SwePub - sökning: WFRF:(Herceg Domagoj)

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1.	Herceg, Domagoj, et al. (författare) Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry 2017 Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 1361-1366 Konferensbidrag (refereegranskat)abstract 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

Herceg, Domagoj, et al. (författare)
Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry
2017
Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 1361-1366
Konferensbidrag (refereegranskat)abstract
- 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

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