Dissolution kinetics of CaO·2Al_(2)O_(3)(CA_(2))particles in a synthetic CaO-Al_(2)O_(3)-SiO_(2)steelmaking slag system have been investigated using the high-temperature confocal laser scanning microscope.Effects...Dissolution kinetics of CaO·2Al_(2)O_(3)(CA_(2))particles in a synthetic CaO-Al_(2)O_(3)-SiO_(2)steelmaking slag system have been investigated using the high-temperature confocal laser scanning microscope.Effects of temperature(i.e.,1500,1550,and 1600℃)and slag composition on the dissolution time of CA_(2)particles are investigated,along with the time dependency of the projection area of the particle during the dissolution process.It is found that the dissolution rate was enhanced by either an increase in temperature or a decrease in slag viscosity.Moreover,a higher ratio of CaO/Al_(2)O_(3)(C/A)leads to an increased dissolution rate of CA_(2)particle at 1600℃.Thermodynamic calculations suggested the dissolution product,i.e.,melilite,formed on the surface of the CA_(2)particle during dissolution in slag with a C/A ratio of 3.8 at 1550℃.Scanning electron microscopy equipped with energy dispersive X-ray spectrometry analysis of as-quenched samples confirmed the dissolution path of CA_(2)particles in slags with C/A ratios of 1.8 and 3.8 as well as the melilite formed on the surface of CA_(2)particle.The formation of this layer during the dissolution process was identified as a hindrance,impeding the dissolution of CA_(2)particle.A valuable reference for designing or/and choosing the composition of top slag for clean steel production is provided,especially using calcium treatment during the secondary refining process.展开更多
Splashing behavior of metal droplets is one of the main phenomena in basic oxygen furnace steelmaking process.The size distribution of metal droplets and the residence time of the metal droplets in the slag have impor...Splashing behavior of metal droplets is one of the main phenomena in basic oxygen furnace steelmaking process.The size distribution of metal droplets and the residence time of the metal droplets in the slag have important effects on the kinetics of the metal–slag reactions.The particle size distribution law,characteristic diameter,splashing velocity and splashing angle of metal droplets were investigated,and an improved prediction model of trajectory and residence time for metal droplets was established based on the combination of expanded droplets theory,decarburization mechanism model and ballistic motion principle.Meanwhile,the trajectory and residence time of metal droplets under different working conditions were analyzed based on this model.The results illustrate that the metal droplets with larger particle size are produced at low lance distance,while the metal droplets with smaller particle size are produced at high lance distance.There is a significant linear relationship between the three diameters(maximum droplet diameter,distribution characteristic diameter,reaction characteristic diameter)and the blowing number.The residence time of decarbonized metal droplets in slag is about 0.2–73 s.Meanwhile,the initial carbon content and diameter of the metal droplets and the FeO content of slag are the main factors affecting the motion state of the metal droplets in the slag,while the splashing velocity,splashing angle and the height of the foam slag have little influence.This model can be used to predict the trajectory and residence time of decarburized metal droplets in a variety of complex multiphase slag conditions,overcoming the limitation that the known model is only applicable to a few specific conditions.展开更多
The conventional steelmaking process emits 1.8 tons of CO_(2) to produce 1 ton of crude steel,making the steel industry the world's largest emitting manufacturing sector.Here,we propose and demonstrate a renewable...The conventional steelmaking process emits 1.8 tons of CO_(2) to produce 1 ton of crude steel,making the steel industry the world's largest emitting manufacturing sector.Here,we propose and demonstrate a renewable route based on electrified carbon cycling,which significantly reduces CO_(2) emission by 83%.The critical step of the route involves electrochemical CO_(2) reduction(CO_(2)RR)to produce CO-rich syngas,which reduces iron ore into metallic iron(Fe_(x)O_(y)-to-Fe),effectively closing the carbon cycling.A technoeconomic analysis(TEA)reveals that the energy efficiency of this novel process is dependent on the operating parameters of CO_(2)RR,with optimal efficiency occurring at the current density range of 150-200mAcm^(-2).As a proof-of-concept study,sulfur vacancy(V_(S))-engineered Ag_(3)CuS_(2) was developed as a high-performance CO_(2)RR electrocatalyst.This catalyst yields a CO-rich syngas at a high Faradaic efficiency(FE)close to 100%at a cell voltage of 2.5 V.The CO_(2)RR-produced syngas effectively reduced iron oxide into metallic iron.The implementation of electrified carbon cycling significantly increases the utilization of electricity in steel production,reaching 88.7%.This research describes a sustainable way to reshape the ironmaking process and ultimately neutralize the steel industry.展开更多
Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF...Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF inputs and outputs,neglecting the associations between smelting operations and RMC.Traditional methods of reducing RMC rely on manual experience and lack a standard operation guidance.A method based on association rules mining and metallurgical mechanism(ARM-MM)was proposed.ARM-MM proposed an improved evaluation indicator of RMC and the indicator independently showed the associations between smelting operations and RMC.On the basis,1265 heats of real EAF data were used to obtain the operation guidance for RMC reduction.According to the ratio of hot metal(HM)in charge metals,data were divided into all dataset,low HM ratio dataset,medium HM ratio dataset,and high HM ratio dataset.ARM algorithm was used in each dataset to obtain specific operation guidance.The real average RMC under all dataset,medium HM ratio dataset,and high HM ratio dataset was reduced by 279,486,and 252 kg/heat,respectively,when obtained operation guidance was applied.展开更多
The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stag...The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.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
Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC...Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC test.To maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.The number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as inputs.First,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time series.Through optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,respectively.Its field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carried out,and dynamic control methods based on prediction values were proposed.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
Energy-saving in China's iron and steel industry still relies on the development and improvement of short-term energy saving technologies.Therefore,a special converter smelting technology incorporating energy savi...Energy-saving in China's iron and steel industry still relies on the development and improvement of short-term energy saving technologies.Therefore,a special converter smelting technology incorporating energy saving was proposed.To evaluate the energy-saving potential of the CO_(2)–O_(2)mixed injection(COMI)technology,collected production data were used to develop an improved techno-economic model.Calculations reveal that the technology can save energy through auxiliary material consumption,sensible heat of solid by-product,iron loss reduction,and energy recovery.The application of COMI technology in an enterprise is cost effective,involving the energy saving potential of 0.206 GJ/t,the cost of conserved energy of−48.83 yuan/GJ,and a simple payback period of 0.35 year for a 60-million-yuan investment.Sensitivity analysis shows that the investment cost and discount rate primarily influence the cost of conserved energy of the technology.As the discount rate increased,the cost of conserved energy also gradually increased.Overall,the COMI technology is an energy-saving technology with good development prospects.展开更多
Sumitomo Metal Ind.is the third place steelmaking company for productivity in Japan.Although,we have some first Japanese innovations results in steelmaking process.In this report,SRP process and some topics for contin...Sumitomo Metal Ind.is the third place steelmaking company for productivity in Japan.Although,we have some first Japanese innovations results in steelmaking process.In this report,SRP process and some topics for continuous casting have been introduced.展开更多
The mass production of steel is inevitably accompanied by large quantities of slags.The treatment of ironmaking and steelmaking slags is a great challenge in the sustainable development of the steel industry.Japan and...The mass production of steel is inevitably accompanied by large quantities of slags.The treatment of ironmaking and steelmaking slags is a great challenge in the sustainable development of the steel industry.Japan and China are two major steel producing countries that have placed a large emphasis on developing new technologies to decrease slag emission or promote slag valorization.Slags are almost completely reused or recycled in Japan.However,due to stagnant infrastructural investments,future applications of slags in conventional sectors are expected to be difficult.Exploring new functions or applications of slags has become a research priority in Japan.For example,the utilization of steelmaking slags in offshore seabeds to create marine forests is under development.China is the top steel producer in the world.The utilization ratios of ironmaking and steelmaking slags have risen steadily in recent years,driven largely by technological advances.For example,hot stage processing of slags for materials as well as heat recovery techniques has been widely applied in steel plants with good results.However,increasing the utilization ratio of basic oxygen furnace slags remains a major challenge.Technological innovations in slag recycling are crucial for the steel industries in Japan and China.Here,the current status and developing trends of utilization technologies of slags in both countries are reviewed.展开更多
In last decade,the utilization of CO?resources in steelmaking has achieved certain metallurgical effects and the technology is maturing.In this review,we summarized the basic reaction theory of CO2,the CO2 conversion,...In last decade,the utilization of CO?resources in steelmaking has achieved certain metallurgical effects and the technology is maturing.In this review,we summarized the basic reaction theory of CO2,the CO2 conversion,and the change of energy-consumption when CO2 was introduced in converter steelmaking process.In the CO2-O2 mixed injection(COMI)process,the CO2 conversion ratio can be obtained as high as 80%or more with a control of the CO2 ratio in mixture gas and the flow rate of CO2,and the energy is saving and even the energy consumption can be reduced by 145.65 MJ/t under certain operations.In addition,a complete route of CO2 disposal technology is proposed combining the comparatively mature technologies of CO2 capture,CO2 compression,and liquid CO2 storage to improve the technology of CO2 utilization.The results are expected to form a large-scale,highly efficient,and valuable method to dispose of CO2.展开更多
With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning te...With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.展开更多
Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. Thi...Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony(IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability,the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.展开更多
In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to ...In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).展开更多
The steelmaking process scheduling problem by considering variable electricity price （SMSPVEP） was in- vestigated. A decomposition approach was proposed for the SMSPVEP. At the first stage, mathematical program-ming...The steelmaking process scheduling problem by considering variable electricity price （SMSPVEP） was in- vestigated. A decomposition approach was proposed for the SMSPVEP. At the first stage, mathematical program-ming was utilized to minimize the maximum completion time for each cast without considering variable electricity price. At the second stage, based on obtained relative schedules of all casts, a mathematical model was formulated with an objective of minimizing the energy cost for all casts scheduling problem. The two-stage models were tested on randomly generated instances based on the practical process in a Chinese steelmaking plant. Computational results demonstrate the effectiveness of the proposed approach.展开更多
The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this...The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.展开更多
The heat transfer in a steelmaking ladle was studied. The evaluation of heat transfer of the steel was performed by measuring steel temperature in points including all refining steel process. In the ladle, the tempera...The heat transfer in a steelmaking ladle was studied. The evaluation of heat transfer of the steel was performed by measuring steel temperature in points including all refining steel process. In the ladle, the temperatures in the refractories and the shell were also measured. To evaluate the thermal profile between the hot and cold faces of the ladle in the slag line position, an experiment which shows the importance of thermal contact resistance was carried out. Higher heat losses in the tapping and the vacuum were verified. The temperature measurements of the ladle indicate distinct thermal profiles in each stage of steel refining. Moreover, as each stage of the process depends on the previous one, the complexity of the ladle thermal control is incremental. So a complete model of heat losses in the ladle is complex.展开更多
In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then...In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.展开更多
The models, algorithms and implementation results of a computerized scheduling system were introduced for the steelmaking-continuous casting process （SCCP） of a steel plant in China. The scheduling of SCCP in this p...The models, algorithms and implementation results of a computerized scheduling system were introduced for the steelmaking-continuous casting process （SCCP） of a steel plant in China. The scheduling of SCCP in this plant required that each cast plan should be processed on time, the charges in the same cast should be processed con- tinuously on the same caster, and the waiting time of the charges which are in front of each caster cannot exceed the given threshold. At the same time, the processing time of charges cannot be conflicted mutually in the same convert- ers or refining furnaces. Based on the research background, a hybrid optimal scheduling approach and its application were discussed. Aiming at the main equipment scheduling, an optimal scheduling method was proposed which con- sisted of equipment assignment algorithm based on dynamic program （DP） technique and conflict elimination algo rithm based on linear program （LP） technique. The approach guarantees that the charges are continuously processed on the same caster. Meanwhile, the requirement for high temperature ladle can also be satisfied due to the ladle matching function. Numerical results demonstrate solution quality, computational efficiency, and values of the mod els and algorithm.展开更多
基金the Natural Sciences and Engineering Research Council of Canada(NSERC)for funding this researchThis research used a high temperature confocal laser scanning microscope-VL2000DX-SVF17SP funded by Canada Foundation for Innovation John Evans Leaders Fund(CFI JELF,Project Number:32826),a PANalytical X’Pert diffraction instrument located at the Centre for crystal growth,Brockhouse Institute for Materials Research,and a scanning electron microscope-JEOL 6610 located at the Canadian Centre for Electron Microscopy at McMaster University.W.Mu would like to acknowledge Swedish Iron and Steel Research Office(Jernkonteret),STINT and SSF for supporting the time for international collaboration research regarding clean steel.
文摘Dissolution kinetics of CaO·2Al_(2)O_(3)(CA_(2))particles in a synthetic CaO-Al_(2)O_(3)-SiO_(2)steelmaking slag system have been investigated using the high-temperature confocal laser scanning microscope.Effects of temperature(i.e.,1500,1550,and 1600℃)and slag composition on the dissolution time of CA_(2)particles are investigated,along with the time dependency of the projection area of the particle during the dissolution process.It is found that the dissolution rate was enhanced by either an increase in temperature or a decrease in slag viscosity.Moreover,a higher ratio of CaO/Al_(2)O_(3)(C/A)leads to an increased dissolution rate of CA_(2)particle at 1600℃.Thermodynamic calculations suggested the dissolution product,i.e.,melilite,formed on the surface of the CA_(2)particle during dissolution in slag with a C/A ratio of 3.8 at 1550℃.Scanning electron microscopy equipped with energy dispersive X-ray spectrometry analysis of as-quenched samples confirmed the dissolution path of CA_(2)particles in slags with C/A ratios of 1.8 and 3.8 as well as the melilite formed on the surface of CA_(2)particle.The formation of this layer during the dissolution process was identified as a hindrance,impeding the dissolution of CA_(2)particle.A valuable reference for designing or/and choosing the composition of top slag for clean steel production is provided,especially using calcium treatment during the secondary refining process.
基金funded by the National Natural Science Foundation of China(Nos.52374321 and 51974023)the funding of State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing(No.41621005)the Youth Science and Technology Innovation Fund of Jianlong Group-University of Science and Technology Beijing(No.20231235).
文摘Splashing behavior of metal droplets is one of the main phenomena in basic oxygen furnace steelmaking process.The size distribution of metal droplets and the residence time of the metal droplets in the slag have important effects on the kinetics of the metal–slag reactions.The particle size distribution law,characteristic diameter,splashing velocity and splashing angle of metal droplets were investigated,and an improved prediction model of trajectory and residence time for metal droplets was established based on the combination of expanded droplets theory,decarburization mechanism model and ballistic motion principle.Meanwhile,the trajectory and residence time of metal droplets under different working conditions were analyzed based on this model.The results illustrate that the metal droplets with larger particle size are produced at low lance distance,while the metal droplets with smaller particle size are produced at high lance distance.There is a significant linear relationship between the three diameters(maximum droplet diameter,distribution characteristic diameter,reaction characteristic diameter)and the blowing number.The residence time of decarbonized metal droplets in slag is about 0.2–73 s.Meanwhile,the initial carbon content and diameter of the metal droplets and the FeO content of slag are the main factors affecting the motion state of the metal droplets in the slag,while the splashing velocity,splashing angle and the height of the foam slag have little influence.This model can be used to predict the trajectory and residence time of decarburized metal droplets in a variety of complex multiphase slag conditions,overcoming the limitation that the known model is only applicable to a few specific conditions.
基金funded by the National Natural Science Foundation of China(U23A20545,52331001)China BaoWu Low Carbon Metallurgy Innovation Foundation-BWLCF202113the Fundamental Research Funds for the Central Universities(N2202012).
文摘The conventional steelmaking process emits 1.8 tons of CO_(2) to produce 1 ton of crude steel,making the steel industry the world's largest emitting manufacturing sector.Here,we propose and demonstrate a renewable route based on electrified carbon cycling,which significantly reduces CO_(2) emission by 83%.The critical step of the route involves electrochemical CO_(2) reduction(CO_(2)RR)to produce CO-rich syngas,which reduces iron ore into metallic iron(Fe_(x)O_(y)-to-Fe),effectively closing the carbon cycling.A technoeconomic analysis(TEA)reveals that the energy efficiency of this novel process is dependent on the operating parameters of CO_(2)RR,with optimal efficiency occurring at the current density range of 150-200mAcm^(-2).As a proof-of-concept study,sulfur vacancy(V_(S))-engineered Ag_(3)CuS_(2) was developed as a high-performance CO_(2)RR electrocatalyst.This catalyst yields a CO-rich syngas at a high Faradaic efficiency(FE)close to 100%at a cell voltage of 2.5 V.The CO_(2)RR-produced syngas effectively reduced iron oxide into metallic iron.The implementation of electrified carbon cycling significantly increases the utilization of electricity in steel production,reaching 88.7%.This research describes a sustainable way to reshape the ironmaking process and ultimately neutralize the steel industry.
基金supported by National Natural Science Foundation of China(Nos.52174328 and 52474368)Fundamental Research Funds for Central Universities of Central South University(Nos.2022ZZTS0084 and 2024ZZTS0062).
文摘Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF inputs and outputs,neglecting the associations between smelting operations and RMC.Traditional methods of reducing RMC rely on manual experience and lack a standard operation guidance.A method based on association rules mining and metallurgical mechanism(ARM-MM)was proposed.ARM-MM proposed an improved evaluation indicator of RMC and the indicator independently showed the associations between smelting operations and RMC.On the basis,1265 heats of real EAF data were used to obtain the operation guidance for RMC reduction.According to the ratio of hot metal(HM)in charge metals,data were divided into all dataset,low HM ratio dataset,medium HM ratio dataset,and high HM ratio dataset.ARM algorithm was used in each dataset to obtain specific operation guidance.The real average RMC under all dataset,medium HM ratio dataset,and high HM ratio dataset was reduced by 279,486,and 252 kg/heat,respectively,when obtained operation guidance was applied.
基金financially supported by the National Natural Science Foundation of China(No.52374320).
文摘The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.
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This research has been supported by the Natural Science Foundation of Hebei Province,China(E2022318002).Thanks are given to Tangsteel Co.,Ltd.of Hesteel Group and Digital Co.,Ltd.of Hesteel Group for providing detailed data,hardware and software support for model development and field production test.
文摘Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC test.To maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.The number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as inputs.First,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time series.Through optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,respectively.Its field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carried out,and dynamic control methods based on prediction values were proposed.
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.
基金the National Natural Science Foundation of China(Nos.52204342 and 52201073)Natural Science Foundation of Hebei Province(No.E2022208019)+1 种基金Natural Science Foundation of Hebei Education Department(Nos.BJK2024194 and BJK2024024)Natural Science Foundation of Hebei Education Department(No.QN2021058)for financial support.
文摘Energy-saving in China's iron and steel industry still relies on the development and improvement of short-term energy saving technologies.Therefore,a special converter smelting technology incorporating energy saving was proposed.To evaluate the energy-saving potential of the CO_(2)–O_(2)mixed injection(COMI)technology,collected production data were used to develop an improved techno-economic model.Calculations reveal that the technology can save energy through auxiliary material consumption,sensible heat of solid by-product,iron loss reduction,and energy recovery.The application of COMI technology in an enterprise is cost effective,involving the energy saving potential of 0.206 GJ/t,the cost of conserved energy of−48.83 yuan/GJ,and a simple payback period of 0.35 year for a 60-million-yuan investment.Sensitivity analysis shows that the investment cost and discount rate primarily influence the cost of conserved energy of the technology.As the discount rate increased,the cost of conserved energy also gradually increased.Overall,the COMI technology is an energy-saving technology with good development prospects.
文摘Sumitomo Metal Ind.is the third place steelmaking company for productivity in Japan.Although,we have some first Japanese innovations results in steelmaking process.In this report,SRP process and some topics for continuous casting have been introduced.
文摘The mass production of steel is inevitably accompanied by large quantities of slags.The treatment of ironmaking and steelmaking slags is a great challenge in the sustainable development of the steel industry.Japan and China are two major steel producing countries that have placed a large emphasis on developing new technologies to decrease slag emission or promote slag valorization.Slags are almost completely reused or recycled in Japan.However,due to stagnant infrastructural investments,future applications of slags in conventional sectors are expected to be difficult.Exploring new functions or applications of slags has become a research priority in Japan.For example,the utilization of steelmaking slags in offshore seabeds to create marine forests is under development.China is the top steel producer in the world.The utilization ratios of ironmaking and steelmaking slags have risen steadily in recent years,driven largely by technological advances.For example,hot stage processing of slags for materials as well as heat recovery techniques has been widely applied in steel plants with good results.However,increasing the utilization ratio of basic oxygen furnace slags remains a major challenge.Technological innovations in slag recycling are crucial for the steel industries in Japan and China.Here,the current status and developing trends of utilization technologies of slags in both countries are reviewed.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51334001,51674021,51574021,and 51734003).
文摘In last decade,the utilization of CO?resources in steelmaking has achieved certain metallurgical effects and the technology is maturing.In this review,we summarized the basic reaction theory of CO2,the CO2 conversion,and the change of energy-consumption when CO2 was introduced in converter steelmaking process.In the CO2-O2 mixed injection(COMI)process,the CO2 conversion ratio can be obtained as high as 80%or more with a control of the CO2 ratio in mixture gas and the flow rate of CO2,and the energy is saving and even the energy consumption can be reduced by 145.65 MJ/t under certain operations.In addition,a complete route of CO2 disposal technology is proposed combining the comparatively mature technologies of CO2 capture,CO2 compression,and liquid CO2 storage to improve the technology of CO2 utilization.The results are expected to form a large-scale,highly efficient,and valuable method to dispose of CO2.
基金supported by the National Natural Science Foundation of China(No.U1960202)。
文摘With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.
基金Supported by the National Natural Science Foundation of China(51705177,51575212)the Program for New Century Excellent Talents in University(NCET-13-0106)the Program for HUST Academic Frontier Youth Team
文摘Steelmaking–refining–Continuous Casting(SCC) scheduling is a worldwide problem, which is NP-hard. Effective SCC scheduling algorithms can help to enhance productivity, and thus make significant monetary savings. This paper develops an Improved Artificial Bee Colony(IABC) algorithm for the SCC scheduling. In the proposed IABC, charge permutation is employed to represent the solutions. In the population initialization, several solutions with certain quality are produced by a heuristic while others are generated randomly. Two variable neighborhood search neighborhood operators are devised to generate new high-quality solutions for the employed bee and onlooker bee phases, respectively. Meanwhile, in order to enhance the exploitation ability, a control parameter is introduced to conduct the search of onlooker bee phase. Moreover, to enhance the exploration ability,the new generated solutions are accepted with a control acceptance criterion. In the scout bee phase, the solution corresponding to a scout bee is updated by performing three swap operators and three insert operators with equal probability. Computational comparisons against several recent algorithms and a state-of-the-art SCC scheduling algorithm have demonstrated the strength and superiority of the IABC.
基金Project(ZR2014FM036)supported by Shandong Provincial Natural Science Foundation of ChinaProject(ZR2010FZ001)supported by the Key Program of Shandong Provincial Natural Science Foundation of China
文摘In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).
基金Item Sponsored by National Natural Science Foundation of China (71171038,71021061 )Fundamental Research Funds for Central Universities of China (N100504001)
文摘The steelmaking process scheduling problem by considering variable electricity price （SMSPVEP） was in- vestigated. A decomposition approach was proposed for the SMSPVEP. At the first stage, mathematical program-ming was utilized to minimize the maximum completion time for each cast without considering variable electricity price. At the second stage, based on obtained relative schedules of all casts, a mathematical model was formulated with an objective of minimizing the energy cost for all casts scheduling problem. The two-stage models were tested on randomly generated instances based on the practical process in a Chinese steelmaking plant. Computational results demonstrate the effectiveness of the proposed approach.
基金financially supported by the National Natural Science Foundation of China (Nos.50874014 and 51974023)the Fundamental Research Funds for Central Universities (No.FRF-BR-17-029A)。
文摘The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.
文摘The heat transfer in a steelmaking ladle was studied. The evaluation of heat transfer of the steel was performed by measuring steel temperature in points including all refining steel process. In the ladle, the temperatures in the refractories and the shell were also measured. To evaluate the thermal profile between the hot and cold faces of the ladle in the slag line position, an experiment which shows the importance of thermal contact resistance was carried out. Higher heat losses in the tapping and the vacuum were verified. The temperature measurements of the ladle indicate distinct thermal profiles in each stage of steel refining. Moreover, as each stage of the process depends on the previous one, the complexity of the ladle thermal control is incremental. So a complete model of heat losses in the ladle is complex.
文摘In the electric arc furnace （EAF） steel production processes, scrap steel is principally used as a raw material instead of iron ore. In the steelmaking process with EAF, scrap is first melted in the furnace and then the desired chemical composition of the steel can be obtained in a special furnace such as ladle furnace （LF）. This kind of furnace process is used for the secondary refining of alloy steel. LF furnace offers strong heating fluxes and enables precise temperature control, thereby allowing for the addition of desired amounts of various alloying elements. It also provides outstanding desulfurization at high-temperature treatment by reducing molten steel fluxes and removing deoxidation products. Elemental analysis with mass balance modeling is important to know the precise amount of required alloys for the LF input with respect to scrap composition. In present study, chemical reactions with mass conservation law in EAF and LF were modeled altogether as a whole system and chemical compositions of the final steel alloy output can be obtained precisely according to different scrap compositions, alloying elements ratios, and other input amounts. Besides, it was found that the mass efficiency for iron element in the system is 95.93%. These efficiencies are calculated for all input elements as 8. 45% for C, 30.31% for Si, 46.36% for Mn, 30.64% for P, 41.96% for S, and 69.79% for Cr, etc. These efficiencies provide valuable ideas about the amount of the input materials that are vanished or combusted for 100 kg of each of the input materials in the EAF and LF system.
基金Item Sponsored by National Natural Science Foundation of China(61174187,71021061,60974091,61104174)Startup Fund of Northeastern University of China(29321006)Basic Scientific Research Foundation of Northeast University of China(N110208001)
文摘The models, algorithms and implementation results of a computerized scheduling system were introduced for the steelmaking-continuous casting process （SCCP） of a steel plant in China. The scheduling of SCCP in this plant required that each cast plan should be processed on time, the charges in the same cast should be processed con- tinuously on the same caster, and the waiting time of the charges which are in front of each caster cannot exceed the given threshold. At the same time, the processing time of charges cannot be conflicted mutually in the same convert- ers or refining furnaces. Based on the research background, a hybrid optimal scheduling approach and its application were discussed. Aiming at the main equipment scheduling, an optimal scheduling method was proposed which con- sisted of equipment assignment algorithm based on dynamic program （DP） technique and conflict elimination algo rithm based on linear program （LP） technique. The approach guarantees that the charges are continuously processed on the same caster. Meanwhile, the requirement for high temperature ladle can also be satisfied due to the ladle matching function. Numerical results demonstrate solution quality, computational efficiency, and values of the mod els and algorithm.
