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...
Ausführliche Beschreibung
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: |
---|
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: |
---|
DOI / URN: |
10.3390/jmmp6020034 |
---|
Katalog-ID: |
DOAJ03194485X |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ03194485X | ||
003 | DE-627 | ||
005 | 20240414075002.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/jmmp6020034 |2 doi | |
035 | |a (DE-627)DOAJ03194485X | ||
035 | |a (DE-599)DOAJ8cf9ac191a244228bebbf5f219145220 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a T58.7-58.8 | |
100 | 0 | |a Álvaro Ojeda Roldán |e verfasserin |4 aut | |
245 | 1 | 0 | |a 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 |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a 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%. | ||
650 | 4 | |a oxygen blowing process | |
650 | 4 | |a cast steel | |
650 | 4 | |a machine learning | |
650 | 4 | |a artificial intelligence | |
650 | 4 | |a reinforcement learning | |
650 | 4 | |a Q-learning | |
653 | 0 | |a Production capacity. Manufacturing capacity | |
700 | 0 | |a Gert Gassner |e verfasserin |4 aut | |
700 | 0 | |a Martin Schlautmann |e verfasserin |4 aut | |
700 | 0 | |a Luis Enrique Acevedo Galicia |e verfasserin |4 aut | |
700 | 0 | |a Doru Stefan Andreiana |e verfasserin |4 aut | |
700 | 0 | |a Mikko Heiskanen |e verfasserin |4 aut | |
700 | 0 | |a Carlos Leyva Guerrero |e verfasserin |4 aut | |
700 | 0 | |a Fernando Dorado Navas |e verfasserin |4 aut | |
700 | 0 | |a Alejandro del Real Torres |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Journal of Manufacturing and Materials Processing |d MDPI AG, 2018 |g 6(2022), 2, p 34 |w (DE-627)1004948336 |x 25044494 |7 nnns |
773 | 1 | 8 | |g volume:6 |g year:2022 |g number:2, p 34 |
856 | 4 | 0 | |u https://doi.org/10.3390/jmmp6020034 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/8cf9ac191a244228bebbf5f219145220 |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2504-4494/6/2/34 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2504-4494 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 6 |j 2022 |e 2, p 34 |
author_variant |
á o r áor g g gg m s ms l e a g leag d s a dsa m h mh c l g clg f d n fdn a d r t adrt |
---|---|
matchkey_str |
article:25044494:2022----::piiainfprtruprsseshogatfcaitliecfrhcsselnutycsfrpiiainfhoyebo |
hierarchy_sort_str |
2022 |
callnumber-subject-code |
T |
publishDate |
2022 |
allfields |
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 |
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 |
sourceStr |
In Journal of Manufacturing and Materials Processing 6(2022), 2, p 34 volume:6 year:2022 number:2, p 34 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
oxygen blowing process cast steel machine learning artificial intelligence reinforcement learning Q-learning Production capacity. Manufacturing capacity |
isfreeaccess_bool |
true |
container_title |
Journal of Manufacturing and Materials Processing |
authorswithroles_txt_mv |
Álvaro Ojeda Roldán @@aut@@ Gert Gassner @@aut@@ Martin Schlautmann @@aut@@ Luis Enrique Acevedo Galicia @@aut@@ Doru Stefan Andreiana @@aut@@ Mikko Heiskanen @@aut@@ Carlos Leyva Guerrero @@aut@@ Fernando Dorado Navas @@aut@@ Alejandro del Real Torres @@aut@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
1004948336 |
id |
DOAJ03194485X |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ03194485X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414075002.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/jmmp6020034</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ03194485X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8cf9ac191a244228bebbf5f219145220</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">T58.7-58.8</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Álvaro Ojeda Roldán</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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%.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">oxygen blowing process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cast steel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">reinforcement learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Q-learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Production capacity. Manufacturing capacity</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gert Gassner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Martin Schlautmann</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Luis Enrique Acevedo Galicia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Doru Stefan Andreiana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mikko Heiskanen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Carlos Leyva Guerrero</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fernando Dorado Navas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alejandro del Real Torres</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Manufacturing and Materials Processing</subfield><subfield code="d">MDPI AG, 2018</subfield><subfield code="g">6(2022), 2, p 34</subfield><subfield code="w">(DE-627)1004948336</subfield><subfield code="x">25044494</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2, p 34</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/jmmp6020034</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/8cf9ac191a244228bebbf5f219145220</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2504-4494/6/2/34</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2504-4494</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2022</subfield><subfield code="e">2, p 34</subfield></datafield></record></collection>
|
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 |
authorStr |
Álvaro Ojeda Roldán |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)1004948336 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
T58 |
illustrated |
Not Illustrated |
issn |
25044494 |
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 |
topic_browse |
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 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Journal of Manufacturing and Materials Processing |
hierarchy_parent_id |
1004948336 |
hierarchy_top_title |
Journal of Manufacturing and Materials Processing |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)1004948336 |
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 |
ctrlnum |
(DE-627)DOAJ03194485X (DE-599)DOAJ8cf9ac191a244228bebbf5f219145220 |
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 |
author_sort |
Álvaro Ojeda Roldán |
journal |
Journal of Manufacturing and Materials Processing |
journalStr |
Journal of Manufacturing and Materials Processing |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
author_browse |
Álvaro Ojeda Roldán Gert Gassner Martin Schlautmann Luis Enrique Acevedo Galicia Doru Stefan Andreiana Mikko Heiskanen Carlos Leyva Guerrero Fernando Dorado Navas Alejandro del Real Torres |
container_volume |
6 |
class |
T58.7-58.8 |
format_se |
Elektronische Aufsätze |
author-letter |
Álvaro Ojeda Roldán |
doi_str_mv |
10.3390/jmmp6020034 |
author2-role |
verfasserin |
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 |
callnumber |
T58.7-58.8 |
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%. |
collection_details |
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 |
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 |
url |
https://doi.org/10.3390/jmmp6020034 https://doaj.org/article/8cf9ac191a244228bebbf5f219145220 https://www.mdpi.com/2504-4494/6/2/34 https://doaj.org/toc/2504-4494 |
remote_bool |
true |
author2 |
Gert Gassner Martin Schlautmann Luis Enrique Acevedo Galicia Doru Stefan Andreiana Mikko Heiskanen Carlos Leyva Guerrero Fernando Dorado Navas Alejandro del Real Torres |
author2Str |
Gert Gassner Martin Schlautmann Luis Enrique Acevedo Galicia Doru Stefan Andreiana Mikko Heiskanen Carlos Leyva Guerrero Fernando Dorado Navas Alejandro del Real Torres |
ppnlink |
1004948336 |
callnumber-subject |
T - General Technology |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/jmmp6020034 |
callnumber-a |
T58.7-58.8 |
up_date |
2024-07-03T23:14:24.586Z |
_version_ |
1803601538802778112 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ03194485X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414075002.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/jmmp6020034</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ03194485X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8cf9ac191a244228bebbf5f219145220</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">T58.7-58.8</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Álvaro Ojeda Roldán</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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%.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">oxygen blowing process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cast steel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">reinforcement learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Q-learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Production capacity. Manufacturing capacity</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gert Gassner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Martin Schlautmann</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Luis Enrique Acevedo Galicia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Doru Stefan Andreiana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mikko Heiskanen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Carlos Leyva Guerrero</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fernando Dorado Navas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alejandro del Real Torres</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Manufacturing and Materials Processing</subfield><subfield code="d">MDPI AG, 2018</subfield><subfield code="g">6(2022), 2, p 34</subfield><subfield code="w">(DE-627)1004948336</subfield><subfield code="x">25044494</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2, p 34</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/jmmp6020034</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/8cf9ac191a244228bebbf5f219145220</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2504-4494/6/2/34</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2504-4494</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2022</subfield><subfield code="e">2, p 34</subfield></datafield></record></collection>
|
score |
7.4004145 |