Unbiased Selection of Decision Variables for Optimization

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.

Details

Authors
Organisations
External organisations
  • Perstorp AB
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Chemical Process Engineering

Keywords

  • Subset Selection Algorithm, Decision Variables, Optimization, Aspen Plus Dynamics, Python, COM
Original languageEnglish
Title of host publication27 European Symposium on Computer Aided Process Engineering
EditorsAntonio Espuña, Moisès Graells, Luis Puigjaner
PublisherElsevier
Pages253-258
Number of pages6
Volume40
ISBN (Print)978-0-444-63965-3
Publication statusPublished - 2017 Oct 1
Publication categoryResearch
Peer-reviewedYes
Event27th European Symposium on Computer Aided Process Engineering - Porta Fira, Barcelona, Spain
Duration: 2017 Oct 12017 Nov 5

Publication series

NameComputer Aided Chemical Engineering
PublisherElsevier
Volume40
ISSN (Print)1570-7946

Conference

Conference27th European Symposium on Computer Aided Process Engineering
Abbreviated titleESCAPE27
CountrySpain
CityBarcelona
Period2017/10/012017/11/05