Reliability of operating window identified from process data

Heimo Ihalainen; Risto Ritala; Olli Saarela

doi:10.1016/B978-0-444-63234-0.50105-6

Reliability of operating window identified from process data

Heimo Ihalainen, Risto Ritala, Olli Saarela

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

This paper discusses data-based operating windows as a tool for process management and development. In particular identification of the operating window and its uncertainty are analyzed. The operating window is determined by maximizing either the mutual information (static) or entropy transfer (dynamic). An industrial example shows that the entropy of the indicator variable is reduced to half by an operating window specified with only few variables, selected amongst over 3000 candidates. Test model based simulations suggest that such few-variable operating windows can be reliably identified from datasets having lengths of a few thousand observations.

Original language	English
Title of host publication	Proceedings of the 23rd European Symposium on Computer Aided Process Engineering
Editors	Andrzej Kraslawski, Ilkka Turunen
Publisher	Elsevier
Pages	625-630
ISBN (Electronic)	978-0-444-63241-8
ISBN (Print)	978-0-444-63234-0
DOIs	https://doi.org/10.1016/B978-0-444-63234-0.50105-6
Publication status	Published - 2013
MoE publication type	A4 Article in a conference publication
Event	23rd European Symposium on Computer Aided Process Engineering, ESCAPE 23 - Lappeenranta, Finland Duration: 9 Jun 2013 → 12 Jun 2013 Conference number: 23

Publication series

Series	Computer Aided Chemical Engineering
Volume	32
ISSN	1570-7946

Conference

Conference	23rd European Symposium on Computer Aided Process Engineering, ESCAPE 23
Abbreviated title	ESCAPE 23
Country	Finland
City	Lappeenranta
Period	9/06/13 → 12/06/13

Keywords

process management
oprating window
mutual information
transfer entropy
uncertainty

Access to Document

10.1016/B978-0-444-63234-0.50105-6

http://www.vtt.fi/inf/julkaisut/muut/2013/OA-ReliabililtyOfOperating.pdf

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Cite this

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title = "Reliability of operating window identified from process data",

abstract = "This paper discusses data-based operating windows as a tool for process management and development. In particular identification of the operating window and its uncertainty are analyzed. The operating window is determined by maximizing either the mutual information (static) or entropy transfer (dynamic). An industrial example shows that the entropy of the indicator variable is reduced to half by an operating window specified with only few variables, selected amongst over 3000 candidates. Test model based simulations suggest that such few-variable operating windows can be reliably identified from datasets having lengths of a few thousand observations.",

keywords = "process management, oprating window, mutual information, transfer entropy, uncertainty",

author = "Heimo Ihalainen and Risto Ritala and Olli Saarela",

note = "LIS: OA ok CA2: TK802 SDA: ICT; 23rd European Symposium on Computer Aided Process Engineering, ESCAPE 23, ESCAPE 23 ; Conference date: 09-06-2013 Through 12-06-2013",

year = "2013",

doi = "10.1016/B978-0-444-63234-0.50105-6",

language = "English",

isbn = "978-0-444-63234-0",

series = "Computer Aided Chemical Engineering",

publisher = "Elsevier",

pages = "625--630",

editor = "Andrzej Kraslawski and Ilkka Turunen",

booktitle = "Proceedings of the 23rd European Symposium on Computer Aided Process Engineering",

address = "Netherlands",

}

Ihalainen, H, Ritala, R & Saarela, O 2013, Reliability of operating window identified from process data. in A Kraslawski & I Turunen (eds), Proceedings of the 23rd European Symposium on Computer Aided Process Engineering. Elsevier, Computer Aided Chemical Engineering, vol. 32, pp. 625-630, 23rd European Symposium on Computer Aided Process Engineering, ESCAPE 23, Lappeenranta, Finland, 9/06/13. https://doi.org/10.1016/B978-0-444-63234-0.50105-6

Reliability of operating window identified from process data. / Ihalainen, Heimo; Ritala, Risto; Saarela, Olli.

Proceedings of the 23rd European Symposium on Computer Aided Process Engineering. ed. / Andrzej Kraslawski; Ilkka Turunen. Elsevier, 2013. p. 625-630 (Computer Aided Chemical Engineering, Vol. 32).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

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N2 - This paper discusses data-based operating windows as a tool for process management and development. In particular identification of the operating window and its uncertainty are analyzed. The operating window is determined by maximizing either the mutual information (static) or entropy transfer (dynamic). An industrial example shows that the entropy of the indicator variable is reduced to half by an operating window specified with only few variables, selected amongst over 3000 candidates. Test model based simulations suggest that such few-variable operating windows can be reliably identified from datasets having lengths of a few thousand observations.

AB - This paper discusses data-based operating windows as a tool for process management and development. In particular identification of the operating window and its uncertainty are analyzed. The operating window is determined by maximizing either the mutual information (static) or entropy transfer (dynamic). An industrial example shows that the entropy of the indicator variable is reduced to half by an operating window specified with only few variables, selected amongst over 3000 candidates. Test model based simulations suggest that such few-variable operating windows can be reliably identified from datasets having lengths of a few thousand observations.

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KW - mutual information

KW - transfer entropy

KW - uncertainty

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DO - 10.1016/B978-0-444-63234-0.50105-6

M3 - Conference article in proceedings

SN - 978-0-444-63234-0

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BT - Proceedings of the 23rd European Symposium on Computer Aided Process Engineering

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T2 - 23rd European Symposium on Computer Aided Process Engineering, ESCAPE 23

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