The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based ...The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.展开更多
The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process,addressing gaps in the field,particularly large-scale dataset sizes and the und...The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process,addressing gaps in the field,particularly large-scale dataset sizes and the underutilization of boosting algorithms.Utilizing a substantial dataset containing over 20,000 heats,significantly bigger than those in previous studies,a comprehensive evaluation of five advanced machine learning models was conducted.These include four ensemble learning algorithms:XGBoost,LightGBM,CatBoost(three boosting algorithms),along with random forest(a bagging algorithm),as well as a neural network model,namely the multilayer perceptron.Our comparative analysis reveals that Bayesian-optimized boosting models demonstrate exceptional robustness and accuracy,achieving the highest R-squared values,the lowest root mean square error,and lowest mean absolute error,along with the best hit ratio.CatBoost exhibited superior performance,with its test R-squared improving by 4.2%compared to that of the random forest and by 0.8%compared to that of the multilayer perceptron.This highlights the efficacy of boosting algorithms in refining complex industrial processes.Additionally,our investigation into the impact of varying dataset sizes,ranging from 500 to 20,000 heats,on model accuracy underscores the importance of leveraging larger-scale datasets to improve the accuracy and stability of predictive models.展开更多
A new technological process involving the introduction of an induction furnace(IF)powered by green electricity was proposed for reducing the CO_(2) emission in the conventional blast furnace–basic oxygen furnace(BF–...A new technological process involving the introduction of an induction furnace(IF)powered by green electricity was proposed for reducing the CO_(2) emission in the conventional blast furnace–basic oxygen furnace(BF–BOF)steelmaking route.The proposed BF–IF–BOF process gains benefits from preheating and smelting scraps utilizing green electricity and further remarkably cuts down the CO_(2) emission in BOF steelmaking.The CO_(2) emissions of conventional and new processes have been comparatively analyzed according to the actual data from a commercial steel plant in China,taking into account the upstream CO_(2) emission,direct CO_(2) emission,and credit CO_(2) emission.The analysis revealed that the CO_(2) emission factor of internal scraps from the steel plant was different from that of purchased scraps from the society but equalled to that of crude steel.The CO_(2) injected into the BOF as a coolant could be defined as the upstream CO_(2) emission source,and there is a net reduction of 1 t CO_(2) emission for each ton of CO_(2) utilized in the BOF.Compared to the BF–BOF process with a scrap ratio of 19.23%,the CO_(2) emission reduction per ton of steel in the new process is 0.278,0.517,0.753,0.987,1.219,1.448,and 1.683 t,respectively,as the scrap ratio increases to 30%,40%,50%,60%,70%,80%,and 90%,and increasing the scrap ratio has a more significant impact on the emission reduction than CO_(2) injecting.A minimum CO_(2) emission model for the BF–IF–BOF process was established,and the minimum CO_(2) emission per ton crude steel was calculated to be 0.677,0.581,0.487,0.393,0.300,0.209,and 0.110 t,for the BF–IF–BOF process with the scrap ratios of 30%,40%,50%,60%,70%,80%,and 90%,respectively.展开更多
Dephosphorisation basic oxygen furnaces (deP-BOFs) greatly differ from conventional BOFs in the melting process, especially its many limits on adding scrap. A mathematical model of the steel scrap melting process was ...Dephosphorisation basic oxygen furnaces (deP-BOFs) greatly differ from conventional BOFs in the melting process, especially its many limits on adding scrap. A mathematical model of the steel scrap melting process was established in MATLAB to investigate the mechanism of scrap melting in deP-BOF in terms of coupling effects of the carbon content of the molten steel, temperature, scrap preheating and converter blowing time on the melting rate and size of the steel scraps. The scrap melting rate was influenced by both the heat and mass transfer during the melting process: at 1350℃, when the carbon content was increased from 4.5 to 5.0 mass%, the scrap melting rate increased by 43%;for the carbon content of 4.5 mass%, when the temperature was increased from 1350 to 1400℃, the scrap melting rate increased by 60%. The carbonisation was found to be the restrictive step of the scrap melting process in deP-BOFs with respect to conventional ones. The scrap heating from room temperature to 800℃ reduced the crusting thickness on the scrap surface but there was no obvious influence on the melting rate. The scrap melting size in the deP-BOF was rather limited by its low melting rate and short melting time.展开更多
Removal kinetics of phosphorus through use of basic oxygen furnace slag（BOF-slag）was investigated through batch experiments. Effects of several parameters such as initial phosphorus concentration, temperature, BOF-s...Removal kinetics of phosphorus through use of basic oxygen furnace slag（BOF-slag）was investigated through batch experiments. Effects of several parameters such as initial phosphorus concentration, temperature, BOF-slag size, initial p H, and BOF-slag dosage on phosphorus removal kinetics were measured in detail. It was demonstrated that the removal process of phosphorus through BOF-slag followed pseudo-first-order reaction kinetics. The apparent rate constant（kobs） significantly decreased with increasing initial phosphorus concentration, BOF-slag size, and initial p H, whereas it exhibited an opposite trend with increasing reaction temperature and BOF-slag dosage.A linear dependence of kobson total removed phosphorus（TRP） was established with kobs=（3.51 ± 0.11） × 10^-4× TRP. Finally, it was suggested that the Langmuir–Rideal（L–R）or Langmuir–Hinshelwood（L–H） mechanism may be used to describe the removal process of phosphorus using BOF-slag.展开更多
Double slag process was adopted to produce low-phosphorus steel from middle-phosphorus hot metal.To achieve a stable dephosphorization operation,conventional process was modified as follows：the blowing time was exten...Double slag process was adopted to produce low-phosphorus steel from middle-phosphorus hot metal.To achieve a stable dephosphorization operation,conventional process was modified as follows：the blowing time was extended by approximately 1min by reducing the oxygen supply flow rate;calcium ferrite pellets were added to adjust the slag composition and viscosity;the dumping temperature was lowered by 30-50°C by the addition of calcium ferrite pellets during the double slag process to prevent phosphorus in the slag from returning to the molten steel;and the bottom-blown gas flow was increased during the blowing process.For 40 heats of comparative experiments,the rate of dephosphorization reached 91% and ranged between 87% and 95%;the phosphorus,sulfur,manganese,and oxygen contents calculated according to the compositions of molten steel and slag as well as the temperature of molten steel at the end-point of the basic oxygen furnace process were similar to the equilibrium values for the reaction between the slag and the steel.Less free calcium oxide and metallic iron were present in the final slag,and the surface of the slag mineral phase was smooth,clear,and well developed,which showed that the slag exhibited better melting effects than that produced using the conventional slag process.A steady phosphorus capacity in the slag and stable dephosphorization effects were achieved.展开更多
Basic oxygen furnace slag（BOFS） has the potential to remove hexavalent chromium（Cr（VI））from wastewater by a redox process due to the presence of minerals containing Fe2＋. The effects of the solution p H, initia...Basic oxygen furnace slag（BOFS） has the potential to remove hexavalent chromium（Cr（VI））from wastewater by a redox process due to the presence of minerals containing Fe2＋. The effects of the solution p H, initial Cr（VI） concentration, BOFS dosage, BOFS particle size, and temperature on the removal of Cr（VI） was investigated in detail through batch tests. The chemical and mineral compositions of fresh and reacted BOFS were characterized using scanning electron microscope（SEM） equipped with an energy dispersive spectrometer（EDS）system and X-ray diffractometer（XRD）. The results show that Cr（VI） in wastewater can be efficiently removed by Fe2＋released from BOFS under appropriate acidic conditions. The removal of Cr（VI） by BOFS significantly depended on the parameters mentioned above. The reaction of Cr（VI） with BOFS followed the pseudo-second-order kinetic model. Fe2＋responsible for Cr（VI） removal was primarily derived from the dissolution of Fe O and Fe3O4 in BOFS. When H2SO4 was used to adjust the solution acidity, gypsum（Ca SO4·2H2O）could be formed and become an armoring precipitate layer on the BOFS surface, hindering the release of Fe2＋and the removal of Cr（VI）. Finally, the main mechanism of Cr（VI） removal by BOFS was described using several consecutive reaction steps.展开更多
Air quenched basic oxygen furnace steel slag （BOF-SS） is processed at very high cooling rate, which is expected to have different cementitious properties from conventional slowly cooled BOF-SS. For this purpose, the...Air quenched basic oxygen furnace steel slag （BOF-SS） is processed at very high cooling rate, which is expected to have different cementitious properties from conventional slowly cooled BOF-SS. For this purpose, the strength activity indexes of air quenched and slowly cooled BOF-SS are investigated. The results reveal that, under the specific surface area （S） of 490 m^2/kg, the compressive strength activity index reaches 1.24 after 28 days with replacement of 15% air quenched BOF-SS and reaches 1.05 after 28 days with replacement of 20% air quenched BOF-SS and 30%granulated blast furnace slag （GBFS）. The cementitious activity of air quenched BOF-SS is obviously higher than that of slowly cooled BOF-SS, mainly because it contains more C3 S and glassy phases.展开更多
In order to improve the end-point hit rate of basic oxygen furnace steelmaking,a novel dynamic control model was proposed based on an improved twin support vector regression algorithm.The controlled objects were the e...In order to improve the end-point hit rate of basic oxygen furnace steelmaking,a novel dynamic control model was proposed based on an improved twin support vector regression algorithm.The controlled objects were the end-point carbon content and temperature.The proposed control model was established by using the low carbon steel samples collected from a steel plant,which consists of two prediction models,a preprocess model,two regulation units,a controller and a basic oxygen furnace.The test results of 100 heats show that the prediction models can achieve a double hit rate of 90%within the error bound of 0.005 wt.%C and 15℃.The preprocess model was used to predict an initial end-blow oxygen volume.However,the double hit rate of the carbon con tent and temperature only achieves 65%.Then,the oxygen volume and coolant additi ons were adjusted by the regulation units to improve the hit rate.Finally,the double hit rate after the regulation is reached up to 90%.The results indicate that the proposed dynamic control model is efficient to guide the real production for low carbon steel,and the modeling method is also suitable for the applications of other steel grades.展开更多
The influence of three different blowing conditions on the slag splashing process in a basic oxygen furnace for steelmaking is analyzed here using two-dimensional transient Computational Fluid Dynamics simulations. Fo...The influence of three different blowing conditions on the slag splashing process in a basic oxygen furnace for steelmaking is analyzed here using two-dimensional transient Computational Fluid Dynamics simulations. Four blowing conditions are considered in the computer runs: top blowing, combined blowing using just a bottom centered nozzle, combined blowing using two bottom lateral nozzles, and full combined blowing using the three top and the three bottom nozzles. Computer simulations show that full combined blowing provides greater slag splashing than conventional top blowing.展开更多
An improved mathematical model to describe the decarburization process in basic oxygen furnaces for steelmaking is presented in this work. This model takes into account those factors or parameters that determine the b...An improved mathematical model to describe the decarburization process in basic oxygen furnaces for steelmaking is presented in this work. This model takes into account those factors or parameters that determine the bath-oxygen impact area, such as the cavity depth, the lance height, the number of nozzles and the nozzles diameter. In the thermal issue, the model includes the targeted carbon content and temperature. The model is numerically solved, and is validated using reported data plant. The oxygen flow rate and the lance height are varied in the numerical simulations to study their effect on the carbon content and decarburization rate.展开更多
Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
To avoid the volume expansion of basic oxygen furnace (BOF) slag for use in building materials, a hot slag modification process was proposed to reduce free CaO (f-CaO) in the molten slag. A transient 3D numerical mode...To avoid the volume expansion of basic oxygen furnace (BOF) slag for use in building materials, a hot slag modification process was proposed to reduce free CaO (f-CaO) in the molten slag. A transient 3D numerical model of BOF molten slag modification by SiO_(2) particles was established. The flow and heat transfer of molten slag, movement and dissolution of the modifier, and concentration distribution of f-CaO in slag during the modification of BOF were studied. The distribution of f-CaO concentration is inhomogeneous all over the molten slag. The mixing effect at the slag surface is weaker than that at the half-height plane of the slag. To consume the f-CaO below 2.0 wt.% in the slag, the optimum quantity of the SiO_(2) modifier is 10.0% of the mass of the slag. The fine SiO_(2) particles help attain a lower final mass fraction of f-CaO and a higher SiO_(2) utilization ratio.展开更多
The integrated steelmaking cycle based on the blast furnace-basic oxygen furnace（BOF）route plays an important role in the production of plain and ultra-low carbon steel,especially for deep drawing operations.BOF ste...The integrated steelmaking cycle based on the blast furnace-basic oxygen furnace（BOF）route plays an important role in the production of plain and ultra-low carbon steel,especially for deep drawing operations.BOF steelmaking is based on the conversion of cast iron in steel by impinging oxygen on the metal bath at supersonic speed.In order to avoid the addition of detrimental chemical elements owing to the introduction of uncontrolled scrap and in order to decrease environmental impact caused by the intensive use of coke for the production of cast iron,HBI（hot briquetted iron）can be used as a source of metal and a fraction of cast iron.Forty industrial experimental tests were performed to evaluate the viability of the use of HBI in BOF.The experimental campaign was supported by a thermal prediction model and realized through the estimation of the oxidation enthalpy.Furthermore,the process was thermodynamically analyzed based on oxygen potentials using the off-gas composition and the bath temperature evolution during the conversion as reference data.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos.51974023 and52374321)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,China (No.41620007)。
文摘The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.
文摘The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process,addressing gaps in the field,particularly large-scale dataset sizes and the underutilization of boosting algorithms.Utilizing a substantial dataset containing over 20,000 heats,significantly bigger than those in previous studies,a comprehensive evaluation of five advanced machine learning models was conducted.These include four ensemble learning algorithms:XGBoost,LightGBM,CatBoost(three boosting algorithms),along with random forest(a bagging algorithm),as well as a neural network model,namely the multilayer perceptron.Our comparative analysis reveals that Bayesian-optimized boosting models demonstrate exceptional robustness and accuracy,achieving the highest R-squared values,the lowest root mean square error,and lowest mean absolute error,along with the best hit ratio.CatBoost exhibited superior performance,with its test R-squared improving by 4.2%compared to that of the random forest and by 0.8%compared to that of the multilayer perceptron.This highlights the efficacy of boosting algorithms in refining complex industrial processes.Additionally,our investigation into the impact of varying dataset sizes,ranging from 500 to 20,000 heats,on model accuracy underscores the importance of leveraging larger-scale datasets to improve the accuracy and stability of predictive models.
基金supported by the National High-end Foreign Expert Introduction Program Project(No.G2023105012L).
文摘A new technological process involving the introduction of an induction furnace(IF)powered by green electricity was proposed for reducing the CO_(2) emission in the conventional blast furnace–basic oxygen furnace(BF–BOF)steelmaking route.The proposed BF–IF–BOF process gains benefits from preheating and smelting scraps utilizing green electricity and further remarkably cuts down the CO_(2) emission in BOF steelmaking.The CO_(2) emissions of conventional and new processes have been comparatively analyzed according to the actual data from a commercial steel plant in China,taking into account the upstream CO_(2) emission,direct CO_(2) emission,and credit CO_(2) emission.The analysis revealed that the CO_(2) emission factor of internal scraps from the steel plant was different from that of purchased scraps from the society but equalled to that of crude steel.The CO_(2) injected into the BOF as a coolant could be defined as the upstream CO_(2) emission source,and there is a net reduction of 1 t CO_(2) emission for each ton of CO_(2) utilized in the BOF.Compared to the BF–BOF process with a scrap ratio of 19.23%,the CO_(2) emission reduction per ton of steel in the new process is 0.278,0.517,0.753,0.987,1.219,1.448,and 1.683 t,respectively,as the scrap ratio increases to 30%,40%,50%,60%,70%,80%,and 90%,and increasing the scrap ratio has a more significant impact on the emission reduction than CO_(2) injecting.A minimum CO_(2) emission model for the BF–IF–BOF process was established,and the minimum CO_(2) emission per ton crude steel was calculated to be 0.677,0.581,0.487,0.393,0.300,0.209,and 0.110 t,for the BF–IF–BOF process with the scrap ratios of 30%,40%,50%,60%,70%,80%,and 90%,respectively.
基金The authors are grateful for the financial support of the National Natural Science Foundation of China(Grant No.51674030)the National Key Research and Development Program of China(Grant No.2016YFB0601301).
文摘Dephosphorisation basic oxygen furnaces (deP-BOFs) greatly differ from conventional BOFs in the melting process, especially its many limits on adding scrap. A mathematical model of the steel scrap melting process was established in MATLAB to investigate the mechanism of scrap melting in deP-BOF in terms of coupling effects of the carbon content of the molten steel, temperature, scrap preheating and converter blowing time on the melting rate and size of the steel scraps. The scrap melting rate was influenced by both the heat and mass transfer during the melting process: at 1350℃, when the carbon content was increased from 4.5 to 5.0 mass%, the scrap melting rate increased by 43%;for the carbon content of 4.5 mass%, when the temperature was increased from 1350 to 1400℃, the scrap melting rate increased by 60%. The carbonisation was found to be the restrictive step of the scrap melting process in deP-BOFs with respect to conventional ones. The scrap heating from room temperature to 800℃ reduced the crusting thickness on the scrap surface but there was no obvious influence on the melting rate. The scrap melting size in the deP-BOF was rather limited by its low melting rate and short melting time.
基金financially supported by the Fundamental Research Fund for the Central Universities (No. N130302004)the National Natural Science Foundation of China (No. U1360204)
文摘Removal kinetics of phosphorus through use of basic oxygen furnace slag（BOF-slag）was investigated through batch experiments. Effects of several parameters such as initial phosphorus concentration, temperature, BOF-slag size, initial p H, and BOF-slag dosage on phosphorus removal kinetics were measured in detail. It was demonstrated that the removal process of phosphorus through BOF-slag followed pseudo-first-order reaction kinetics. The apparent rate constant（kobs） significantly decreased with increasing initial phosphorus concentration, BOF-slag size, and initial p H, whereas it exhibited an opposite trend with increasing reaction temperature and BOF-slag dosage.A linear dependence of kobson total removed phosphorus（TRP） was established with kobs=（3.51 ± 0.11） × 10^-4× TRP. Finally, it was suggested that the Langmuir–Rideal（L–R）or Langmuir–Hinshelwood（L–H） mechanism may be used to describe the removal process of phosphorus using BOF-slag.
基金financially supported by the Beijing Natural Science Foundation of China(No.2172057)
文摘Double slag process was adopted to produce low-phosphorus steel from middle-phosphorus hot metal.To achieve a stable dephosphorization operation,conventional process was modified as follows：the blowing time was extended by approximately 1min by reducing the oxygen supply flow rate;calcium ferrite pellets were added to adjust the slag composition and viscosity;the dumping temperature was lowered by 30-50°C by the addition of calcium ferrite pellets during the double slag process to prevent phosphorus in the slag from returning to the molten steel;and the bottom-blown gas flow was increased during the blowing process.For 40 heats of comparative experiments,the rate of dephosphorization reached 91% and ranged between 87% and 95%;the phosphorus,sulfur,manganese,and oxygen contents calculated according to the compositions of molten steel and slag as well as the temperature of molten steel at the end-point of the basic oxygen furnace process were similar to the equilibrium values for the reaction between the slag and the steel.Less free calcium oxide and metallic iron were present in the final slag,and the surface of the slag mineral phase was smooth,clear,and well developed,which showed that the slag exhibited better melting effects than that produced using the conventional slag process.A steady phosphorus capacity in the slag and stable dephosphorization effects were achieved.
基金financially supported by the Fundamental Research Fund for the Central Universities(No.N130302004)the National Natural Science Foundation of China(No.21407020)
文摘Basic oxygen furnace slag（BOFS） has the potential to remove hexavalent chromium（Cr（VI））from wastewater by a redox process due to the presence of minerals containing Fe2＋. The effects of the solution p H, initial Cr（VI） concentration, BOFS dosage, BOFS particle size, and temperature on the removal of Cr（VI） was investigated in detail through batch tests. The chemical and mineral compositions of fresh and reacted BOFS were characterized using scanning electron microscope（SEM） equipped with an energy dispersive spectrometer（EDS）system and X-ray diffractometer（XRD）. The results show that Cr（VI） in wastewater can be efficiently removed by Fe2＋released from BOFS under appropriate acidic conditions. The removal of Cr（VI） by BOFS significantly depended on the parameters mentioned above. The reaction of Cr（VI） with BOFS followed the pseudo-second-order kinetic model. Fe2＋responsible for Cr（VI） removal was primarily derived from the dissolution of Fe O and Fe3O4 in BOFS. When H2SO4 was used to adjust the solution acidity, gypsum（Ca SO4·2H2O）could be formed and become an armoring precipitate layer on the BOFS surface, hindering the release of Fe2＋and the removal of Cr（VI）. Finally, the main mechanism of Cr（VI） removal by BOFS was described using several consecutive reaction steps.
基金Item Sponsored by National Natural Science Foundation of China(51234003)
文摘Air quenched basic oxygen furnace steel slag （BOF-SS） is processed at very high cooling rate, which is expected to have different cementitious properties from conventional slowly cooled BOF-SS. For this purpose, the strength activity indexes of air quenched and slowly cooled BOF-SS are investigated. The results reveal that, under the specific surface area （S） of 490 m^2/kg, the compressive strength activity index reaches 1.24 after 28 days with replacement of 15% air quenched BOF-SS and reaches 1.05 after 28 days with replacement of 20% air quenched BOF-SS and 30%granulated blast furnace slag （GBFS）. The cementitious activity of air quenched BOF-SS is obviously higher than that of slowly cooled BOF-SS, mainly because it contains more C3 S and glassy phases.
基金This work was supported by Liaoning Province PhD Start-up Fund(No.201601291)Liaoning Province Ministry of Education Scientific Study Project(No.2O17LNQN11).
文摘In order to improve the end-point hit rate of basic oxygen furnace steelmaking,a novel dynamic control model was proposed based on an improved twin support vector regression algorithm.The controlled objects were the end-point carbon content and temperature.The proposed control model was established by using the low carbon steel samples collected from a steel plant,which consists of two prediction models,a preprocess model,two regulation units,a controller and a basic oxygen furnace.The test results of 100 heats show that the prediction models can achieve a double hit rate of 90%within the error bound of 0.005 wt.%C and 15℃.The preprocess model was used to predict an initial end-blow oxygen volume.However,the double hit rate of the carbon con tent and temperature only achieves 65%.Then,the oxygen volume and coolant additi ons were adjusted by the regulation units to improve the hit rate.Finally,the double hit rate after the regulation is reached up to 90%.The results indicate that the proposed dynamic control model is efficient to guide the real production for low carbon steel,and the modeling method is also suitable for the applications of other steel grades.
文摘The influence of three different blowing conditions on the slag splashing process in a basic oxygen furnace for steelmaking is analyzed here using two-dimensional transient Computational Fluid Dynamics simulations. Four blowing conditions are considered in the computer runs: top blowing, combined blowing using just a bottom centered nozzle, combined blowing using two bottom lateral nozzles, and full combined blowing using the three top and the three bottom nozzles. Computer simulations show that full combined blowing provides greater slag splashing than conventional top blowing.
文摘An improved mathematical model to describe the decarburization process in basic oxygen furnaces for steelmaking is presented in this work. This model takes into account those factors or parameters that determine the bath-oxygen impact area, such as the cavity depth, the lance height, the number of nozzles and the nozzles diameter. In the thermal issue, the model includes the targeted carbon content and temperature. The model is numerically solved, and is validated using reported data plant. The oxygen flow rate and the lance height are varied in the numerical simulations to study their effect on the carbon content and decarburization rate.
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
基金the National Natural Science Foundation of China(Grant Nos.U1860205 and 52204352)Young Elite Scientist Sponsorship Program by China Association for Science and Technology(Grant No.YESS20200210)Youth Project of Hubei Natural Science Foundation(Grant No.2022CFB593).
文摘To avoid the volume expansion of basic oxygen furnace (BOF) slag for use in building materials, a hot slag modification process was proposed to reduce free CaO (f-CaO) in the molten slag. A transient 3D numerical model of BOF molten slag modification by SiO_(2) particles was established. The flow and heat transfer of molten slag, movement and dissolution of the modifier, and concentration distribution of f-CaO in slag during the modification of BOF were studied. The distribution of f-CaO concentration is inhomogeneous all over the molten slag. The mixing effect at the slag surface is weaker than that at the half-height plane of the slag. To consume the f-CaO below 2.0 wt.% in the slag, the optimum quantity of the SiO_(2) modifier is 10.0% of the mass of the slag. The fine SiO_(2) particles help attain a lower final mass fraction of f-CaO and a higher SiO_(2) utilization ratio.
文摘The integrated steelmaking cycle based on the blast furnace-basic oxygen furnace（BOF）route plays an important role in the production of plain and ultra-low carbon steel,especially for deep drawing operations.BOF steelmaking is based on the conversion of cast iron in steel by impinging oxygen on the metal bath at supersonic speed.In order to avoid the addition of detrimental chemical elements owing to the introduction of uncontrolled scrap and in order to decrease environmental impact caused by the intensive use of coke for the production of cast iron,HBI（hot briquetted iron）can be used as a source of metal and a fraction of cast iron.Forty industrial experimental tests were performed to evaluate the viability of the use of HBI in BOF.The experimental campaign was supported by a thermal prediction model and realized through the estimation of the oxidation enthalpy.Furthermore,the process was thermodynamically analyzed based on oxygen potentials using the off-gas composition and the bath temperature evolution during the conversion as reference data.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
