Blast Furnace System Research Articles

Understanding cohesive zone behaviour of blast furnace based on computed tomography flow modelling in a fused bed of ferrous and coke particles

Performance of ironmaking blast furnace systems critically depends on the permeability of the cohesive zone which involves flow of reducing gas through a packed bed of fused ferrous and coke particles at high temperature. This study systematically investigated the effect of operating temperature on bed permeability for two different ferrous burdens - pellets, and pellets-sinter mixture supported on coke particles. To quantify the structural changes in the bed, interrupted tests at various temperatures were conducted and bed porosity was estimated using synchrotron X-ray computed tomography (CT) technique. Bed porosity showed decreasing trends with increasing temperature. A CT image based computational flow dynamics (CFD) model was developed which showed linearly decreasing trends of bed permeability parameter with increasing temperature indicating a significant loss of bed permeability in the high temperature cases. This behaviour was more pronounced for the pure pellet case compared to the mixed burden case.

Numerical simulation of air flow and distribution in the blast system of blast furnace

A three-dimensional geometric model of the blast system of the 5500 m3 blast furnace was established in this study. The velocity distribution of the blast system was calculated by OpenFOAM, and the velocity distribution at cross sections of the round pipe and the branch pipe was analysed. The main conclusions are as follows: (1) When the hot air enters the branch pipe, the air flow speed gradually increases and reaches its maximum value (approximately 266.7 m/s) at the tuyere outlet. (2) When the hot air enters the round pipe from the main pipe, there is momentum loss. When the hot air further reaches the opposite side of the junction between the round pipe and the main (Tuyere 1#), the speed of the hot air in the round pipe decreases to the minimum. (3) The maximum air flow average speed of the tuyere outlet is located at Tuyeres 20# and 21# at the junction of the main pipe and the round pipe, which can reach 264.5 m/s. The minimum air flow average velocity of the tuyere outlet is located at Tuyeres 19# and 22#, approximately 258.5 m/s.

Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy

For complex, difficult‐to‐control, and hour‐delay blast furnace (BF) systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag and iron discharge during the entire BF process are analyzed, with a total of 171 variables and 5033 groups of data. Based on the knowledge of BF technology, a comprehensive index of hearth activity is then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method is verified from the two aspects of furnace heat level and furnace status coincidence. Compared with the traditional single‐machine learning algorithm, the performance of the proposed method that combines genetic algorithm and stacking exhibits significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity are 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions are also simultaneously pushed. The model of BF hearth activity is successfully applied in practical online production. During the application period, the average furnace hearth activity increases by 10% compared to the historical value.

A Hybrid Cluster Variational Autoencoder Model for Monitoring the Multimode Blast Furnace System

Efficient monitoring of the blast furnace system is crucial for maintaining high production efficiency and ensuring product quality. This article introduces a hybrid cluster variational autoencoder model for monitoring the blast furnace ironmaking process which exhibits multimode behaviors. In contrast to traditional approaches, this method utilizes neural networks to learn data features and effectively handles the diverse feature types observed in different production modes. Through the utilization of a clustering process within the hidden layer of the variational autoencoder, the proposed technique facilitates efficient fault detection in the context of multimodal blast furnace data. Based on the variational autoencoder model, this study further establishes a unified monitoring index and defines a method for computing the control limits. The application of the model to real blast furnace data reveals its proficiency in accurately identifying faults across diverse modes; compared with the probabilistic principal component analysis based on the local nearest neighbor standardization method and the recursive probabilistic principal component analysis, the model shows a reduction in false positives by up to 10.3% and a substantial reduction of 19.2% in the missed detection rate. This method achieves a remarkable false detection rate of only 0.2% and 0 instances of missed detection.

Recovery Of Zinc from Scrap Steel Using Zinc-Bromine Battery Technology.

Secondary production of steel is known to significantly decrease the CO2 emissions of steelmaking, but only 40 % of steel is produced through recycling, which is made difficult by contamination of scrap resources with nonferrous metals and nonmetal debris. These contaminants include zinc, towards which blast furnace and electric arc systems have a low tolerance (<0.02 wt %). In this work, clean and efficient recovery of zinc from the surface of steel substrates was investigated using a custom-made low-cost membrane-free non-flow zinc-bromine battery (ZBB) that enabled rapid and straightforward integration and removal of steel substrates. The electrical performance of the cell was characterized by charge-discharge profiles, and zinc removal and recovery onto electrodes was characterized by using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Upon discharging, the cell efficiently removed >99.9 wt % zinc from steel surfaces. On recharging the cell, zinc was re-electroplated onto a carbon foam electrode in an easily recoverable form and with high purity. The process was repeated over 30 cycles to demonstrate robustness. The work shows the importance of the cutoff voltage upon discharging: if less than 0.5 V, the cell co-extracted iron into the electrolyte solution, affecting cell durability and zinc purity. A two-stage process for recovering zinc from scrap steel is proposed, illustrating how ZBB technology could enable efficient and clean recovery of zinc from complex scrap steel resources in the steel industry.

Motion trajectory mathematical model of burden flow at the top of bell-less blast furnace based on coordinate transformation

The motion trajectory mathematical model of burden flow is the basis for implementing blast furnace (BF) charging operation, which is of great significance for the stable and efficient operation of BFs. To calculate the motion trajectory of the burden flow at the top of the BF, static and dynamic coordinate systems are first established. Then, coordinate transformation and point composite motion methods are proposed and implemented to calculate this motion trajectory. Finally, the motion trajectories of the single-particle, multi-particle, and entire burden flows are analyzed and compared with the simulation results of a 1:8 scale BF discrete element method model. The results indicate that the absolute percentage errors (APEs) of the particle’s radial and Z-axis velocities are 7.89% and 4.65%, respectively, when the particle is at the chute tip in the single-particle flow trajectory. And the APEs of the radial and Z-axis velocities are 8.79% and 4.38%, respectively, when the burden flow is regarded as a particle. The foregoing demonstrates that the proposed mathematical model can effectively calculate the motion trajectory of the burden flow at the top of BF.

Burden circumferential mass segregation at the blast furnace with parallel hoppers

Burden circumferential mass segregation has a negative influence on the smooth operation of blast furnace. A three-dimensional model of charging system of blast furnace with parallel hoppers based on the discrete element method (DEM) was established to investigate the effects of Y-tube diameter and mass flow rate on the segregation. The static and rolling friction coefficients of sinter and pellet used in the model were calibrated by the experiment of angle of repose. The simulation revealed that the change of force from chute curvature and Coriolis force with the change of collision point led to the deflection of particle flow. The deflection was the main cause of circumferential mass segregation. The circumferential region of blast furnace was divided into the negative segregation region (0°-120° and 210°-240°) and positive segregation region (120°-210° and 240°-360°). The degree of segregation varied linearly with the changes of Y-tube diameter and mass flow rate, and the effect of Y-tube diameter on segregation was greater than that of mass flow rate. In the future, it is expected to establish a circumferential mass segregation evaluation model that considers the main factors for exploring appropriate changing operations to reduce the segregation. • Friction coefficients of sinter and pellet are calibrated by physical experiment. • Key reason of circumferential mass segregation was detailed clarified • Two important factors for mass segregation were investigated • Future expectation about this work was proposed

A Data-Based Compact High-Order Volterra Model for Complex Blast Furnace System

In this article, a data-based compact Volterra model is constructed to carry out the silicon prediction task. The main motivations are to, on the one hand, make better use of the strong memory ability of the Volterra series, which is suitable for reflecting the large inertia of the blast furnace process, on the other hand, overcome the difficulty that high complexity of the original Volterra models may result in serious overfitting. Six kinds of models, including single-input and two-input linear, second-order and third-order compact Volterra models, are successively designed and verified through a real blast furnace case. The reasonable agreement between the predicted values and the observed values indicates the compact Volterra model, especially the single-input third-order Volterra model, are powerful and competitive for describing the complex blast furnace system. The experimental results can serve as a guide for the blast furnace operators to judge the in-furnace thermal state change in time and further provide an indication on how to control the blast furnace in advance.

Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model

Anomaly detection and early warning of the blast furnace ironmaking process is an important research direction of blast furnace production. In this paper, through the analysis and optimization of the multivariate statistical process control (MSPC) model, MSPC based on TOPSIS and the grey (GT-MSPC) model is established based on the problems of missing alarms, false alarms and untimely prediction in the abnormal detection process of the traditional blast furnace ironmaking production process. First, a composite volatility (CV) that consists of the squared prediction error and T2 is computed to form the MSPC model. The parameters of different training set sizes and different alarm conditions are optimized by using the technique for order preference by similarity to an ideal solution model to optimize the effect of anomaly detection and fault diagnosis. Second, according to the results of the contribution plot, the fault diagnosis of abnormal occurrence is carried out, the main influencing factors of abnormal occurrence are analysed, and the monitoring is strengthened. Finally, the grey model is used to predict CV to realize anomaly early warning and form the GT-MSPC model. By collecting the field data of blast furnace production and performing empirical analysis, the results show that the GT-MSPC model has higher anomaly detectability. The detection rate is 50 % higher than that of the manual observation method, and the GT-MSPC model alarms 16.4861 min earlier than manual detection method. Fault diagnosis can accurately locate the main influencing factors of blast furnace anomalies, such as the furnace body static pressure and differential pressure. In summary, the GT-MSPC model realizes the accurate identification and prediction of anomalies and greatly reduces the occurrence of missing and false alarms and untimely prediction in the blast furnace system, thus reducing the risk of blast furnaces. It can be used to assist field engineers in dealing with abnormal faults and improve the product quality and production safety of blast furnace ironmaking.

Causality analysis and prediction of blast furnace state based on convergence cross mapping

ABSTRACT Blast furnace (BF) ironmaking system is a complex industrial system so this paper proposes a BF state causality analysis method based on the use of convergent cross-mapping method (CCM). This method can accurately describe the causal relationships between states at different locations in the BF system. It can also be used as a feature selection method for prediction models. After obtaining accurate causal characteristics of the BF state covariates, the BF system process theory is used for validation. The causal characteristics are used as input variables to the extreme gradient boosting model (XGboost) for predicting BF state parameters. After testing with industrial data, the model predicted an absolute error control within 2% with an accuracy of over 88%. The CCM approach mentioned in this paper is more suitable for state causal impact analysis and predictive model feature selection for BF systems.

Optimizing the acid resistance of concrete with granulated blast-furnace slag

Concrete structures exposed to high levels of chemical attacks are assigned to exposure class XA3, which recommends separate concrete protection or a special expert solution to ensure durability. Due to the partial substitution of Portland cement by blast-furnace slag, an increased resistance to acid attacks could be achieved within the framework of a research project. The technical and ecological advantages of cements containing granulated blast-furnace slag were exploited through chemical, granulometric and concrete technological optimizations. Despite extensive parameters, a statistical test design (DoE) was able to limit the experimental effort, thus defining principles for the conception of binder systems with increased chemical resistance.Mortar prisms indicated that the use of (ultrafine) blast-furnace slags (up to 13,000 cm2/g according to Blaine) with a broad particle size distribution can have a positive effect both on the capillary/gel pore ratio and on the calcium hydroxide content in the cement stone. Furthermore, the chemical composition of the blast-furnace slag as well as the water-binder ratio are decisive influencing factors for the acid-resistance, which was confirmed in accelerated acid resistance tests on concretes (pH-stat method). After 13 weeks of storing concrete specimens in sulfuric acid (H2SO4, pH 3.5), reduced damage depths and lower weight losses were observed compared to conventional binder compositions. The results serve as a basis for the development of highly acid-resistant concretes using blast-furnace slag-containing binder systems. Currently, the acid resistance of those concretes is being investigated in a long-term study by outsourcing representative test specimens into the Emscher sewer.

Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system

Hot metal silicon content (HMSC) is usually utilized to measure the quality of hot metal and reflect the thermal status of blast furnace (BF) system. However, most state-of-the-arts ignore the time-varying behavior of BF ironmaking process, which are impractical. Accordingly, a novel dual ensemble online sequential extreme learning machine (DE-OS-ELM) is proposed to establish the online estimation model of HMSC, which can update the data-driven model with the latest operation data. Specifically, an online learning method with recursive modification is first proposed based on OS-ELM (referred to as RM-OS-ELM) to address the modeling with uncertainty. To heel, a dynamic forgetting factor is presented for the dynamic tracking capability enhancement and convergence acceleration. Furthermore, a final updating rule for sequential implementation is constructed by combining the output weights of OS-ELM and RM-OS-ELM based on their corresponding contributions on modeling. Considering the modeling accuracy and curve trend consistency, multiobjective parameter optimization model is also implemented to achieve the satisfactory performance. By taking the proposed DE-OS-ELM, the estimation model of HMSC is established using industrial data. Comprehensive experiments demonstrate that DE-OS-ELM-based HMSC estimation model is more feasible and practical.

Performance analysis of hot metal temperature prediction in a blast furnace and expert suggestion system proposal using neural, statistical and fuzzy models

Blast Furnace (BF) production methodology is one of the most complex process of iron &amp; steel plants as it is dependent on multi-variable process inputs and disturbances to be modelled properly. Due to expensive investment costs, it is critical to operate a BF by reducing operational expenses, increasing the performance of raw material and fuel consumptions to optimize overall furnace efficiency and stability, also to maximize the lifetime. The chemical compositions and temperature of hot metal are important indicators while evaluating the operation, therefore, if the future values of hot metal temperature can be predicted in advance instead of subsequent measuring, then the BF staff can take earlier counteractions on several operational parameters such as coke to ore ratio, distribution matrix, oxygen enrichment rate, blast moisture rate, permeability, flame temperature, cold blast temperature, cold blast flow and pulverized coal injection rate, etc. to control the furnace optimally. In this study, Artificial Neural Networks (ANN) model is proposed combined with NARX (Nonlinear autoregressive exogenous model) time series approach to track and predict furnace hot metal temperature by selecting the most suitable process inputs and past values of hot metal temperatures using the real data which is collected from the BF operated in Turkey during 2 months of operation. Various data mining techniques are applied due to requirements of charge cycling and operating speed of the furnace which secures novelty and effectiveness of this study comparing previous articles. Furthermore, a statistical tool, Autoregressive Integrated Moving Average (ARIMA) model, is also executed for comparison. ANN prediction results of 0.92, 8.59 and 0.41 are found very satisfactory comparing ARIMA (1,1,1) model outputs of 0.73, 97.4 and 9.32 for R2 (Coefficient of determination), RMSE (Root mean squared error) and MAPE (Mean absolute percentage error) respectively. Consequently, an expert suggestion system is proposed using fuzzy if-then rules with 5 × 5 probability matrix design using the last predicted HMT value and the average of the last 5 HMT values to decide furnace’s warming or cooling movements state in mid-term and maintain the operational actions interactively in advance.

Investigation of the system of blast furnace charging with uneven coke distribution in skips

Investigation of the system of blast furnace charging with uneven coke distribution in skips

A Nonuniform Delay-Coordinate Embedding-Based Multiscale Predictor for Blast Furnace Systems

Blast furnace is the main equipment in modern ironmaking industry. To achieve hot metal of high quality, the temperature in the blast furnace rector, often indicated by the silicon content in hot metal, should be controlled within a proper range. This brief captures the multiscale features of the blast furnace system and proposes a multiscale predictor based on nonuniform delay-coordinate embedding for silicon prediction. We use the discrete binary particle swarm optimization method to optimize nonuniform embedding parameters, including embedding dimension and time lags. Then, the predictor is trained with the nonuniform embedding features under the framework of the Taylor expansion model. Some experiments on a real blast furnace are conducted to verify the superiority of the proposed method, and the results show that the nonuniform embedding model obtains better performance with lower embedding dimension and higher accuracy than the uniform embedding model, which can be seen as a great improvement of our previous work.

Numerical Study of the Influence of Burden Batch Weight on Blast Furnace Performance

Burden batch weight is a critical operating parameter in the blast furnace (BF) system. It directly affects solid distribution such as coke-layer and ore-layer thickness and thus gas permeability and thermal–chemical conditions inside a BF. However, in the open literature, few quantitative studies have been reported on the influence of burden batch weight on BF performance, such as cohesive zone (CZ) shape and location, reducing gas evolution, and iron oxide reduction behaviors. In this study, a multi-fluid BF model is used to investigate the BF performance with different burden batch weights systematically. This model features the layered burden structure with respective chemical reactions in respective coke and ore layers and a burden batch weight sub-model. The results show that, under the given simulation conditions, with the increased burden batch weight from 112 to 140 tons, the average gas velocity in central regions is significantly suppressed by ~ 1 m/s; the average gas and solid temperatures are decreased by ~ 160 K and the position of CZ decreased by ~ 7 m near the BF center; moreover, the gas utilization efficiency is improved by ~ 2.5 pct; the reduction load of the BF becomes heavier, particularly by ~ 0.05 near the BF center; the fuel rate is decreased by ~ 2.5 kg/thm, meaning a higher furnace efficiency. This study provides theoretical support for batch weight selection and optimization in BF ironmaking practice.

Optimum Distribution of Raw Materials and Fuel/Energy Resources Across Multiple Blast Furnaces

Metallurgical companies are currently facing an unstable market for iron ore and fuel/energy, which means that it is important to optimize the distribution of raw materials across multiple blast furnaces and determine the optimum composition of the blast-furnace charge for each furnace. Our analysis of current mathematical modeling approaches for determining the optimum distribution of iron-ore raw material across multiple blast furnaces indicated that the most promising approach involves full-scale modeling and the concept of perturbed object trajectories relative to a reference trajectory. The paper describes algorithm design, mathematical tools, and software for a system to determine the optimum distribution of raw materials and fuel/energy resources across multiple blast furnaces. The optimum raw-material resource distribution scheme implemented in the blast-furnace management system must support integration of the physical and chemical laws governing basic control-target and translator-model processes. The problem was solved by assuming small deviations of parameters from baseline conditions for the perturbed trajectory relative to a reference trajectory. We illustrate the blast-furnace operations planning capabilities of this software and methodology with respect to the cast-iron smelting process and improvement of performance statistics using the Magnitogorsk Metallurgical Complex PJSC (MMC PJSC) as an example.

A Novel MIMO T–S Fuzzy Modeling for Prediction of Blast Furnace Molten Iron Quality With Missing Outputs

For complex and difficult-to-control blast furnace systems with hour-level delay, accurate prediction of molten iron quality plays a very important role in guaranteeing the stable and smooth operation. Recently, some data-driven multi-input multi-output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature, silicon content ([Si]), phosphorus content ([P]), and sulfur content ([S]). However, those data-driven MIMO models ignore the interindicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. Moreover, the above methods do not pay attention to the molten iron quality indicators missing issue, which often occurs on blast furnace. To address the above two issues, this article proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model by utilizing an output transfer matrix. In the novel method, the interindicator correlation was explicitly modeled by a low-rank learning of the correlation matrix that overcame the great challenge of jointly determining the fuzzy rules of the MIMO T-S model and the interindicator correlation. Moreover, a new complete complementary matrix can be obtained by the output transfer from the original incomplete matrix resulting from molten iron quality indicators missing issues. For the corresponding optimization problem, an effective alternating optimization algorithm is presented, and the convergence of the optimization algorithm is also rigorously proved. The validity of the proposed method is verified by comparison with some related methods on real blast furnace data.

Two Derivative Algorithms of Gradient Boosting Decision Tree for Silicon Content in Blast Furnace System Prediction

The background of the present study complies with silicon content prediction in hot metal in the blast furnace system. The blast furnace system is a highly complex industrial reactor in the conventional process. The system is subject to several problems (e.g., system automation, the thermal state of the blast furnace, and the life prediction of blast furnace) that should be addressed by professionals. To determine the prediction state of the heat in the blast furnace, the silicon content in the blast furnace molten iron commonly acts as a key indicator. Based on the assumption that the blast furnace system exhibits a stable state, the accuracy of hot metal silicon is analyzed by using a range of machine learning algorithms. In the present study, two derivative algorithms of gradient boosting decision tree are adopted to develop a strong boosting predictor based on the extreme gradient boosting (XGBoost) algorithm and the light gradient boosting machine (LightGBM) algorithm for prediction. Compared with the conventional algorithms (e.g., lasso, random forest, support vector machine and gradient boosting decision tree), the prediction by using the two boosting algorithms is capable of more effectively guiding and determining the state of the blast furnace. As revealed from experimentally simulated results, the mentioned two boosting algorithms exhibit better comprehensive prediction performance than the conventional algorithms on the datasets of two practical blast furnace systems, demonstrating that the R-square of the two blast furnaces in the training set is over 0.7. The mentioned two algorithms are of certain guiding significance for exploring blast furnace problems.

Gasification kinetics of bulk coke in the CO2/CO/H2/H2O/N2 system simulating the atmosphere in the industrial blast furnace

The gasification characteristics and gasification kinetics of coke in complex CO2/CO/H2/H2O/N2 systems similar to the gas system of industrial blast furnace (BF) were studied by the method of isothermal thermogravimetric analysis. The experimental gas compositions and the corresponding temperature were chosen according to data reported for industrial BFs. The gasification behavior of coke was described by the Random Pore Model (RPM), Volumetric Model (VM), and Grain Model (GM). Results showed that the gas composition of the coke gasification zone in BF changes slightly and that the temperature is the most important factor affecting coke gasification. The lower activation energy of coke samples (Coke Reaction Index (CRI) > 50) is due to the high Fe2O3 in the ash, lower degree of graphitization, and larger pore structure. In addition, the choice of kinetic model does not differ substantially in describing the gasification mechanism of coke in a BF.