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Data-driven modelling, learning and stochastic predictive control for the steel industry
IMT School for Advanced Studies Lucca, Italy.
KU Leuven, Belgium.
Lulea University of Technology, Sweden.
KU Leuven, Belgium.
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2017 (Engelska)Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, 2017, s. 1361-1366Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The steel industry involves energy-intensive processes such as combustion processes whose accurate modelling via first principles is both challenging and unlikely to lead to accurate models let alone cast time-varying dynamics and describe the inevitable wear and tear. In this paper we address the main objective which is the reduction of energy consumption and emissions along with the enhancement of the autonomy of the controlled process by online modelling and uncertainty-aware predictive control. We propose a risk-sensitive model selection procedure which makes use of the modern theory of risk measures and obtain dynamical models using process data from our experimental setting: a walking beam furnace at Swerea MEFOS. We use a scenario-based model predictive controller to track given temperature references at the three heating zones of the furnace and we train a classifier which predicts possible drops in the excess of Oxygen in each heating zone below acceptable levels. This information is then used to recalibrate the controller in order to maintain a high quality of combustion, therefore, higher thermal efficiency and lower emissions.

Ort, förlag, år, upplaga, sidor
2017. s. 1361-1366
Nyckelord [en]
Advanced Process Control, Cyber-Physical Systems, Machine Learning, Risk-sensitive Model Selection, Stochastic Model Predictive Control
Nationell ämneskategori
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:ri:diva-33130DOI: 10.1109/MED.2017.7984308Scopus ID: 2-s2.0-85027858483ISBN: 9781509045334 (tryckt)OAI: oai:DiVA.org:ri-33130DiVA, id: diva2:1179118
Konferens
25th Mediterranean Conference on Control and Automation, MED 2017, 3 July 2017 through 6 July 2017
Anmärkning

Funding details: KU Leuven; Funding details: BOF/STG-15-043, KU Leuven; Funding details: University of Engineering and Technology, Peshawar; Funding details: McDonnell Center for Systems Neuroscience; Funding details: 636834, Technische Universiteit Delft; Funding details: Fédération Wallonie-Bruxelles; Funding details: Luleå Tekniska Universitet; Funding details: Department of Electrical Engineering, Chulalongkorn University; Funding details: H2020 LEIT Advanced Manufacturing and Processing

Tillgänglig från: 2018-01-31 Skapad: 2018-01-31 Senast uppdaterad: 2018-01-31Bibliografiskt granskad

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