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Unbiased Selection of Decision Variables for Optimization

Yamanee-Nolin, Mikael LU ; Andersson, Niklas LU orcid ; Nilsson, Bernt LU orcid ; Max-Hansen, Mark LU and Pajalic, Oleg (2017) 27th European Symposium on Computer Aided Process Engineering In Computer Aided Chemical Engineering 40. p.253-258
Abstract
Complex chemical processes require complex simulation models. Selecting decision variables for optimization is increasingly difficult. This paper presents a study of a Subset Selection Algorithm (SSA) applied to the selection of decision variables to facili-tate a reduction of the decision variable combination sets to consider for a process designer, aimed towards improving said selection, optimization, and thereby resource efficiency. The results help conclude that SSA is able to reduce the consideration set of decision variable combinations for the process designer, and selects combination sets that are more effective in terms of minimizing the objective.
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Subset Selection Algorithm, Decision Variables, Optimization, Aspen Plus Dynamics, Python, COM
host publication
27 European Symposium on Computer Aided Process Engineering
series title
Computer Aided Chemical Engineering
editor
Espuña, Antonio ; Graells, Moisès and Puigjaner, Luis
volume
40
pages
6 pages
publisher
Elsevier
conference name
27th European Symposium on Computer Aided Process Engineering
conference location
Barcelona, Spain
conference dates
2017-10-01 - 2017-11-05
external identifiers
  • wos:000417380000044
  • scopus:85041384259
ISSN
1570-7946
ISBN
978-0-444-63965-3
DOI
10.1016/B978-0-444-63965-3.50044-1
language
English
LU publication?
yes
id
f912ab99-50c8-4357-8e33-582ad0bcfd4a
date added to LUP
2017-11-29 09:30:09
date last changed
2024-11-20 02:18:23
@inproceedings{f912ab99-50c8-4357-8e33-582ad0bcfd4a,
  abstract     = {{Complex chemical processes require complex simulation models. Selecting decision variables for optimization is increasingly difficult. This paper presents a study of a Subset Selection Algorithm (SSA) applied to the selection of decision variables to facili-tate a reduction of the decision variable combination sets to consider for a process designer, aimed towards improving said selection, optimization, and thereby resource efficiency. The results help conclude that SSA is able to reduce the consideration set of decision variable combinations for the process designer, and selects combination sets that are more effective in terms of minimizing the objective.}},
  author       = {{Yamanee-Nolin, Mikael and Andersson, Niklas and Nilsson, Bernt and Max-Hansen, Mark and Pajalic, Oleg}},
  booktitle    = {{27 European Symposium on Computer Aided Process Engineering}},
  editor       = {{Espuña, Antonio and Graells, Moisès and Puigjaner, Luis}},
  isbn         = {{978-0-444-63965-3}},
  issn         = {{1570-7946}},
  keywords     = {{Subset Selection Algorithm; Decision Variables; Optimization; Aspen Plus Dynamics; Python; COM}},
  language     = {{eng}},
  month        = {{10}},
  pages        = {{253--258}},
  publisher    = {{Elsevier}},
  series       = {{Computer Aided Chemical Engineering}},
  title        = {{Unbiased Selection of Decision Variables for Optimization}},
  url          = {{http://dx.doi.org/10.1016/B978-0-444-63965-3.50044-1}},
  doi          = {{10.1016/B978-0-444-63965-3.50044-1}},
  volume       = {{40}},
  year         = {{2017}},
}