Mechanism of the Direct Reduction of Chromite Process as a Clean Ferrochrome Technology

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-12-01 DOI:10.1021/acsengineeringau.3c00057
Dogan Paktunc*, Jason P. Coumans, David Carter, Nail Zagrtdenov and Dominique Duguay, 
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Abstract

Direct reduction of chromite (DRC) is a promising alternative process for ferrochrome production with the potential to significantly reduce energy consumption and greenhouse gas emissions compared to conventional smelting. In DRC, chromium (Cr) and iron (Fe) from chromite ore incongruently dissolve into a molten salt, which facilitates mass transfer to a carbon (C) reductant where in situ metallization occurs. Consequently, ferrochrome is produced below the slag melting temperatures, achieving substantial energy savings relative to smelting. However, there are significant knowledge gaps in the kinetics, Cr solubility, speciation, and coordination environment which are critical to understanding the fundamental mechanisms of molten salt-assisted carbothermic reactions. To address these knowledge gaps, we performed pyrometallurgical experiments with variable temperature and residence times and analyzed the composition of chromite, ferrochrome, and slag products along with determining the speciation of Cr. Our results indicate that the DRC mechanism can be explained by the following sequential steps: (1) incongruent dissolution of chromite, (2) reduction of dissolved Cr in molten salt/slag, (3) transport of Cr and Fe species in molten media, and (4) reduction on C particles and metallization as Cr–Fe alloys. The discovery of four types of reduced Cr species in the slag indicates that the reduction of Cr3+ to Cr2+ and Cr0 occurred in the molten phase before metallization on solid carbon particles. Thermodynamically, the reduction of CrO(l) to Cr metal is more feasible at a lower temperature than it is for Cr2O3(l) corroborating the accelerated reduction efficiency of the DRC process.

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铬铁矿直接还原法作为清洁铬铁技术的机理研究
铬铁矿直接还原(DRC)是一种很有前途的替代铬铁生产工艺,与传统冶炼相比,具有显著降低能耗和温室气体排放的潜力。在刚果民主共和国,铬铁矿中的铬(Cr)和铁(Fe)不一致地溶解在熔盐中,这有助于将质量传递给碳(C)还原剂,从而发生原位金属化。因此,铬铁的生产低于熔渣温度,相对于冶炼实现了大量的节能。然而,在动力学、Cr溶解度、形态形成和配位环境方面存在着重大的知识空白,这些对理解熔盐辅助碳热反应的基本机制至关重要。为了解决这些知识空白,我们进行了不同温度和停留时间的火法冶金实验,分析了铬铁矿、铬铁和渣产品的组成,并确定了Cr的形态。我们的研究结果表明,DRC机制可以通过以下连续步骤来解释:(1)铬铁矿的不均匀溶解,(2)熔融盐/熔渣中溶解Cr的还原,(3)熔融介质中Cr和Fe的迁移,(4)C颗粒的还原和金属化为Cr - Fe合金。在渣中发现了四种类型的Cr还原态,表明在固体碳颗粒金属化之前,Cr3+在熔融相中就已还原为Cr2+和Cr0。热力学上,CrO(l)在较低温度下还原为Cr金属比Cr2O3(l)更可行,证实了DRC过程的加速还原效率。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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