Data-driven modelling, learning and stochastic predictive control for the steel industry

D. Herceg; G. Georgoulas; P. Sopasakis; M. Castaño; P. Patrinos; A. Bemporad; J. Niemi; G. Nikolakopoulos

doi:10.1109/MED.2017.7984308

Data-driven modelling, learning and stochastic predictive control for the steel industry

D. Herceg, G. Georgoulas, P. Sopasakis, M. Castaño, P. Patrinos, A. Bemporad, J. Niemi, G. Nikolakopoulos

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Original language	Undefined/Unknown
Title of host publication	2017 25th Mediterranean Conference on Control and Automation (MED)
Pages	1361-1366
Number of pages	6
DOIs	https://doi.org/10.1109/MED.2017.7984308
Publication status	Published - 01 Jul 2017

Keywords

air pollution control
combustion
energy consumption
furnaces
heating
learning systems
predictive control
steel industry
stochastic processes
learning control
stochastic predictive control
data-driven modelling
energy-intensive processes
controlled process
online modelling
uncertainty-aware predictive control
risk-sensitive model selection
risk measures
dynamical models
process data
walking beam furnace
Swerea MEFOS
scenario-based model predictive controller
temperature references
heating zones
classifier training
oxygen
thermal efficiency
Furnaces
Predictive models
Heating systems
Combustion
Legged locomotion
Computational modeling
Process control
Advanced Process Control
Machine Learning
Stochastic Model Predictive Control
Risk-sensitive Model Selection
Cyber-Physical Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MED.2017.7984308

Cite this

@inproceedings{a81e7e70a97f4ed9adb0652604cc0db5,

title = "Data-driven modelling, learning and stochastic predictive control for the steel industry",

keywords = "air pollution control, combustion, energy consumption, furnaces, heating, learning systems, predictive control, steel industry, stochastic processes, learning control, stochastic predictive control, data-driven modelling, energy-intensive processes, controlled process, online modelling, uncertainty-aware predictive control, risk-sensitive model selection, risk measures, dynamical models, process data, walking beam furnace, Swerea MEFOS, scenario-based model predictive controller, temperature references, heating zones, classifier training, oxygen, thermal efficiency, Furnaces, Predictive models, Heating systems, Combustion, Legged locomotion, Computational modeling, Process control, Advanced Process Control, Machine Learning, Stochastic Model Predictive Control, Risk-sensitive Model Selection, Cyber-Physical Systems",

author = "D. Herceg and G. Georgoulas and P. Sopasakis and M. Casta{\~n}o and P. Patrinos and A. Bemporad and J. Niemi and G. Nikolakopoulos",

year = "2017",

month = jul,

day = "1",

doi = "10.1109/MED.2017.7984308",

language = "Undefined/Unknown",

pages = "1361--1366",

booktitle = "2017 25th Mediterranean Conference on Control and Automation (MED)",

}

Data-driven modelling, learning and stochastic predictive control for the steel industry. / Herceg, D.; Georgoulas, G.; Sopasakis, P.; Castaño, M.; Patrinos, P.; Bemporad, A.; Niemi, J.; Nikolakopoulos, G.

2017 25th Mediterranean Conference on Control and Automation (MED). 2017. p. 1361-1366.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Data-driven modelling, learning and stochastic predictive control for the steel industry

AU - Herceg, D.

AU - Georgoulas, G.

AU - Sopasakis, P.

AU - Castaño, M.

AU - Patrinos, P.

AU - Bemporad, A.

AU - Niemi, J.

AU - Nikolakopoulos, G.

PY - 2017/7/1

Y1 - 2017/7/1

KW - air pollution control

KW - combustion

KW - energy consumption

KW - furnaces

KW - heating

KW - learning systems

KW - predictive control

KW - steel industry

KW - stochastic processes

KW - learning control

KW - stochastic predictive control

KW - data-driven modelling

KW - energy-intensive processes

KW - controlled process

KW - online modelling

KW - uncertainty-aware predictive control

KW - risk-sensitive model selection

KW - risk measures

KW - dynamical models

KW - process data

KW - walking beam furnace

KW - Swerea MEFOS

KW - scenario-based model predictive controller

KW - temperature references

KW - heating zones

KW - classifier training

KW - oxygen

KW - thermal efficiency

KW - Furnaces

KW - Predictive models

KW - Heating systems

KW - Combustion

KW - Legged locomotion

KW - Computational modeling

KW - Process control

KW - Advanced Process Control

KW - Machine Learning

KW - Stochastic Model Predictive Control

KW - Risk-sensitive Model Selection

KW - Cyber-Physical Systems

U2 - 10.1109/MED.2017.7984308

DO - 10.1109/MED.2017.7984308

M3 - Conference contribution

SP - 1361

EP - 1366

BT - 2017 25th Mediterranean Conference on Control and Automation (MED)

ER -