{"title":"BOF Gas Cleaning System Upgrades for Increased Efficiency and Off–Gas Quality","authors":"E. Engel, P. Klut, R. Herold, M. Meyn","doi":"10.33313/380/013","DOIUrl":"https://doi.org/10.33313/380/013","url":null,"abstract":"","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122614943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Ortner, L. Demuner, M. Schuster, O. Láng, F. Ramstorfer
{"title":"Assessment of the Peritectic Behavior in the Continuous Casting Mold","authors":"C. Ortner, L. Demuner, M. Schuster, O. Láng, F. Ramstorfer","doi":"10.33313/380/088","DOIUrl":"https://doi.org/10.33313/380/088","url":null,"abstract":"","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132810637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Formation and Distribution of Ti(C,N) to Prevent Blast Furnace Refractory Wear","authors":"P. Pistorius, T. Britt","doi":"10.33313/380/040","DOIUrl":"https://doi.org/10.33313/380/040","url":null,"abstract":"","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132619474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Through hardened steel ball fatigue failure is an atypical mode of failure in a rolling element bearing. A recent full-scale bench test resulted in ball spalling well below calculated bearing life. Subsequent metallurgical analysis of the spalled balls found inferior microstructure and manufacturing methods. Microstructural analysis revealed significant carbide segregation and inclusions in the steel. These can result from substandard spheroidized annealing and steel making practices. In addition, the grain flow of the balls revealed a manufacturing anomaly which produced a stress riser in the material making it more susceptible to crack initiation. The inferior manufactured balls caused at least an 80% reduction in rolling contact fatigue life of the bearing.
{"title":"Ball Spalling in Rolling Element Bearings: Decrease in Rolling Contact Fatigue Life Due to Inferior Microstructure and Manufacturing Processes","authors":"G. Keep, M. Wolka, E. Brazitis","doi":"10.33313/380/226","DOIUrl":"https://doi.org/10.33313/380/226","url":null,"abstract":"Through hardened steel ball fatigue failure is an atypical mode of failure in a rolling element bearing. A recent full-scale bench test resulted in ball spalling well below calculated bearing life. Subsequent metallurgical analysis of the spalled balls found inferior microstructure and manufacturing methods. Microstructural analysis revealed significant carbide segregation and inclusions in the steel. These can result from substandard spheroidized annealing and steel making practices. In addition, the grain flow of the balls revealed a manufacturing anomaly which produced a stress riser in the material making it more susceptible to crack initiation. The inferior manufactured balls caused at least an 80% reduction in rolling contact fatigue life of the bearing.","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126377727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Phull, J. Egas, S. Barui, S. Mukherjee, K. Chattopadhyay
Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio ( l p ) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of l p , the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (‘High’, ‘Moderate’, ‘Low’, and ‘Very Low’) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (≥97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF.
{"title":"Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking","authors":"J. Phull, J. Egas, S. Barui, S. Mukherjee, K. Chattopadhyay","doi":"10.3390/met10010025","DOIUrl":"https://doi.org/10.3390/met10010025","url":null,"abstract":"Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio ( l p ) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of l p , the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (‘High’, ‘Moderate’, ‘Low’, and ‘Very Low’) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (≥97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF.","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128429288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Bathla, S. Agashe, T. Popławski, V. Devabhaktuni, C. Elkin
Digital technologies are transforming industry at all levels. Steel has the opportunity to lead all heavy industries as an early adopter of specific digital technologies to improve our sustainability and competitiveness. This column is part of AIST’s strategy to become the epicenter for steel’s digital transformation, by providing a variety of platforms to showcase and disseminate Industry 4.0 knowledge specific for steel manufacturing, from big-picture concepts to specific processes. Improved Prediction of Steel Hardness Through Neural Network Regression
{"title":"Improved Prediction of Steel Hardness Through Neural Network Regression","authors":"R. Bathla, S. Agashe, T. Popławski, V. Devabhaktuni, C. Elkin","doi":"10.33313/380/216","DOIUrl":"https://doi.org/10.33313/380/216","url":null,"abstract":"Digital technologies are transforming industry at all levels. Steel has the opportunity to lead all heavy industries as an early adopter of specific digital technologies to improve our sustainability and competitiveness. This column is part of AIST’s strategy to become the epicenter for steel’s digital transformation, by providing a variety of platforms to showcase and disseminate Industry 4.0 knowledge specific for steel manufacturing, from big-picture concepts to specific processes. Improved Prediction of Steel Hardness Through Neural Network Regression","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122696693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Hackmann, K. Huang, V. Berenzon, X. Liu, J. Gnauk
Digital technologies are transforming industry at all levels. Steel has the opportunity to lead all heavy industries as an early adopter of specific digital technologies to improve our sustainability and competitiveness. This column is part of AIST’s strategy to become the epicenter for steel’s digital transformation, by providing a variety of platforms to showcase and disseminate Industry 4.0 knowledge specific for steel manufacturing, from big-picture concepts to specific processes. Integrated Overall Quality Management
{"title":"Integrated Overall Quality Management","authors":"J. Hackmann, K. Huang, V. Berenzon, X. Liu, J. Gnauk","doi":"10.33313/380/203","DOIUrl":"https://doi.org/10.33313/380/203","url":null,"abstract":"Digital technologies are transforming industry at all levels. Steel has the opportunity to lead all heavy industries as an early adopter of specific digital technologies to improve our sustainability and competitiveness. This column is part of AIST’s strategy to become the epicenter for steel’s digital transformation, by providing a variety of platforms to showcase and disseminate Industry 4.0 knowledge specific for steel manufacturing, from big-picture concepts to specific processes. Integrated Overall Quality Management","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125668411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital technologies are transforming industry at all levels. Steel has the opportunity to lead all heavy industries as an early adopter of specific digital technologies to improve our sustainability and competitiveness. This column is part of AIST’s strategy to become the epicenter for steel’s digital transformation, by providing a variety of platforms to showcase and disseminate Industry 4.0 knowledge specific for steel manufacturing, from big-picture concepts to specific processes. AI Application to Melting Temperature Prediction in an Electric Arc Furnace
{"title":"AI Application to Melting Temperature Prediction in an Electric Arc Furnace","authors":"F. Monti, J. Ibarra, M. Saparrat","doi":"10.33313/380/060","DOIUrl":"https://doi.org/10.33313/380/060","url":null,"abstract":"Digital technologies are transforming industry at all levels. Steel has the opportunity to lead all heavy industries as an early adopter of specific digital technologies to improve our sustainability and competitiveness. This column is part of AIST’s strategy to become the epicenter for steel’s digital transformation, by providing a variety of platforms to showcase and disseminate Industry 4.0 knowledge specific for steel manufacturing, from big-picture concepts to specific processes. AI Application to Melting Temperature Prediction in an Electric Arc Furnace","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116208442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Zugliano, A. Martinis, A. H. Giraldo, D. D. Nogare, D. Pauluzzi
Alessandro Martinis Vice President Ironmaking DRI, Danieli & Officine Meccaniche, Buttrio (UD), Italy a.martinis@danieli.it Climate change is one of the defining challenges of our era and the iron and steel sector is responsible for approximately 7% of global CO2 emissions. Innovative hydrogen-based technologies are being developed to decrease the carbon footprint of tomorrow’s steelmaking plants. In this context, Energiron is a mature direct reduction technology that maximizes the efficient use of hydrogen for direct reduced iron (DRI) production. This paper presents a computational fluid dynamics analysis of an Energiron reactor operating with different levels of hydrogen; the resulting momentum, species and enthalpy balances for both the DRI and the gas phases are described and analyzed.
{"title":"CFD Study of an Energiron Reactor Fed With Different Concentrations of Hydrogen","authors":"A. Zugliano, A. Martinis, A. H. Giraldo, D. D. Nogare, D. Pauluzzi","doi":"10.33313/380/056","DOIUrl":"https://doi.org/10.33313/380/056","url":null,"abstract":"Alessandro Martinis Vice President Ironmaking DRI, Danieli & Officine Meccaniche, Buttrio (UD), Italy a.martinis@danieli.it Climate change is one of the defining challenges of our era and the iron and steel sector is responsible for approximately 7% of global CO2 emissions. Innovative hydrogen-based technologies are being developed to decrease the carbon footprint of tomorrow’s steelmaking plants. In this context, Energiron is a mature direct reduction technology that maximizes the efficient use of hydrogen for direct reduced iron (DRI) production. This paper presents a computational fluid dynamics analysis of an Energiron reactor operating with different levels of hydrogen; the resulting momentum, species and enthalpy balances for both the DRI and the gas phases are described and analyzed.","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133668346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Thomas, H. Yang, M. Zappulla, M. Liang, S. Cho, H. Olia
The pressure distribution in the flow delivery system is very important to steel quality, since the minimum pressure in the nozzle can cause air aspiration through cracks, joints, or porous refractory. A new MATLAB-based modeling tool has been developed to predict Pressure-drop Flow-rate relations in a Slide Gate system (PFSG) that enables researchers to investigate these phenomena. This model is validated with three-dimensional finite-difference model calculations and plant measurements and is applied to conduct parametric studies. The slide gate opening at which the minimum pressure occurs depends only on the nozzle diameter and is not affected by tundish height or casting speed. Decreasing lower diameter of the Submerged Entry Nozzle requires an increase in the slide gate opening to maintain casting speed. Furthermore, changing all diameters of the nozzle together has even more effect on the slide gate opening. This effect is beneficial to increase the minimum pressure in the system and lessen air aspiration problems.
{"title":"Pressure-Drop and Flowrate Model of Slidegate Metal Delivery Systems (PFSG)","authors":"B. Thomas, H. Yang, M. Zappulla, M. Liang, S. Cho, H. Olia","doi":"10.33313/380/212","DOIUrl":"https://doi.org/10.33313/380/212","url":null,"abstract":"The pressure distribution in the flow delivery system is very important to steel quality, since the minimum pressure in the nozzle can cause air aspiration through cracks, joints, or porous refractory. A new MATLAB-based modeling tool has been developed to predict Pressure-drop Flow-rate relations in a Slide Gate system (PFSG) that enables researchers to investigate these phenomena. This model is validated with three-dimensional finite-difference model calculations and plant measurements and is applied to conduct parametric studies. The slide gate opening at which the minimum pressure occurs depends only on the nozzle diameter and is not affected by tundish height or casting speed. Decreasing lower diameter of the Submerged Entry Nozzle requires an increase in the slide gate opening to maintain casting speed. Furthermore, changing all diameters of the nozzle together has even more effect on the slide gate opening. This effect is beneficial to increase the minimum pressure in the system and lessen air aspiration problems.","PeriodicalId":226675,"journal":{"name":"AISTech2020 Proceedings of the Iron and Steel Technology Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131790168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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