Interaction of Pure Alumina Refractory with FeO–SiO 2 and FeO–SiO 2 –CaO Slags Relevant to the Novel Flash Ironmaking Technology
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Metallurgy & Metallurgical Engineering↗
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Experimental Investigation and Computational Fluid Dynamics Simulation of a Novel Flash Ironmaking Process Based on Partial Combustion of Natural Gas in a Reactor
Not provided.
Metallurgy & Metallurgical Engineering↗
Interaction of ferrous oxide with alumina refractory under flash ironmaking conditions
Not provided.
Materials Science↗
Interaction of magnesia-carbon refractory with ferrous oxide under flash ironmaking conditions
Not provided.
Materials Science↗
Energy Consumption and CO 2 Emissions in Ironmaking and Development of a Novel Flash Technology
The issues of energy consumption and CO 2 emissions of major ironmaking processes, including several new technologies, are assessed. These two issues are interconnected in that the production and use of fuels to generate energy add to the total amount of CO 2 emissions and the efforts to sequester or convert CO 2 require energy. The amounts of emissions and energy consumption in alternate ironmaking processes are compared with those for the blast furnace, currently the dominant ironmaking process. Although more than 90% of iron production is currently through the blast furnace, intense efforts are devoted to developing alternative technologies. Recent developments in alternate ironmaking processes, which are largely driven by the needs to decrease CO 2 emissions and energy consumption, are discussed in this article. This discussion will include the description of the recently developed novel flash ironmaking technology. This technology bypasses the cokemaking and pelletization/sintering steps, which are pollution prone and energy intensive, by using iron ore concentrate. This transformational technology renders large energy saving and decreased CO 2 emissions compared with the blast furnace process. Economic analysis indicated that this new technology, when operated using natural gas, would be economically feasible. As a related topic, we will also discuss different methods for computing process energy and total energy requirements in ironmaking.
36 MATERIALS SCIENCE↗
Design of Novel Flash Ironmaking Reactors for Greatly Reduced Energy Consumption and CO2 Emissions
The development of a novel ironmaking technology based on fine iron ore concentrate in a flash reactor is summarized. The design of potential industrial reactors for flash ironmaking based on the computational fluid dynamics technique is described. Overall, this simulation work has shown that the size of the reactor used in the novel flash ironmaking technology (FIT) can be quite reasonable vis-à-vis the blast furnaces. A flash reactor of 12 m diameter and 35 m height with a single burner operating at atmospheric pressure would produce 1.0 million tons of iron per year. The height can be further reduced by either using multiple burners, preheating the feed gas, or both. The computational fluid dynamics (CFD)-based design of potential industrial reactors for flash ironmaking pointed to a number of features that should be incorporated. The flow field should be designed in such a way that a larger portion of the reactor is used for the reduction reaction but at the same time excessive collision of particles with the wall must be avoided. Further, a large diameter-to-height ratio that still allows a high reduction degree should be used from the viewpoint of decreased heat loss. This may require the incorporation of multiple burners and solid feeding ports.
36 MATERIALS SCIENCE↗
Interaction of Iron with Alumina Refractory Under Flash Ironmaking Conditions
A novel flash ironmaking technology (FIT) based on the direct reduction of iron ore concentrate by a reducing gas such as hydrogen, natural gas, or coal gas has been developed at the University of Utah. A proper choice of refractories is expected to play an important role in the development of FIT and its scale-up. In this work, the interaction of iron with alumina refractory under flash ironmaking conditions has been investigated. A thermodynamic basis for the interaction phenomena has been developed by considering the Fe-Al-O system, and a kinetic model based on solid-state diffusion is proposed to describe the growth of the hercynite (FeAl{sub 2}O{sub 4}) spinel formed as a result of the interaction between Fe, O (from H{sub 2}O and CO{sub 2}), and Al{sub 2}O{sub 3}. Experiments were conducted with Fe powders and pure Al{sub 2}O{sub 3} in the temperature range 1200 to 1500 °C under gas atmospheres relevant to flash ironmaking, and the reacted samples were analyzed using XRD, SEM-EDX, and EPMA techniques. The analyses of reacted samples confirmed the formation of the hercynite (FeAl{sub 2}O{sub 4}) spinel phase and the thickness of the FeAl{sub 2}O{sub 4} spinel as measured using elemental line scans in SEM-EDX and EPMA varied in the range of 0.5 to 1.3 mm. Results showed that the proposed model suitably describes the growth of the hercynite spinel layer, which obeyed the parabolic rate law at all temperatures. The parabolic rate constants were obtained and the diffusion-controlled growth of the FeAl{sub 2}O{sub 4} layer strongly depended on temperature. Furthermore, using the kinetic model, an expression for the effective diffusivity ( (D{sub eff})-bar ) was obtained and its values at the experimental temperatures were determined. The solid-state diffusion was an activated process, as expected, with an activation energy value of 231 kJ/mol.
36 MATERIALS SCIENCE↗
Pathways to Electrochemical Ironmaking at Scale Via the Direct Reduction of Fe 2 O 3
Electrochemical ironmaking can provide an energy efficient, zero-emissions alternative to traditional methods of ironmaking, but the scalability of low-temperature electrochemical cells may be constrained by reactor throughput and the availability of acceptable feedstocks. Electrodes directly converting solid iron-oxide particles to metal circumvent traditional mass-transport limitations but are sensitive to both the particle size and nanoscale morphology of reactants. Furthermore, the effect of these properties on reactor throughput has not been systematically studied at model electrowinning surfaces. Here, we have used size-controlled, homologous α-Fe 2 O 3 particles to study how the nanoscale morphology of oxides influences the obtainable current density toward Fe metal and integrated these results in a technoeconomic model for alkaline iron electrowinning systems. Micron-scale α-Fe 2 O 3 with nanoscale porosity can be used to form Fe at current densities commensurate with industrial water electrolysis (>0.6 A cm –2 ) in the absence of external convection, providing a path to cost-competitive and scalable ironmaking using electrochemistry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Activating magnetite ores for aqueous ironmaking at high current densities
Low-temperature electrochemical cells reducing iron oxides to metal in alkaline electrolytes can support fully electrified steelmaking processes. Previous studies on these cells have primarily focused on high-surface-area hematite, Fe 2 O 3 , reactants whereas attempts to reduce suspensions of magnetite, Fe 3 O 4 —one of the two feedstocks for existing ironmaking reactors—have generally been limited to low rates of reaction (<30 mA cm −2 ). Here, in this study, we control the crystalline domain size of Fe 2 O 3 and Fe 3 O 4 particles in 10 M NaOH electrolytes to study how the nanoscale morphology of oxides controls the rate of electrochemical ironmaking. Rotating-ring disk electrode measurements of Fe 2+ , in situ Raman spectroscopy of the electrode surface, and ex situ electron microscopy were consistent with a hypothesized passivation process at Fe 3 O 4 surfaces that may prevent the continuous formation of soluble intermediates. Sufficiently small (<100 nm diameter) oxide particles yielded Fe partial current densities >160 mA cm −2 , a fivefold increase relative to previously reported rates for Fe 3 O 4 suspensions and comparable to active Fe 2 O 3 . Electron microscopy revealed that electrodeposited films were composed of micron-scale crystalline Fe domains with a porous film of Fe 3 O 4 nanoparticles and supports a model where Fe is grown primarily from soluble Fe 2+ intermediates. Based on these insights, inactive blast-furnace-grade iron-oxides were transformed into high surface area nanoparticles (1.6 to 229.2 m 2 g −1 ) via reprecipitation, leading to a ninefold enhancement in faradaic efficiency and an Fe partial current density of 120 mA cm −2 . When integrated with chlor-iron cells producing reagents for reprecipitation, this approach could lead to a cost-competitive process for electrochemical ironmaking from industrially relevant feedstocks.
TEM↗
Interaction of magnesia-carbon refractory with metallic iron under flash ironmaking conditions
The interaction of a magnesia-carbon refractory with iron under flash ironmaking conditions has been investigated in this work. Oxidation of carbon from the magnesia-carbon refractory took place and the formation of magnesiowustite (Mg x Fe 1-x O) solid solution occurred as a result of the interaction between magnesia (MgO) in the refractory with iron (Fe). A kinetics model for the growth of magnesiowustite (Mg x Fe 1-x O) was developed based on the counterdiffusion of Fe 2+ and Mg 2+ cations through it. Experiments were conducted with iron powder and magnesia-carbon refractory in the temperature range 1200–1400 °C under gas atmospheres relevant to flash ironmaking and analyses of reacted samples using SEM-EDX and EPMA techniques confirmed the formation of magnesiowustite (Mg x Fe 1-x O). Using the kinetics model and the composition profiles determined with EPMA, the values of interdiffusion coefficient ( D ¯ F e - M g ), averaged over the composition range, were calculated. The activation energy ( E a ) for the solid-state diffusion process was determined to be 377 kJ/mol.
36 MATERIALS SCIENCE↗
Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems with Application of an Ironmaking Blast Furnace
In a world where the increasing complexity of modern industrial processes brings difficulties for accurate mathematical modeling, taking advantage of data has become an efficient solution to complex dynamic process modeling issue. In this paper, we develop a novel robust online sequential version of random vector functional-link networks (RVFLNs) for data-driven modeling of dynamic time-varying system and applied it in a blast furnace (BF) ironmaking process. First, to overcome the time-varying dynamics of process and to enable the RVFLNs to learn online with avoiding data saturation, an improved online sequential version of RVFLNs (OS-RFVLNs) is first presented by online sequential learning with forgetting factor. This improved OS-RVFLNs algorithm is not only suitable for the real-time and large data transfer situation, but also can adjust the sensitivity of the algorithm to different samples with the help of the introduced forgetting factor. Second, since the output weights of the improved OS-RVFLNs as well as other RVFLNs algorithms are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with various outliers. To solve this problem, a Cauchy distribution weighted M-estimator is introduced to improve the robustness of the improved OS- RVFLNs. For this proposed robust OS-RVFLNs (R-OS- RVFLNs), since the weights of different outlier data are properly determined by the Cauchy distribution function, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Experiments using actual industrial data of BF ironmaking process and comparative studies have demonstrated that the proposed method produces a better estimation accuracy and stronger robustness than other methods.
Blast furnace (BF), Modelling, Dynamic systems↗
Increased Use of Natural Gas in Blast Furnace Ironmaking: Mass and Energy Balance Calculations
In blast furnace ironmaking, coke can be partially replaced by injected natural gas. Tuyère injection of cold natural gas is commonly practiced in North America. In this work, limits to the tuyère injection rate have been quantified with mass and energy balances. The fundamental origin of the limits is the endothermicity of methane injection. In principle, shaft injection of preheated and partially combusted methane would obviate the endothermic effect. However, the thermal and chemical energy parameter indicates that the energy requirement in the lower part of the furnace—to melt hot metal and slag—might not be met in the case of shaft injection. Tuyère injection of preheated methane might be a feasible alternative. Calculated combustion kinetics support the feasibility of partial combustion of preheated methane (with sub-stoichiometric oxygen) before injection.
36 MATERIALS SCIENCE↗
An Investigation of Carburization Behavior of Molten Iron for the Flash Ironmaking Process
In order to recognize the dripping and the carburizing behaviors of the molten iron within the coke packed bed in the flash ironmaking process, a transient three-dimensional numerical model was developed. The volume of fluid (VOF) approach is used to describe the movement of the molten iron and the argon gas. The porous medium module is employed to define the momentum, heat, and mass transfer between the coke packed bed and the molten iron. Moreover, a factor is introduced to consider the influence of the ash film on the carburization process. A reasonable agreement between the experiment and simulation is obtained. The results indicate that the molten iron flows downward from the upper crucible to middle crucible. After entering the middle crucible, the molten iron spreads around within the coke packed bed and simultaneously moves downward. The carbon is, therefore, transferred from the coke to the molten iron. With the 47-mm-height coke packed bed, the carbon content in the molten iron after the carburization decreases from 3.19 to 1.97 pct, while the coke diameter ranges from 2 to 5 mm. With the 2-mm-diameter coke, the carbon content in the molten iron after the carburization increases from 2.84 to 4.81 pct, while the coke packed bed height increases from 37 to 97 mm.
36 MATERIALS SCIENCE↗
Flash ironmaking drop tube furnace system
A flash ironmaking drop tube furnace includes a primary reaction section having a refractory, an induction coil around the refractory, insulation located between the refractory and the induction coil, and a susceptor located inside the refractory, the susceptor being formed of a material that is heated by induction when electrical current flows through the induction coil, and having at least one interior channel through which particles can pass. The furnace further includes a muffle, located below the primary reaction section; an outer shell surrounding the muffle; at least one heater located adjacent to the muffle; insulation located between the at least one heater and the outer shell; at least one particle feeder that feeds a predetermined volume of particles into the furnace above the primary reaction section; and an inlet port for injecting gas into the furnace, the inlet port being located so that the gas flows through the susceptor and muffle in parallel with the particles.
Green, Edward↗
Novel Flash Ironmaking Technology Based on Iron Ore Concentrate and Partial Combustion of Natural Gas: A CFD Study
Not provided.
Materials Science↗
Kinetics of hydrogen reduction of magnetite concentrate particles at 1623–1873 K relevant to flash ironmaking
Not provided.
Metallurgy & Metallurgical Engineering↗
Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation
Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R 2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.
97 MATHEMATICS AND COMPUTING↗
Modeling the First Hydrogen Direct Reduction Pilot Reactor for Ironmaking in the USA Using Finite Element Analysis and Its Validation Using Pilot Plant Trial Data
Direct reduction of hematite pellets with hydrogen (H 2 ) was used to produce directly reduced iron (DRI) in a pilot scale reactor at a pellet feed rate of 21.4 kg/h. At a steady state, operational parameters of the pilot plant (gas recycling rate and inlet temperature) along with key reactor output parameters, the pellet metallization, and the internal temperature profile of the reactor were reported for two scenarios with high recycle and low recycle rate of H 2 . Scenario 1, with a high recycle rate of 400 L/min H 2 along with external heating of 870 °C, gave an average metallization of 91.8%, while Scenario 2, with low recycle rate of 100 L/min H 2 and external heating of 850 °C gave a metallization of 67.8% due to the higher moles of H 2 available for reduction and the external energy required for the endothermic reduction reaction in Scenario 1 as compared with Scenario 2. Finite element analysis was used to build a model of the shaft reactor, which was validated against the metallization and internal temperature profile data. The average metallization values predicted by the model were very close to the metallization values obtained from the pilot plant samples, with 90.9% average metallization for Scenario 1 and 65.6% average metallization for Scenario 2. The internal temperature profiles in the lower region of the reactor obtained from the model were very close to these pilot plant data, with a maximum difference of 52.7 °C and 67.6 °C for Scenarios 1 and 2, respectively. The pilot plant reactor model was used extensively in the commissioning of the pilot plant and to predict the startup outcomes for a given set of operating parameters.
08 HYDROGEN↗