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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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
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
2019-08-28 04:57:55
@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},
  isbn         = {978-0-12-818634-3},
  issn         = {1570-7946},
  keyword      = {Aspen Plus Dynamics,co-simulation,optimization,PyMoC,Python},
  language     = {eng},
  location     = {Eindhoven, Netherlands},
  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},
  volume       = {46},
  year         = {2019},
}