The novel process of hydrogen-based shaft furnaces(HSFs)has attracted considerable attention because of their significant reduction of CO_(2)emissions.In this study,the interaction of H_(2)and CO with Fe_(tet1)-and Fe...The novel process of hydrogen-based shaft furnaces(HSFs)has attracted considerable attention because of their significant reduction of CO_(2)emissions.In this study,the interaction of H_(2)and CO with Fe_(tet1)-and Fe_(oct2)-terminated Fe_(3)O_(4)(111)surfaces under HSF conditions,including their adsorption and reduction behaviors,was investigated using the density functional theory method.The results indicated that the H_(2)molecule adsorbed onto the Fe_(tet1)-terminated surface with an adsorption energy(AE)of-1.36 eV,whereas the CO molecule preferentially adsorbed on the Fe_(oct2)-terminated surface with an AE of-1.56 eV.Both H_(2)and CO can readily undergo reduction on the Fe_(tet1)-terminated surface(corresponding to energy barriers of 0.83 eV and 2.23 eV,respectively),but kinetically the reaction of H2is more favorable than that of CO.With regard to the thermodynamics at 400-1400 K,the H_(2)was easy to be adsorbed,while the CO would like to react on the Fe_(tet1)-terminated surface.These thermodynamically tendencies were reversed on the Fe_(oct2)-terminated surface.The thermodynamic disadvantage of the reaction of H_(2)on the Fe_(tet1)-terminated surface was offset by an increase in the temperature.Furthermore,the adsorption of H2 and CO on the Fe_(tet1)-terminated surface was competitive,whereas the adsorption of them on the Fe_(oct2)-terminated surface was synergistic.Therefore,iron ores with a higher proportion of Fe_(tet1)-terminated surface can be applied for the HSF process.In conjunction with the increases in the reduction temperature and the ratio of H_(2)in the reducing gas would promote efficient HSF smelting.These observations provide effective guidance for optimizing the practical operation parameters and advancing the development of the HSF process.展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredi...The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.展开更多
The phenomena of tuyere upward-warp have been found at No.6 blast furnace in Kunming Steel Company China after its blow-in, which has made a great impact on the practical production of the furnace. Thus, a number of e...The phenomena of tuyere upward-warp have been found at No.6 blast furnace in Kunming Steel Company China after its blow-in, which has made a great impact on the practical production of the furnace. Thus, a number of efforts have been made to elucidate the mechanism of this phenomenon. The results of investigation and tests revealed that the enrichment and expansion of zinc in the tuyere bricks is the main factor leading to the tuyere upward-warp. The eroding behavior of zinc is that the inner structure of the tuyere bricks turns from dense to loose with entering, enriching and expanding of zinc, which forms spot-like→stripe-like→ditch-like→vein-like→tumorlike eroding passage. Additionally, it is found that the sequence of deleterious ele- ments entering the tuyere refractory is K, Na, Zn and Pb, respectively. Finally, the phenomena and process of zinc crystallization and growth in the refractory have been clearly observed and recorded during this investigation.展开更多
Hydrogen-based shaft furnace process is gaining more and more attention due to its low carbon emission, and the reduction behavior of iron bearing burdens significantly affects its operation. In this work, the effects...Hydrogen-based shaft furnace process is gaining more and more attention due to its low carbon emission, and the reduction behavior of iron bearing burdens significantly affects its operation. In this work, the effects of reduction degree, temperature, and atmosphere on the swelling behavior of pellet has been studied thoroughly under typical hydrogen metallurgy conditions. The results show that the pellets swelled rapidly in the early reduction stage, then reached a maximum reduction swelling index (RSI) at approximately 40%reduction degree. The crystalline transformation of the iron oxides during the reduction process was the main reason of pellets swelling. The RSI increased significantly with increasing temperature in the range of 850-1050℃, the maximum RSI increased from 6.66%to 25.0%in the gas composition of 100%H_(2). With the temperature increased, the pellets suffered more thermal stress resulting in an increase of the volume. The maximum RSI decreased from 19.78%to 17.35%with the volume proportion of H_(2) in the atmosphere increased from 55%to 100%at the temperature of 950℃.The metallic iron tended to precipitate in a lamellar structure rather than whiskers. Consequently, the inside of the pellets became regular, so the RSI decreased. Overall, controlling a reasonable temperature and increasing the H_(2) proportion is an effective way to decrease the RSI of pellets.展开更多
Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF iron...Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.展开更多
The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,...The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed.The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed.A novel method for the delay analysis of furnace heat indicators was established.The extracted delay variables were found to play an important role in modeling.The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model.The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4%,and that for the chemical heat of hot metal in the error range of±0.1wt%was 93.3%.On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels.These sugges-tions were highly accepted by BF operators.Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system.It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9%to 84.9%.This improvement achieved considerable economic benefits.展开更多
Direct reduction based on hydrogen metallurgical gas-based shaft furnace is a promising technology for the efficient and low-carbon smelting of vanadium-titanium magnetite.However,in this process,the sticking of pelle...Direct reduction based on hydrogen metallurgical gas-based shaft furnace is a promising technology for the efficient and low-carbon smelting of vanadium-titanium magnetite.However,in this process,the sticking of pellets occurs due to the aggregation of metal-lic iron between the contact surfaces of adjacent pellets and has a serious negative effect on the continuous operation.This paper presents a detailed experimental study of the effect of TiO2 on the sticking behavior of pellets during direct reduction under different conditions.Results showed that the sticking index(SI)decreased linearly with the increasing TiO2 addition.This phenomenon can be attributed to the increase in unreduced FeTiO3 during reduction,leading to a decrease in the number and strength of metallic iron interconnections at the sticking interface.When the TiO2 addition amount was raised from 0 to 15wt%at 1100°C,the SI also increased from 0.71%to 59.91%.The connection of the slag phase could be attributed to the sticking at a low reduction temperature,corresponding to the low sticking strength.Moreover,the interconnection of metallic iron became the dominant factor,and the SI increased sharply with the increase in re-duction temperature.TiO2 had a greater effect on SI at a high reduction temperature than at a low reduction temperature.展开更多
With the intensified depletion of high-grade iron ores,the increased aluminum content in iron ore concentrates has become unavoidable,which is detrimental to the pelletization process.Therefore,the effect mechanism of...With the intensified depletion of high-grade iron ores,the increased aluminum content in iron ore concentrates has become unavoidable,which is detrimental to the pelletization process.Therefore,the effect mechanism of aluminum on pellet quality must be identified.In this study,the influence of aluminum occurrence and content on the induration of hematite(H)and magnetite(M)pellets was investigated through the addition of corresponding Al-containing additives,including alumina,alumogoethite,gibbsite,and kaolinite.Systematic mineralogical analysis,combined with the thermodynamic properties of different aluminum occurrences and the quantitative characterization of consolidation behaviors,were conducted to determine the related mechanism.The results showed that the alumina from various aluminum occurrences adversely affected the induration characteristics of pellets,especially at an aluminum content of more than 2.0wt%.The thermal decomposition of gibbsite and kaolinite tends to generate internal stress and fine cracks,which hinder the respective microcrystalline bonding and recrystallization between Fe2O3particles.The adverse effect on the induration characteristics of fired pellets with different aluminum occurrences can be relieved to varying degrees through the formation of liquid phase bonds between the hematite particles.Kaolinite is more beneficial to the induration process than the other three aluminum occurrences because of the formation of more liquid phase,which improves pellet consolidation.The research results can further provide insights into the effect of aluminum occurrence and content in iron ore concentrates on downstream processing and serve as a guide for the utilization of high-alumina iron ore concentrates in pelletization.展开更多
基金financially supported by the Key Program of National Natural Science Foundation of China(No.U23A20608)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project),China(No.2023JH2/101800058)+3 种基金the Science&Technology Plan Project of Hebei Province,China(No.23314601L)the Project of Hydrogen-Based Shaft Furnace Reduction-Electric Furnace Melting And Separation Technology Research and Application for High-Titanium Magnetite Iron Ore(No.HG2023239)the General Program of National Natural Science Foundation of China(No.52274253)the Special Project for Major Scientific and Technological Achievements Transformation in Hebei Province,China(No.23284101Z)。
文摘The novel process of hydrogen-based shaft furnaces(HSFs)has attracted considerable attention because of their significant reduction of CO_(2)emissions.In this study,the interaction of H_(2)and CO with Fe_(tet1)-and Fe_(oct2)-terminated Fe_(3)O_(4)(111)surfaces under HSF conditions,including their adsorption and reduction behaviors,was investigated using the density functional theory method.The results indicated that the H_(2)molecule adsorbed onto the Fe_(tet1)-terminated surface with an adsorption energy(AE)of-1.36 eV,whereas the CO molecule preferentially adsorbed on the Fe_(oct2)-terminated surface with an AE of-1.56 eV.Both H_(2)and CO can readily undergo reduction on the Fe_(tet1)-terminated surface(corresponding to energy barriers of 0.83 eV and 2.23 eV,respectively),but kinetically the reaction of H2is more favorable than that of CO.With regard to the thermodynamics at 400-1400 K,the H_(2)was easy to be adsorbed,while the CO would like to react on the Fe_(tet1)-terminated surface.These thermodynamically tendencies were reversed on the Fe_(oct2)-terminated surface.The thermodynamic disadvantage of the reaction of H_(2)on the Fe_(tet1)-terminated surface was offset by an increase in the temperature.Furthermore,the adsorption of H2 and CO on the Fe_(tet1)-terminated surface was competitive,whereas the adsorption of them on the Fe_(oct2)-terminated surface was synergistic.Therefore,iron ores with a higher proportion of Fe_(tet1)-terminated surface can be applied for the HSF process.In conjunction with the increases in the reduction temperature and the ratio of H_(2)in the reducing gas would promote efficient HSF smelting.These observations provide effective guidance for optimizing the practical operation parameters and advancing the development of the HSF process.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
基金supported by the Youth Program of National Natural Science Foundation of China(No.52404343)the General Program of National Natural Science Foundation of China(No.52274326)+2 种基金the Fundamental Research Funds for the Central Universities,China(No.N2425031)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553)the Liaoning Province Science and Technology Plan Joint Program,China(Key Research and Development Program Project)(No.2023JH2/101800058).
文摘The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.
基金supported by Program for New Century Excellent Talents in University(NCET-2008-0099)
文摘The phenomena of tuyere upward-warp have been found at No.6 blast furnace in Kunming Steel Company China after its blow-in, which has made a great impact on the practical production of the furnace. Thus, a number of efforts have been made to elucidate the mechanism of this phenomenon. The results of investigation and tests revealed that the enrichment and expansion of zinc in the tuyere bricks is the main factor leading to the tuyere upward-warp. The eroding behavior of zinc is that the inner structure of the tuyere bricks turns from dense to loose with entering, enriching and expanding of zinc, which forms spot-like→stripe-like→ditch-like→vein-like→tumorlike eroding passage. Additionally, it is found that the sequence of deleterious ele- ments entering the tuyere refractory is K, Na, Zn and Pb, respectively. Finally, the phenomena and process of zinc crystallization and growth in the refractory have been clearly observed and recorded during this investigation.
基金financially supported by the National Natural Science Foundation of China (No.51904063)the China Postdoctoral Science Foundation (No.2018M640259)+2 种基金the Fundamental Research Funds for the Central Universities(No.N2025023)the Key research and development project of Hebei Province (No.21314001D)the Plan of Xingliao Talents,China (No.XLYC1902118)。
文摘Hydrogen-based shaft furnace process is gaining more and more attention due to its low carbon emission, and the reduction behavior of iron bearing burdens significantly affects its operation. In this work, the effects of reduction degree, temperature, and atmosphere on the swelling behavior of pellet has been studied thoroughly under typical hydrogen metallurgy conditions. The results show that the pellets swelled rapidly in the early reduction stage, then reached a maximum reduction swelling index (RSI) at approximately 40%reduction degree. The crystalline transformation of the iron oxides during the reduction process was the main reason of pellets swelling. The RSI increased significantly with increasing temperature in the range of 850-1050℃, the maximum RSI increased from 6.66%to 25.0%in the gas composition of 100%H_(2). With the temperature increased, the pellets suffered more thermal stress resulting in an increase of the volume. The maximum RSI decreased from 19.78%to 17.35%with the volume proportion of H_(2) in the atmosphere increased from 55%to 100%at the temperature of 950℃.The metallic iron tended to precipitate in a lamellar structure rather than whiskers. Consequently, the inside of the pellets became regular, so the RSI decreased. Overall, controlling a reasonable temperature and increasing the H_(2) proportion is an effective way to decrease the RSI of pellets.
基金financially supported by the General Program of the National Natural Science Foundation of China(No.52274326)the Fundamental Research Funds for the Central Universities (Nos.2125018 and 2225008)China Baowu Low Carbon Metallurgy Innovation Foundation(BWLCF202109)。
文摘Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.
基金financially supported by the General Program of the National Natural Science Foundation of China (No. 52274326)the Fundamental Research Funds for the Central Universities (No. N2425031)+3 种基金Seventh Batch of Ten Thousand Talents Plan (No. ZX20220553)China Baowu Low Carbon Metallurgy Innovation Foundation (No. BWLCF202109)The key technology research and development and application of digital transformation throughout the iron and steel production process (No. 2023JH2/101800058)Liaoning Province Science and Technology Plan Joint Program (Key Research and Development Program Project)
文摘The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed.The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed.A novel method for the delay analysis of furnace heat indicators was established.The extracted delay variables were found to play an important role in modeling.The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model.The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4%,and that for the chemical heat of hot metal in the error range of±0.1wt%was 93.3%.On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels.These sugges-tions were highly accepted by BF operators.Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system.It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9%to 84.9%.This improvement achieved considerable economic benefits.
基金the National Natural Science Foundation of China(No.51904063)the Science and Technology Plan Project of Liaoning Province,China(No.2022JH24/10200027)+1 种基金the Key Research and Development Project of Hebei Province,China(No.21314001D)the seventh batch of the Ten Thousand Talents Plan(No.ZX20220553).
文摘Direct reduction based on hydrogen metallurgical gas-based shaft furnace is a promising technology for the efficient and low-carbon smelting of vanadium-titanium magnetite.However,in this process,the sticking of pellets occurs due to the aggregation of metal-lic iron between the contact surfaces of adjacent pellets and has a serious negative effect on the continuous operation.This paper presents a detailed experimental study of the effect of TiO2 on the sticking behavior of pellets during direct reduction under different conditions.Results showed that the sticking index(SI)decreased linearly with the increasing TiO2 addition.This phenomenon can be attributed to the increase in unreduced FeTiO3 during reduction,leading to a decrease in the number and strength of metallic iron interconnections at the sticking interface.When the TiO2 addition amount was raised from 0 to 15wt%at 1100°C,the SI also increased from 0.71%to 59.91%.The connection of the slag phase could be attributed to the sticking at a low reduction temperature,corresponding to the low sticking strength.Moreover,the interconnection of metallic iron became the dominant factor,and the SI increased sharply with the increase in re-duction temperature.TiO2 had a greater effect on SI at a high reduction temperature than at a low reduction temperature.
基金financially supported by the National Natural Science Foundation of China(Nos.52004339 and 52174329)the Fundamental Research Funds for the Central Universities,China(No.N2325031)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202216)。
文摘With the intensified depletion of high-grade iron ores,the increased aluminum content in iron ore concentrates has become unavoidable,which is detrimental to the pelletization process.Therefore,the effect mechanism of aluminum on pellet quality must be identified.In this study,the influence of aluminum occurrence and content on the induration of hematite(H)and magnetite(M)pellets was investigated through the addition of corresponding Al-containing additives,including alumina,alumogoethite,gibbsite,and kaolinite.Systematic mineralogical analysis,combined with the thermodynamic properties of different aluminum occurrences and the quantitative characterization of consolidation behaviors,were conducted to determine the related mechanism.The results showed that the alumina from various aluminum occurrences adversely affected the induration characteristics of pellets,especially at an aluminum content of more than 2.0wt%.The thermal decomposition of gibbsite and kaolinite tends to generate internal stress and fine cracks,which hinder the respective microcrystalline bonding and recrystallization between Fe2O3particles.The adverse effect on the induration characteristics of fired pellets with different aluminum occurrences can be relieved to varying degrees through the formation of liquid phase bonds between the hematite particles.Kaolinite is more beneficial to the induration process than the other three aluminum occurrences because of the formation of more liquid phase,which improves pellet consolidation.The research results can further provide insights into the effect of aluminum occurrence and content in iron ore concentrates on downstream processing and serve as a guide for the utilization of high-alumina iron ore concentrates in pelletization.
