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

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Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	27 European Symposium on Computer Aided Process Engineering
Redaktörer	Antonio Espuña, Moisès Graells, Luis Puigjaner
Förlag	Elsevier
Sidor	253-258
Antal sidor	6
Volym	40
ISBN (tryckt)	978-0-444-63965-3
DOI	https://doi.org/10.1016/B978-0-444-63965-3.50044-1
Status	Published - 2017 okt. 1
Evenemang	27th European Symposium on Computer Aided Process Engineering - Porta Fira, Barcelona, Spanien Varaktighet: 2017 okt. 1 → 2017 nov. 5

Publikationsserier

Namn	Computer Aided Chemical Engineering
Förlag	Elsevier
Volym	40
ISSN (tryckt)	1570-7946

Konferens

Konferens	27th European Symposium on Computer Aided Process Engineering
Förkortad titel	ESCAPE27
Land/Territorium	Spanien
Ort	Barcelona
Period	2017/10/01 → 2017/11/05

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

1 Doktorsavhandling (sammanläggning)

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 s.
Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)

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Yamanee-Nolin, M., Andersson, N., Nilsson, B., Max-Hansen, M., & Pajalic, O. (2017). Unbiased Selection of Decision Variables for Optimization. I A. Espuña, M. Graells, & L. Puigjaner (Red.), 27 European Symposium on Computer Aided Process Engineering (Vol. 40, s. 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",

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Yamanee-Nolin, M, Andersson, N , Nilsson, B, Max-Hansen, M & Pajalic, O 2017, Unbiased Selection of Decision Variables for Optimization. i A Espuña, M Graells & L Puigjaner (red), 27 European Symposium on Computer Aided Process Engineering. vol. 40, Computer Aided Chemical Engineering, vol. 40, Elsevier, s. 253-258, 27th European Symposium on Computer Aided Process Engineering, Barcelona, Spanien, 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. red. / Antonio Espuña; Moisès Graells; Luis Puigjaner. Vol. 40 Elsevier, 2017. s. 253-258 (Computer Aided Chemical Engineering; Vol. 40).

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i 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

Sammanfattning

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Konferens

Ämnesklassifikation (UKÄ)

FN:s Globala mål

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Forskningsoutput

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

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