Blast Furnace Production Research Articles

Study on zinc removal behavior in simulated pre-reduction sintering process

In this study, blast furnace dust and iron concentrate were used as raw materials and graphite as reducing agent for mixed compacts. The briquettes were roasted in a high temperature tube furnace at 1200°C and kept for a certain time to simulate the pre-reduction sintering dezincification process. The reduced briquettes were characterized by X-ray diffraction, scanning electron microscopy, Energy Dispersive Spectroscopy and flame atomic absorption spectrometry. The effects of C/O molar ratio, basicity and pre-reduction time on the zinc removal rate and pre-reduction degree of the pre-reduction sintered products were studied, and the removal mechanism of zinc was further investigated. The results show that the pre-reduction sintering process can effectively achieve the zinc volatility removal while meeting the requirements of subsequent blast furnace production. The zinc removal rate of pre-reduced sintered products increased with the prolongation of pre-reduction time, increased first and then decreased with the increase of C/O molar ratio and basicity. The pre-reduction degree and Zn removal rate showed the same variation pattern. When the briquettes with C/O molar ratio of 1.0 and basicity of 1.9 were reduced for 20 min, the zinc removal rate and pre-reduction degree could reach 90.40% and 87.74%, respectively, indicating that a large amount of zinc was removed and most of the metallic iron had been reduced. The research results can provide some theoretical basis for industrial production.

Process Metallurgy and Genetic Algorithm Multi‐Objective Optimization of Blast Furnace Hearth Working State

The working state of the blast furnace (BF) hearth is vital for achieving high efficiency, quality, low fuel consumption, and extended lifespan in BF production. In this study, optimizing the BF hearth by considering three key factors is focused on: hot metal temperature (HMT), silicon content ([Si]), and hearth activity index (HAI). A multi‐objective and multistep optimization method, combining prediction models of HMT, [Si], and HAI with a genetic algorithm, is proposed to enhance the hearth's performance. A Pareto optimal solution set screening method is also established based on target thresholds, weights, and operation priorities, improving its applicability in industrial production. Through this model, significant improvement in hearth working state is demonstrated, verified through case analysis and industrial applications. The prediction model achieves high accuracy in forecasting HMT, [Si], and HAI, with hit rates of 92.26%, 93.45%, and 94.64%, respectively. During the application, the pass rates of these indicators increase by 8.32%, 16.67%, and 13.51%, showcasing the effectiveness of the approach in industrial settings.

Spatio-temporal and multi-mode prediction for blast furnace gas flow

The reasonable and stable distribution of blast furnace (BF) gas flow is the basis for maintaining the smooth operation of BF. Therefore, the accurate detection of the gas flow distribution is essential in the BF ironmaking process due to the direct impact on productivity, stability, and efficiency. However, there is a significant challenge to capture the complex interactions and dynamic changes of the ironmaking process by single predictive mode and two-dimensional (2D) distribution, leading to a lack of flexibility and interpretability in dealing with different abnormalities. To address this issue, a novel spatio-temporal multi-mode approach for three-dimensional (3D) BF gas flow prediction is proposed in this article. First, Pearson correlation analysis is employed to evaluate correlated variables in the spatial dimension. The precise temporal correlations among the multiple variables are matched with mutual information (MI) to extract spatio-temporal variables. Next, the spatio-temporal variables are decomposed utilizing variation mode decomposition (VMD), and the noise is removed with integrated correlation analysis and Fourier transform (FT) to identify and retain the relevant information. Finally, the MI-VMD-Informer is innovatively proposed to establish three different prediction modes based on spatio-temporal features, thus obtaining 2D and 3D gas flow distributions. The superiority of the proposed method is verified by actual BF production data.

Research on Molten Iron Quality Prediction Based on Machine Learning

The quality of molten iron not only has a significant impact on the strength, toughness, smelting cost and service life of cast iron but also directly affects the satisfaction of users. The establishment of timely and accurate blast furnace molten iron quality prediction models is of great significance for the improvement of the production efficiency of blast furnace. In this paper, Si, S and P content in molten iron is taken as the important index to measure the quality of molten iron, and the 989 sets of production data from a No.1 blast furnace from August to October 2020 are selected as the experimental data source, predicting the quality of molten iron by the I-GWO-CNN-BiLSTM model. First of all, on the basis of the traditional data processing method, the missing data values are classified into correlation data, temporal data, periodic data and manual input data, and random forest, the Lagrangian interpolation method, the KNN algorithm and the SVD algorithm are used to complete them, so as to obtain a more practical data set. Secondly, CNN and BiLSTM models are integrated and I-GWO optimized hyperparameters are used to form the I-GWO-CNN-BiLSTM model, which is used to predict Si, S and P content in molten iron. Then, it is concluded that using the I-GWO-CNN-BiLSTM model to predict the molten iron quality can obtain high prediction accuracy, which can provide data support for the regulation of blast furnace parameters. Finally, the MCMC algorithm is used to analyze the influence of the input variables on the Si, S and P content in molten iron, which helps the steel staff control the quality of molten iron in a timely manner, which is conducive to the smooth running of blast furnace production.

Research on Blast Furnace Ingredient Optimization Based on Improved Grey Wolf Optimization Algorithm

Blast furnace ironmaking plays an important role in modern industry and the development of the economy. A reasonable ingredient scheme is crucial for energy efficiency and emission reduction in blast furnace production. Determining the right blast furnace ingredients is a complicated process; therefore, this study examines the optimization of the ingredient ratio. In this paper a model of the blast furnace ingredients is established by considering cost of per ton iron, CO2 emissions, and the theoretical coke ratio as the objective functions; ingredient parameters, process parameters, main and by-product parameters as variables; and the blast furnace smelting theory and equilibrium equation as constraints. Then, the model is solved by using an improved grey wolf optimization algorithm and an improved multi-objective grey wolf optimization algorithm. Using the data collected from the steel mill, the conclusion is that multi-objective optimization can consider the indexes of each target, so that the values of all the targets are excellent; we also compared the multi-objective solution results with the original production scheme of the steel mill, and we found that using the blast furnace ingredient scheme optimized in this study can reduce the cost of iron per ton, CO2 emissions per ton, and the theoretical coke ratio in blast furnace production by 350 CNY/t, 1000 kg/t, and 20 kg/t, respectively, compared with the original production plan. Thus, steel mill decision makers can choose the blast furnace ingredients according to different business strategies and the actual needs of steel mills can be better met.

Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control.

The blast furnaces of Anshan Iron and Steel have completed large-scale modernization, and a large amount of information technology has been popularized and applied to the process of blast furnaces. This paper takes the Anshan Iron and Steel blast furnace group as the research background. Based on big data and industrial Internet technology, combining the smelting process mechanism of blast furnace production and using artificial intelligence, cloud analysis, and other technologies, the data management platform was used to effectively integrate the data of each process of the blast furnace and design the data asset catalogue. The big data application platform for the intensive control of the blast furnace was established. The data were in multidimensional in-depth mining, and the intelligent application model of the blast furnace was established. The visual intelligent monitoring of the safe production and operation of the blast furnace was realized, and the production operation of the blast furnace was guided. The overall information and intelligent level of production operation and management of the blast furnace have been improved.

Blast furnace operation profile monitoring system based on DB-Mini-KMeans and multi-layer perceptron

This article proposes a system for monitoring a blast furnace operation based on DB-Mini-KMeans and multi-layer perceptron (MLP), which solves a difficult problem categorising the furnace operation types. The system uses the correlation between cooling wall temperature and furnace operation type, creates an furnace operation type recognition model with optimised K-Means and MLP algorithms and makes a comparison test with other recognition models; evaluates all kinds of operation types by combining six blast furnace production indexes, such as gas utilisation rate and airflow and establishes a monitoring system of furnace operation types in blast furnaces. The clustering performance of the system has been improved by nearly 40%, and the recognition accuracy of various operation types has reached more than 90%, covering functions such as two-dimensional schematic diagrams of furnace operation types and status warnings, which provide help and support for people to analyse furnace conditions.

Study of Tuyere Combustion Flame Temperature in Vanadium and Titanium Blast Furnaces by Machine Vision and Colorimetric Thermometry

The steel industry is an important foundation of the national economy and the livelihood of the people, producing a large amount of carbon dioxide gas, accounting for about 70% of the carbon dioxide gas generated in the steel industry, which occurs during the ironmaking process. Therefore, the key technology to reduce the pollution and improve competitiveness is to increase the stability of blast furnace production and the quality of hot metal. Since the operation requirements for temperature control in the vanadium-titanium blast furnace are dramatically different compared to the traditional ones due to the low fluidity of vanadium-titanium slag, maintaining the required hot metal temperature within a narrow range with smaller fluctuations is essential. In addition, the adjustment parameters of the lower part have a significant influence on the tuyere combustion flame temperature during the daily operation of blast furnaces. At present, there is no relevant research on the online detection and analysis of vanadium-titanium blast furnace tuyere combustion flame temperature. In this study, the temperature of four tuyeres in a 500 m3 vanadium and titanium blast furnace at Jianlong Steel was detected by an online detection system. The tuyere combustion flame temperature was then calculated using colorimetric temperature measuring methodology at various times and at four distinct locations. After that, the calibration analyses, imaging parameter and the temperature tendencies in different directions of the blast furnace were investigated. This study not only offers new methods for understanding the regularity of operation and increasing the degree of visualization in vanadium and titanium smelting blast furnaces but also provides technical support for intelligent and low-carbon operation in blast furnaces.

Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI+3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI+3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production.

Greater Energy Independence with Sustainable Steel Production

Global energy market price volatility and an upward trajectory of prices per unit of electricity have sent all industrial sectors and many economies to the brink of recession. Alongside the urgent need for decarbonisation of all industries, achieving a globally higher level of energy independence across all sectors seems imperative. A multi-disciplinary approach with a proposed system of CO2 emissions reduction and capture technologies has the potential for short-term emissions reduction to near-zero in the steel industry—although some of the mechanisms can be implemented across most heavy industries. The findings of this research show a CO2 emissions reduction of ~30% from 977 t of CO2 to 684 t in one single blast furnace production cycle (based on 330 tonnes of liquid iron production capacity, with the mean of 2.1–3.2 tonnes CO2/t of steel and chemical reactions emissions applied), by switching the electricity provider for operating the electric heaters to providers generating energy exclusively from renewable sources. Replacing coal with biomass and adding post-combustion capture units to the blast furnace operation, will add carbon neutrality into the process—resulting in CO2 emissions reduction to near-zero. Carbon capture from biomass utilisation (BECCS) will add the benefit of carbon-negative emissions to the cycle. Simultaneously, energy-saving and process improvement measures implementation (up to 60% efficiency increase), excess heat recovery <30% of energy savings, and retrofitting renewable energy technology resulted in an energy independence of 88%. Engineering solutions, partly subsidised in the UK, are readily available for implementation in the iron and steel manufacturing industry.

Раціональні технологічні засади отримання коксу із заданими показниками питомого електричного опору

DOI: 10.31081/1681-309X-2024-0-1-8-15 Specialty 161. U.D.C. 661.66.2:537.311/.312 RATIONAL TECHNOLOGICAL BASES OF PRODUCTION OF COKE WITH SPECIFIED ELECTRICAL RESISTIVITY © I.V. Shulga, Ph.D. in Technical Sciences, O.V. Sytnyk, Ph.D. in Technical Sciences (State Enterprise “Ukrainian State Research Institute for Carbochemistry (UKHIN)”, 7 Vesnina str., Kharkiv, 61023, Ukraine), O.I. Zelenskii, Ph.D. in Technical Sciences, V.V. Vladymirenko (National Technical University "Kharkiv Polytechnic Institute", 2, Kyrpychova str., Kharkiv, 61002, Ukraine) The article is devoted to the practically important issue of obtaining coke with a specific electrical resistance of no more than 0.1 Ohm∙cm for the needs of blast furnace production. Achievement of this indicator should occur simultaneously with the improvement of the entire range of properties of blast furnace coke. It is noted that the concept of production of blast furnace coke of improved quality consists of the formation of a rational raw material base, ensuring proper coking technology and post-furnace coke treatment. It has been shown that the production of high-quality blast furnace coke with low resistivity requires: high sinterability and coking capacity of the charge based on highly sinterable coals; coking pressure of the coal charge not exceeding 7 kPa; coking speed not exceeding 24 mm/hour; final coking temperature of 1050-1100 °C; maximum temperature level in the heating system not exceeding 1410 °C. It is confirmed that in order to obtain a low electrical resistivity, it is necessary to produce coke with the most ordered anisotropic structure. It is shown that the resistivity of coke naturally changes when used in blast furnace production: the destruction of lumps leads to the formation of fragments of different sizes. At the same time, coarser fragments have lower resistivity values, and smaller ones, characterized by an increased concentration of structural defects that are the centers of crack formation, have a higher resistivity. After gasification with carbon dioxide, the number of structural defects increases in grains of any size due to the weakening of the surface due to chemical interaction, so the resistivity of coke increases and becomes almost equal for both coarser and finer grains. Keywords: hard coal coke, blast furnace production, resistivity, vertical temperature, final coking temperature, coke gasification. Corresponding author I.V. Shulga, e-mail: ko@ukhin.org.ua

Circular boron steel: a case study on high performance materials for a zero emission automobile industry

The European automotive sector is under pressure to transform into a zero net emission industry. This involves lightweight, highly efficient vehicles, but also zero emission structural materials. At the same time, a steel industry with automotion as its main customer faces a similar conundrum; it is only natural that this synergy is explored. One of the possible ways this need can be solved is by producing the high performance sheet steel consumed by the Auto industry through scrap-intensive Electric Arc Furnace (EAF) routes, saving an approximate 1.5 tCO2/tsteel compared through the integrated steelmaking route. However, in order to do this, the effect of Residual or Tramp elements inherited from the scrap needs to be considered into the downstream process and use phase. In this scenario, hot stamping of Boron steel sheet presents itself as an excellent use case. This process has become a mainstay in lightweight, high performance safety cage components in passenger cars. It is also gaining traction into light transport vehicles and trucks, and all future trends point to stable usage in the future. Transformed through hot stamping, boron steel shows incredible flexibility to cover different usage scenarios, all while having a simple chemical composition and reasonable cost. And as an added benefit, forming at high temperature bypasses many of the difficulties posited by the presence of residuals affecting springback and formability. This work shows a preliminary study on the concept of circular boron steel for automotive applications, produced by EAF instead of blast furnace. First, sources of scrap commonly used in steelmaking are analyzed to determine the residual elements and inclusions present in this raw material. From this study, studies have been performed to determine the effect on the steel CCT of the residuals with highest impact (in this case, Mo, Cr and Cu). Finally, an industrial cast has been produced and rolled into 4 mm thick sheet, such as it is being used in components like bumpers or in light trucks. Subject to common heat treatments, this material has shown performance on par with commercial, blast furnace products. Results show that scrap-intensive EAF production of Boron steel is possible, and that the impact of moderate amounts of residual elements can be acceptable in this application.

Ефективність використання коксу із заданими значеннями питомого електричного опору

DOI: 10.31081/1681-309X-2024-0-2-28-32 Specialty161, 051. U.D.C. 661.66.2:537.311/.312 EFFICIENCY OF COKE UTILISATION WITH SPECIFIED VALUES OF ELECTRICAL RESISTIVITY © V.V. Vladymirenko, N.M. Dyakova (National Technical University "Kharkiv Polytechnic Institute", 2, Kyrpychova str., Kharkiv, 61002, Ukraine), I.V. Shulga, Ph.D. in Technical Sciences, (State Enterprise “Ukrainian State Research Institute for Carbochemistry (UKHIN)”, 7 Vesnina str., Kharkiv, 61023, Ukraine) The article shows that ensuring the specified values of coke resistivity for different directions of its use allows achieving a positive effect in metallurgical production. It is noted that the resistivity of coke for modern blast furnaces using pulverised coal should be minimal. This is ensured by the proper final coking temperature, which makes it possible to obtain coke with the required level of readiness – the final temperature should be at least 1100 оС. This reduces not only the specific electrical resistance of coke, but also its reactivity. The post-reaction strength of the coke is also significantly increased. This makes it possible to reduce coke consumption for pig iron production and increase the productivity of blast furnaces. As a result, it can be achieved the generation of an additional profit of UAH 377.20 per ton of coke. The resistivity of ferroalloy coke, unlike blast furnace coke, should be as high as possible, which will increase the amount of heat generated in the electric circuit of ferroalloy furnaces and used for endothermic reduction reactions. Therefore, lower coke readiness and lower final coking temperatures are sufficient to produce ferroalloy coke. The use of such coke makes it possible to increase the productivity of electric furnaces for smelting ferroalloys and reduce the consumption of raw materials and electricity. According to the results presented in this article, the additional profit is 4741.37 UAH per ton of coke. The weighted average efficiency of the use of coke with given values of resistivity in the national economy, determined by the volume of coke use in various industries, is 464.48 UAH per ton of coke. Keywords: coal coke, electrical resistivity, post-reaction strength, blast furnace production, ferroalloy production, economic efficiency. Corresponding author I.V. Shulga, e-mail: ko@ukhin.org.ua

Predicting blast furnace permeability index: a deep learning approach with limited time-series data

The blast furnace permeability index is one of the crucial technical indicators in the ironmaking process of a blast furnace. Given that the conventional models are not entirely suitable for accommodating the intricate characteristics of blast furnace production, this paper explores a comprehensive approach that involves data mining, the sparrow search algorithm (SSA), convolutional neural networks (CNNs), and gated recurrent unit networks (GRUs) for predicting the blast furnace permeability index. Initially, to address the multi-noise nature of blast furnaces, outliers are eliminated, and a Kalman filter is devised for denoising purposes. Subsequently, in consideration of the nonlinear and substantial time-delay features of blast furnaces, the maximal information coefficient (MIC) method is employed for time-delay alignment, followed by the selection of model input variables based on process analysis and relevance. Subsequent to this, the SSA-CNN-GRU model is established. Within the modeling process, a one-dimensional convolutional neural network is utilized to extract distinct process variable features, thus further resolving the interdependence among blast furnace data. Ultimately, the effectiveness, accuracy, and advancement of the proposed method are validated using real production data.

Розвиток ресурсозберігаючих технологій металургійного виробництва на основі українського вугілля

The article shows that the post-war restoration of Ukraine's ferrous metallurgy should be based on a new technical and environmental basis - the so-called ‘green metallurgy’ aimed at reducing the harmful environmental impact of iron and iron-based alloys. One of the main elements of the development of ‘green metallurgy’ is the replacement of traditional blast furnace production with the reduction of iron ore with coke with the technology of direct reduction of iron without the use of coke. It is noted that the most promising for the conditions of Ukraine is the process of direct reduction of iron with synthesis gas, which is obtained during the gasification of low-metamorphosed coal. Ukraine has all the necessary prerequisites for the development of low-carbon metallurgy, namely: large deposits of iron ore and its enrichment facilities, which ensure the production of semi-finished products (pellets) of proper quality; the presence of about two-thirds of fossil coal reserves in Ukraine in the balance sheet of lowmetamorphosed coal grades B, D, DG and G. It is emphasised that the destruction of most coal-fired power generation facilities has led to a surplus of coal mined in Ukraine, which is quite suitable for gasification and synthesis gas production. Therefore, the development of metallurgy based on the use of synthesis gas from low-metamorphosed coal makes it possible not only to provide the country's economy with high-quality steel, but also to preserve the coal industry and, moreover, to provide it with new opportunities by ensuring sustainable sales at affordable prices. It has been shown that in practic carbon neutrality of the technology of iron recovery from ores can be achieved by using pure hydrogen as a reducing agent with the possible use of carbon fuels only to provide heat for the reduction reactions. Therefore, in order to reduce the environmental impact of the proposed iron production technology, it is necessary to find new ways to produce hydrogen. Keywords: ‘green’ metallurgy, coke-free metallurgy, direct reduction of iron, low-metamorphosed coal, coal gasification, hydrogen production. Corresponding author I.V. Shulga, e-mail: ko@ukhin.org.ua

Effect of structure of unfluxed burnt titanomagnetite pellets on strength under static compression

Burnt pellets must retain their strength from the moment they are taken out of an induration machine until they are loaded into a blast furnace. One of the indicators of the burnt pellets’ strength is the compressive strength, i.e. the ultimate force. In experiments to determine compressive strength, the main type of fracture is occurrence and development of cracks that pass through the core center of pellets (where the maximum radial tensile stresses present) or near it. The paper presents the requirements for static compression strength imposed by blast furnace production to iron ore pellets. Using an optical and scanning electron microscope equipped with an energy-dispersive microanalyzer, we analyzed the relationship of structural components and pores in the core of burnt unfluxed iron ore titanomagnetite pellets with the ultimate force under static compression. By scanning electron microscopy and X-ray spectral microanalysis, it was established that the core of pellets is a multiphase material, and its main phases are titanomagnetite, magnetite, titanohematite, hematite and aluminosilicate binder. Optical microscopy made it possible to establish the microstructure of the pellet core, which has three types of microstructures: non-oxidized core (magnetite or titanomagnetite), partially oxidized core – around (magnetite or titanomagnetite) hematite grains (titanohematite) and oxidized core (hematite and titanohematite). The main factors for obtaining pellets with an ultimate force of more than 2.5 kN/pellet according to the requirements of blast furnace production are: the number of closed macropores and the number of large grains in the core. It is shown that with an increase in the number of closed macropores and the number of large grains in the core, the ultimate force is reduced from 3.5 kN to 0.87kN/pellet.

Examination of factors and mechanisms contributing to surface wear on copper cooling staves within large blast furnaces

Copper cooling stave is an important cooling equipment to ensure the safe production of blast furnaces. However, the breakage mechanism of copper cooling staves in large blast furnaces is still controversial. The field measurements and samples of the replacement copper cooling stave were performed to elucidate the breakage cause and mechanism. Additionally, the wear parameters of the copper cooling stave were analyzed using SEM, EDS, microhardness, and optical microscopy. According to measurement data, the most problematic areas of copper cooling stave, are situated at the junction of shaft and belly, the junction of belly and bosh, and the joints of the two cooling staves. The annual wear rate in these areas can be as high as 5.64 mm per year. The microanalysis results reveal numerous furrows on the surface of the copper cooling stave and the furrows through the pure copper and the surface of the slag are continuous. It has been observed that abrasive wear is the predominant type of wear, with a minor contribution from oxidative wear. After wear, the subsurface of the copper cooling staves develops wavy undulations, accompanied by the formation of microcracks. In certain specific areas, a multi-layer loose structure was formed which weakened the surface strength of the cooling stave and speeded up the wear of the cooling stave. It was eventually determined that slag peeling was directly responsible for the wear of the copper cooling stave, and the surface heating caused by the high-temperature gas contributed to the wear of the copper cooling stave.

PROSPECTS OF USING ALTERNATIVE FUELS FOR HEATING OF REGENERATIVE AIR HEATERS FOR BLASТ FURNACES

Landfill gas as one of the promising artificial gaseous fuels is analyzed for use in heating system of regenerative air heaters for blast furnaces. Increasing the temperature of blast is one of the most effective ways for metallurgical coke saving and increase the productivity of blast furnaces. To increase this temperature, a mixture of blast-furnace and coke oven gases is used as fuel for regenerative heat exchangers. Considering the current shortage of coke oven gas, landfill gas, the main combustible element of which is methane, is proposed as a high-calorie additive to the main fuel. Calculations were made for the combustion of a mixture of gaseous fuels in three combinations: blast-furnace gas, blast-furnace and coke gases mixture, blast-furnace and landfill gases mixture. The possibility of increasing the temperature of the hot blast to 1250 °C for the air supply system of a blast furnace with a volume of 1033 m³ was considered. Calculations showed that in order to achieve such a temperature, the adiabatic combustion temperature should be 1423 °C, and the flue gas temperature in the crown should be 1300 °C. Such temperatures cannot be reached using only blast-furnace gas, so two possible options were considered. The first option is the use of a coke and blast furnace gases mixture with a coke gas content of 6.3 % and combustion air heating to 180 °C by exhaust gases from blast furnace air heaters. The second option is combustion of a mixture of blast-furnace and landfill gas with combustion air heating to 180 °C (the content of landfill gas is 7.6 %). The flow rate of the fuel mixture for the second option is 68,523 m³ /h, and the required amount of landfill gas is 5208 m³ /h. The volumes of gas output from landfills in large cities are reported to be 5–10 MCM a year, which is less than the amount of biogas required to heat an air heater. Therefore, to achieve the required temperature it makes sense to consider a tree gas mixture that is blast-furnace, coke and landfill gases an appropriate proportions. The use of landfill gas contributes to the solution of an important environmental problem of land and atmosphere pollution during the accumulation of municipal solid waste.

Overall particle size distribution estimation method based on kinetic modeling and transformer prediction

Estimating the overall particle size distribution (PSD) on the conveyor is one of the most important means of monitoring blast furnace production. Due to the harsh production environment and limited detection conditions, existing online detection methods can only estimate part of the PSD, but it is difficult to predict the overall PSD. Unlike segmentation methods that can only obtain surface PSD, we propose an overall PSD estimation method based on mechanistic modeling and local–global fused prediction networks. First, we constructed a kinetic mechanism model of particle accumulation, from which global field distribution features such as coordination number, moment of inertia, and spatial distribution were extracted. Then, a particle segmentation model is trained using surface images collected by detection means such as cameras to obtain surface PSDs as local surface features. Next, to predict the overall PSD, we propose a Local surface and Global field distribution feature Fusion-based Transformer network (LGFT). Among them, in order to better aggregate local and global features, we introduce orthogonal fusion and point cloud extractors in the encoder. Finally, we verify the accuracy and efficiency of our method through extensive ablation experiments.

Enhancement of coal tar pitch carbonization with biochar: A metallurgical formed biocoke product produced by waste coke breeze and bamboo powder

The combustion of pulverized fuels in industrial production is inefficient and aggravates haze formation. A novel and green approach via synergetic using two solid waste resources (waste bamboo powder and waste coke breeze) for producing formed biocoke products as a metallurgical coke substitute was proposed in this study. A biochar with high fixed carbon (87.32 %) and low volatiles (2.49 %) was obtained by bamboo powder pyrolysis at 1000 °C for 30 min. Adding this biochar can enhance the strength of the formed coke product prepared using coke breeze with coal tar pitch as the binder. The cracks and pores between coke breeze particles inside the product can be filled by biochar, increasing the product's density. Furthermore, the carbonization of coal tar pitch as the binder can be improved by adding biochar, further enhancing the product strength. A formed biocoke product, with 34.5 MPa compressive strength, 41.29 % CSR and 30.59 % CRI, was obtained at a 2:8 mass ratio (biochar to coke breeze) by carbonization at 1100 °C under a nitrogen atmosphere for 30 min. The experiments of simulating blast furnace production using mixtures of the formed biocoke product with the metallurgical coke (with 21.12 % CRI and 64.58 % CSR) in different mass ratios were carried out. 4 % of the metallurgical coke for blast furnace ironmaking can be replaced by obtained formed biocoke product. This study achieved sustainable high-value-added recycling of coke solid waste resources. It can be an essential reference for recycling other pulverized solid fuels.