CFD-DEM study of coke combustion in the raceway cavity of an ironmaking blast furnace

Journal Pre-proof CFD-DEM study of coke combustion in the raceway cavity of an ironmaking blast furnace

Jiaxin Cui, Qinfu Hou, Yansong Shen PII:

S0032-5910(19)31112-X

DOI:

https://doi.org/10.1016/j.powtec.2019.12.012

Reference:

PTEC 15018

To appear in:

Powder Technology

Received date:

13 June 2019

Revised date:

1 November 2019

Accepted date:

6 December 2019

Please cite this article as: J. Cui, Q. Hou and Y. Shen, CFD-DEM study of coke combustion in the raceway cavity of an ironmaking blast furnace, Powder Technology(2019), https://doi.org/10.1016/j.powtec.2019.12.012

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© 2019 Published by Elsevier.

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CFD-DEM STUDY OF COKE COMBUSTION IN THE RACEWAY CAVITY OF AN IRONMAKING BLAST FURNACE Jiaxin Cuia, Qinfu Houb, and Yansong Shena, *1

a

School of Chemical Engineering, University of New South Wales, Sydney, NSW 2052,

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Department of Chemical Engineering, Monash University, Clayton, VIC 3800, Australia

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b

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Australia

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* Corresponding author. Tel: +61 2 9385 4448; E-mail address: [email protected]

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Journal Pre-proof ABSTRACT A void zone, the so-called raceway, may be formed when gas is laterally injected into a particle packed bed and the char particles may combust when the gas enters at a high temperature for example coke combustion in the raceway of ironmaking blast furnaces. Experimental and numerical studies of multiphase flow in the raceway have been conducted widely in recent years, however, the study of reacting flows in the raceway is still limited at a particle scale. This work is to study the gas-solid reacting flows in the raceway using a

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particle scale CFD-DEM approach, featuring heat and mass transfers and chemical reactions

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between the gas and solid phases. The simulation results are comparable with the measurements in the physical experiments and previously calculated results. The key

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phenomena of flow and thermochemical behaviour related to the raceway formation at the

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particle scale are obtained. Then the model is used to study the dependency of raceway formation and thermochemical behaviour on several key raceway variables including gas

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inlet velocity, particle size, bed height and particle discharge rate. The simulation results indicate that before generating a stable raceway, a larger blast inlet velocity or a larger

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discharge rate can form a larger raceway cavity, while the effect of packed bed level shows

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an opposite trend. The model provides fundamental insights into the complex reacting flows in the raceway zone for a better understanding and optimization in operations.

Modelling

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Keywords : Raceway, Thermo-chemical behaviour, Gas-solid flow, CFD-DEM, Blast furnace,

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1

INTRODUCTION

“Raceway” refers to the solid particles recirculation region driven by a high-speed lateral gas jet in a packed bed. It is distinctly characterized by a region of high void fraction next to the gas inlet and with intermittent peeling of particles from the cavity surface. In practice, the raceway process can be commonly observed in many industrial applications such as ironmaking blast furnace (BF), catalyst regeneration, granular drying, coal gasification and combustion, for efficient heat transfer and chemical reactions within the packed beds [1]. For

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example, BF practice represents one of the most commercially important applications of raceway process. BF is a metallurgical furnace used for smelting iron ore and producing

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liquid iron at industrial scale. In an integrated steelwork, BFs together with the associated units (pelletising-sintering machine and coke oven) represent over 90% of the CO2 emission

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[2-4] and 70% of the energy consumption [5-7]. Thus, minimizing the rate of reducing agent

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and increasing the energy consumption efficiency in the BF ironmaking process have attracted considerable attentions [8, 9]. In practice, the high-speed air is injected into the coke

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packed bed at the lower part of BFs, forming the void region termed raceway. Raceway is the

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combustion zone where coke particles are burnt with hot air to supply heat and reducing gases for the reduction of iron-bearing materials in the upper part of the furnace [10-14].

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Therefore, the size and shape of the raceway plays an important role in determining the gas species distributions and heat supply to the whole BF, and affects the productivity of the furnace significantly. For this reason, the raceway should be studied comprehensively, especially the fundamentals at particle scale.

The raceway zone has been studied extensively both experimentally and theoretically in the past [10, 15, 16]. Experimentally, the raceway has been measured through sampling coke density through the tuyeres [17]. For example, Matsui et al.[18] discussed the BF raceway formation under the intensive coal injection rate conditions by means of measuring the microwave reflection gunned through a tuyere. Due to the harsh practical environment (high temperature, high pressure etc.) in an actual BF operation, the raceway phenomenon has been studied using mathematical modelling. For example, Sastry et al.[19] studied the mixed 3

Journal Pre-proof particle systems in a two-dimensional cold model. Similarly, Luo et al. developed particle velocity contours to define the raceway boundary based on the data from a cold physical model of the raceway [20]. In numerical modelling perspective, various numerical models have been developed to predict raceway shape and size, for example, the 2D cold model using combined continuum and discrete model (CCDM) reported by Feng et al. [21], Eulerian-Eulerian model by Mondal et al.[22], and 3D cold model by Rangarajan et al. using the two-fluid model [23]. The formulated computational fluid dynamics (CFD)-based continuum model had been studied some operational conditions on raceway size and shape

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[22, 23], and the coal/coke combustion in the raceway [11]. In practice, the continuum approach is preferred for large scale computations, but detailed microscopic dynamic

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information for fundamental understanding cannot be achieved using this approach. Such

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difficulties can be overcome by the particle scale approach discrete element method (DEM) coupled with CFD. In recent years, CFD-DEM has been used to investigate the region of

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raceway by many researchers. For example, Feng et al.[21] developed a 2D slot cold model and discussed the effect of some factors such as gas velocity and the load from the bed top on

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the raceway formation. Yuu et al. [24] reported the flow characteristics of solid particles, the

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air flow, and the packing fraction distribution around raceway. Hilton and Cleary [1] observed that particle shape plays a critical role in the formation and maintenance of raceway.

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Wei et al.[25] reported a study of flow and heat transfer in the raceway using CFD-DEM approach. Generally, it should be noted that the majority of these numerical studies only focus on flows at cold state and did not consider the heat and mass transfers at hot state.

In this work, a particle scale CFD-DEM model, considering heat and mass transfers related to chemical reactions, is developed and used to study the reacting flow in the raceway formation. The key phenomena of raceway formation are described. The effects of some key operational variables including gas inlet velocity, bed height, raceway discharge rate and packed bed particle size on raceway formation are also examined and analyzed. 2

MODEL DESCRIPTION

The present mathematical model is formulated based on the combined CFD-DEM approach, 4

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from flow [26-28] to heat and mass transfer models [29, 30]. The detailed descriptions are shown as follows. 2.1

Governing equations for discrete solid particles

The translational and rotational motions of solid particles are described by the DEM. It has been well established [26-28, 31] and outlined below for completeness. The interactions

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between a specific particle and its neighbouring particles and/or walls can occur through which the momentum exchange takes place. At any time, t, the governing equations of the

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motions of particle i of mass mi and radius Ri are written as:

(1)

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𝑚 𝑖 𝑑𝐯𝑖 ⁄𝑑𝑡 = ∑𝑗 (𝐟𝑒,𝑖𝑗 + 𝐟𝑑,𝑖𝑗 ) + 𝐟𝑝𝑓 ,𝑖 + 𝑚 𝑖 𝐠

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and 𝐼𝑖 𝑑𝛚i⁄𝑑𝑡 = ∑𝑗 (𝐓𝑡,𝑖𝑗 + 𝐓𝑟 ,𝑖𝑗 )

(2)

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where vi and  i are the translational and rotational velocities, and Ii (= 2/5𝑚 𝑖 𝑅𝑖2 ) is the

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moment of inertia of the particle. Particle- fluid interaction force fpf,i, the gravitational force

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mig and the forces between particles (and between particles and walls) are involved. The elastic force fe,ij and viscous damping force fd,ij are the forces between particles. Specifically, the particle-fluid interaction forces on a specific particle i is the sum of particle- fluid drag force, fd,i and pressure gradient force fpg,i. Tt,ij, which is generated by the tangential force and causes particle i to rotate, and Tr,ij, which, commonly known as the rolling friction torque [32] are the two torques acting on particle i due to particle j. For possible multiple interactions of particle i, the interaction forces and torques between each pair of particles are summed up. Most of the equations to determine the forces and torques are well recorded in the literature as, for example, reviewed by Zhu et al. [33]. The equations used in previous studies [28] are 5

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adopted for the present work as given in Table 1.

Particle i exchanges heat in three modes: convective heat transfer with surrounding fluid, conductive heat transfer to other particles or walls, and radiative heat transfer to its surrounding environment [34]. The governing equations of energy balance and species concentration for particle i are written as: (3)

𝑚 𝑖 𝑑𝑐𝑖 ,𝑚 ⁄𝑑𝑡 = 𝑠𝑖 ,𝑚

(4)

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𝑚 𝑖 𝑐𝑝,𝑖 𝑑𝑇𝑖 ⁄𝑑𝑡 = ∑𝑗 𝑄̇𝑖,𝑗 + 𝑄̇𝑖 ,𝑓 + 𝑄̇𝑖 ,𝑟𝑎𝑑 + 𝑄̇𝑖,𝑤𝑎𝑙𝑙 + 𝑄̇𝑖,𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛

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where 𝑄̇𝑖 ,𝑗 (= 𝑄̇𝑖,𝑗,𝑓 + 𝑄̇𝑖,𝑗,𝑠𝑡𝑎𝑡𝑖𝑐 + 𝑄̇𝑖,𝑗,𝑑𝑦𝑛𝑎𝑚𝑖𝑐 ), 𝑄̇𝑖,𝑓 , 𝑄̇𝑖,𝑟𝑎𝑑 , 𝑄̇𝑖,𝑤𝑎𝑙𝑙 , are the heat exchange

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rate between particles i and j by conduction, between particle i and its local surrounding fluid by convection, between particle i and its local surrounding environment by radiation, between

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particle i and wall by conduction, respectively. For particle-wall conduction, 𝑄̇𝑖,𝑤𝑎𝑙𝑙 , a wall is

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treated as a particle with an infinite diameter and mass, as commonly adopted in the DEM

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work [30]. 𝑄̇𝑖,𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 is the heating rate due to chemical reactions. Eq. (3) is the same as the so-called lumped-capacity formulation, where the thermal resistance within a particle can be neglected [35]. However, as noted by Ref. [30], Eq. (3) is established on the basis of heat balance at the particle scale and it can be applied by using representative properties at a particle scale even for the Biot number higher than 0.1 [36]. ci,m is the concentration of species m in particle i and si,m is the reaction rate of species m with the surrounding environment. The equations for the calculation of conductive, convective and radiative heat exchange rates in Eq. (3) are given in Table 2 [30, 34].

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2.2

Governing equations for gas phase

The continuum fluid phase is modeled similarly to the one widely used in the conventional two- fluid model [28, 37]. Set II and in particular set I can be used generally among the present three sets of governing equations [28]. In this work, the general Set I is used. Thus, the conservations of mass and momentum in terms of the locally averaged variables over a

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computational cell are given by:

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𝜕(𝜌𝑓 𝜀𝑓 )⁄𝜕𝑡 + 𝛻 ⋅ (𝜌𝑓 𝜀𝑓 𝐮) = 0

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and

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𝜕(𝜌𝑓 𝜀𝑓 𝐮)⁄𝜕𝑡 + 𝛻 ⋅ (𝜌𝑓 𝜀𝑓 𝐮𝐮) = −𝛻𝑝 − 𝐅𝑓𝑝 + 𝛻 ⋅ 𝜏 + 𝜌𝑓 𝜀𝑓 𝐠

(5)

(6)

The corresponding energy equation for heat transfer can be written as:

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𝜕(𝜌𝑓 𝜀𝑓 𝑐𝑝𝑓 𝑇𝑓 )⁄𝜕𝑡 + 𝛻 ⋅ (𝜌𝑓 𝜀𝑓 𝐮𝑐𝑝𝑓 𝑇𝑓 ) = 𝛻 ⋅ (𝑘𝑒 𝛻𝑇𝑓 ) + 𝑄̇ + 𝑄̇𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛

(7)

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The species transport equation for different gas components can be written as: (8)

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𝜕(𝜌𝑓 𝜀𝑓 𝐶𝑚 )⁄𝜕𝑡 + 𝛻 ⋅ (𝜌𝑓 𝜀𝑓 𝐮𝐶𝑚 ) = 𝛻 ⋅ (𝜀𝑓 𝛤𝑚 𝛻𝐶𝑚 ) + 𝑅𝑚 + 𝑆𝑚

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where u, f, p and Ffp are the fluid velocity, density, pressure and volumetric fluid-particle interaction force, respectively;  and  f are the fluid viscous stress tensor and porosity respectively. Note that  i is the local porosity for particle i for calculating particle- fluid drag force and  f is determined over a CFD cell. k e is the fluid thermal conductivity. The volumetric particle- fluid interaction force Ffp in Eq. (6) can be determined as 𝐅𝑓𝑝 = 𝑣 ∑𝑘𝑖 =1 (𝐟𝑑,𝑖 + 𝐟𝑝𝑔,𝑖 ) /ΔV. The volumetric heat exchange rate Q in Eq. (7) is determined as 𝑣 𝑄̇ = (∑𝑘𝑖=1 𝑄̇𝑓,𝑖 + 𝑄̇𝑓,𝑤𝑎𝑙𝑙 + 𝑄̇𝑓,𝑟𝑎𝑑 ) /ΔV, where 𝑄̇𝑓,𝑖 , 𝑄̇𝑓,𝑤𝑎𝑙𝑙 , 𝑄̇𝑓 ,𝑟𝑎𝑑 , are the heat exchange

rate between the fluid and particle i by convection, between the fluid and a wall by convection and between the fluid and its environment by radiation, respectively. 𝑄̇𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 in 7

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Eq. (7) is due to chemical reactions between gas species and solid particles in the local cell, where the heat sink or source is shared by discrete and continuum phases. This work simply considers one continuum gas phase and Cm is the concentration of species m. Rm is the source of m due to chemical reactions in continuum phase and Sm is the source term due to the reaction between discrete and continuum phases which equals to the sum of si,m over all particles in a CFD cell. Γm is the diffusion coefficient of species m. The rate for coke

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combustion reaction is given in Table 3, where N c, yo2 and Ef′ are the number of coke

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particles in unit volume of bed, oxygen mole fraction in gas phase and effectiveness factor,

Treatments and numerical solution procedure

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2.3

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respectively.

The numerical method for CFD-DEM coupling has been well documented [27, 28, 38, 39]

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and the same coupling scheme is used here. The exchange of information between discrete

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and continuum phases is illustrated in Fig. 1. In each time step, the information of individual

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particles such as position, velocity, temperature and carbon content is determined by the DEM which is then used to evaluate porosity, particle- fluid interaction forces, heat flux and carbon consumption in a computational cell. The information is then used for the CFD to calculate fluid flow, temperature field and carbon transportation and to find particle- fluid interaction forces, heat transfer between the fluid and coke particles or wall, and the generation rate of carbon monoxide, respectively. Incorporation of the resulting forces, the heat fluxes and the carbon consumption into the DEM produces the information of the position, the velocity, the temperature and the carbon content of individual particles for next time step [40]. 8

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The sequential solution procedure of the CFD-DEM raceway model includes the following major steps and treatments: a) The bed is initially charged with coke particles to a certain level, which presents the existing coke in the lower part of BF (Fig. 2(c)).

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b) To reduce the computational time to achieve a steady state, an initial solid temperature

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field as shown in Fig. 2(c) is pre-set, largely according to experimental data [41].

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Otherwise, it will take a very long time to achieve the steady state if the packed bed is

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heated from the ambient temperature.

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c) Coupled gas-solid flow and heat and mass transfer due to chemical reactions will be calculated and temperature field is updated continuously. Here, the coke particles in the

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raceway region are discharged at a given rate, resembling the coke combustion. And the

SIMULATION CONDITIONS

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3

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charging rate equals to the discharged coke particles from the raceway region.

The present work aims to investigate the reacting flow in the raceway and its dependency on some operational variables based on the developed CFD-DEM model. In order to confirm the functionality and applicability of this model, the model is applied to a commercial BF at Baosteel. The geometry is set as shown in Fig. 2(a). To reduce computational cost, a two-dimensional (2D) slot model is used with the thickness of four particle diameters. Periodic boundary conditions are applied to solid particles along the front and rear directions to eliminate the effect of the corresponding walls. For simplicity, the properties of sidewall and furnace center are assumed to be the same as particles, but the size of a wall is infinitely 9

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large. The mesh size in Fig. 2(b) has been examined, and the results do not improve much if a finer mesh is used. The gas composition set at the inlet and the properties of coke particles in the present model are given in Table 4.

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Typical phenomena of reacting flows in the raceway

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4.1

RESULTS AND DISCUSSION

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The formation of the raceway is a dynamic process at a fixed gas velocity as shown in Fig. 3

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under the base case conditions (N=30,000; uf=220 m/s). Initially, a small cavity is formed and

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enlarged along the horizontal direction next to the tuyere (Fig. 3(b)). Then the raceway keeps

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enlarging and reaches a steady state (Fig. 3(c)) gradually as more gas is introduced. It is indicated that the raceway zone could be observed under the specified dynamic condition of

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gas flow before fluidizing, and a dynamic particle recirculation is generated due to intensive

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gas penetration and the particles rotational motion macroscopically. In addition, it can be

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presented that the generation of the raceway cavity is a gradual process, but stable raceway will occur eventually under the suitable gas flow rate and bed width (Fig. 3(c)) when the interactions between gas and particles reach a relative balance. The evolutional process of raceway formation can be detected macroscopically from bed porosity as shown in Fig. 3. This trend agrees well with the findings of previous works [16, 42], confirming the model validity.

In principle, the interaction forces between particle-fluid and particle-particle dominate the dynamic process in the raceway and cause the large-scale recirculating particle flows [16].

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The variation of the contact force distribution is illustrated in Fig. 4. The gas recirculating zones observed in the raceway will drive the particles recirculate, leading to smaller contact force among particles (Fig. 4c). The large vortex is formed on the both sides of the jet axis. These vortices are the large recirculating flow, and the gas above the tuyere tip region with large kinetic energy is recirculating in an anti-clockwise, at the same time, the gas below the

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tuyere is recirculating clockwise (red arrow in Fig. 4(c)). The dynamic balance between the

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incoming kinetic energy and the bed gravitational energy contributes to the formation of

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raceway. The simulated results are generally consistent with the early work observed in cold

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models and experiment [1, 15, 16, 24].

In Fig. 5, it shows the variation of the bed temperature in the formation of the raceway. As

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the time goes, the bed temperature tends to become higher, and the high-temperature area

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(dotted line frame in Fig. 5(a)) is extended gradually with the gas flow as shown in Fig. 5(e).

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As the gas-solid reaction (C+O 2 →CO) is exothermic and the path of gas recirculating is followed with an anti-clockwise direction upon the inlet as shown in Fig. 4(c), the temperature vortex recirculating flow is formed. This behavior of bed temperature is comparable with the results in a previous work [41], confirming the model validity in terms of heat transfer.

Fig. 6 shows the carbon content inside and around the raceway. As the time goes, the consumption of carbon is rapid in the high-temperature area as given in Fig. 6(e), and an elliptical recirculation region of carbon content is formed above the inlet. The reason is that

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the reaction of carbon and oxygen could occur immediately and completely when the temperature is high enough for the reaction of C+O 2 →CO. It is presented that the coke consumption ratio follows the gas flow trajectory and temperature distribution. Coke is consumed more in the centre of the rotational gas flow where the temperature is higher. This calculated result correlates well with the measured one by Ryota [43], confirming the model

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validity in terms of mass transfer.

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Fig. 7 shows the distribution of gas compositions inside and around the raceway. It is shown

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that oxygen is blown into raceway zone and is consumed quickly due to coke combustion at

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high temperature. CO is generated and decrease progressively from the wall towards to the

Effects of operational variables on raceway shape and size

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4.2

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centre due to simultaneous oxygen consumption.

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In this section, the effects of key variables on the raceway shape and size are investigated, in terms of inlet velocity, bed height, particle discharge rate around raceway, and particle size.

4.2.1

Effects of inlet velocity

Generally, different raceway features would be a result of the different operating settings in almost every chemical reactor process. In practice, the raceway size and shape are difficult to predict owing to the complexity and severe circumstance in a real reactor. In this work, the formation of raceways is investigated with the variation of inlet gas velocity as shown in Fig. 8. It shows typical flow patterns of particles in the raceway region at gas inlet velocities of

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200 m/s, 210 m/s, and 220 m/s, respectively. The voided region formed adjacent to the inflow for every case is visible on the left side of each figure. When gas inflow velocity is at 200 m/s, the bed remains static with non-noticeable void region because the gas simply percolated through packed bed. For the 210 and 220 m/s cases, a cavity region formed almost immediately, and the raceway size increases with inlet gas velocity, which can be clearly

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found in Fig. 8c where the raceway is quantitatively defined as the region in which its void

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fraction is over 0.95. It is because that the rise in the horizontal momentum of the gas is

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transferred to particles, which could drag the particles further into the bed before the

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retardation caused by the packed bed preventing any further horizontal penetration. This trend

Effects of packed bed height

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4.2.2

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agrees well with the findings of other authors. [21-23, 44, 45]

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In a real chemical reactor operation, different furnace shapes and operating conditions

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generate different solid loading. For this reason, it is necessary to investigate the effects of packed bed height on raceway formation. The initial packed bed height determines the upper load of raceway. Here, the different packed bed heights are presented by different particle number existing in the geometry. Fig. 9 shows that increasing the initial packed bed height decreases the size and shape of the raceway under the same inlet gas velocity. The reason is that the growth in bed height increases upper load, which offers a higher resistance to gas flow through it. Generally, the raceway size decreases distinctly with bed height increase, then causing smaller raceways. This relation between packed bed height and raceway size is comparable with the findings of other researchers [22, 23]. 13

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4.2.3

Effects of discharge rate

Fig. 10 shows the raceway profile under different discharge rates for the default conditions (t=600s; uf=220 m/s). It is shown that the size of raceway becomes larger with increasing the discharge rate. This suggests that the particle flow produced by extraction breaks the

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interparticle locking around the cavity periphery. This makes it easier for the gas momentum

Effects of particle size

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4.2.4

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discharge rate was also found in other work [21, 23].

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rearranging the particles, and expanding the raceway size. This trend regarding the effects of

Fig. 11 presents the effects of particle size in the raceway region. Under the conditions of

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same packed bed height and inlet velocity (220 m/s), raceway zone can be generated with an

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appropriate particle size (d=0.05m) as shown in Fig. 11(b). For the small particle size (Fig.

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11(a)), it means more particles existing in the packed bed to maintain the same bed height leading to a slightly larger packed bed density around wall boundary, which results in a relatively higher resistance to gas flow through the packed bed from the boundary inlet. A relatively smaller void region forms adjacent to gas inlet tuyere, causing similar raceway zone profile. For the large particle case (Fig. 11(c)), it can be explained by a combination of a decrease in the drag force and increase in the particle resistance (solids frictional viscosity) with increasing particle size. This means that the large particles are not dragged sufficiently far horizontally, and higher particle resistance to flow prevents a larger region of particles from taking part in the entrainment and recirculation process. Fig. 11(d) shows the 14

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quantitative raceway profiles of these three cases. It is suggested that there is not an obvious tendency between particle size and raceway profile because the similar raceway profiles could be observed with particle size increasing in a certain range, but there would appear a smaller raceway cavity by increasing the particle size continuously. This qualitative behavior with respect to particle size is in partial agreement with others’ work [23, 41] due to the

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different conditions on the particle size. Given the limited research on particle size, it may

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need a further investigation regarding the effect of particle size on the formation of the

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raceway.

CONCLUSIONS

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A multiscale CFD-DEM model is used to study the raceway cavity shape and size, from flow

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to heat and mass transfers. Specifically, the coke combustion is involved. The main features

Key raceway features of solid temperature and gas chemical compositions distributions

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

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at the current stage of development, and some significant conclusions are summarized below:

are reproduced qualitatively. 

The gas inlet velocity is an important factor in forming a stable raceway. Below the critical velocity that can fluidize the bed, a larger velocity can form a larger raceway cavity. The relation between raceway profile and discharge rate shows the similar trend.



Higher packed bed level or load could exert a larger resistance for forming the raceway zone. As for the effect of particle size on raceway size and shape, it cannot directly conclude an obvious relation under the condition of the same bed height. Therefore, it needs further investigations for clear understanding.

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ACKNOWLEDGEMENTS This work is financially supported by Australian Research Council (LP150100112, DP180101232) and Baosteel. The first author wishes to acknowledge the financial support from China Scholarship Council.

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REFERENCES

[1] J.E. Hilton, P.W. Cleary, Raceway formation in laterally gas-driven particle beds, Chem.

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Eng. Sci., 80 (2012) 306-316.

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[2] C.B. Xu, D.Q. Cang, A brief overview of low CO 2 emission technologies for iron and steel making, J. Iron Steel Res. Int., 17 (2010) 1-7.

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[3] A. Orth, N. Anastasijevic, H. Eichberger, Low CO 2 emission technologies for iron and steelmaking as well as titania slag production, Miner. Eng., 20 (2007) 854-861.

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[4] Y.S. Shen, B.Y. Guo, S. Chew, P. Austin, A.B. Yu, Three-Dimensional Modeling of Flow

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and Thermochemical Behavior in a Blast Furnace, Metallurgical and Materials Transactions B - Process Metallurgy and Materials Processing Science, 46 (2015) 432-448.

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[5] A. Babich, H. Gudenau, K. Mavrommatis, C. Froehling, A. Formoso, A. Cores, L. Garcia, Choice of technological regimes of a blast furnace operation with injection of hot reducing gases, Revista de metalurgia, 38 (2002) 288-305. [6] Y.S. Shen, A.B. Yu, P. Zulli, CFD Modelling and Analysis of Pulverized Coal Injection in Blast Furnace: An Overview, Steel Research International, 82 (2011) 532-542. [7] Y.S. Shen, B.Y. Guo, A.B. Yu, P. Zulli, A three-dimensional numerical study of the combustion of coal blends in blast furnace, Fuel, 88 (2009) 255-263. [8] M. Naito, K. Takeda, Y. Matsui, Ironmaking technology for the last 100 years: Deployment to advanced technologies from introductio n of technological know-how, and evolution to next-generation process, ISIJ Int., 55 (2015) 7-35. [9] J. Liao, A.B. Yu, Y. Shen, Modelling the injection of upgraded brown coals in an ironmaking blast furnace, Powder Technology, 314 (2017) 550-556. 16

Journal Pre-proof [10] J.M. Burgess, Fuel combustion in the blast furnace raceway zone, Progress in Energy and Combustion Science, 11 (1985) 61-82. [11] Y.S. Shen, B.Y. Guo, A.B. Yu, P.R. Austin, P. Zulli, Three-dimensional modelling of in-furnace coal/coke combustion in a blast furnace, Fuel, 90 (2011) 728-738. [12] P.J. Flint, J.M. Burgess, A fundamental study of raceway size in two dimensions, Metallurgical Transactions B, 23 (1992) 267-283. [13] S.C. Barman, K.P. Mrunmaya, M. Ranjan, Mathematical Model Development of Raceway Parameters and Their Effects on Corex Process, J. Iron Steel Res. Int., 18 (2011)

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[14] Y. Sun, R. Chen, G.X. Wu, L.N. Sun, L.H. Han, Raceway Definition Using Digital

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Image Processing Technology in Blast Furnace Model, Applied Mechanics and Materials,

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268 (2013) 1794-1800.

[15] Z. Miao, Z.Y. Zhou, A.B. Yu, Y.S. Shen, CFD-DEM simulation of raceway formation in

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an ironmaking blast furnace, Powder Technol., 314 (2017) 542-549. [16] Q.F. Hou, D.Y. E, A.B. Yu, Discrete particle modeling of lateral jets into a packed bed

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and micromechanical analysis of the stability of raceways, AIChE J., 62 (2016) 4240-4250.

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[17] J.K. Chung, J.W. Han, J.H. Lee, Coke properties at tuyere level in blast furnace with pulverized coal injection, Metals and Materials, 2 (1996) 1-7.

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[18] Y. Matsui, Y. Yamaguchi, M. Sawayama, S. Kitano, N. Nagai, T. Imai, Analyses on blast furnace raceway formation by micro wave reflection gunned through tuyere, ISIJ Int., 45 (2005) 1432-1438.

[19] G.S.S.R.K. Sastry, G.S. Gupta, A.K. Lahiri, Cold model study of raceway under mixed particle conditions, Ironmak. Steelmak., 30 (2003) 61-65. [20] Z.G. Luo, Z.X. Di, Z.S. Zou, R. Chen, Application of fractal theory on raceway boundary in COREX melter-gasifier model, Ironmak. Steelmak., 38 (2011) 417-421. [21] Y.Q. Feng, D. Pinson, A.B. Yu, S.J. Chew, P. Zulli, Numerical study of gas-solid flow in the raceway of a blast furnace, Steel Res. Int., 74 (2003) 523-530. [22] S.S. Mondal, S.K. Som, S.K. Dash, Numerical predictions on the influences of the air blast velocity, initial bed porosity and bed height on the shape and size of raceway zone in a blast furnace, Journal of Physics D: Applied Physics, 38 (2005) 1301-1307. 17

Journal Pre-proof [23] D. Rangarajan, T. Shiozawa, Y.S. Shen, J.S. Curtis, A.B. Yu, Influence of operating parameters on raceway properties in a model blast furnace using a two- fluid model, Ind. Eng. Chem. Res., 53 (2013) 4983-4990. [24] S. Yuu, T. Umekage, M. Kadowaki, Numerical simulation of particle and air velocity fields in raceway in model blast furnace and co mparison with experimental data (cold model), ISIJ Int., 50 (2010) 1107-1116. [25] G.C. Wei, H. Zhang, X.Z. An, B. Xiong, S.Q. Jiang, CFD-DEM study on heat transfer characteristics and microstructure of the blast furnace raceway with ellipsoidal particles,

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Powder Technol., 346 (2019) 350-362.

[26] Y.Q. Feng, A.B. Yu, Assessment of model formulations in the discrete particle

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simulation of gas-solid flow, Ind. Eng. Chem. Res., 43 (2004) 8378-8390.

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[27] B.H. Xu, A.B. Yu, Numerical simulation of the gas-solid flow in a fluidized bed by combining discrete particle method with computational fluid dynamics, Chem. Eng. Sci., 52

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(1997) 2785-2809.

[28] Z.Y. Zhou, S.B. Kuang, K.W. Chu, A.B. Yu, Discrete particle simulation of

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482-510.

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particle- fluid flow: Model formulations and their applicability, J. Fluid Mech., 661 (2010)

[29] Q.F. Hou, Z.Y. Zhou, A.B. Yu, Computational study of the heat transfer in bubbling

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fluidized beds with a horizontal tube, AIChE J., 58 (2012) 1422-1434. [30] Z.Y. Zhou, A.B. Yu, P. Zulli, Particle scale study of heat transfer in packed and bubbling fluidized beds, AIChE J., 55 (2009) 868-884. [31] K.J. Dong, C.C. Wang, A.B. Yu, A novel method based on orientation discretization for discrete element modeling of non-spherical particles, Chem. Eng. Sci., 126 (2015) 500-516. [32] Q.J. Zheng, H.P. Zhu, A.B. Yu, Finite element analysis of the rolling friction of a viscous particle on a rigid plane, Powder Technol., 207 (2011) 401-406. [33] H.P. Zhu, Z.Y. Zhou, R.Y. Yang, A.B. Yu, Discrete particle simulation of particulate systems: Theoretical developments, Chem. Eng. Sci., 62 (2007) 3378-3396. [34] Q.F. Hou, J.Q. Gan, Z.Y. Zhou, A.B. Yu, Particle scale study of heat transfer in packed and fluidized beds, in: G.B. Marin, J.H. Li (Eds.) Focusing Mesoscales of Multiscale Problems in Chemical Engineering, Academic Press2015, pp. 193-243. 18

Journal Pre-proof [35] F.P. Incropera, D.P. Dewitt, Fundamentals of Heat and Mass Transfer, Fifth ed., John Wiley & Sons, New York, 2002. [36] Q.F. Hou, Z.Y. Zhou, A.B. Yu, Computational study of the effects of material properties on heat transfer in gas fluidization, Ind. Eng. Chem. Res., 51 (2012) 11572-11586. [37] T.B. Anderson, R. Jackson, Fluid mechanical description of fluidized beds: Equations of motion, Industrial & Engineering Chemistry Fundamentals, 6 (1967) 527-539. [38] Y.Q. Feng, B.H. Xu, S.J. Zhang, A.B. Yu, P. Zulli, Discrete particle simulation of gas fluidization of particle mixtures, AIChE J., 50 (2004) 1713-1728.

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[39] M. Sakai, M. Abe, Y. Shigeto, S. Mizutani, H. Takahashi, A. Viré, J.R. Percival, J. Xiang, C.C. Pain, Verification and validation of a coarse grain model of the DEM in a

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bubbling fluidized bed, Chem. Eng. J., 244 (2014) 33-43.

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[40] Q.F. Hou, D.Y. E, S.B. Kuang, Z.Y. Li, A.B. Yu, DEM-based virtual experimental blast furnace: A quasi-steady state model, Powder Technol., 314 (2017) 557-566.

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[41] H. Nogami, H. Yamaoka, K. Takatani, Raceway design for the innovative blast furnace, ISIJ Int., 44 (2004) 2150-2158.

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[42] Z. Miao, Z. Zhou, A. Yu, Y. Shen, CFD-DEM simulation of raceway formation in an

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ironmaking blast furnace, Powder Technol, (2016). [43] R. Murai, M. Asanuma, M. Sato, T. Inoguchi, K. Terada, Flow Behavior of Plastic

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Particles in the Lower Part of Blast Furnace, ISIJ Int., 55 (2015) 528-535. [44] S. Sarkar, G.S. Gupta, S.-y. Kitamura, Prediction of raceway shape and size, ISIJ international, 47 (2007) 1738-1744. [45] G.S. Gupta, V. Rudolph, Comparison of blast furnace raceway size with theory, ISIJ Int., 46 (2006) 195-201. [46] Y. Omori, Blast furnace phenomena and modelling, Elsevier Applied Science1987.

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Journal Pre-proof Table 1. Equations to calculate the forces and torques exerting on particle i. Force or torque

Equation √𝑅

/2 𝑛

Normal elastic force, fen,ij

−

Normal damping force, fdn,ij

−𝑐𝑛 (6𝑚𝑖𝑗

Tangential elastic force, fet,ij

−𝜇 𝑆 |𝐟𝑒𝑛,𝑖𝑗 |(1 − (1 −

√𝑅

𝑛)

1/2𝐯 𝑛,𝑖𝑗 𝑡 / 𝑡,𝑚𝑎𝑥 )

/2

)𝜹̑𝑡

Coulomb friction force, ft,ij

−𝑐𝑡 (6𝜇𝑠 𝑚𝑖𝑗 |𝐟𝑒𝑛,𝑖𝑗 |√1 − 1/2 𝐯 𝑡,𝑚𝑎𝑥 ) 𝑡,𝑖𝑗 | | −𝜇 𝑆 𝐟𝑒𝑛,𝑖𝑗 𝛅̑𝑡

Torque by tangential forces, Tt,ij

𝐑 𝑖𝑗 × (𝐟𝑒𝑡 ,𝑖𝑗 + 𝐟𝑑𝑡 ,𝑖𝑗 )

Rolling friction torque, Tr,ij

−𝜇 𝑟,𝑖𝑗 |𝐟𝑒𝑛,𝑖𝑗 |𝛚̑𝑛𝑖𝑗

Particle-fluid drag force, fd,i

2 𝜀 2 |𝐮 − 𝐯 |(𝐮 − 𝐯 )𝜀 −𝜒 0.125𝐶𝑑0,𝑖 𝜌𝑓 𝜋𝑑𝑝𝑖 𝑖 𝑖 𝑖 𝑖 𝑖 𝑖

𝑡 / 𝑡,𝑚𝑎𝑥 /

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Tangential damping force, fdt,ij

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Pressure gradient force, fpg,i −𝑉𝑖 𝛻𝑝𝑖 where 1/𝑚 𝑖𝑗 = 1/𝑚 𝑖 + 1/𝑚𝑗 , 1/𝑅 = 1/𝑅𝑖 + 1/𝑅𝑗 , = /[2(1 − 𝑣 2 )], 𝛚̑𝑛𝑖𝑗 = 𝛚𝑛𝑖𝑗 /|𝛚𝑛𝑖𝑗 |, 𝑡 = |𝜹𝑡 |, 𝜹̑𝑡 = 𝜹𝑡/ |𝜹𝑡 |, 𝐑 𝑖𝑗 = 𝑅𝑖 (𝐫𝑗 − 𝐫𝑖 )/(𝑅𝑖 + 𝑅𝑗 ), , = 𝜇 (2 − 𝑣)/(2(1 − 𝑣)), 𝐯𝑖𝑗 = 𝐯𝑗 − 𝐯𝑖 + 𝛚𝑗 × 𝐑𝑗 − 𝛚𝑖 × 𝐑 𝑖 , 𝑡,𝑚𝑎𝑥 𝑠 𝑛 𝑣 𝐯n,ij = (𝐯ij ⋅ ) ⋅ , 𝐯t,ij = (𝐯ij × ) × , 𝜀𝑖 = 1 − ∑𝑘𝑖=1 𝑉𝑖 /Δ𝑉, 𝜒 = 3.7 − 2 0.65 exp[ − (1.5 − log 10 Re𝑖)2 /2], 𝐶𝑑0,𝑖 = (0.63 + 4.8/Re0.5 𝑖 ) , Re𝑖 = 𝜌𝑓 𝑑𝑝𝑖 𝜀𝑖 |𝐮𝑖 − 𝐯𝑖 |/𝜇𝑓. Note that tangential forces (fet,ij + fdt,ij) should be replaced by ft, ij when 𝑡 ≥ 𝑡,𝑚𝑎𝑥 .

20

Journal Pre-proof Table 2. Heat transfer models and the equations for heat exchange rates. Heat exchange rate Convective

Equation 𝑄̇𝑖,𝑓 = (2.0 + 𝑎 𝑅𝑒𝑖𝑏 𝑃𝑟 1/ ) 𝑘𝑓 𝐴𝑖 𝛥𝑇 /𝑑 𝑝𝑖

(a)

𝑄̇𝑓,𝑤𝑎𝑙𝑙 = 0.037 𝑅𝑒 0.8 𝑃𝑟 1/ 𝑘𝑓 𝐴𝑤 𝛥𝑇/𝐿

(b)

𝑟

𝑠𝑓 𝑄̇𝑖,𝑗,𝑓 = (𝑇𝑗 − 𝑇𝑖 ) ∫𝑟 2𝜋 ⋅ 𝑟((√𝑅𝑖2 − 𝑟 2 − 𝑟(𝑅𝑖2 + 𝐻)/𝑟𝑖𝑗 ) ⋅ (1/ 𝑠𝑖𝑗

Conductive

(c)

𝑘𝑝𝑖 + 1/𝑘𝑝𝑗 ) + 2[(𝑅𝑖2 + 𝐻) − √𝑅𝑖2 − 𝑟 2 ]/𝑘𝑓 ) −1 𝑑𝑟 𝑄̇𝑖,𝑗,𝑠𝑡𝑎𝑡𝑖𝑐 = 4𝑟𝑐 (𝑇𝑗 − 𝑇𝑖 )/(1/𝑘𝑝𝑖 + 1/𝑘𝑝𝑗 )

(d) (e)

f

𝑄̇𝑖,𝑗,𝑑𝑦𝑛𝑎𝑚𝑖𝑐 =

−1/2 𝑐(𝑇𝑗 − 𝑇𝑖 )𝜋𝑟𝑐2 𝑡𝑐 /((𝜌𝑝𝑖 𝑐 𝑝𝑖𝑘 𝑝𝑖 )−1/2 + (𝜌𝑝𝑗 𝑐𝑝𝑗 𝑘𝑝𝑗 ) −1/2 )

𝑘

where 𝑇𝑙𝑜𝑐𝑎𝑙 ,𝑖 = 𝜀𝑓 𝑇𝑓 ,𝛺 + (1 − 𝜀𝑓 ) ∑𝑗 𝛺=1 𝑇𝑗 (𝑗 ≠ 𝑖)/𝑘𝛺 , Ω denotes a sphere space with the diameter of 1.55 times of particle diameter.

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Radiative

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𝑄̇𝑖,𝑟𝑎𝑑 = 𝜎𝑒𝐴𝑖 (𝑇𝑙𝑜𝑐𝑎𝑙,𝑖 − 𝑇𝑖 ), 𝑄̇ 𝑓,𝑟𝑎𝑑 = 𝜎𝑒𝑓 𝐴𝑓 (𝑇𝑙𝑜𝑐𝑎𝑙,𝑖 − 𝑇𝑓 )

21

(f)

Journal Pre-proof Table 3. Coke combustion reaction and its reaction rate. Chemical reaction

Reaction rate

𝟐𝑪(𝒔) + 𝑶𝟐 (𝒈) = 𝟐𝑪𝑶(𝒈)

𝜋𝑑2𝑐 𝜑𝑐−1 𝑁𝑐 ⋅27 𝑦𝑂2 /(22. 𝑇𝑔 ) 1/𝑘 𝑓 +6/(𝑑𝑐𝜌𝑐 𝜀 𝑐𝐸𝑓′ 𝑘 )

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𝑅𝑐 =

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Reference [46]

Journal Pre-proof Table 4. Physical properties of gas, iron ore, and coke, and inlet gas composition.

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Values 30,000/35,000/40,000 45/50/55 1000 1.70 850 0.3 0.01dp 0.8 1107 0.3 1230 220 1.2 1.8  10-5 2.62  10-2 1000 3.25  10-4 /6.5  10-4 /13  10-4 0.337/0.965

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Variables Number of particles (N), Particle diameter of iron coke dp , mm Particle density of coke ρ, kg/m3 Thermal conductivity of coke k p , W/(mK) Specific heat of coke cp , J/(kgK) Particle-particle/wall sliding friction s, Particle-particle/wall rolling friction r, mm Restitution coefficient, Particle Young’s modulus E, kg/(ms2 ) Particle Poisson ratio  , Inlet gas temperature Tin , C Inlet gas velocity uin , m/s Fluid density f, kg/m3 Fluid molecular viscosity f, Pa∙s Fluid thermal conductivity k f, W/(mK) Fluid specific heat cpf, J/(kgK) Coke discharge rate, kg/s Inlet gas composition, O 2 /N2 , kg/m3

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Journal Pre-proof

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Fig. 1. Exchange of information between discrete and continuum phases.

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(b)

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Journal Pre-proof

(c)

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Fig. 2 (a) Geometry of the model raceway (unit: mm); (b) CFD mesh, and (c) initial solid temperature distribution.

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(a)

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(b)

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Fig. 3. The evolution of raceway formation at particle and cell scales (uf=220 m/s): (a) t=0s;

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(b) t=900s; and (c) t=1800s.

26

Journal Pre-proof

(a)

(b)

(c)

Fig. 4. Variation of the contact force in the formation of the stable raceway (uf=220 m/s):

f

snapshots for the spatial distribution of the contact force at (a) t=0s; (b) t=900s; and (c)

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t=1800s. The force magnitude is the unit of the gravitational force of a single particle.

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Journal Pre-proof

(b)

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(a)

(d)

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(c)

(e) Fig. 5. Variation of the bed temperature in the formation of the stable raceway (uf=220 m/s): snapshots for the spatial distribution of the bed temperature at (a) t=150s; (b) t=300s; (c) t=450s; (d) t=600s; and (e) average coke temperature around raceway zone.

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Journal Pre-proof

(b)

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(a)

(d)

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(c)

(e) Fig. 6. Variation of the carbon content in the formation of the stable raceway (uf=220 m/s): snapshots for the spatial distribution of the carbon content at (a) t=150s; (b) t=300s; (c) t=450s; (d) t=600s; and (e) average carbon consumption rate around raceway zone.

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Journal Pre-proof

(a)

(b)

Fig. 7. Gas compositions distribution at 600s: (a) O 2 composition and (b) CO composition.

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(unit: g/m3 )

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Journal Pre-proof

(b)

(c)

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(a)

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(d)

Fig. 8. The raceway profile under different gas velocities at t=600s: (a) uf=200 m/s; (b)

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uf=210 m/s; (c) uf=220 m/s (d) raceway profiles of (a)-(c).

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Journal Pre-proof

(b)

(c)

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(a)

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(d)

Fig. 9. The raceway profile under different bed height at t=600s with uf=220m/s: (a)

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N=30,000; (b) N=35,000; (c) N=40,000; and (d) raceway profiles of (a)-(c).

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Journal Pre-proof

(b)

(c)

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(a)

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Fig. 10. The raceway profile under different discharge rate at t=600s with uf=220m/s: (a)

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3.25×10-4 kg/s; (b) 6.5×10-4 kg/s; (c) 13×10-4 kg/s; and (d) raceway profiles of (a)-(c).

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Journal Pre-proof

(b)

(c)

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(a)

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(d)

Fig. 11 The raceway profile under different particle diameters: (a) d=0.045m; (b) d=0.05m; (c)

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d=0.055m at t=600s; and (d) raceway profiles of (a)-(c).

34

Journal Pre-proof Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Journal Pre-proof

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Graphical Abstract

36

Journal Pre-proof Highlights

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A CFD-DEM model is developed for describing gas-solid reacting flow. The raceway cavity and thermochemical behaviour is simulated at particle scale. Raceway size is quantified under various conditions. A larger air inlet velocity or a larger discharge rate can form a larger raceway cavity.

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   

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11