Converter Steelmaking Research Articles

Energy saving analysis for CO2–O2 mixed injection technology in converter steelmaking

Energy saving analysis for CO2–O2 mixed injection technology in converter steelmaking

A hybrid PCA-SOA-BP approach for predicting converter endpoint temperature in steelmaking

Accurately controlling the temperature of the converter end is a crucial element of the steelmaking process. To enhance the accuracy of predicting the converter end temperature, we propose a hybrid model that utilizes principal component analysis (PCA) and snake optimization algorithm (SOA) in conjunction with a backpropagation algorithm (BP) neural network. The 16 parameters for smelting in converter steelmaking were reduced using principal component analysis to remove shared features. The nine principal components derived from this analysis were then used as input variables for an optimized BP neural network. An optimization algorithm was then employed to refine the initialized weights and thresholds of the BP neural network. The impact of neuron node quantity in the hidden layer on the BP neural network was examined. Results show that the ideal BP neural network is achieved with 19 neuron nodes in the hidden layer. Compared with ordinary BP neural network, PCA-BP neural network and SOA-BP neural network, the model proposed in this study can predict the end temperature of converter most accurately. In the temperature error range of ±10 °C and ±15 °C, the prediction accuracy of the model is 93% and 96%, respectively. Meanwhile, the model has been effectively applied in the industrial production of a steel plant in China. The results show that the prediction results of the model are in good agreement with the actual production data in the field. This accurate prediction can optimize the field operation process and realize the stable control of product quality.

Effect of Furnace Lining Structure on the Flow Field in the 35t Top-blowing Converter Steelmaking Process

Effect of Furnace Lining Structure on the Flow Field in the 35t Top-blowing Converter Steelmaking Process

Improvement of Electrical Isotropic Low-Carbon Converter Steelmaking. Part 3

Improvement of Electrical Isotropic Low-Carbon Converter Steelmaking. Part 3

Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning

Abstract The accurate control of the endpoint in converter steelmaking is of great significance and value for energy saving, emission reduction, and steel quality improvement. The key to endpoint control lies in accurately predicting the carbon content and temperature. Converter steelmaking is a dynamic process with a large fluctuation of samples, and traditional ensemble learning methods ignore the differences among the query samples and use all the sub-models to predict. The different performances of each sub-model lead to the performance degradation of ensemble learning. To address this issue, we propose a soft sensor method based on multi-cluster dynamic adaptive selection (MC-DAS) ensemble learning for converter steelmaking endpoint carbon content and temperature prediction. First, to ensure the diversity of the ensemble learning base model, we propose a clustering algorithm with different data partition characteristics to construct a pool of diverse base models. Second, a model adaptive selection strategy is proposed, which involves constructing diverse similarity regions for individual query samples and assessing the model’s performance in these regions to identify the most suitable model and weight combination for each respective query sample. Compared with the traditional ensemble learning method, the simulation results of actual converter steelmaking process data show that the prediction accuracy of carbon content within ±0.02% error range reaches 92.8%, and temperature within ±10°C error range reaches 91.6%.

Behavior of residual Zn oxides in metallized pellets applied in the converter steelmaking process

Metallized pellets serve as a significant recycling product derived from the iron and steel production, comprising dust and sludge. However, the substantial presence of residual zinc within these pellets poses challenges such as vaporization, condensation, and accumulation, ultimately leading to severe clogging in off-gas pipes and potential erosion of refractory materials. To address this, a novel approach is proposed to efficiently harness the potential of metallized pellets within the converter process, focusing on investigating the behavior and mechanism of zinc oxides. Results reveal that metallized pellets introduce Zn elements with a distribution ratio of approximately 1:1 in the steel-gas phase and only around 3% in the slag. The Zn element can remain stable in the over-oxidized molten steel, and an inverse proportional relationship between [C] and [Zn] contents in the molten steel has been derived from industrial and laboratory experiments as follows.[%C]0.84·[%Zn]=0.0055Furthermore, the end-point Zn content in the molten steel is closely related to the C content in the molten steel before the addition of the metallized pellets. The ZnO contained within the metallized pellets must undergo reduction by the [C] present in the molten steel, as well as by the [C] to facilitate vaporization and escape. Consequently, a critical carbon content (approximately 0.3%) emerges, wherein the addition of metallized pellets enables the maximum and enduring presence of Zn elements within the molten steel while minimizing vaporization and escape of Zn into the off-gas pipeline.

Study on Efficient Dephosphorization in Converter Based on Thermodynamic Calculation

Given the accelerating depletion of iron ore resources, there is growing concern within the steel industry regarding the availability of high-phosphorus iron ore. However, it is important to note that the utilization of high-phosphorus iron ore may result in elevated phosphorus content and notable fluctuations in molten iron, thereby imposing additional challenges on the dephosphorization process in steelmaking. The most urgent issue in the process of converter steelmaking is how to achieve efficient dephosphorization. In this study, the influence of various factors on the logarithm of the phosphorus balance distribution ratio (lgLp), the logarithm of the P2O5 activity coefficient (lgγP2O5), and the logarithm of the phosphorus capacity (lgCp) were examined through thermodynamic calculations. The impact of each factor on dephosphorization was analyzed, and the optimal conditions for the dephosphorization stage of the converter were determined. Furthermore, the influence of basicity and FetO content on the form of phosphorus in the slag was analyzed using FactSage 7.2 software, and the precipitation rules of the slag phases were explored. The thermodynamic calculation results indicated that increasing the basicity of the dephosphorization slag was beneficial for dephosphorization, but it should be maintained below 3. The best dephosphorization effect was achieved when the FetO content was around 20%. The reaction temperature during the dephosphorization stage should be kept low, as the dephosphorization efficiency decreased sharply with the increasing temperature. In dephosphorization slag, Ca3(PO4)2 usually formed a solid solution with Ca2SiO4, so the form of phosphorus in the slag was mainly determined by the precipitation form and content of Ca2SiO4. The phases in the dephosphorization slag mainly consisted of a phosphorus-rich phase, an iron-rich phase, and a matrix phase. The results of scanning electron microscopy and X-ray diffraction analyses were consistent with the thermodynamic calculation results.

Effect of Supersonic Oxygen Lance on Post-Combustion in Converter Steelmaking Process – Experiment and Analysis with Converter Simulator

Employing post-combustion technology in the converter, using the sensible heat of the hot metal and the oxidation reaction heat as a heat source, is known to compensate for insufficient heat in the converter process. However, most studies on post-combustion have been conducted using subsonic nozzles, whereas actual converter processes use supersonic nozzles. Therefore, research on the combustion behavior of supersonic jets is needed. In this study, experiments and analyses were conducted using a converter simulator and a supersonic nozzle to investigate the effect of nozzle height on the post-combustion behavior. The reaction was set to complete combustion, with an O2gas flow rate of 150 L/min blown through the upper lance and a CO gas flow rate of 300 L/min blown at the bottom of the simulator to represent the surface of the molten metal. The combustion reaction of CO gas was calculated to be rate-controlled by reactant mixing. The nozzle heights were set to 250, 380, and 530 mm from the surface of the molten metal. Post-combustion analysis showed that the lowest gas velocity was observed under the condition of the highest nozzle height of 530 mm, and the high temperature and reaction zones were widely distributed in the lower region. Therefore, to facilitate heat compensation to the molten metal, it is necessary to control the gas velocity of the molten steelgas interface slowly.

Improving the Durability of Steel Ladle Linings in Converter Shop of Nizhniy Tagil Iron and Steel Works (to the 60th Anniversary of the Development of Converter Steelmaking in Russia)

Improving the Durability of Steel Ladle Linings in Converter Shop of Nizhniy Tagil Iron and Steel Works (to the 60th Anniversary of the Development of Converter Steelmaking in Russia)

Prediction model of BOF end-point phosphorus content and sulfur content based on LWOA-TSVR

ABSTRACT Precise control of the end-point phosphorus and sulfur content in converter steelmaking is critical to ensuring steel quality. An end-point prediction model based on LWOA-TSVR is established to better control the BOF end-point content of phosphorus and sulfur. The prediction impact is compared to the models BP, SVM, and TSVR. The results indicate that the LWOA-TSVR model outperforms the other three models in terms of accuracy. And the prediction model is applied to a steel mill. The results showed that the hit rates of phosphorus content and sulfur content were: 96.3%, 81.7%, and 94.8%, 76.9% in the range of ±0.005% and ±0.003%, respectively. The double hit rate was: 87.63% in the range of ±0.005%. Thus, it is demonstrated that the LWOA-TSVR prediction model performs effective prediction of end-point phosphorus and sulfur content with prediction accuracy that exceeds that required by the real steelmaking process in a steel mill.

End Point Temperature Prediction of Converter Steelmaking Based on Characteristics of Flame Image and Spectrum

End Point Temperature Prediction of Converter Steelmaking Based on Characteristics of Flame Image and Spectrum

Effect of Bottom Tuyere Arrangement Based on Impact Cavity Morphology on Kinetic Behavior of Molten Bath in Converter

In article 2200532, Liu and co-workers illustrate and study the hydrodynamic interaction between the top-blowing supersonic jets and the bottom-blowing plumes during the top and bottom combined blowing converter steelmaking process. Four bottom tuyere arrangement cases are designed considering the influence of cavity morphology.

Research Progress on Injection Technology in Converter Steelmaking Process

During the converter steelmaking process, gas-slag-metal three-phase emulsification is realized by injecting gas to complete metallurgical tasks such as slagging, dephosphorization, decarburization, and heating. As green and intelligent development of the steel industry progresses, converter steelmaking injection technology is also constantly innovating. In this review, the types and applications of top blowing injection elements, bottom blowing injection elements, and injection the medium are reviewed. Three different types of combined blowing processes are compared. At the same time, the advantages and disadvantages of different bottom blowing elements and injection media are respectively discussed. Finally, based on the research and application status of converter injection technology, the development direction of converter steelmaking injection technology is discussed. Accelerating the innovation of converter steelmaking injection technology, especially the improvement and breakthrough of high efficiency, reductions the environmental burden, and long life technology, will play an important role in promoting the transformation and improvement of the steel industry.

Activation of low-activity calcium silicate in converter steelmaking slag based on synergy of multiple solid wastes in cementitious material

This research aims to activate the low-activity calcium silicate in converter steelmaking slag (CSS) under the synergy of multiple solid wastes, and further increase the dosage of CSS in cementitious materials. In this work, the CSS-based cementitious material (RB) is composed by converter steelmaking slag (CSS), Bayer red mud (BRM), blast furnace slag (BFS), and pulverized coal furnace fly ash (PFA). More importantly, the low-activity calcium silicate in CSS is activated to generate extra CSH gel based on the synergy of CSS, BRM, BFS, and PFA in RB4 (RB4 is an RB with a mass ratio of (Ca + Na)/(Si + Al) is 1.33). Thus, the compressive strength of RB4 in 28 days grows to 53.40 MPa. In parallel, the dosage of CSS in RB is increased to 30 wt%. The main hydration products are CSH gel, ettringite, NASH gel, and CASH gel, which facilitate the performance growth of RBs. The dense microstructure and better pore structure of RB are presented when the mass ratio of (Ca + Na)/(Si + Al) is 1.33. Additionally, the consolidation of harmful elements in RB4 is improved by synergy of CSS, BRM, BFS, and PFA, and their leaching results meet the relevant standards of drinking water. This work is expected to provide an effective method for the synergistic utilization of CSS, BRM, BFS, and PFA in green cementitious materials.

FIGAN: A Missing Industrial Data Imputation Method Customized for Soft Sensor Application

Missing data is quite common in the industrial field, resulting in problems in downstream applications, as most data driven methods used in these applications rely on complete and high-quality dataset to build a high-quality model. Existing methods deal with missing data individually regardless of its downstream application, treating all variables equally without considering their different roles in the downstream application. This would affect imputation performance for key variables, thus deteriorating the accuracy of the downstream model. A considerable challenge is how to refine the missing data imputation task. In this paper, a new method termed fine-tuned imputation GAN (FIGAN) is designed to achieve customized data imputation for industrial soft sensor. The major contribution of the paper lies in two aspects: 1) different from the original imputation GAN (GAIN) which treats all variables equally, FIGAN is guided by a soft sensor module so as to achieve customized data imputation by performing improved data imputation on quality-related variables. Enhanced accuracy for the final industrial soft sensor would be possible; 2) in addition, since labels of the soft sensor might also have missing data, a soft sensor with pseudo labeling is designed to conquer the problem with data imputation and label prediction being optimized interactively. Case studies on a converter steelmaking process and a penicillin fermentation process show the feasibility of the proposed FIGAN. It is noted that such customized imputation could be readily transferred to other downstream applications with missing data. Note to Practitioners—Industrial data is often incomplete and needs proper treatment. Meanwhile, downstream applications with preprocessed data vary under different industrial scenes. The focus of this study is to develop a customized data imputation method for specific downstream applications such as soft sensing. A fine-tuned imputation GAN is designed with a soft sensor module so as to guide the data imputation for good imputation on key variables of the soft sensor. Considering that missing data exists not only in measurement variables but also in soft sensor labels, a semi-supervised soft sensor is designed to handle missing data in the labels, optimized together with the imputation model. The customized data imputation can thus improve the final performance of the downstream model which is a soft sensor in this work. The customization could be transferred to other applications such as an anomaly detection model as well.

Improvement of Converter Steelmaking of Cold-Rolled Non Grain-Oriented Electrical Low Carbon Steel. Report 1

Improvement of Converter Steelmaking of Cold-Rolled Non Grain-Oriented Electrical Low Carbon Steel. Report 1

Thermal Effect and Metallurgical Characteristics of Hydrogen Bottom Blowing in Top–Bottom Combined Blowing Converter

A new technology of hydrogen bottom blowing instead of traditional argon blowing in the current converter steelmaking process is proposed herein, in the aim of overcoming problems such as energy shortages caused by increased scrap charging, the low stirring intensity of bottom blowing, high CO2 emissions, and endpoint carbon content control. The thermal effect and metallurgical characteristics of hydrogen bottom blowing were investigated based on the production data of a steelmaking converter in Pangang Group Xichang Steel & Vanadium Co., Ltd. This study shows that hydrogen bottom blowing at an intensity of 0.1–0.5 m3·min−1·t−1—rather than argon blowing at an intensity of 0.1 m3·min−1·t−1—can increase the smelting temperature by 16–73 K, increase the scrap charging ratio by 0.89–5.19%, and reduce CO2 emissions by 19.79–115.96 kg per ton of steel. Intensive hydrogen blowing could significantly reduce the oxygen content of molten steel in the late stage of steelmaking and be beneficial to controlling oxygen at the endpoint. Hydrogen can also reduce the (FeO) content in slag, and the equilibrium partial pressure ratios of H2O/H2 for the reaction H2 + (FeO) = H2O + Fe in the middle and late periods are 0.41 and 0.11, respectively. Hydrogen can also slightly suppress the decarbonization reaction in the late period of steelmaking, and the equilibrium partial pressure ratio of H2O/H2 for the reaction H2 + (CO) = [C] + H2O in the late period is 9.65 × 10−2, which means that hydrogen is beneficial in preventing the rapid decrease in [C] and, in turn, helps control the endpoint carbon content. By comparing the degree of the reaction (P2O5) + 5H2 = P2(g) + 5H2O and the reaction (P2O5) + 5H2 = 2[P] + 5H2O, it can be seen that intensive bottom-blown hydrogen may have a slight positive effect on slag gasification dephosphorization. The FactSage simulation results further verify the conclusions of the above analysis.

Synergistic enhancement of converter steelmaking slag, blast furnace slag, Bayer red mud in cementitious materials: Strength, phase composition, and microstructure

The dosage of converter steelmaking slag (CSS) is less than 20 wt% in the field of cementitious materials. This work aims to increase the dosage of CSS in cementitious materials containing multiple types of solid wastes. It is found that the dosage of CSS in the cementitious material is 30 wt% in this work, which is beneficial to promote the synergy of CSS, Bayer red mud (BRM), and blast furnace slag (BFS). In this research, the strength of P·Ⅰ 42.5 cement is raised to the P·C 52.5 cement level due to the synergy of CSS, BRM, and BFS. The compressive strength values in CSS-based cementitious material No. 5 (CSSM5) at 3 days (22.90 MPa) and 28 days (56.30 MPa) are highest when the (CaO+Na2O)/(SiO2+Al2O3) mass ratio is 1.64, which meet strength standard of P·C 52.5 cement. The major phase composition is C–S–H gels, ettringite, and N, C-A-S-H gels that facilitate the improvement of compressive strength. The density of microstructure and pore structure of CSSM are optimal, with a (CaO+Na2O)/(SiO2+Al2O3) mass ratio of 1.64. In addition, the thermogravimetric mass loss of ettringite and C–S–H gels in CSSM5 is the maximum (7.79 wt%), which is beneficial to the development of strength. The results of this work have potential as a valuable reference to the synergy of CSS, BFS, and BRM in cementitious materials, and also highlight the potential to utilize solid waste as a secondary material in the production of useful resources.

Effect of the wear of supersonic oxygen lance on the stirring characteristics and metallurgical effects in the converter steelmaking process

ABSTRACT Studying the variation in jet characteristics of an oxygen lance nozzle is of great significance for the converter smelting process. A full-size geometric model of a 120-t converter was established and the effects of a worn oxygen lance nozzle exit on the gas jet, stirring characteristics, and molten bath velocity distribution were studied. An increasing wear angle increases the nozzle exit velocity, increases the jet velocity attenuation, decreases the area of the longitudinal high-velocity zone of the molten bath, increases the area of the low-velocity and dead zones, worsens the molten steel flow, and decelerates the reaction rate. Then, industrial tests were conducted using a 120-t converter. When the wear angle increased from 0 to 20°, the phosphorus content, carbon–oxygen equilibrium, and TFe content in the slag increased from 0.029%, 0.0023, and 12.92% to 0.032%, 0.0028, and 14.58%, respectively. This study has important implications for optimizing the oxygen supply in converters.

Study on jet characteristics of a dual-structure converter oxygen lance based on hydrodynamics

Oxygen lance is the key piece of equipment for iron and steel smelting. During the entire steelmaking process, the performance of the oxygen lance determines the occurrence of decarburization, slagging, heating, and splashing. In this paper, the influence of structural and process parameters on converter steelmaking is studied using a six-nozzle staggered oxygen lance and the traditional six-nozzle oxygen lance by performing jet tests, numerical simulation, and water model experiments. The research results show that, compared with the traditional oxygen lance nozzle, a stepwise fusion of the jet streams of the staggered oxygen lance nozzles occurs, which increases the fusion distance to varying degrees, reduces the amount of splash at the furnace mouth, and increases the effective impact area. For the six-nozzle staggered oxygen lance nozzle, the most ideal structure and maximum performance are obtained at an inner nozzle angle of 14° with a flow ratio of 55% and an outer nozzle angle of 18° with a flow ratio of 45%. The results show that the fusion distance of the inner nozzle and outer nozzle is 2.1 and 2.25 m, respectively; compared to the traditional oxygen lance nozzle, the splash amount of 0.19 g at the furnace mouth is reduced to only 16%, while the effective impact area has doubled to reach a value of 0.47 m2. With the increase in the oxygen lance position, the effective impact area of the jet of the oxygen lance nozzle on the molten pool first increases and then decreases, and the best position of the traditional and staggered oxygen lance nozzles is determined as 30.0 and 35.0–37.5 de, respectively.