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
Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry
IMT School for Advanced Studies Lucca.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-9992-7791
Show others and affiliations
2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, 1361-1366 p., 7984308Conference paper, Published paper (Refereed)
Abstract [en]

The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. 1361-1366 p., 7984308
Keyword [en]
Advanced Process Control; Machine Learning; Stochastic Model Predictive Control; Risk-sensitive Model Selection; Cyber-Physical Systems
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-64960DOI: 10.1109/MED.2017.7984308Scopus ID: 2-s2.0-85027858483OAI: oai:DiVA.org:ltu-64960DiVA: diva2:1129607
Conference
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
Projects
DISIRE
Note

Författaruppgifterna på fulltexten/DOI är omkastade (andbra)

Available from: 2017-08-04 Created: 2017-08-04 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Georgoulas, GeorgiosCastaño Arranz, MiguelNikolakopoulos, George
By organisation
Signals and Systems
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 91 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