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A new methodology involving the integration of a three-dimensional (3D) Computational Fluid Dynamics (CFD) model and a two-dimensional (2D) heat transfer model for the study of the slab reheating process in a walking beam reheating furnace has been proposed. A comprehensive 3D CFD model has been developed to simulate the flow characteristics, combustion process and multi-mode heat transfer phenomena inside an industrial slab reheating furnace. A customized 2D heat transfer model for the slab reheating process has also been developed based on the finite difference method. The simulation results from the 3D CFD model provide detailed heat transfer boundary conditions for the 2D heat transfer model. Instrumented slab trials were conducted under typical operating conditions of the furnace and the results were used to calibrate the 2D model. By comparing the slab temperatures measured from the instrumented slab trials and those predicted using the models, it was demonstrated that the 3D CFD and 2D heat transfer models predict the reheating process reasonably well. The proposed methodology is fairly easy to be used by mill engineers for trouble shooting and optimizing of the slab reheating process. [ABSTRACT FROM AUTHOR]

Iron ore (pellet, sinter and lump ore) is gradually reduced to iron in blast furnace. The reductions are a serial of heterogeneous gas solid reactions that take place from the top of the furnace and throughout the shaft region of the furnace. Two major categories of iron ore reaction model had been developed and applied for modeling the iron ore reduction: Un-Reacted shrinking Core (URC) model and grain model. The URC model had been widely adopted in blast furnace numerical model due to its simplicity for numerical implementation. However, the URC model assumes the reaction taking place in a sharp interface inside the iron ore. The sharp interface assumption may not be valid in the entire reaction stage due to the porous natural of the iron ore. The grain model was proposed to overcome such limitation. But the complexity of the grain model restricts its application to single ore granule reaction process and it has not been applied full blast furnace modeling. In this paper, a novel methodology is proposed to implement the grain model to predict iron ore reduction in blast furnace. The model considers the gas diffusion in porous iron ore, the dynamic composition changes of iron ore along the radius of the iron ore, the iron ore transport in blast furnace. The gas flow and interphase heat transfer are also considered. The result reveals the detailed heat and mass transfer process for the iron ore reduction in blast furnace shaft. [ABSTRACT FROM AUTHOR]

In steel rolling mills, reheat furnaces are used to reheat slabs to high temperatures in a highly oxidizing environment; this results in the formation of iron oxide scale on the slab surface. Scale formation poses an ongoing material and economic loss to industry and should be minimized where feasible. The kinetics of scale growth are complex and still not fully understood. Previous studies that modeled scale formation with mathematical methods are limited to simple case studies. Here, computational fluid dynamics (CFD) is used to simulate slab reheat furnace operations and to investigate complicated physical phenomenon. This paper proposes a new numerical method to model scale growth under varying conditions/characteristics including temperature, gas atmosphere composition, and steel grade. This method uses a mixed linear‐parabolic equation to model both the initial surface reaction (and scale formation) and the subsequent solid‐state ion diffusion through the developed scale. This model can be used to predict the amount of scale that will form on a slab under certain conditions. Model predictions were found to be consistent with experimental data. In this paper, a new numerical method to simulate scale formation is introduced. An acceptable agreement between CFD model results and actual experimental measurements is achieved for the scale formation model under different gas conditions. The model estimates the scale thickness with fair accuracy compared with both in‐lab experimental work and industrial reheat furnace data. [ABSTRACT FROM AUTHOR]