Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Surrogate models for design and study of underground mine ventilation
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. ABB Corporate Research, Sweden.
2018 (English)In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

Ventilation is vital for the production in an underground mine. Therefore, it is important to have efficient and accurate design tools in order to ensure and optimize the airflows in the mine. There are several commercial software for airflow simulation based on first principles. However, the computational cost of simulation together with integrational obstacles when connecting simulation to control strategies limits the benefit of these tools. In this paper an approach utilizing surrogate models as a complementary design tool is presented. It is shown that using surrogate models one can with rather low computational expense evaluate and benchmark different control strategies. It is also shown that the models can be used for identifying possible bottlenecks in the system in advance. Moreover, the use of surrogate models transfer the simulation into a development-friendly environment (such as Matlab). A test case is used based on a real underground mine ventilation design. Two types of surrogate models are fitted to process data; multiple least squares regression and a Gaussian process model. Sensitivity analysis on the surrogate shows the potential of using surrogate models for identifying bottlenecks. Furthermore, the surrogate is used to benchmark two different control strategies for mine ventilation.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1-8
Series
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, ISSN 1946-0740
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-225493DOI: 10.1109/ETFA.2017.8247618ISI: 000427812000053Scopus ID: 2-s2.0-85044445062ISBN: 9781509065059 (print)OAI: oai:DiVA.org:kth-225493DiVA, id: diva2:1195734
Conference
22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017, Limassol, Cyprus, 12 September 2017 through 15 September 2017
Note

QC 20180406

Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-04-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Åstrand, Max

Search in DiVA

By author/editor
Åstrand, Max
By organisation
Automatic Control
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 37 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf