis › Doctoral Thesis (compilation)

Open Access
File

Cite this

Yamanee-Nolin, M., Löfgren, A., Andersson, N., Nilsson, B., Max-Hansen, M., & Pajalic, O. (2019). Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model. In Computer Aided Chemical Engineering (pp. 721-726). (Computer Aided Chemical Engineering; Vol. 46). Elsevier Science Publishers B.V.. https://doi.org/10.1016/B978-0-12-818634-3.50121-1

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abstract = "The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.",

keywords = "Aspen Plus Dynamics, co-simulation, optimization, PyMoC, Python",

author = "Mikael Yamanee-Nolin and Anton L{\"o}fgren and Niklas Andersson and Bernt Nilsson and Mark Max-Hansen and Oleg Pajalic",

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Yamanee-Nolin, M, Löfgren, A, Andersson, N , Nilsson, B, Max-Hansen, M & Pajalic, O 2019, Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model. in Computer Aided Chemical Engineering. Computer Aided Chemical Engineering, vol. 46, Elsevier Science Publishers B.V., pp. 721-726, 29th European Symposium on Computer Aided Process Engineering, Eindhoven, Netherlands, 2019/06/16. https://doi.org/10.1016/B978-0-12-818634-3.50121-1

Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model. / Yamanee-Nolin, Mikael; Löfgren, Anton; Andersson, Niklas et al.
Computer Aided Chemical Engineering. Elsevier Science Publishers B.V., 2019. p. 721-726 (Computer Aided Chemical Engineering; Vol. 46).

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

TY - CHAP

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AU - Yamanee-Nolin, Mikael

AU - Löfgren, Anton

AU - Andersson, Niklas

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AU - Max-Hansen, Mark

AU - Pajalic, Oleg

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N2 - The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.

AB - The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.

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DO - 10.1016/B978-0-12-818634-3.50121-1

M3 - Book chapter

AN - SCOPUS:85069666085

SN - 978-0-12-818634-3

T3 - Computer Aided Chemical Engineering

SP - 721

EP - 726

BT - Computer Aided Chemical Engineering

PB - Elsevier Science Publishers B.V.

T2 - 29th European Symposium on Computer Aided Process Engineering

Y2 - 16 June 2019 through 19 June 2019

ER -

Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model

Abstract

Publication series

Conference

Subject classification (UKÄ)

Free keywords

Access to Document

Fingerprint

Research output

Computer-aided optimization of complex processes in production systems and urban environments

Cite this