Ironmaking Process Research Articles

Applicability of the Teetered Bed Separator for Beneficiating Indian Iron Ore Fines: An Experimental Study

Higher alumina and lower iron content in Indian iron ore fines is a major problem for its effective utilization in the ironmaking process. India is the second largest steel producer in the world with an annual production of 200 million tons in the year 2020, whereas iron ore beneficiation of this country is highly derisory. Only 52 concentrators are required to produce 220 MTPA of iron ore. Most of the concentrator's benefits are limited to sizing and washing. In recent years, the Teetered Bed Separator (TBS) has gained significant importance and appeared as a viable option for beneficiating a variety of fine minerals. So, a systematic study has been carried out to verify the suitability of the TBS to reduce the alumina content and improve the iron grade in Indian iron ore fines. As the performance of gravity separation processes strongly depends on the feed particle size, a size-by-size beneficiation study was carried out to delineate the role of particle size on the performance of the TBS. Based on this study, the TBS is established to be a possible alternative to the other conventional equipment used for the beneficiation of iron ore fines.

Analysis of Blast Furnace Permeability Regulation Strategy Based on Machine Learning

The permeability index of a blast furnace is an important parameter to characterize the reasonable countercurrent movement between the charge and the gas flow. The prediction modeling and regulation of the permeability index are of great significance for energy saving and emission reduction in the ironmaking process. Herein, predictive modeling of the permeability index after one hour (PI‐1h) is carried out by selecting appropriate machine learning models for the different clusters separately based on six machine learning methods and fuzzy‐C‐means. In addition, The SHapley Additive exPlanations (SHAP) method is used to gain insight into the relevance of each parameter to the PI‐1h. The simulation results show that within the allowable error range, the prediction accuracy of the support vector regression and Gaussian process regression models reaches 95.2% and 99.5%, respectively. Based on the data used in this article and the parameter importance analysis of SHAP, permeability index, pressure difference, wind velocity, and hourly coal injection rate are the main parameters to regulate PI‐1h.

Impact of chute cross-sectional shape on the ironmaking blast furnace performance: A numerical investigation

Rotating chutes play a critical role in determining burden distribution on the throat of blast furnaces, thereby influencing the overall ironmaking process. This study aims to explore the influence of chute cross-sectional shape at an industrial scale using a numerical approach. The simulation results demonstrate that the semicircular chute results a lower ore/coke ratio in the central area compared to the trapezoidal chute, while exhibiting a higher ratio at the periphery. Correspondingly, this leads to a lower productivity, higher coke rate, and increased pressure drop within the furnace. These effects can be attributed to the more coke is loaded in central region which has an elevated cohesive zone. Furthermore, with the chute angle for ore increasing, the blast furnace performance with the trapezoidal chute exhibits greater stability compared to that with the semicircular chute. This investigation provides valuable insights for selecting appropriate chute designs to enhance performance and efficiency.

Numerical study of the feasibility of injecting CO2-containing off-gas in an ironmaking blast furnace

The injection of CO2-containing blast furnace top gas (BFTG) to replace reducing gas partially in an ironmaking blast furnace (BF) is a promising approach to achieve carbon neutralisation while potentially maintaining or improving BF performance. However, the feasible injection operating strategy for using CO2-containing BFTG and its effects on in-furnace phenomena are not well understood. In this study, a multi-fluid industrial-scale BF model with a tuyere injection submodel is developed to investigate the impacts of injecting CO2-containing BFTG at the BF tuyere on the in-furnace phenomena in terms of gas flow, temperature field, iron oxides distribution, and BF performance. The results show that: the maximum reducing gas replacement ratio by BFTG is ∼ 15 %, and a further increase leads to an unacceptable flame temperature. As more reducing gas is replaced by the BFTG, the start temperature of the intensified reduction for hematite and magnetite increases while those for wustite and iron decreases; the indirect reduction of iron oxides is improved in 1073–1273 K. The coke rate is increased by around 0.87 kg-coke/tHM with every 1 % reducing gas replaced by the BFTG. The top CO2 emission rate is decreased. This work provides a cost-effective tool to understand the flow-thermal-chemical behaviours inside a BF when CO2-containing gases are recycled in the BF ironmaking process.

Improved back propagation neural network method for predicting sulfur content in hot metal

Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be stabilized by using proper hot metal. Sulfur is an important indicator reflecting the quality of hot metal, it is necessary to establish an accurate prediction model to predict the sulfur content of hot metal, to effectively guide the production process. There is a non-linear relationship among the factors influencing the desulfurization effect during the blast furnace smelting process, and the back propagation neural network (BPNN) model has a strong ability to solve nonlinear problems. However, BPNN has the disadvantages of slow convergence speed and easy to fall into local minima. To improve the prediction accuracy, an improved algorithm combining Kmeans and BPNN is proposed in this paper. The study showed that compared with the BPNN model and case-based reasoning (CBR) model, the Kmeans-BPNN model has the lowest RMSE and MAPE values, which indicates a high degree of fit and a low degree of dispersion. The Kmeans-BPNN model has the largest HR value, which indicates the highest prediction accuracy. The proposed Kmeans-BPNN prediction model achieves a hit rate of 96%, which is 4.5% higher than before the improvement. It can effectively improve the prediction accuracy of hot metal sulfur content.

Characteristics and applications of iron oxide reduction processes

Abstract The present review handles the main characteristics of iron oxide reduction and its industrial applications. The reduction of iron oxide is the basis of all ironmaking processes, whether in a blast furnace or by direct reduction and/or direct smelting processes. The reduction characteristics of iron ores control the efficiency of any ironmaking process and the quality of the produced iron as well. Many controlling parameters should be considered when discussing the reducibility of iron ores such as equilibrium phase diagrams, reduction temperature, pressure, gas composition, and the nature of both iron ores and reducing agent. The different factors affecting the main routes of ironmaking will be highlighted in the present review to give a clear picture of each technology. Moreover, further innovations regarding the reduction of iron oxides such as the reduction by green hydrogen will be discussed.

Multichannel Dynamic Graph Convolutional Network-Based Fault Diagnosis and Its Application in Blast Furnace Ironmaking Process

Multichannel Dynamic Graph Convolutional Network-Based Fault Diagnosis and Its Application in Blast Furnace Ironmaking Process

Fault Detection-Based Multiple Local Manifold Learning and Its Application to Blast Furnace Ironmaking Process

Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. This paper proposes fused local manifold learning (FLML), which synthesizes the typical manifold learning methods to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for process data dimension reduction. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process whose average MDR and average FAR of FLML T2 are 7.58% and 0.21% and a real blast furnace ironmaking process whose MDR and FAR of FLML T2 are 2.80% and 0.00% are carried out to demonstrate the superiority and effectiveness of the proposed method.

Deep Learning Based Feature Selection and Ensemble Learning for Sintering State Recognition.

Sintering is a commonly used agglomeration process to prepare iron ore fines for blast furnace. The quality of sinter significantly impacts the blast furnace ironmaking process. In the vast majority of sintering plants, the judgment of sintering quality still relies on the intuitive observation of the cross section at sintering machine tail by operators, which is susceptible to the external environment and the experience of operators. In this paper, we propose a new sintering state recognition method using deep learning based feature selection and ensemble learning. First, features from the infrared thermal images of sinter cross section at the tail of the sinterer are extracted based on ResNeXt. Then, to eliminate the irrelevant, redundant and noisy features, an efficient feature selection method based on binary state transition algorithm (BSTA) is proposed to find the truly useful features. Subsequently, an ensemble learning (EL) method based on group decision making (GDM) is proposed to recognize the sintering states. Novel combination strategies considering the varying performance of the base learners are designed to further improve recognition accuracy. Industrial experiments conducted at a steel plant verify the effectiveness and superiority of the proposed method.

Effects of pellet ratio on the burden movement and distribution characteristics in the BF throat

In order to solve the urgent problem of energy conservation and emission reduction in the iron and steel industry, the use of high proportion pellets in blast furnace smelting will be the trend of ironmaking development in recent years. In the present work, a geometric model of a serial-hopper bell-less type charging system based on the discrete element method was built, which was used to investigate the distribution behavior of burden under the condition of multi-ring charging and different pellet ratios. The results show: (1) the overall velocity and particle flow width of the burden at the chute outlet become larger which results the average radius of falling point increases. (2) The pellet ratio has a significant effect on the distribution characteristics and the burden profiles at the furnace throat. When the pellet ratio increases from 0% to 100%, the relative radius of peak position of the burden layer increases from 0.491 R to 0.639 R, while the peak height decreases from 0.860 m to 0.543 m. (3) The burden segregation occurs at both the radial and longitudinal positions of the layer. In the radial position, the distribution of pellets near the center is relatively concentrated compared with sinter and there is no burden segregation occurs near the relative radius of 0.590 R. In the longitudinal position, the pellets are mainly distributed at the bottom of the burden layer, while the upper part is relatively less distributed. The research content and results may give a reference for the charging operation of BF under the condition of large pellet ratio, and consequently reduce CO2 emission in ironmaking process.

Review on the Application and Development of Biochar in Ironmaking Production

In recent years, the concept of green, low-carbon and clean energy consumption has been deeply rooted in the hearts of the people, and countries have actively advocated the use of new energy. In the face of problems such as resource shortage and environmental pollution, we began to explore the use of new fuels instead of coal for production. Biomass resources have the characteristics of being renewable and carbon neutral and having large output. As an energy utilization, it is helpful to promote the transformation of the energy structure in various countries. Applying it to ironmaking production is not only conducive to energy conservation and emission reduction in the ironmaking process but also can achieve efficient utilization of crop waste. By introducing the source and main preparation methods of biochar, this paper expounds the main links and advantages of biochar in the ironmaking process and puts forward the direction of biochar in ironmaking in the future.

Thermodynamic Model of MIDREX Ironmaking Process Using FactSageTM and Macro Facility

Thermodynamic Model of MIDREX Ironmaking Process Using FactSageTM and Macro Facility

Non-isothermal kinetics of coke and iron ore melting reduction with variable activation energy model

Due to the significant consumption of coke in blast furnace ironmaking, a large amount of carbon emissions is generated. Thus, addressing the issues of energy conservation and carbon emission reduction in blast furnace ironmaking is imperative. The melting reduction reaction constitutes the predominant portion of carbon consumption during the ironmaking process. However, its kinetic behavior remains unclear to date, impeding energy-saving and carbon reduction efforts in blast furnaces. Consequently, this study conducted melting reduction experiments on two types of sinters and coke under simulated blast furnace conditions. A variable activation energy model (VAEM) was established for kinetic analysis of melting reduction. The conclusion is as follows: the variable activation energies of two types of sinters were determined to vary between 110 and 50 kJ/mol and 115–60 kJ/mol, the pre-exponential factors were 1.36 and 2.57. The deviation analysis showed that the predicted values using VAEM deviated from the experimental values by 1.17 % and 0.95 %. The reliability of VAEM was demonstrated. In turn, the kinetic behavior of coke and iron ore melting reduction was declared. It offers a theoretical basis for promoting the rational use of coke resources in blast furnace ironmaking and reducing carbon emissions.

Separation and recovery of zinc from blast furnace dust via coordination leaching of Cl− and hydrolysis of NH4+

Blast furnace dust (BFD) is a by-product of iron-making process, a typical hazardous waste containing abundant recyclable components. In this study, a NH4Cl cooperative coordination leaching method was proposed for the selective separation and recovery of zinc from BFD. The thermodynamic analysis results revealed that ZnO and ZnSO4 from BFD can be converted to zinc-chlorine coordination compounds (ZnCli2−i, i = 1,2,3,4) in the cooperative actions of NH4+ and Cl− ions. The experimental results showed that the leaching efficiency of zinc reached 85.64% while iron and carbon was enriched from 46.76% to 57.96% under the optimal experimental conditions. The leaching residue with a total content 2.53% of Zn, Cl, K and Na can be returned to sintering for the recovery of iron and carbon. Moreover, the XRD, SEM-EDS and chemical phase analysis results of BFD and residue demonstrated that a small amount ZnO, ZnS and ZnFe2O4 were not leached and remained in the leaching residue. Additionally, a novel method of low-temperature crystallization was adopted to recover zinc from filtrate. The crystallization efficiency of zinc reached 80.96% and the obtained crystal was confirmed to be special Zn(NH3)2Cl2. Thus, this work not only achieved the separation and recovery of zinc from BFD, but also solved the disposal problem of leaching residue and crystallization solution, providing a guidance for the resource utilization of hazardous BFD.

Dynamic stationary subspace analysis based on Gaussian mixture models for ironmaking process monitoring

AbstractBlast furnace ironmaking process monitoring is an important and challenging task. Due to the influence of hot blast stove switching and large fluctuations in the quality of raw materials, the measurements of ironmaking processes show obvious non‐stationary characteristics, and in addition, the observed data are also characterized by time‐series dynamic and non‐Gaussian characteristics. In this paper, a dynamic stationary subspace analysis method based on the Gaussian mixture model (DSSA–GMM) is proposed to address the difficulties in blast furnace ironmaking process monitoring. The time‐series dynamic relationship of the data is conducted by introducing a sliding time window. The Gaussian mixture model (GMM) is used to deal with the non‐Gaussian characteristics of the data, and the parameters of the GMMs are estimated using the expectation–maximization algorithm. The stationary projection matrix is obtained by optimizing the Kullback–Leibler (K–L) divergence between GMMs of different periods to realize the stationary subspace separation. Finally, the convex hull of the stationary subspace is established for fault detection, thus realizing the monitoring for non‐stationary and non‐Gaussian dynamic processes. The effectiveness of the DSSA–GMM method is verified by a numerical simulation and a dataset collected from an actual blast furnace ironmaking process.

Development and Problems of Fluidized Bed Ironmaking Process: An Overview

Development and Problems of Fluidized Bed Ironmaking Process: An Overview

Muti-objective optimization on energy consumption, CO2 emission and production cost for iron and steel industry

Due to high material density, high energy consumption density and CO2 emission density, it is not only difficult but significant to clarify the relationship between energy consumption, the CO2 emission and the production cost in different conditions. However, the previous researches rarely refer how to balance the energy consumption, the CO2 emission and the production cost after the fluctuation of material, energy and carbon price as well as what will happen to them if production structure changes. Therefore, based on the conservation law of mass and energy, to study iron and steel manufacturing process (ISMP), this paper, taking carbon price into consideration, establishes a muti-optimization model of energy consumption, CO2 emission and cost. After optimization with different objectives, the production cost per tonne of crude steel is reduced by 192.03 CNY (7.71%), the CO2 emission per tonne of crude steel is reduced by 224.22 kg (13.37%), and the energy consumption per tonne of steel is reduced by 51.20 kgce (9.10%). Moreover, based on the optimization results under different objectives, it is ironmaking process (coal ratio and ore ratio) and steelmaking process (amount of scrap steel) that has more impact on three above as well as ore blending and coal blending have a great influence on production cost but little effect on energy consumption and CO2 emission.

How much hydrogen is in green steel?

Hydrogen-based reduction of iron ores is the key technology for future sustainable ironmaking, to mitigate the CO2 burden from the steel industry, accounting for ~7–8% of all global emissions. However, using hydrogen as a reductant prompts concerns about hydrogen embrittlement in steel products. This raises the question of how much hydrogen remains from green ironmaking in the metal produced. We answer this question here by quantifying the amount of hydrogen in iron produced via two hydrogen-based ironmaking processes, namely, direct reduction and plasma smelting reduction. Results suggest no threat of hydrogen embrittlement resulting from using hydrogen in green steel production.

An unsupervised fault monitoring framework for blast furnace: Information extraction enhanced GRU-GMM-autoencoder

Fault monitoring of the blast furnace ironmaking process (BFIP) is challenging due to the nonlinearity and dynamic characteristics. The lack of fault labels and the existence of outliers further increase the difficulty of fault monitoring. In particular, these outliers are considered normal operations in BFIP. In this case, an unsupervised autoencoder (AE)-based monitoring framework is proposed. Firstly, the gate recurrent unit (GRU) is used as an encoder to adaptively capture the nonlinear dynamic features of data. The extracted features are proved to be directly related to monitoring. Second, the variance constraint of latent variables (LVs) is added to the loss function to enhance the ability of information extraction. Third, an approach that combines AE structure and GMM to deal with outliers is proposed. Based on the probability clustering results of GMM, monitoring thresholds can be calculated adaptively for different samples. Meanwhile, the fault variables can also be located according to the increment of reconstruction error of GRU-AE. Finally, the proposed method is validated by a numerical example and the practical data gathered from the BF of a Chinese steel group. Compared to unsupervised statistical methods and deep learning methods, the proposed algorithm achieves the lowest false alarm rate (FAR) + missing alarm rate (MAR).

Interfacial phenomenon and Marangoni convection of Fe–C melt on coke substrate under in situ observation

The interfacial phenomenon between liqiuid iron and coke is important for determining the melting efficiency in the blast furnace iron-making process. In this study, the interaction observed in the case of the iron-carbon (Fe–C) melt on coke substrate was investigated using a high-temperature vacuum wettability test equipment. The Fe–C melt did not wet and spread on the coke substrate with different graphitization degrees (r0) at a high temperature of 1450 °C. The contact angles changed from 124.5° to 105.3°, and the r0 increased from 9.30 to 50.00%, thus indicating a nonwetting state. The deepening of graphitization decreased the contact angle. Thereby, increasing the contact area between liquid iron and the carbonaceous material, which facilitated carbon dissolution. The irregular movements of Fe–C melt were observed in situ during the wetting process. The horizontal force of the droplet caused by interfacial tension and the contact angle; the Marangoni convection owing to the gradient of carbon concentration; and the impulse force caused by the generation, aggregation, and release of SiO bubbles at the interface were attributed to the driving force.