Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model
Yamanee-Nolin, Mikael LU ; Löfgren, Anton LU
; Andersson, Niklas
LU
; Nilsson, Bernt
LU
; Max-Hansen, Mark
LU
and Pajalic, Oleg
(2019)
29th European Symposium on Computer Aided Process Engineering
In Computer Aided Chemical Engineering
46.
p.721-726
- 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... (More)
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.
(Less)
- author
- Yamanee-Nolin, Mikael
LU
; Löfgren, Anton
LU
; Andersson, Niklas
LU
; Nilsson, Bernt
LU
; Max-Hansen, Mark
LU
and Pajalic, Oleg
- organization
- publishing date
- 2019-06-16
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Aspen Plus Dynamics, co-simulation, optimization, PyMoC, Python
- host publication
- Computer Aided Chemical Engineering
- series title
- Computer Aided Chemical Engineering
- volume
- 46
- pages
- 6 pages
- publisher
- Elsevier Science Publishers B.V.
- conference name
- 29th European Symposium on Computer Aided Process Engineering
- conference location
- Eindhoven, Netherlands
- conference dates
- 2019-06-16 - 2019-06-19
- external identifiers
-
- scopus:85069666085
- ISSN
- 1570-7946
- ISBN
- 978-0-12-818634-3
- DOI
- 10.1016/B978-0-12-818634-3.50121-1
- language
- English
- LU publication?
- yes
- id
- 47032dee-94de-4530-b645-c380bc0af93b
- date added to LUP
- 2019-08-08 10:20:25
- date last changed
- 2023-12-18 05:27:59
@inbook{47032dee-94de-4530-b645-c380bc0af93b,
abstract = {{<p>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.</p>}},
author = {{Yamanee-Nolin, Mikael and Löfgren, Anton and Andersson, Niklas and Nilsson, Bernt and Max-Hansen, Mark and Pajalic, Oleg}},
booktitle = {{Computer Aided Chemical Engineering}},
isbn = {{978-0-12-818634-3}},
issn = {{1570-7946}},
keywords = {{Aspen Plus Dynamics; co-simulation; optimization; PyMoC; Python}},
language = {{eng}},
month = {{06}},
pages = {{721--726}},
publisher = {{Elsevier Science Publishers B.V.}},
series = {{Computer Aided Chemical Engineering}},
title = {{Single-shooting optimization of an industrial process through co-simulation of a modularized Aspen Plus Dynamics model}},
url = {{http://dx.doi.org/10.1016/B978-0-12-818634-3.50121-1}},
doi = {{10.1016/B978-0-12-818634-3.50121-1}},
volume = {{46}},
year = {{2019}},
}