Unbiased Selection of Decision Variables for Optimization

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

doi:10.1016/B978-0-444-63965-3.50044-1

Unbiased Selection of Decision Variables for Optimization

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

Division of Chemical Engineering

Perstorp AB

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

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.

Original language	English
Title of host publication	27 European Symposium on Computer Aided Process Engineering
Editors	Antonio Espuña, Moisès Graells, Luis Puigjaner
Publisher	Elsevier
Pages	253-258
Number of pages	6
Volume	40
ISBN (Print)	978-0-444-63965-3
DOIs	https://doi.org/10.1016/B978-0-444-63965-3.50044-1
Publication status	Published - 2017 Oct 1
Event	27th European Symposium on Computer Aided Process Engineering - Porta Fira, Barcelona, Spain Duration: 2017 Oct 1 → 2017 Nov 5

Publication series

Name	Computer Aided Chemical Engineering
Publisher	Elsevier
Volume	40
ISSN (Print)	1570-7946

Conference

Conference	27th European Symposium on Computer Aided Process Engineering
Abbreviated title	ESCAPE27
Country/Territory	Spain
City	Barcelona
Period	2017/10/01 → 2017/11/05

Subject classification (UKÄ)

Chemical Engineering

Free keywords

Subset Selection Algorithm
Decision Variables
Optimization
Aspen Plus Dynamics
Python
COM

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/B978-0-444-63965-3.50044-1

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., Andersson, N., Nilsson, B., Max-Hansen, M., & Pajalic, O. (2017). Unbiased Selection of Decision Variables for Optimization. In A. Espuña, M. Graells, & L. Puigjaner (Eds.), 27 European Symposium on Computer Aided Process Engineering (Vol. 40, pp. 253-258). (Computer Aided Chemical Engineering; Vol. 40). Elsevier. https://doi.org/10.1016/B978-0-444-63965-3.50044-1

@inproceedings{f912ab9950c843578e33582ad0bcfd4a,

title = "Unbiased Selection of Decision Variables for Optimization",

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. ",

keywords = "Subset Selection Algorithm, Decision Variables, Optimization, Aspen Plus Dynamics, Python, COM",

author = "Mikael Yamanee-Nolin and Niklas Andersson and Bernt Nilsson and Mark Max-Hansen and Oleg Pajalic",

year = "2017",

month = oct,

day = "1",

doi = "10.1016/B978-0-444-63965-3.50044-1",

language = "English",

isbn = "978-0-444-63965-3",

volume = "40",

series = "Computer Aided Chemical Engineering",

publisher = "Elsevier",

pages = "253--258",

editor = "Antonio Espu{\~n}a and Mois{\`e}s Graells and Luis Puigjaner",

booktitle = "27 European Symposium on Computer Aided Process Engineering",

address = "United States",

note = "27th European Symposium on Computer Aided Process Engineering, ESCAPE27 ; Conference date: 01-10-2017 Through 05-11-2017",

}

Yamanee-Nolin, M, Andersson, N , Nilsson, B, Max-Hansen, M & Pajalic, O 2017, Unbiased Selection of Decision Variables for Optimization. in A Espuña, M Graells & L Puigjaner (eds), 27 European Symposium on Computer Aided Process Engineering. vol. 40, Computer Aided Chemical Engineering, vol. 40, Elsevier, pp. 253-258, 27th European Symposium on Computer Aided Process Engineering, Barcelona, Spain, 2017/10/01. https://doi.org/10.1016/B978-0-444-63965-3.50044-1

Unbiased Selection of Decision Variables for Optimization. / Yamanee-Nolin, Mikael; Andersson, Niklas ; Nilsson, Bernt et al.
27 European Symposium on Computer Aided Process Engineering. ed. / Antonio Espuña; Moisès Graells; Luis Puigjaner. Vol. 40 Elsevier, 2017. p. 253-258 (Computer Aided Chemical Engineering; Vol. 40).

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

TY - GEN

T1 - Unbiased Selection of Decision Variables for Optimization

AU - Yamanee-Nolin, Mikael

AU - Andersson, Niklas

AU - Nilsson, Bernt

AU - Max-Hansen, Mark

AU - Pajalic, Oleg

PY - 2017/10/1

Y1 - 2017/10/1

N2 - 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.

AB - 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.

KW - Subset Selection Algorithm

KW - Decision Variables

KW - Optimization

KW - Aspen Plus Dynamics

KW - Python

KW - COM

U2 - 10.1016/B978-0-444-63965-3.50044-1

DO - 10.1016/B978-0-444-63965-3.50044-1

M3 - Paper in conference proceeding

SN - 978-0-444-63965-3

VL - 40

T3 - Computer Aided Chemical Engineering

SP - 253

EP - 258

BT - 27 European Symposium on Computer Aided Process Engineering

A2 - Espuña, Antonio

A2 - Graells, Moisès

A2 - Puigjaner, Luis

PB - Elsevier

T2 - 27th European Symposium on Computer Aided Process Engineering

Y2 - 1 October 2017 through 5 November 2017

ER -

Unbiased Selection of Decision Variables for Optimization

Abstract

Publication series

Conference

Subject classification (UKÄ)

Free keywords

UN SDGs

Access to Document

Fingerprint

Research output

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

Cite this