Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms

The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator...
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Autor*in:	Álvaro Ojeda Roldán [verfasserIn] Gert Gassner [verfasserIn] Martin Schlautmann [verfasserIn] Luis Enrique Acevedo Galicia [verfasserIn] Doru Stefan Andreiana [verfasserIn] Mikko Heiskanen [verfasserIn] Carlos Leyva Guerrero [verfasserIn] Fernando Dorado Navas [verfasserIn] Alejandro del Real Torres [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning

Übergeordnetes Werk:	In: Journal of Manufacturing and Materials Processing - MDPI AG, 2018, 6(2022), 2, p 34
Übergeordnetes Werk:	volume:6 ; year:2022 ; number:2, p 34

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jmmp6020034

Katalog-ID:	DOAJ03194485X

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spelling	10.3390/jmmp6020034 doi (DE-627)DOAJ03194485X (DE-599)DOAJ8cf9ac191a244228bebbf5f219145220 DE-627 ger DE-627 rakwb eng T58.7-58.8 Álvaro Ojeda Roldán verfasserin aut Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%. oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning Production capacity. Manufacturing capacity Gert Gassner verfasserin aut Martin Schlautmann verfasserin aut Luis Enrique Acevedo Galicia verfasserin aut Doru Stefan Andreiana verfasserin aut Mikko Heiskanen verfasserin aut Carlos Leyva Guerrero verfasserin aut Fernando Dorado Navas verfasserin aut Alejandro del Real Torres verfasserin aut In Journal of Manufacturing and Materials Processing MDPI AG, 2018 6(2022), 2, p 34 (DE-627)1004948336 25044494 nnns volume:6 year:2022 number:2, p 34 https://doi.org/10.3390/jmmp6020034 kostenfrei https://doaj.org/article/8cf9ac191a244228bebbf5f219145220 kostenfrei https://www.mdpi.com/2504-4494/6/2/34 kostenfrei https://doaj.org/toc/2504-4494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2022 2, p 34
allfields_unstemmed	10.3390/jmmp6020034 doi (DE-627)DOAJ03194485X (DE-599)DOAJ8cf9ac191a244228bebbf5f219145220 DE-627 ger DE-627 rakwb eng T58.7-58.8 Álvaro Ojeda Roldán verfasserin aut Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%. oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning Production capacity. Manufacturing capacity Gert Gassner verfasserin aut Martin Schlautmann verfasserin aut Luis Enrique Acevedo Galicia verfasserin aut Doru Stefan Andreiana verfasserin aut Mikko Heiskanen verfasserin aut Carlos Leyva Guerrero verfasserin aut Fernando Dorado Navas verfasserin aut Alejandro del Real Torres verfasserin aut In Journal of Manufacturing and Materials Processing MDPI AG, 2018 6(2022), 2, p 34 (DE-627)1004948336 25044494 nnns volume:6 year:2022 number:2, p 34 https://doi.org/10.3390/jmmp6020034 kostenfrei https://doaj.org/article/8cf9ac191a244228bebbf5f219145220 kostenfrei https://www.mdpi.com/2504-4494/6/2/34 kostenfrei https://doaj.org/toc/2504-4494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2022 2, p 34
allfieldsGer	10.3390/jmmp6020034 doi (DE-627)DOAJ03194485X (DE-599)DOAJ8cf9ac191a244228bebbf5f219145220 DE-627 ger DE-627 rakwb eng T58.7-58.8 Álvaro Ojeda Roldán verfasserin aut Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%. oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning Production capacity. Manufacturing capacity Gert Gassner verfasserin aut Martin Schlautmann verfasserin aut Luis Enrique Acevedo Galicia verfasserin aut Doru Stefan Andreiana verfasserin aut Mikko Heiskanen verfasserin aut Carlos Leyva Guerrero verfasserin aut Fernando Dorado Navas verfasserin aut Alejandro del Real Torres verfasserin aut In Journal of Manufacturing and Materials Processing MDPI AG, 2018 6(2022), 2, p 34 (DE-627)1004948336 25044494 nnns volume:6 year:2022 number:2, p 34 https://doi.org/10.3390/jmmp6020034 kostenfrei https://doaj.org/article/8cf9ac191a244228bebbf5f219145220 kostenfrei https://www.mdpi.com/2504-4494/6/2/34 kostenfrei https://doaj.org/toc/2504-4494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2022 2, p 34
allfieldsSound	10.3390/jmmp6020034 doi (DE-627)DOAJ03194485X (DE-599)DOAJ8cf9ac191a244228bebbf5f219145220 DE-627 ger DE-627 rakwb eng T58.7-58.8 Álvaro Ojeda Roldán verfasserin aut Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%. oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning Production capacity. Manufacturing capacity Gert Gassner verfasserin aut Martin Schlautmann verfasserin aut Luis Enrique Acevedo Galicia verfasserin aut Doru Stefan Andreiana verfasserin aut Mikko Heiskanen verfasserin aut Carlos Leyva Guerrero verfasserin aut Fernando Dorado Navas verfasserin aut Alejandro del Real Torres verfasserin aut In Journal of Manufacturing and Materials Processing MDPI AG, 2018 6(2022), 2, p 34 (DE-627)1004948336 25044494 nnns volume:6 year:2022 number:2, p 34 https://doi.org/10.3390/jmmp6020034 kostenfrei https://doaj.org/article/8cf9ac191a244228bebbf5f219145220 kostenfrei https://www.mdpi.com/2504-4494/6/2/34 kostenfrei https://doaj.org/toc/2504-4494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2022 2, p 34
language	English
source	In Journal of Manufacturing and Materials Processing 6(2022), 2, p 34 volume:6 year:2022 number:2, p 34
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callnumber-first	T - Technology
author	Álvaro Ojeda Roldán
spellingShingle	Álvaro Ojeda Roldán misc T58.7-58.8 misc oxygen blowing process misc cast steel misc machine learning misc artificial intelligence misc reinforcement learning misc Q-learning misc Production capacity. Manufacturing capacity Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms
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topic_title	T58.7-58.8 Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning
topic	misc T58.7-58.8 misc oxygen blowing process misc cast steel misc machine learning misc artificial intelligence misc reinforcement learning misc Q-learning misc Production capacity. Manufacturing capacity
topic_unstemmed	misc T58.7-58.8 misc oxygen blowing process misc cast steel misc machine learning misc artificial intelligence misc reinforcement learning misc Q-learning misc Production capacity. Manufacturing capacity
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title	Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms
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title_full	Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms
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title_sort	optimisation of operator support systems through artificial intelligence for the cast steel industry: a case for optimisation of the oxygen blowing process based on machine learning algorithms
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title_auth	Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms
abstract	The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%.
abstractGer	The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%.
abstract_unstemmed	The processes involved in the metallurgical industry consume significant amounts of energy and materials, so improving their control would result in considerable improvements in the efficient use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an operator support system that improves the efficiency of the oxygen blowing process of a real cast steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory. This reinforcement learning agent is trained with both historical data provided by the company and data generated by an external model. The trained agent will be the basis of the operator support system that will be integrated into the factory, allowing the agent to continue improving with new and real experience. The results show that the suggestions of the agent improve as it gains experience, and consequently the efficiency of the process also improves. As a result, the success rate of the process increases by 12%.
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container_issue	2, p 34
title_short	Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms
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up_date	2024-07-03T23:14:24.586Z
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