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Iron speciation in blast furnace slag cements
Slag-containing pastes and concretes were analysed by element-specific synchrotron-based techniques to determine the speciation of iron on crushed materials through spatially resolved micro-spectroscopic studies. The investigated cement samples were hydrated either in the laboratory, or exposed to river or sea water. Metallic iron, along with minor proportions of iron sulphide and magnetite was detected in the laboratory sample. Iron sulphide, goethite, and siliceous hydrogarnet were discovered in the blended slag cements hydrated in contact with river water for up to 7 years. In contrast, no Fe(0) was observed in blended concretes exposed to sea water. Instead, iron sulphide, iron(II)-hydroxide and -oxide, hematite, magnetite, siliceous hydrogarnet, and goethite were detected as well as ilmenite (FeTiO{sub 3}) in the aggregates. The strong acceleration of Fe oxidation in samples exposed to sea water and the long-term passivation observed in the other samples indicate comparable processes as those occurring on steel bars.
Transient Interaction Between Reduction and Slagging Reactions of Wustite in Simulated Cohesive Zone of Blast Furnace
The blast furnace cohesive zone plays an important role in the gas flow distribution and heat-transfer efficiency. Previous work mainly employed temperature-based indices to evaluate and predict the shape and thickness of the cohesive zone, whereas the internal reactions and related effects on the softening and melting properties of a complex burden are ignored. In this study, an innovative index, namely, shrinkage rate (SR), is first proposed to directly estimate the shrinkage behavior of wustite (FeO)-packed bed inside a simulated cohesive zone. The index is applied as the temperature increases to elucidate the transient interaction between reduction and slagging reactions. Results show that the thermally induced slagging reaction causes the packed bed to shrink at lower temperature, and the SR doubles when compounds with low melting temperature are generated by adding a reasonable concentration of CaO or SiO{sub 2}. The reduction reaction becomes the driving force during the shrinkage of the packed bed between 1173 K and 1273 K when CO is introduced in the mixture gas. Then, the dominating factors for further shrinkage include slagging, reduction, or both factors. These factors vary with respect to the added compounds or temperature.
Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking
Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improve modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking
Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ), which is not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for online estimation and control of MIQ indices. First, to completely capture the nonlinear dynamics of the BF process, the nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multi-output problem, the multi- task transfer learning is proposed to design a novel multi-output LS-SVR (M-LS-SVR) for learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS- SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can be properly distinguished, thus a robust modeling result can be achieved. Finally, a novel multi- objective evaluation index on modeling performance is developed by comprehensively considering the root mean square error of modeling and the correlation coefficient on trend fitting, based on which the NSGA-II algorithm is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently, which indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.
Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking
In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.
Numerical Methods for Simulating the Reduction of Iron Ore in Blast Furnace Shaft
The blast furnace process is a counter-current moving bed chemical reactor to reduce iron oxides to iron, which involves complex transport phenomena and chemical reactions. The iron ore and coke are alternatively charged into the blast furnace, forming a layer by layer structural burden which is slowly descending in the counter-current direction of the ascending gas flow. We proposed a new methodology to efficiently simulate the gas and solid burden flow in the counter current moving bed in blast furnace shaft. The gas dynamics, burden movement, chemical reactions, heat and mass transfer between the gas phase and solid phase are included. Furthermore, the new methodology has been developed to explicitly consider the effects of the layer thickness thermally and chemically in the CFD model.
CFD modeling of multiphase reacting flow in blast furnace shaft with layered burden
The ironmaking blast furnace is a counter-current chemical reactor which includes the ascending gas flow and the counter-current descending porous bed (burden). A Computational Fluid Dynamics (CFD) model has been developed to simulate the multiphase reacting flow in blast furnace shaft. The gas flow dynamics, burden movement, chemical reactions, heat and mass transfer between the gas phase and burden phase are included in the CFD model. The blast furnace burden consists of alternative layers of iron ore and coke. We propose a novel methodology in order to efficiently model the effects of alternative burden layer structure on gas flow, heat transfer, mass transfer and chemical reactions. Different reactions and heat transfer characteristics are applied for difference types of layer. In addition, the layered CFD model accurately predicts the Cohesive Zone (CZ) shape where the melting of solid burden taking place. Furthermore, we determine the shape and location of the CZ by an iterative method based on the ore temperature distribution. The theoretical formation and the methodology of the CFD model are presented and the model is applied to simulate industry blast furnaces. Finally, the proposed method can be applied to investigate the blast furnace shaft process and other moving bed system with periodic burden structure configuration.
Precursor Analysis Report: Cyber Attack on Thyssenkrupp Blast Furnace 2014
The Cyber Attack on Thyssenkrupp Blast Furnace 2014 Precursor Analysis Report leverages publicly available information about the Thyssenkrupp Steel Mill cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. In December 2014, the German Government’s Federal Office for Information Security (BSI) released a report detailing a cyber attack on a German steel mill that occurred earlier that year, though exact dates and details of the attack were not revealed. While the report did not specify the name of the company, multiple sources identified the victim as one of Europe’s largest steel manufacturers, Thyssenkrupp AG. Further, Thyssenkrupp announced on 16 May of that year that Europe’s largest blast furnace, “Schwelgern 2,” located at its facility in Duisburg, Germany, would be offline for several weeks for repairs and upgrades, suggesting Schwelgern 2 was likely the target of the attack. The attack began in early 2014, when adversaries infiltrated the victim steel mill’s Information Technology (IT) network via a spearphishing campaign, then worked their way into the Operational Technology (OT) environment, where they executed software that caused denial of service, denial of control, and eventually a loss of control. This led to the blast furnace shutting down without proper safety procedures, resulting in catastrophic physical damage. No lives were lost in the incident, but ThyssenKrupp suffered $4 million in damage to the blast furnace and an additional $6 million in lost revenue. The adversaries required specialized knowledge and expertise in steel production, which enabled them to compromise a variety of internal systems and components across both IT and OT networks. The attack also demonstrated detailed knowledge of the industrial control systems (ICS) and production processes being used. This combination resulted in one of the earliest known publicly reported cybersecurity incidents resulting in physical damage to ICS equipment. Researchers and analysts identified 19 unique techniques (used in a sequence of 20 steps) utilized during the attack with a total of 454 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Fifteen of the identified techniques used during the Thyssenkrupp cyber attack were precursors to the triggering event. Analysis identified 369 observables associated with these precursor techniques, 316 of which were assessed to have an increased likelihood of being perceived in the 120 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.
Numerical Investigation of Novel Oxygen Blast Furnace Ironmaking Processes
Oxygen blast furnace (OBF) ironmaking process has the potential to realize “zero carbon footprint” production, but suffers from the “thermal shortage” problem. This paper presents three novel OBF processes, featured by belly injection of reformed coke oven gas, burden hot-charge operation, and their combination, respectively. These processes were studied by a multifluid process model. The applicability of the model was confirmed by comparing the numerical results against the measured key performance indicators of an experimental OBF operated with or without injection of reformed coke oven gas. Then, these different OBF processes together with a pure OBF were numerically examined in aspects of in-furnace states and global performance, assuming that the burden quality can be maintained during the hot-charge operation. The numerical results show that under the present conditions, belly injection and hot charge, as auxiliary measures, are useful for reducing the fuel rate and increasing the productivity for OBFs but in different manners. Hot charge should be more suitable for OBFs of different sizes because it improves the thermochemical states throughout the dry zone rather than within a narrow region in the case of belly injection. The simultaneous application of belly injection and hot charge leads to the best process performance, at the same time, lowering down hot-charge temperature to achieve the same carbon consumption and hot metal temperature as that achieved when applying the hot charge alone. This feature will be practically beneficial in the application of hot-charge operation. In addition, a systematic study of hot-charge temperature reveals that optimal hot-charge temperatures can be identified according to the utilization efficiency of the sensible heat of hot burden.
Mathematical model for burden distribution in blast furnace
Burden distribution in a blast furnace is vital to its smooth running. However, it is difficult to directly measure the burden distribution for an operating blast furnace. Therefore, mathematical models have been applied to guide the charging process to achieve the desired burden distribution. The accuracies of such models depend on the prediction of falling curve, stockline profile formation, and burden descent mode. Here, a new stockline profile formation model is proposed in which new equations have been developed for the inner and outer repose angle by considering the influence of the burden flow's vertical and horizontal velocity at the apex of the stockline profile. Furthermore, validation of this new stockline profile formation model is provided through comparison between calculated results and experimental data for stockline profile. A stepped burden descending strategy, in which the burden would descend through a specified distance after each ring charging process, is proposed corresponding to the successive charging process. The influence of the burden descending strategy on the falling point, the final burden profile and radial depth ratio of ore to coke is also analysed. Our results show that the burden descending strategy greatly affects the final burden distribution, especially in the peripheral region.
Evaluation of stock profile models for burden distribution in blast furnace
Burden distribution in a blast furnace is vital to its smooth running. We have applied mathematical models to guide the charging process to achieve desired burden distribution. Furthermore, the accuracies of such models depend on the prediction of falling curve, stock profile and burden descent mode. Among them, the stock profile model greatly influences the final burden distribution. In this study, a special evaluation index for the accuracy of the modelled burden profile based on charging volume is established. Six existing stock profile models were evaluated and compared with the published experiment data of a scaled blast furnace. It is found that all the models predicted the stock profile well. But, certain models showed increased accuracy for the particular case.
Reactions in the Tuyere Zone of Ironmaking Blast Furnace
A series of slags can be formed in the lower part of the ironmaking blast furnace that play important roles in smooth furnace operation, and in determining iron quality and productivity. The final slag tapped from the BF has been investigated extensively as it can be collected directly. Unfortunately, difficulties in accessing the interiors of the blast furnace limit the full understanding of other slags such as primary and bosh slags. In this study, different types of samples directly obtained from the tuyere zone of the blast furnace have been systematically analyzed and characterized using scanning electron microscopy (SEM), electron probe X-ray microanalysis (EPMA), and X-ray fluorescence (XRF), with focus on the characteristics of slags formed in the tuyere level. The samples were identified into three groups according to their morphological, mineralogical, and chemical properties: (1) tuyere slags originating from the reactions between ash and dripping slags; (2) bosh slags in the CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO-FeO system, with a CaO/SiO{sub 2} weight ratio of around 1.50, and Al{sub 2}O{sub 3} and MgO concentrations close to those of final slags; and (3) coke ash that did not react with bosh slags. These findings will provide useful information on the evaluation of slags inside the blast furnace and the reactions in the tuyere zone.
Minimization of Blast furnace Fuel Rate by Optimizing Burden and Gas Distribution
The goal of the research is to improve the competitive edge of steel mills by using the advanced CFD technology to optimize the gas and burden distributions inside a blast furnace for achieving the best gas utilization. A state-of-the-art 3-D CFD model has been developed for simulating the gas distribution inside a blast furnace at given burden conditions, burden distribution and blast parameters. The comprehensive 3-D CFD model has been validated by plant measurement data from an actual blast furnace. Validation of the sub-models is also achieved. The user friendly software package named Blast Furnace Shaft Simulator (BFSS) has been developed to simulate the blast furnace shaft process. The research has significant benefits to the steel industry with high productivity, low energy consumption, and improved environment.
Recovery Act: ArcelorMittal USA Blast Furnace Gas Flare Capture
The U.S. Department of Energy (DOE) awarded a financial assistance grant under the American Recovery and Reinvestment Act of 2009 (Recovery Act) to ArcelorMittal USA, Inc. (ArcelorMittal) for a project to construct and operate a blast furnace gas recovery boiler and supporting infrastructure at ArcelorMittal’s Indiana Harbor Steel Mill in East Chicago, Indiana. Blast furnace gas (BFG) is a by-product of blast furnaces that is generated when iron ore is reduced with coke to create metallic iron. BFG has a very low heating value, about 1/10th the heating value of natural gas. BFG is commonly used as a boiler fuel; however, before installation of the gas recovery boiler, ArcelorMittal flared 22 percent of the blast furnace gas produced at the No. 7 Blast Furnace at Indiana Harbor. The project uses the previously flared BFG to power a new high efficiency boiler which produces 350,000 pounds of steam per hour. The steam produced is used to drive existing turbines to generate electricity and for other requirements at the facility. The goals of the project included job creation and preservation, reduced energy consumption, reduced energy costs, environmental improvement, and sustainability.
Model Study of Blast Furnace Operation with Central Coke Charging
Blast furnace (BF) remains the dominant ironmaking process worldwide. Central coke charging (CCC) operation is a promising technology for stabilizing BF operations, but it needs reliable and quantified process design and control. In this work, a multi-fluid BF model is further developed for quantitatively investigating flow-thermal-chemical phenomena of a BF under CCC operation. This model features the respective chemical reactions in the respective coke and ore layers, and a specific sub-model of layer profile for the burden structure for the CCC operation. The simulation results confirm that the gas flow patterns and cohesive zone’s shape and location under the CCC operation are quite different from the non-CCC operation. Under the CCC operation, the heat is overloaded at the furnace center while the reduction load is much heavier at the periphery regions; the profiles of top gas temperature and gas utilization show bell-shape and inverse-bell-shape patterns, respectively. More importantly, these differences are characterized quantitatively. In this given case, when the CCC opening radius at the throat is 0.35 m, the cohesive zone top opening radius is around 0.50 m, and the isotherms of CCC operation become much steeper (~ 80 deg) than those of non-CCC operation (~ 60 deg) near BF central regions. In addition, it is confirmed that carbon solution-loss reaction rate can be decreased significantly at BF central regions under CCC operation. The model helps to understand CCC operation and provides a cost-effective method for optimizing BF practice.
Investigation of Skull Formation in a Blast Furnace Hearth
A stable skull layer formed on the inner surface of the hearth could effectively protect the lining and extend blast furnace campaign life. Our first comprehensive work of using integrated heat transfer, fluid flow dynamics and solidification numerical simulation to investigate skull formation phenomena in a blast furnace hearth is presented here. The impact of hearth design and furnace operation parameters on skull formation is examined, including lining property and structure, cooling water temperature and flow rate, hot metal production rate, temperature and viscosity, and cast practice. Our findings are compared with operating experience of United States Steel blast furnaces.