Date Envelopment Analysis of Steelmakers＇ Economical Scale in China

In view of the fact that Chinese steelmakers are not in a developed competitive market, the economical scale of Chinese iron and steel enterprises is measured by Date Envelopment Analysis （DEA） in batches compared with international one. The deep reasons are revealed why Chinese large steelmakers are in batches not in economical scale.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

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.

Consideration of green intelligent steel processes and narrow window stability control technology on steel quality

In order to promote the intelligent transformation and upgrading of the steel industry, intelligent technology features based on the current situation and challenges of the steel industry are discussed in this paper. Based on both domestic and global research, functional analysis, reasonable positioning, and process optimization of each aspect of steel making are expounded. The current state of molten steel quality and implementation under narrow window control is analyzed. A method for maintaining stability in the narrow window control technology of steel quality is proposed, controlled by factors including composition, temperature, time, cleanliness, and consumption(raw material). Important guidance is provided for the future development of a green and intelligent steel manufacturing process.

A review of carbon dioxide disposal technology in the converter steelmaking process

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.

Physical model of fluid flow characteristics in RH-TOP vacuum refining process

To understand the characteristic of circulation flow rate in 250-t RH-TOP vacuum refining process, the 1：4 water model test was established through the bubble behavior and gas holdup in the up-leg to investigate the effects of different processes and equipment parame- ters on the RH circulation flow rate. With the increases of lifting gas flow rate, liffing bubble travel, and the internal diameter of the up-leg, and the decrease of nozzle diameter, the work done by bubble floatage and the circulation flow rate increase. The expression of circulation flow rate was derived fi＂om the regression analysis of experiment data. Meanwhile, the influences of vacuum chamber pressure and nozzle blockage situation on the circulation flow rate were discussed in detail by the bubble behavior and gas holdup in the up-leg. It is necessary to maintain a certain vacuum chamber liquid level in the molten steel circulation flow. Compared with a nozzle with symmetrical blockage in the up-leg, when a nozzle with non-symmetrical blockage is applied, the lifting gas distribution is non-uniform, causing a great effect on the molten steel circulation flow and making the circulation flow drop largely.

An improved cross entropy algorithm for steelmaking-continuous casting production scheduling with complicated technological routes

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).

Recycling of ironmaking and steelmaking slags in Japan and China

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.

An improved artificial bee colony algorithm for steelmaking–refining–continuous casting scheduling problem

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.

A process model for BOF process based on bath mixing degree

The process model for BOF process can be applied to predict the liquid steel composition and bath temperature during the whole steelmaking process. On the basis of the traditional three-stage decarburization theory, the concept of mixing degree was put forward, which was used to indicate the effect of oxygen jet on decarburization. Furthermore, a more practical process model for BOF steelmaking was developed by analyzing the effect of silicon, manganese, oxygen injection rate, oxygen lance height, and bath temperature on decarburization. Process verification and end-point verification for the process model have been carried out, and the verification results show that the predic- tion accuracy of carbon content reaches 82.6% （the range of carbon content at the end-point is less than 0. 1wt%） and 85.7% （the range of carbon content at end-point is 0. 1wt% -0.7wt%） when the absolute error is less than 0.02wt% and 0.05wt%, respectively.

Endpoint temperature prediction of molten steel in RH using improved case-based reasoning

An improved case-based reasoning （CBR） method was proposed to predict the endpoint temperature of molten steel in Ruhrstahl Heraeus （RH） process. Firstly, production data were analyzed by multiple linear regressions and a pairwise comparison matrix in analytic hierarchy process （AHP） was determined by this linear regression＇s coefficient. The weights of various influencing factors were obtained by AHP. Secondly, the dividable principles of case base including ＂0-1＂ and ＂breakpoint＂ were proposed, and the case base was divided into several homogeneous parts. Finally, the improved CBR was compared with ordinary CBR, which is based on the even weight and the single base. The results show that the improved CBR has a higher hit rate for predicting the endpoint temperature of molten steel in RH.

State of the art in applications of machine learning in steelmaking process modeling

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.

Mass Balance Modeling for Electric Arc Furnace and Ladle Furnace System in Steelmaking Facility in Turkey

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.

Behavior of phosphorus enrichment in dephosphorization slag at low temperature and low basicity

At low basicity and low temperature, the dephosphorization behavior and phosphorus distribution ratio(LP) between slag and molten steel in the double slag and remaining slag process were studied with a 180 t basic oxygen furnace industrial experiment.The dephosphorization slags with different basicities were quantitatively analyzed.At the lower basicity range of 0.9–2.59, both LP and dephosphorization ratio were increased as the basicity of dephosphorization slag increased.Dephosphorization slag consisted of dark gray P-rich, light gray liquid slag,and white Fe-rich phases.With increasing basicity, not only did the morphologies of different phases in the dephosphorization slag change greatly, but the area fractions and P2O5 content of the P-rich phase also increased.The transfer route of P during dephosphorization can be deduced as hot metal → liquid slag phase + Fe-rich phase → P-rich phase.

Experimental Research on Refining Slag Systems Containing BaO for Ultra-Low Sulphur Steel

The experimental research on refining slag systems for ultra-low sulphur steel was carried out in a 10 kg induction furnace.It was proved that sulphur element in molten steel can be removed to less than 5×10^(-6) by adding CaO-Al_2O_3-SiO_2-MgO-CaF_2 slag on the surface of molten steel and feeding CaO-BaO-CaF2 wire into molten steel.And L_s,which is the coefficient of sulphur between slag and molten steel,that is ω(s)/ω[s],increases by increasing I(I = ωBaO/ωCaO).When I=5/3,L_s can be up to its maximum of 633.The CaSi is effective for deep desulphurization,especially when it is added to the slag of wire feeding.

Simulation study on factors influencing the entrainment behavior of liquid steel as bubbles pass through the steel/slag interface

In this study, a water/silicone oil interface was used to simulate the steel/slag interface in a converter. A high-speed camera was used to record the entrainment process of droplets when air bubbles were passed through the water/silicone oil interface. Motion parameters of the bubbles and droplets were obtained using particle kinematic analysis software, and the entrainment rate of the droplets was calculated. It was found that the entrainment rate decreased from 29.5% to 0 when the viscosity of the silicone oil was increased from 60 mPa.s to 820 mPa.s in the case of bubbles with a 5 mm equivalent diameter passing through the water/silicone oil interface. The results indicate that in- creasing the viscosity of the silicone oil is conducive to reducing the entrainment rate. The entrainment rate increased from 0 to 136.3% in the case of silicone oil with a viscosity of 60 mPa.s when the equivalent diameter of the bubbles was increased from 3 mm to 7 ram. We there- fore conclude that small bubbles are also conductive to reducing the entrainment rate. The force analysis results for the water colmnn indicate that the entrainment rate of droplets is affected by the velocity of the bubble passing through the water/silicone oil interface and that the en- trainment rate decreases with the bubble velocity.

Phosphate enrichment mechanism in CaO–SiO_2–FeO–Fe_2O_3–P_2O_5 steelmaking slags with lower binary basicity

The addition of silica to steelmaking slags to decrease the binary basicity can promote phosphate enrichment in quenched slag samples. In this study, we experimentally investigated phosphate enrichment behavior in CaO-SiO2-FeO-Fe203-P205 slags with a P205 content of 5.00% and the binary basicity B ranging from 1.0 to 2.0, where the （%Fe/O）/（%CaO） mass percentage ratio was maintained at 0.955. The experimental results are explained by the defined enrichment degree c, of solid solution 2CaO·SiO2-3CaO·P205 （C2S-C3P）, where R_C2S-C3P is a component of the developed ion and molecule coexistence theory （IMCT）-Ni model for calculating the mass action concentrations Ni of structural units in the slags on the basis of the IMCT. The asymmetrically inverse V-shaped relation be- tween phosphate enrichment and binary basicity B was observed to be correlated in the slags under applied two-stage cooling conditions. The maximum content of PROs in the C2S-C3P solid solution reached approximately 30.0% when the binary basicity B was controlled at 1.3.

Mass transfer of phosphorus in high-phosphorus hot-metal refining

Mass transfer of phosphorus in high-phosphorus hot-metal refining was investigated using CaO-FetO-SiO2 slags at 1623 K. Based on a two-film theory kinetic model and experimental results, it was found that the overall mass transfer coefficient, which includes the effects of mass transfer in both the slag phase and metal phase, is in the range of 0.0047 to 0.0240 cm/s. With the addition of a small amount of fluxing agents A1203 or Na20 into the slag, the overall mass transfer coefficient has an obvious increase. Silicon content in the hot metal also influences the overall mass transfer coefficient. The overall mass transfer coefficient in the lower [Si] heat is much higher than that in the higher [Si] heat. It is concluded that both fluxing agents and lower [Si] hot metal facilitate mass transfer of phosphorus in liquid phases. Fur- thermore, the addition of Na20 could also prevent rephosphorization at the end of the experiment.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

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.

Thermodynamic calculation on the smelting slag of direct recycling of electric arc furnace stainless steelmaking dust

Thermodynamic calculation on the smelting slag of direct recycling of electric arc furnace stainless steelmaking dust was presented. An induction furnace was used to simulate electric arc furnace smelting to recover the metals from the dust. The elements of iron, chromium and nickel in the ingot and the components of metal oxides in the slag were analyzed. The thermodynamic model for FeO Cr 2O 3 MgO SiO 2 slag was set up and the active concentrations of substances in the slag at 1 550 ℃ were determined by thermodynamic calculation according to the experimental data. The results show that the apparent equilibrium constant and quantitative distribution of chromium between slag and steel are unstable and affected by the mass ratios of pellets to start iron and metal reducing agent to the pellets. In order to get satisfactory chromium recovery from the direct recycling of electric arc furnace stainless steelmaking dust, it is important to ensure the mass ratio of pellets to the steel below 0.20 and the mass ratio of metal reducing agent to pellets over 0.18 in practical smelting runs.

Reaction mechanism of phosphorus with multi phase fluxes during hot metal dephosphorization

The formation of solid solution combined with tricalcium phosphate and dicalcium silicate could promote a considerable removal of phosphorus from liquid slag to solid during hot metal dephosphorization,and thus the dephosphorization by using multi phase fluxes could significantly decrease the consumption of lime. However,the reaction mechanism of multi phase fluxes has not been understood clearly.In the present study,the phase diagram for the CaO-SiO_2-FeO-P_2O_5 system has been measured with certain oxygen partial pressure at hot metal pretreatment temperature.Comparing with the CaO-SiO_2-FeO system,shrinkage of liquid phase area at higher FeO contents was observed at 1 673 K with oxygen partial pressure of 9.2×10^(-11) atm.