竖炉氢冶金建模与仿真综述:化学工程视角

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-12-12 DOI:10.1021/acsengineeringau.3c00033
Yang Fei, Xiaoping Guan, Shibo Kuang, Aibing Yu and Ning Yang*, 
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引用次数: 0

摘要

氢基竖炉技术为低碳氢冶金带来了希望。由于氢气辅助铁矿石还原具有很高的内热性,因此与气体和致密矿石接触相关的热量供应不足可能会降低还原速度和效率。解决这一问题的关键在于了解矿石周围的气体流动和矿石内部反应所驱动的热量供应、热量传递和热量损失之间的竞争。建模和模拟对于揭示内在机制、促进工艺放大和强化至关重要。本综述总结了以往在物理建模和模型应用方面为提高还原性能所做的努力。离散元素法(DEM)和计算流体动力学(CFD)-DEM 模型已被用于颗粒尺度模拟,以研究非均质颗粒下降和相关的颗粒-颗粒相互作用。对于宏观模拟,通过考虑热量和质量传递,开发了稳态简化模型,如塞流和 REDUCTOR 以及欧拉两相模型。在这些模型应用的基础上,提出了包括优化操作条件和供气方法在内的策略,以提高熔炉性能。还需要进一步开展数值工作,分析炉内热量的演变和减少,并通过将多尺度物理学纳入竖炉来揭示流动、传输和反应的竞争性。此外,还可以关注颗粒粘附和降解对还原的影响,当块状矿石比例增加时,这种影响可能会更加严重。在评估相对优化策略时,希望在各种还原和冷却气体操作条件和炉型下,对铁矿石还原度、气体利用率、能耗和经济可行性进行综合比较,为工业设计和强化提供实用指南。
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A Review on the Modeling and Simulation of Shaft Furnace Hydrogen Metallurgy: A Chemical Engineering Perspective

Hydrogen-based shaft furnace technology holds promise for low-carbon hydrogen metallurgy. Since hydrogen-assisted iron ore reduction is highly endothermic, inadequate heat supply relevant to the contact of gas and densely packed ores may reduce the rate and efficiency of reductions. The key to addressing this issue lies in understanding the competition among heat supply, heat transfer, and heat loss driven by the gas flow around ores and reactions within them. Modeling and simulation are crucial for revealing the underlying mechanisms and promoting process scale-up and intensification. This review summarizes previous efforts in physical modeling and model applications for improving the reduction performance. The discrete element method (DEM) and computational fluid dynamics (CFD)–DEM models have been used for particle-scale simulation to investigate inhomogeneous particle descent and relevant particle–particle interactions. For macroscale simulations, steady-state simplified models such as plug flow and REDUCTOR, as well as the Eulerian two-phase model, have been developed by considering heat and mass transfer. Based on these model applications, strategies including the optimization of operating conditions and gas-feeding methods have been proposed to improve the furnace performance. Further numerical efforts are needed to analyze the in-furnace heat evolution and reduction and reveal the competitiveness of flow, transport, and reaction by incorporating multiscale physics in shaft furnaces. Additionally, attention could be paid to the effects of particle sticking and degradation on reduction, which may be more serious when the proportion of lump ores increases. When evaluating relative optimization strategies, comprehensive comparisons are expected in terms of iron ore reduction degree, gas utilization rate, energy consumption, and economic feasibility under various reducing and cooling gas operating conditions and furnace profiles to offer practical guidelines for industrial design and intensification.

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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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