The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, t...The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, the concept of mixing degree was put forward, which was used to indicate the effect of oxygen jet on decarburization. Furthermore, a more practical process model for BOF steelmaking was developed by analyzing the effect of silicon, manganese, oxygen injection rate, oxygen lance height, and bath temperature on decarburization. Process verification and end-point verification for the process model have been carried out, and the verification results show that the predic- tion accuracy of carbon content reaches 82.6% （the range of carbon content at the end-point is less than 0. 1wt%） and 85.7% （the range of carbon content at end-point is 0. 1wt% -0.7wt%） when the absolute error is less than 0.02wt% and 0.05wt%, respectively.展开更多
For further research on the control model of muhifunctional hot metal ladles between the ironmaking and steelmaking interface, the hot metal ladies of K steel plant were taken as the object to analyze the operation pr...For further research on the control model of muhifunctional hot metal ladles between the ironmaking and steelmaking interface, the hot metal ladies of K steel plant were taken as the object to analyze the operation process. The factors of blast furnace supply and basic oxygen furnace demand were proposed. According to the principle of supply and demand balance, the control model of hot metal was researched under the following factor conditions： equal to, greater than, and less than 1, respectively. The distribution model of the blast furnace, sleelmaking works, and online buffering was proposed. When the supply and demand factor is equal to ） , the turnover number of hot metal ladles equals 16 and the turnover cycle of hot metal ladles equals 512 min. When the factor is greater than 1, the total number of hot metal ladles is equal to the normal turnover number plus the turnover number of the cast iron machine. When the factor is less than 1, the total number of hot metal ladles is equal tO the normal turnover number plus the accumulating number. Satisfactory effects were obtained by applying the control model in produc tion. The numbers of turnover ladles and accumulating ladles were reduced.展开更多
Energy balances are a general fundamental approach for analyzing the heat requirements for metallurgical processes.The formulation of heat balance equations was involved by computing the various components of heat goi...Energy balances are a general fundamental approach for analyzing the heat requirements for metallurgical processes.The formulation of heat balance equations was involved by computing the various components of heat going in and coming out of the oxygen steelmaking furnace.The developed model was validated against the calculations of Healy and McBride.The overall heat losses that have not been analyzed in previous studies were quantified by back-calculating heat loss from 35 industrial data provided by Tata Steel.The results from the model infer that the heat losses range from 1.3%to 5.9%of the total heat input and it can be controlled by optimizing the silicon in hot metal,the amount of scrap added and the postcombustion ratio.The model prediction shows that sensible heat available from the hot metal accounts for around 66%of total heat input and the rest from the exothermic oxidation reactions.Out of 34%of the heat from exothermic reactions,between 20%and 25%of heat is evolved from the oxidation of carbon to carbon monoxide and carbon dioxide.This model can be applied to predict the heat balance of any top blown oxygen steelmaking technology but needs further validation for a range of oxygen steelmaking operations and conditions.展开更多
Basic oxygen furnace(BOF)steelmaking is the most widely used process in global steel production today,accounting for around 70%of the industry's output.Due to the physical,mechanical,and chemical complexities invo...Basic oxygen furnace(BOF)steelmaking is the most widely used process in global steel production today,accounting for around 70%of the industry's output.Due to the physical,mechanical,and chemical complexities involved in the process,conventional monitoring and control methods are often pushed to their limits.The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process.Over the past decade,Machine Learning(ML)techniques have garnered substantial attention,offering a promising pathway to enhance efficiency and suitability of steel production.This paper presents the first comprehensive review of ML applications in the BOF steelmaking process.We provide an introduction to both fields:an overview of the BOF steelmaking process and ML.We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories,supporting the identification of common use cases and approaches.This analysis concludes with the elaboration of challenges,potential solutions,and a future outlook for further research directions.展开更多
基金supported by the New Century Excellent Talents Program of the Ministry of Education of China (No.NCET 07-0067)the National Natural Science Foundation of China (No.50874014)
文摘The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, the concept of mixing degree was put forward, which was used to indicate the effect of oxygen jet on decarburization. Furthermore, a more practical process model for BOF steelmaking was developed by analyzing the effect of silicon, manganese, oxygen injection rate, oxygen lance height, and bath temperature on decarburization. Process verification and end-point verification for the process model have been carried out, and the verification results show that the predic- tion accuracy of carbon content reaches 82.6% （the range of carbon content at the end-point is less than 0. 1wt%） and 85.7% （the range of carbon content at end-point is 0. 1wt% -0.7wt%） when the absolute error is less than 0.02wt% and 0.05wt%, respectively.
基金Item Sponsored by China Postdoctoral Science Foundation(2015M572647XB)Science Research Program of Yunnan Province Education Department of China(2014Y069)
文摘For further research on the control model of muhifunctional hot metal ladles between the ironmaking and steelmaking interface, the hot metal ladies of K steel plant were taken as the object to analyze the operation process. The factors of blast furnace supply and basic oxygen furnace demand were proposed. According to the principle of supply and demand balance, the control model of hot metal was researched under the following factor conditions： equal to, greater than, and less than 1, respectively. The distribution model of the blast furnace, sleelmaking works, and online buffering was proposed. When the supply and demand factor is equal to ） , the turnover number of hot metal ladles equals 16 and the turnover cycle of hot metal ladles equals 512 min. When the factor is greater than 1, the total number of hot metal ladles is equal to the normal turnover number plus the turnover number of the cast iron machine. When the factor is less than 1, the total number of hot metal ladles is equal tO the normal turnover number plus the accumulating number. Satisfactory effects were obtained by applying the control model in produc tion. The numbers of turnover ladles and accumulating ladles were reduced.
文摘Energy balances are a general fundamental approach for analyzing the heat requirements for metallurgical processes.The formulation of heat balance equations was involved by computing the various components of heat going in and coming out of the oxygen steelmaking furnace.The developed model was validated against the calculations of Healy and McBride.The overall heat losses that have not been analyzed in previous studies were quantified by back-calculating heat loss from 35 industrial data provided by Tata Steel.The results from the model infer that the heat losses range from 1.3%to 5.9%of the total heat input and it can be controlled by optimizing the silicon in hot metal,the amount of scrap added and the postcombustion ratio.The model prediction shows that sensible heat available from the hot metal accounts for around 66%of total heat input and the rest from the exothermic oxidation reactions.Out of 34%of the heat from exothermic reactions,between 20%and 25%of heat is evolved from the oxidation of carbon to carbon monoxide and carbon dioxide.This model can be applied to predict the heat balance of any top blown oxygen steelmaking technology but needs further validation for a range of oxygen steelmaking operations and conditions.
文摘Basic oxygen furnace(BOF)steelmaking is the most widely used process in global steel production today,accounting for around 70%of the industry's output.Due to the physical,mechanical,and chemical complexities involved in the process,conventional monitoring and control methods are often pushed to their limits.The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process.Over the past decade,Machine Learning(ML)techniques have garnered substantial attention,offering a promising pathway to enhance efficiency and suitability of steel production.This paper presents the first comprehensive review of ML applications in the BOF steelmaking process.We provide an introduction to both fields:an overview of the BOF steelmaking process and ML.We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories,supporting the identification of common use cases and approaches.This analysis concludes with the elaboration of challenges,potential solutions,and a future outlook for further research directions.
