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Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry
IMT School for Advanced Studies Lucca.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-9992-7791
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2017 (Engelska)Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 1361-1366, artikel-id 7984308Konferensbidrag, Publicerat paper (Refereegranskat)
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

Ort, förlag, år, upplaga, sidor
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. s. 1361-1366, artikel-id 7984308
Serie
Mediterranean Conference on Control and Automation, ISSN 2325-369X
Nyckelord [en]
Advanced Process Control, Machine Learning, Stochastic Model Predictive Control, Risk-sensitive Model Selection, Cyber-Physical Systems
Nationell ämneskategori
Reglerteknik
Forskningsämne
Reglerteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-64960DOI: 10.1109/MED.2017.7984308ISI: 000426926300223Scopus ID: 2-s2.0-85027858483OAI: oai:DiVA.org:ltu-64960DiVA, id: diva2:1129607
Konferens
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
Projekt
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Forskningsfinansiär
EU, Horisont 2020, 636834
Anmärkning

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Tillgänglig från: 2017-08-04 Skapad: 2017-08-04 Senast uppdaterad: 2019-09-13Bibliografiskt granskad

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Georgoulas, GeorgiosCastaño Arranz, MiguelNikolakopoulos, George

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Totalt: 130 träffar
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