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Cite This: Energy Fuels XXXX, XXX, XXX−XXX

Computational Fluid Dynamics Study of Biomass Combustion in a Simulated Ironmaking Blast Furnace: Effect of the Particle Shape Yiran Liu and Yansong Shen* School of Chemical Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia ABSTRACT: Biomass is a carbon-neutral fuel and has potential to be used in pulverized coal injection (PCI) technology in ironmaking blast furnaces (BFs). In comparison to pulverized coal particles, biomass particles vary considerably in particle shape, and thus, the change of the aspect ratio of biomass particles may affect the motion and conversion of biomass particles. In this study, a computational fluid dynamics (CFD) model is developed to simulate the flow and thermochemical behaviors related to biomass injection into BFs. The model features non-spherical particle shapes and an improved devolatilization model. The model is then applied to a pilot-scale test of charcoal injection using a pilot-scale PCI test rig under simulated BF conditions for model validation. The burnout comparisons between simulation and measurement indicate that, in comparison to spherical particles, the non-spherical particles show smaller burnouts as a result of the shorter traveling time in the chamber; moreover, the burnouts predicted by the model considering cylindrical particles and improved devolatilization model are more comparable to the measurements. This confirms the model validity and also concludes that it is necessary to include the effect of the particle shape in the modeling of biomass injection in BFs. Then, to give a full picture of charcoal injection, other typical phenomena of flow and combustion behaviors are analyzed in detail for charcoals in cylindrical particle shape, aspects of gas−charcoal flow, their temperature distribution, and gas composition distribution. This model provides an effective way for understanding and optimizing biomass injection in BF practice. exceeded coal at the same volatile matter (VM) content,13 and the solution loss reaction for biomass went faster than pulverized coal,14 making full substitution possible.15 BHP developed a pilot-scale PCI test rig, tested the charcoal injection (Figure 1a), and found that charcoal injection into BF lowered the pressure drop and its fluctuation amplitude.16,17 Moreover, charcoal injection was tested under actual production conditions using mini-BFs in Brazil,18 where the charcoal injection rate of 100−190 kg/tonne of heavy metal (HM) was achieved19 and 200−225 kg/tonne of HM should be feasible in large BFs.20 However, all of these experiments conducted at either lab or plant scale failed to provide the detailed information on multiphase flow and thermochemical processes related to charcoal injection, such as flow distribution, turbulent mixing, reaction zones, and species distribution.21 At present, mathematical modeling, especially computational fluid dynamics (CFD), was used to overcome these problems. Wijayanta et al.22,23 reported a two-dimensional (2D) CFD investigation of pulverized biochar injection into a BF and found that a longer raceway was required to obtain the same combustibility of biochar as coal. However, this 2D model did not consider the three-dimensional (3D) features and can only generate useful results for practice. Wu et al.24 developed a 3D CFD model to evaluate the flow characteristics of charcoal utilization in a PCI blowpipe and tuyere and optimize the position of the blowpipe. However, the raceway was not considered.

1. INTRODUCTION As an environmentally friendly and carbon-balance renewable energy, biomass comes from a variety of sources in the world.1,2 It has been widely used in coal-fired boilers for power generation.3 On the other hand, blast furnaces (BFs) are the predominant route of ironmaking, representing the main energy consumer, nearly 70% of the whole plant as a result of coke combustion.4 By means of pulverized coal injection (PCI), coal is injected into the raceways to partially replace expensive metallurgical coke.5−7 As a result, a large amount of CO2 is emitted from the combustion of coal and coke. Therefore, biomass, as a carbon-balance source, has potential to be used in ironmaking BFs. That is, substituting traditional pulverized coal with biomass is significant for not only meeting the energy needs of ironmaking BFs but also the mitigation of greenhouse gases.8,9 Practically, biomass injection technology was reported to decrease CO2 emission by 21−28%10 up to 37−45%11 compared to PCI technology. However, biomass and coal differ significantly from physicochemical properties, such as the size, density, and especially non-spherical shape, where the latter could greatly affect the motion and conversion of fuel particles in combustors.12 On the other hand, the flow and thermochemical behavior of PCI operation in BF differs significantly from coal combustion in boilers, such as a higher temperature, more intense heating rate and mixing, larger velocity, and more complex gas concentration. Therefore, it is of importance to understand the thermochemical behavior of biomass injection in BFs and, in particular, consider the irregular shape in studying the biomass injection in BFs. Biomass combustion has been studied under BF conditions using various methods. Lab- or pilot-scale experimental studies of biomass combustion in BF indicated that the combustion performance of the charcoal, a biomass-derived product, © XXXX American Chemical Society

Special Issue: 6th Sino-Australian Symposium on Advanced Coal and Biomass Utilisation Technologies Received: October 15, 2017 Revised: December 7, 2017 Published: December 12, 2017 A

DOI: 10.1021/acs.energyfuels.7b03150 Energy Fuels XXXX, XXX, XXX−XXX

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Figure 1. Schematic of the PCI test rig: (a) schematic of the PCI test rig and (b) geometry details of the computational domain (mm).30

Table 1. Governing Equations for the Gas and Particle Phases in the Model25 for the Gas Phase mass

momentum

∇(ρU) =

∑ ṁ np

⎛ 2 ⎞ ∇(ρUU) − ∇((μ + μt )(∇U + (∇U)T )) = −∇⎜p + ρk ⎟ + ⎝ 3 ⎠

energy

⎛ ⎞ ⎛ λ μ ⎞ ∇⎜⎜ρUH − ⎜ + t ⎟∇H ⎟⎟ = σH ⎠ ⎝ CP ⎝ ⎠

gas species i

⎛ ⎛ μ ⎞ ⎞ ∇⎜⎜ρUYi − ⎜⎜Γi + t ⎟⎟∇Yi ⎟⎟ = Wi σYi ⎠ ⎠ ⎝ ⎝

turbulent kinetic energy

⎛ ⎛ μ⎞ ⎞ ∇⎜⎜ρUk − ⎜μ + t ⎟∇k ⎟⎟ = (Pk − ρε) σ ⎝ ⎠ ⎝ k⎠

turbulent dissipation rate

⎛ ⎛ μ⎞ ⎞ ε ∇⎜⎜ρUε − ⎜μ + t ⎟∇ε⎟⎟ = (C1Pk − C2ρε) σε ⎠ ⎠ k ⎝ ⎝

mass

∑ fD np

∑q np

for a Particle in the Particle Phase dm p = − ṁ dt

mp momentum

dUp

= −fD

dt

−fD = mpCp energy

1 πd p2ρC D|U − Up|(U − Up) 8

dTp dt

= −q

− q = πd pλNu(Tg − Tp) +

Shen et al.25 developed a 3D CFD model to simulate the injection of different charcoals in BFs to evaluate the effect of

∑

dm p dt

Hreac + A pεp(πI − σBTp 4)

the charcoal type, such as the VM content. Castro et al.26,27 reported CFD studies of simultaneous injection of pulverized B


a

A = 2.3 × 107 s−1; E = 125.5 kJ mol−1; [i] = [CH4]−0.3[O2]1.3 A = 1014.6 s−1 mol−1 cm2.25; E = 167.3 kJ mol−1; [i] = [CO][O2]0.5

⎛ E ⎞ k = [i]A exp⎜− ⎟ ⎝ RTP ⎠

⎛ [i] ⎞ ε ri = CA min⎜ ⎟ κ ⎝ vt′ ⎠

CH4 + 0.5O2 → CO + 2H2

CO + 0.5O2 → CO2

2

A2 = 1.46 × 1013 s−1; E2 = 251 kJ mol−1; α2 = Q × VM (daf) + 0.14; CS = 0

ṁ d d = CSd0 ref dt mref,0

k v2

C

2

k1 = D/RP ; k2 = (1 − e)kc/RP; k3 = kcTp(β coth β − 1)/β α; kc = AcTp exp(−Tc/Tp); β = R(kc/Dpea) ; Dp = effic × D; D = Dref/ρfluid((Tp + Tg)/2Tref)α.

Ac = 6069 m s−1 K−1; Tc = 32406 K; As = 0.0004; T s = 6240 K

Ac = 202300 m s−1 K−1; Tc = 39743 K; As = 0.0004; Ts = 6240 K

dmC 3ϕ MC ρ∞ −1 =− (k1 + (k 2 + k 3)−1mC) dt 1− e MO2 ρC

char + CO2 → 2CO

0.5

Ac = 140 m s−1 K−1; Tc = 21580 K; As = 2500; Ts = 6240 K

⎛ T⎞ 2(ϕ − 1) = AS exp⎜⎜− s ⎟⎟ 2−ϕ ⎝ Tp ⎠

ϕchar + O2 → 2(ϕ − 1)CO + (2 − ϕ)CO2

char + H2O → CO + H2

CA = 4.0; [i] = [H2O]

⎛ [i] ⎞ ε ri = CA min⎜ ⎟ κ ⎝ vt′ ⎠

H2O → H2 + 0.5O2

CO2 → CO + 0.5O2 H2 + 0.5O2 → H2O

charcoal ⎯→ ⎯ α2 fuel gas + (1 − α2)char

A = 5 × 108 s−1; E = 167.3 kJ mol−1; [i] = [CO2]1 A = 540 m3 kg−1 s−1; E = 15.1 kJ mol−1; [i] = [H2][O2]0.5

A1 = 4.3 × 107 s−1; E1 = 136 kJ mol−1; α1 = VM (daf); CS = 0

charcoal ⎯→ ⎯ α1fuel gas + (1 − α1)char

k v1

moisture(l) → H 2O(g)

k

⎛ E ⎞ k = A exp⎜− ⎟ ⎝ RTP ⎠

reaction kinetics38 A = 5.13 × 108 s−1; E = 88 kJ mol−1

reaction rate expression

⎛ E ⎞ k = A exp⎜− ⎟ ⎝ RTP ⎠

reaction

Table 2. Reactions Considered in This Charcoal Injection Model with Respective Reaction Kineticsa

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(ii) Devolatilization is modeled using two-competing reaction models30,35 rather than a one-step global model used in the past.25 In the experimental study,10 it was found that the charcoal particles used in the test do not swell much during the devolatilization. Thus, in this numerical study based on the conditions of ref 10, the swelling coefficient of charcoal particles is set to zero (Cs = 0). (iii) Gaseous combustion reactions of fuel gas are modeled by the three-fuel gas model and controlled by the combined eddy dissipation model and finite rate chemistry. (iv) Char oxidation and gasification are modeled by Gibb’s model. The models are summarized in Table 2. The models are developed on the basis of the framework of software package ANSYS-CFX, version 17.2.36

coal and charcoal into a BF. However, in these model studies, the irregular shape of biomass particles that may greatly affect the motion and conversion of fuel particles in combustors28,29 was not considered. To date, the studies considering irregular particle shape have not been conducted for biomass injection into BFs. In this study, a 3D CFD model is improved to describe the flow and thermochemical behaviors related to biomass injection in BFs and study the effects of non-spherical particle shapes. The model is applied to charcoal in a pilot-scale PCI test rig. The comparisons against measurements show that the simulations using the cylindrical particle shape give the most comparable burnouts to the measurements. Then, other key phenomena of injecting charcoal in cylinder shape are described, including gas−particle flow, temperature, and concentrations.

3. SIMULATION CONDITIONS 3.1. Properties of Charcoal. In this study, the model of biomass injection is applied to charcoal in the base cases for model validation. Charcoal, derived by carbonizing biomass, is typically low in oxygen, VM, and ash contents compared to biomass/cellulosic materials, while they all tend to remain the original shape. The proximate (ad) and ultimate (daf) analyses and mean particle size (de) of the pulverized charcoal are listed in Table 3. For the injectant, 50 particle size groups ranging

2. MODEL DEVELOPMENT 2.1. Model Outline. The mathematical model of the biomass injection is based on our previous PCI models.30−32 The governing equations for the gas−solid flow and associated heat transfer and the reactions of charcoal are outlined below for completion. The gas phase is modeled using a 3D steady-state Reynoldsaveraged Navier−Strokes equation closed by a standard k−ε turbulence model. The particle phase is modeled using the Lagrangian method, where the trajectories of the discrete particles are determined by integrating Newton’s second law of motion, where drag force and turbulence dispersion are included. The heat transfer of the gas−particle phase is modeled by considering three physical processes: convective heat transfer, latent heat transfer associated with mass transfer, and radiative heat transfer. Full coupling of continuity, momentum, and energy are applied to the particle−gas phase interaction. The models are summarized in Table 1. 2.2. Model Improvements. Some model improvements specific to biomass combustion are also included in this part, including the model of the non-spherical particle shape and the improved devolatilization model. 2.2.1. Non-spherical Particle Shape. Unlike coal that will soften and form a spherical particle shape during devolatilization, biomass particles will largely retain their initial irregularly shaped form.33 This large aspect ratio may affect gas−particle drag force and heat and mass transfer correlations. This necessitates the introduction of a nonspherical shape factor. In this model, two shape factors, namely, crosssectional area factor and surface area factor, are used to describe the influence of the particle shape on the drag force and mass and heat transfer correlations, respectively. The former factor is multiplied by the calculated cross-sectional area when assuming spherical particles. The latter factor is defined as the ratio of the surface area of the nonspherical particle to the surface area of the spherical particle with the equivalent diameter. With the cylindrical spherical particle taken for example, the two shape factors are defined as eqs 1 and 2. The particle shape factors are considered in the governing equations in the form of the volume-equivalent sphere diameter of particle, de. For consistency and tidiness, their definitions (i.e., eqs 1 and 2) are not specified in the governing equations in Table 1.

fcross‐sectional =

fsurface =

scross‐cylinder Scross‐sphere

ssurface cylinder Ssurface sphere

=

=

πr 2 π(1/2de)2

2πr 2 + 2πrd 4π(1/2de)2

Table 3. Proximate (ad) and Ultimate (daf) Analyses of the Pulverized Charcoal Used in This Study25 moisture (%)

ash (%)

VM (%)

fixed carbon (%)

9.3 C (%)

1.8

4.3 H (%)

84.6 O (%)

32.48 N (%)

1150 S (%)

0.99

3.7

0.33

0.03

95.0

calorific value (MJ/kg)

density (kg/m3)

from 1 to 250 μm are sampled and 500 representative particles are tracked in the simulations. In this model, de is the volumeequivalent sphere diameter for different particle shapes, 27 μm in this study. In this study, charcoal is in the shape of a cylinder, as shown in the scanning electron microscopy (SEM) characterization.10 3.2. Geometry and Boundary Conditions. The model is applied to a pilot-scale PCI test rig (Figure 1a).17 The test rig was designed to simulate the flow and combustion in the region of lance−tuyere−raceway (along the coal plume) related to PCI operation under simulated BF conditions. The computational domain (Figure 1b) includes lance, tuyere, and raceway centerline. The main duct is used for injecting the hot blast (i.e., oxygen-enriched air). The coaxial lance is installed at an inclination angle of 6° with respect to the centerline of the duct. The inner tube is used for injecting pulverized charcoal and the conveying gas (i.e., 100% nitrogen), and the outer tube is used for injecting cooling gas (i.e., air). The geometry is plane-symmetric. The exit at the end of the chamber is set as an outlet, and the wall of the chamber is assumed non-slippery and adiabatic. A boundaryfitted, multi-block structured finite volume mesh is used, with a highly fine mesh around the lance and tuyere and along the centerline of the chamber. Table 4 shows main parameters of operating conditions. 3.3. Model Validation. The charcoal injection model is validated against the measured burnouts obtained from the pilot-scale test rig using two charcoal materials, as shown in Table 5.10,25 The burnout is calculated according to ash balance, as below. The ash data were collected at three positions, +50 mm, centerline, and −50 mm, at 925 mm downstream from the injection point.

(1)

(2)

2.2.2. Improved Devolatilization Model. Similar to pulverized coal combustion, biomass combustion is considered to be a four-stage chemical reaction; however, the parameters of reactivity and inner particle structure are quite different, which gives highly different reaction rates.34 They are modeled by means of the following: (i) The moisture evaporation process is controlled by finite rate chemistry. D


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(case 2), and flat chip (case 3), where the cylindrical shape is common for charcoal particles and the flat chip is common for wood chip particles: case 1, spherical charcoal injection; case 2, cylindrical charcoal injection; and case 3, flat-chip-shaped charcoal injection.

Table 4. Operating Conditions in the PCI Test Rig Used in This Study26 flow rate (Nm3/h)

temperature (K)

300 3.2 2 38 kg/h

1473 600 323 320

blast gas cooling gas conveying gas coal/charcoal

O2 (%) 20.9 20.9 0 (i.e., 100% N2)

eat‐upCO = mfCO /(mfCO + mfCO ) 2

(4)

2

Figure 2 compares the burnout evolutions along the centerline of the raceway between the three cases. They are discussed in

Table 5. Experimental Conditions for Model Validation charcoal materiala

case 1 2 3 4

charcoal charcoal charcoal charcoal

1 1 2 2

blast rate (Nm3/h)

blast temperature (K)

blast O2 (%)

charcoal rate (kg/h)

301 304 302 301

1474 1475 1478 1477

22.4 22.5 22.4 22.3

33.5 25.2 29.4 48.4

a

The injectants: charcoal 1, referenced in Table 3; charcoal 2, 9.5% moisture, 2.9% ash, 10.2% VM, 77.4% fixed carbon, and de of 25 μm.

⎛ ma,0 ⎞ burnout = ⎜1 − ⎟ /(1 − ma,0) ma ⎠ ⎝

(3)

Table 6 compares the measured burnouts with the predicted burnouts of particles in different shapes, i.e., sphere and cylinder. Figure 2. Comparison of burnout evolutions along the centerline among spherical (case 1), cylindrical (case 2), and flat-chip-shaped (case 3) charcoals.

Table 6. Model Validation in Terms of Burnout for Different Particle Shapes predicted burnout (%) number

measured burnout (%)

sphere-shaped charcoal

cylinder-shaped charcoal

1 2 3 4

70.1 80.8 70.4 72.1

72.12 81.44 75.10 80.40

70.79 79.82 73.75 71.61

two aspects: overall trend and difference between them. First, it is indicated that the overall trends are similar for all three curves of the three cases. That is, there are two fast increases along the burnout curves around 0.1−0.3 and 0.7−1.0 m. They resulting from the release and combustion of VM at a lower temperature upstream and higher temperature downstream, along the two competing reaction paths in the two-competing reaction model of devolatilization (Table 2), respectively. Second, the burnout evolutions along the centerline and burnout at 1.1 m, traveling time, and maximum temperature are then compared among spherical (case 1), cylindrical (case 2), and flat-chip-shaped (case 3) charcoals. It is indicated that the burnout evolution of spherical charcoal particles is always larger than those of the two irregularly shaped charcoal particles along the centerline. The same trend can be found in burnout at the end of the raceway, i.e., 1.1 m, and maximum temperature. The difference may result from the longer traveling time of the spherical particle of 0.404 s than those of the corresponding irregularly shaped cases, 0.328 and 0.241 s, as shown in Table 7.

It is indicated that the improved model can reasonably predict burnouts for both spherical and cylindrical particles. Moreover, it is also indicated that, in comparison to spherical particles, the burnout predicted by cylindrical particle shapes is more comparable to the measurements; that is, it is necessary to consider the particle shape when injecting non-spherical materials into the BF.

4. RESULTS AND DISCUSSION After the model is validated against the measurements from the pilot-scale test rig, the model is then used to investigate the effects of the particle shape on the flow and thermochemical behaviors of biomass injection in BFs and the significance of improving the devolatilization model from a one-step global reaction model to a two-completing reaction model in the modeling of biomass injection in BFs. Finally, to give a full picture of charcoal injection into BFs specifically, in additional burnout and eat-up, other key flow and thermochemical behaviors are overviewed for cylindrical charcoals under simulated BF conditions. 4.1. Effects of the Particle Shape. To clarify the effects of particle shapes in the modeling of biomass injection in BFs, burnout and the so-called “eat-up” (defined as the rate of gas conversion, shown in eq 4) along the centerline are compared for charcoal particles in the shapes of a sphere (case 1), cylinder

Table 7. Comparisons of Burnout at 1.1 m, Traveling Time, and Maximum Temperature among Spherical (Case 1), Cylindrical (Case 2), and Flat-Chip-Shaped (Case 3) Charcoals number

predicted burnout at 1.1 m (%)

traveling time (s)

maximum temperature (K)

1 2 3

65.50 64.74 64.39

0.404 0.328 0.241

1900 1882 1872

This is because the deviation of a particle from the spherical shape causes a decrease of its terminal velocity in the fluid. E


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Energy & Fuels This implies a higher drag force of the non-spherical particles compared to the spherical particles.37 It should be noted that the small difference in traveling time as a result of the PCI process is a highly intense process, where the mixing rate is very high and the traveling time is very short. Figures 3 and 4 compare the effect of particle shapes on gas concentrations among the three cases, along the symmetry

Figure 5. Comparison of “eat-up” among sphere (case 1), cylinder (case 2), and flat chip (case 3).

Figure 6. Comparison of burnout evolution of cylindrical charcoals using two different devolatilization models.

Figure 3. Comparison of the CO2 concentration among sphere (case 1), cylinder (case 2), and flat chip (case 3).

Figure 4. Comparison of gas composition along the centerline among sphere (case 1), cylinder (case 2), and flat chip (case 3).

Figure 7. (a) Gas velocity vector and (b) charcoal particle trajectories colored by the particle size in the case of cylindrical charcoal injection. F


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concentrations than those of the spherical particle cases. The non-spherical charcoal particle cases with a relatively smaller burnout, especially the cylindrical case, show a slower conversion from O2 to CO2 and CO, as shown in Figure 4. However, in Figure 5, the comparison between “eat-up” curves (rate of gas conversion) along the longitudinal direction of different shaped particles shows that, when the particle shape is taken into consideration, the rate of gas consumption of the cylindrical particle is higher than those of other two cases in the upstream (in the range of 0−0.4 m). In the downstream, although the “eat-up” of non-spherical particles has a larger value than that of the spherical counterpart, the final value of eat-up is similar. In view of “eat-up” of one particle shape, a rapid gas conversion upstream and slow convergence downstream are found for charcoal, indicating that homogeneous reactions are the dominant reactions in determining the burnout of charcoal. 4.2. Effect of the Improved Devolatilization Model. The one-step global model is a relatively simple model widely used in the modeling of coal combustion. This may lead to insufficient accuracy in the prediction of the product yield distributions with temperature and heating conditions. The two-competing reaction model includes a primary path and a secondary path, during which biomass materials undergo additional cracking to produce gases, and, thus, has potential to overcome the inaccuracy in the one-step global model. Figure 6 compares the burnout evolutions predicted by the two models, i.e., one-step global devolatilization and two-competing reaction devolatilization, and the average burnout measurement at 0.925 m in the validation case 1 (Table 6), i.e., 70.1%. It can be seen that the predicted burnout using the two-competing reaction model is comparable to the measurement, while the model using the one-step global model gives a much lower burnout evolution. That is, the present model using the two-competing reaction model with a suitable Q factor is adequate to illustrate the impacts of shape on charcoal combustion. Thus, the two-competing reaction model rather than the one-step global model is suggested for the modeling of biomass combustion in the future. 4.3. Typical Results of Other Flow and Thermochemical Behaviors of Charcoal Particles in a Cylindrical Particle Shape. In this study, the charcoal particles are largely

Figure 8. (a) Gas-phase temperature contours and (b) radiation intensity in the case of cylindrical charcoal injection.

Figure 9. Gas-phase temperature contours in the case of pulverized coal injection.30

plane and along the centerline, respectively. It is indicated that the non-spherical charcoal injection cases obtain smaller CO2

Figure 10. Contours of gas concentrations of cylindrical charcoal injection, in terms of (a) H2O, (b) H2, (c) CO, and (d) O2. G


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4.3.1. Flow Patterns. Figure 7a shows the velocity vector of the gas phase along the symmetry plane for cylindrical charcoal injection. The results show the velocity of the conveying gas at the lance tip to be ∼9 m/s and the gas velocity at the tuyere to be ∼125 m/s. The gas stream forms a high-speed inclined jet after exiting the tuyere, and then the jet starts to expand in the radial direction and finally dispersing near the exit. A low-speed recirculation occurs near the wall. Similar phenomena are also observed in the particle flow (Figure 7b), where the charcoal plume is concentrated until exiting the jet and then starts to rapidly disperse, mainly fine particles toward the exit of the chamber. Some fine particles recirculate above, behind, and below the jet. 4.3.2. Temperature Field. Figure 8a shows the gas-phase temperature along the symmetry plane for cylindrical charcoal injection. The asymmetric temperature distribution shows that the temperature in the lower part is higher toward the exit, which is due to the inclined lance arrangement. The hightemperature part surrounding the lance tip corresponds to the combustion of volatile, which starts to release after a preheating stage of a high heating rate in front of the tuyere. Different from PCI simulations, the charcoal injection does not show a flamefront high temperature over the coal plume surface in front of tuyere, as observed in Figure 9.30 In the chamber, the high temperature resulted from the combustion of the recirculated small charcoal particles. In the downstream, the highest temperature was achieved because of the combustion of char released from the charcoal particles. From Figure 8b, similar to the temperature field, the radiation intensity contour shows that there are different dominant reactions along the distance from the lance tip. 4.3.3. Gas Concentrations. Figure 10 shows the gas species distributions (H2O, H2, CO, and O2) along the symmetry plane in charcoal injection. As the combustion proceeds, the moisture evaporation process for charcoal combustion resulted in releasing more H2O at the upstream, which is observed at the upstream for the charcoal injection (Figure 10a). The significant moisture content makes the evaporation process obvious and may delay the ignition process. As one of the products of devolatilization of charcoal, H2 has two relatively high-concentration parts along the symmetry plane (Figure 10b), which may result from the two-competing reaction model of devolatilization. Partial O2, CO, and CO2 gasification conditions produced similar concentration profiles (panels c and d of Figure 10 and Figure 3b), and the following changes in the concentration were observed: rapid consumption of the O2 concentration and a rapid increase of CO and CO2 at the downstream. This indicates a fast devolatilization reaction at a low temperature and a fast char combustion at a higher temperature. 4.3.4. Combustion Characteristics Analyzed by Particle Size Groups. Combustion characteristics related to charcoal injection are further analyzed by particle size groups. Figure 11 shows the combustion characteristics in terms of burnout, VM content, and particle temperature in six particle size groups. Although similar to the overall performance of burnout, the burnout evolution curve for each size group (Figure 11a) varies for different size groups. The particle of a smaller size has a larger specific surface area and faster devolatilization (Figure 11b) and, thus, achieves a higher particle temperature (Figure 11c). The smaller particles are going through a higher heating rate (Figure 11c) and start the devolatilization process earlier than larger particles (Figure 11b); thus, the burnout of

Figure 11. Combustion characteristics along the centerline for different size groups for cylindrical charcoal injection: (a) burnout, (b) VM content, and (c) particle temperature.

in a cylindrical shape, as observed in ref 10, as confirmed in the model validation that the predicted burnout using a cylindrical particle shape is more comparable to the measurements. Some typical results of a cylindrical particle shape have been discussed above, including burnout evolution and gas conversation. In this section, other typical results of flow and thermochemical phenomena related to charcoal injection using a cylindrical shape are studied under simulated BF conditions, including spatial distribution of flow and temperature distributions in the entire chamber and, more quantitatively, evolutions of burnout, VM content, and particle temperature along the centerline. H


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d = particle diameter (μm) de = volume-equivalent sphere diameter (μm) e = void fraction of char particles E1 and E2 = activation energy of devolatilization reactions (kJ/mol) fD = drag force from a particle (N) fcross‑sectional = cross-sectional area factor fsurface = surface area factor H = enthalpy (J kg−1) Hreac = reaction heat (J kg−1) I = radiation intensity on the particle surface (W m−2) [i] = molar concentration of component i K = turbulent kinetic energy (m2 s−2) kv1 and kv2 = devolatilization rate constants (s−1) k1 = rate of external diffusion in the Gibb model (s−1) k2 = rate of surface reaction in the Gibb model (s−1) k3 = rate of internal diffusion and surface reaction in the Gibb model (s−1) kc = carbon oxidation rate in the Gibb model (m s−1) ṁ = mass transfer rate from a particle (kg s−1) ma = ash mass fraction ma,0 = original ash mass fraction mc = mass of char (kg) mfCO = mass fraction of carbon monoxide mfCO2 = mass fraction of carbon dioxide Mc = molecular weight of carbon MO2 = molecular weight of the oxygen molecule np = particle number per unit volume (m−3) Nu = Nusselt number Pk = turbulence production by viscous force p = pressure (Pa) q = heat transfer from a particle (W) Q = Q factor for the devolatilization model rp = particle radius (m) ri = reaction rate of gas species i (mol m−3 s−1) Re = Reynolds number T = temperature (K) Tblast = blast temperature (K) Tc = activation temperature (E/R) in the Gibb model (K) Tg = gas temperature (K) Tp = particle temperature (K) Tref = reference temperature in the Gibb model (293 K) Ts = constant in the Gibb model (6240 K) U = mean velocity of gas (m s−1) Up = mean velocity of the particle (m s−1) u, v, and w = gas velocity components (m s−1) VM = volatile matter vi = stoichiometric coefficient of species i. Wi = reaction rate of species i (per unit volume) (kg m−3 s−1) Yi = mass fraction of species i

smaller particles starts earlier and achieves a higher value than that of larger particles.

5. CONCLUSION A 3D CFD model is developed for describing the injection of irregularly shaped charcoal particles and applied to a pilot-scale PCI test rig to simulate the charcoal injection under simulated BF conditions. The model is validated against the measurements in terms of burnout. The model is then used to investigate the effects of particle shapes on irregularly shaped charcoal particles and the significance of the improved devolatilization model and illustrate other flow and thermochemical behaviors of charcoal particles in a cylindrical particle shape. The following is found: (1) This model with the introduced shape factors can better describe the evolutions of irregularly shaped charcoal particles in terms of burnout. For the charcoal injection, the burnouts using a cylindrical shape are more comparable to the measurements, consistent with the practice. (2) The present model using the two-competing reaction model with a suitable Q factor is more suitable to illustrate the impacts of particle shape on charcoal combustion than that using the one-step global model. (3) In comparison to spherical charcoal cases, the two cases of irregularly shaped charcoal particles have a slower combustion process and lower burnouts. (4) The significant moisture content of charcoal makes the evaporation process obvious at the upstream and may delay the ignition process for the charcoal injection. (5) Different from PCI simulations, the charcoal injection does not show a flame-front high temperature over the coal plume surface in front of tuyere.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Yansong Shen: 0000-0001-8472-8805 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the financial support from the Australian Research Council and Baosteel (LP150100112) and the first author wishes to acknowledge the financial support from China Scholarship Council.

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NOMENCLATURE A1 and A2 = pre-exponential factors of devolatilization reactions (s−1) Ac = pre-exponential factors in the Gibb model (m s−1 K−1) Ap = particle area (m2) As = constant in the Gibb model (0.0004) a = exponent in the Gibb model (0.75) C0 = mass of raw charcoal (kg) C1 and C2 = turbulent model constants CD = drag coefficient Cp = particle heat capacity (J kg−1 K−1) D = external diffusion coefficient of oxygen in the Gibb model (m2 s−1) Dref = reference dynamic diffusivity in the Gibb model (1.8 × 10−5 kg m−1 s−1) Dp = pore diffusivity daf = dry and ash free

Greek Letters

α = volume/internal surface area ratio in the Gibb model α1 and α2 = volatile yields ε = turbulent dissipation rate (m2 s−3) εp = particle emissivity λ = thermal conductivity (W m−1 K−1) σB = Stefan−Boltzmann constant (5.67 × 10−8 W m−2 K−4) σk and σε = turbulence model constants σH = turbulent Ptandtl number for enthalpy ϕ = mechanism factor in the Gibb model ρ = density (kg m−3) μ = dynamic viscosity (Pa s) μt = turbulent viscosity (Pa s)

I


Article

Energy & Fuels Γi = molecular diffusivity of species i (kg m−1 s−1)

(27) de Castro, J. A.; Araújo, G. M.; da Mota, I. O.; Sasaki, Y.; Yagi, J. J. Mater. Res. Technol. 2013, 2 (4), 308−314. (28) Zhang, X.; Chen, Q.; Bradford, R.; Sharifi, V.; Swithenbank, J. Fuel Process. Technol. 2010, 91 (11), 1491−1499. (29) Momeni, M.; Yin, C.; Kaer, S. K.; Hansen, T. B.; Jensen, P. A.; Glarborg, P. Energy Fuels 2013, 27, 507−514. (30) Shen, Y.; Guo, B.; Yu, A.; Maldonado, D.; Austin, P.; Zulli, P. ISIJ Int. 2008, 48 (6), 777−786. (31) Shen, Y.; Maldonado, D.; Guo, B.; Yu, A.; Austin, P.; Zulli, P. Ind. Eng. Chem. Res. 2009, 48, 10314−10323. (32) Shen, Y.; Guo, B.; Yu, A.; Zulli, P. ISIJ Int. 2009, 49 (6), 819− 826. (33) Backreedy, R. I.; Fletcher, L. M.; Jones, J. M.; Ma, L.; Pourkashanian, M.; Williams, A. Proc. Combust. Inst. 2005, 30, 2955− 2964. (34) Shen, F.; Ding, Z.; Yang, W.; Jiang, X.; Mu, L.; Shen, Y. J. Iron Steel Res. Int. 2009, 16, 753−757. (35) Guo, B.; Zulli, P.; Rogers, H.; Mathieson, J. G.; Yu, A. ISIJ Int. 2005, 45 (9), 1272−1281. (36) ANSYS, Inc. ANSYS-CFX Documentation; ANSYS, Inc.: Canonsburg, PA, 2017. (37) Tran-Cong, S.; Gay, M.; Michaelides, E. E. Powder Technol. 2004, 139, 21−32. (38) Shiozawa, T. Numerical Modelling of Multiphase Flow in Raceway of Ironmaking Blast Furnace. Ph.D. Thesis, Materials Science & Engineering, Faculty of Science, The University of New South Wales, Sydney, New South Wales, Australia, 2013.

Subscripts

C = char G = gas P = particle

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