Effects of Particle Shrinkage and Devolatilization Models on High

Publication Date (Web): July 7, 2015 .... In the CRD model, the volatiles are released at a constant rate and the devolatilization rate is defined as ...
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Effects of Particle Shrinkage and Devolatilization Models on HighTemperature Biomass Pyrolysis and Gasification Xiaoke Ku,* Tian Li, and Terese Løvås Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway ABSTRACT: The effects of two different particle shrinkage and devolatilization models on biomass pyrolysis and gasification behavior in a high-temperature (1400 °C) entrained-flow reactor have been studied employing a three-dimensional Eulerian− Lagrangian computational fluid dynamic model in the framework of open-source codes, OpenFOAM. Both qualitative results (temperature distribution, gas composition, and particle distribution) and quantitative results (pyrolysis time, syngas production, carbon conversion, and particle residence time) are presented and analyzed. Results show that particle shrinkage models significantly affect the simulation results and the constant volume model predicts a faster devolatilization rate, higher H2, CO, and CH4 productions, lower CO2 production, higher carbon conversion, and longer particle residence time than the constant density model. However, the two devolatilization models employed give consistent results on the exit syngas production and carbon conversion, although the constant rate devolatilization model predicts a faster devolatilization rate and a longer particle residence time than the single kinetic rate devolatilization model. In addition, the sensitivity of the kinetic constants for the constant rate devolatilization model is also tested. These trends are the same for both the biomass pyrolysis and gasification applications. Moreover, the predicted results are also compared to the experimental data available in the literature, and the differences resulting from different particle shrinkage and devolatilization models are highlighted.

1. INTRODUCTION Because of the limited supply of conventional fossil fuels and global environmental problems, more and more attention has been paid to the renewable and clean energy resources, among which biomass is one of the most promising resources because of its carbon-neutral property and wide availability. Many research works have been performed on syngas production from biomass by various methods, such as pyrolysis1,2 and gasification.3−9 Several types of reactors are currently commercially available, including fluidized bed,10 fixed bed,11 and entrained-flow reactors.12 Entrained-flow reactors are particularly attractive because high carbon conversion efficiency and low-tar syngas can be achieved. However, requiring very fine particle sizes, they are for biomass associated with operational challenges and accurately predicting the gas-flow dynamics throughout the reactor, as well as the devolatilization becomes crucial for optimum operation. Computational fluid dynamic (CFD) models have become more and more popular in recognizing the gas−solid flow dynamics13−15 and chemical reactions.16−18 Generally speaking, multiphase CFD models can be categorized into two theoretical approaches, namely, Eulerian−Eulerian and Eulerian−Lagrangian approaches. In comparison to the Eulerian−Eulerian approach, the Eulerian−Lagrangian approach has several unique advantages. For example, it does not only provide detailed information on the particle level but also has no closure difficulties for polydisperse particle systems. Therefore, a Eulerian−Lagrangian approach has been extensively applied to study thermochemical conversion of coal in the past decade.19−26 However, very limited work on the Eulerian− Lagrangian model of biomass conversion in an entrained-flow reactor has been reported, to our knowledge. For example, Chen et al.27 used a Eulerian−Lagrangian method to study the gasification behavior of three different fuels (torrefied bamboo, © XXXX American Chemical Society

raw bamboo, and coal) in an entrained-flow reactor, although their simulations were strongly simplified; e.g., they assumed that the flow was two-dimensional and incompressible, and the body force of the flow and radiation were also ignored. In our earlier paper,12 we presented the three-dimensional (3D) Eulerian−Lagrangian CFD model, which has been developed in the framework of open-source codes, OpenFOAM (version 2.1.1),28 and used to investigate hightemperature biomass conversion in entrained-flow reactors. The purpose was to integrate a full model, including submodels for turbulence, heat and mass transfer, radiation, pyrolysis, and homo- and heterogeneous reactions. The proposed model represents a useful tool for reactor design because it accounts for the major factors that would affect the progress of biomass pyrolysis and gasification, from the time that the particle is injected into the reactor until it reaches the outlet of the reactor. The proposed model was thoroughly validated against a wide range of experimental data found in the literature.8 As a continuation, in this paper, the CFD model already developed by the authors12 is further extended and applied to explore the sensitivity of two important submodels on the prediction of high-temperature biomass pyrolysis and gasification, namely, particle shrinkage and devolatilization models. Different submodels with similar complexity are common in use, the choice of which are often poorly motivated. Both qualitative results (temperature distribution, gas composition, and particle distribution) and quantitative results (pyrolysis time, syngas production, carbon conversion, and particle residence time) are presented and analyzed, and the effect of steam/carbon molar Received: April 29, 2015 Revised: June 25, 2015

A

DOI: 10.1021/acs.energyfuels.5b00953 Energy Fuels XXXX, XXX, XXX−XXX

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and elemental analyses of the fuel and the elemental conservation relationships:

ratios is also explored. Besides, the predicted results are also compared to the experimental data of Qin et al.,8 and the differences resulting from different particle shrinkage and devolatilization models are highlighted.

biomass → α1H 2O + α2CH4 + α3H 2 + α4CO2 + α5CO + α6char(s) + α7ash(s),

2. MATHEMATICAL MODELING In the Eulerian−Lagrangian CFD model, the gas phase is treated as a continuum by solving a set of transport equations, whereas each of the discrete particles is tracked in a Lagrangian frame of reference through the calculated flow filed. The interphase interaction is considered by treating the exchange of mass, momentum, and energy between the two systems as source terms in the governing equations. Details on the implementation as well as the homo- and heterogeneous reactions and their kinetic constants are available in our earlier publication,12 which are not repeated here for the sake of shortness. The current section will introduce the two different particle shrinkage and devolatilization models for the discrete particles, which are under investigation in the present study. 2.1. Particle Shrinkage Models. During thermal conversion of biomass, the fuel particles change in mass as a result of the loss of water vapor, devolatilization, and char reactions. At the same time, the particles shrink as they suffer from the loss of mass. Two commonly used particle shrinkage models are employed in this study. One is the constant volume (CV) model with variable density, and the other is the constant density (CD) model with variable volume. For the CV model, the particle diameter keeps constant and the particle density “shrinks” as follows: 6mp ρp = πd p 3 (1)

i

(3)

Two frequently used devolatilization models are adopted and compared in the present paper for biomass, namely, the single kinetic rate devolatilization (SKRD) model and the constant rate devolatilization (CRD) model. From a physical point of view, the devolatilization kinetics are dependent upon many factors, for example, the inert or oxidizing environment, the steam/carbon molar ratio, etc. However, from the CFD viewpoint, to predict accurately some of the indicative parameters of reactor performance, such as carbon conversion and syngas production, we have implemented a complete set of submodels, such as turbulence, heat and mass transfer, radiation, pyrolysis, particle turbulent dispersion, and homoand heterogeneous chemical reactions. However, the coupling and interactions between the various physical and chemical processes involved are very complex, and considering there have been very few suitable kinetic constants at different environments reported in the literature, the effects of the existing air and steam/carbon molar ratio on the particle devolatilization process have been ignored in this paper. Moreover, in the model, the devolatilization of biomass is treated as an energetic neutral process, which is common in CFD modeling for pulverized fuels and justified by the fact that the heat of devolatilization is generally negligible compared to the heat of char and gas reactions.19 2.2.1. SKRD Model. In the SKRD model, the devolatilization rate depends upon the amount of volatiles remaining in the biomass particle and is modeled by a single-step first-order Arrhenius reaction19

For the CD model, the particle density keeps constant and the particle diameter shrinks as follows: ⎛ 6m ⎞1/3 p ⎟ d p = ⎜⎜ ⎟ πρ ⎝ p⎠

∑ αi = 1

⎛ E ⎞ dmdevol ⎟⎟mdevol = −A exp⎜⎜ − dt ⎝ RTp ⎠

(2)

where mp, ρp, and dp are the mass, density, and diameter of the biomass particle, respectively. On the basis of eqs 1 and 2, we see that the CV and CD models will make the fuel particles have different sizes and densities, which will affect not only the particle motion equation but also the particle energy equation. The convective heat transfer and radiation heat transfer in the particle energy equation are proportional to the particle surface area, which means that, under the same particle mass condition, larger particles allow for transfer of a greater amount of heat from hot gas and will have a higher particle temperature. For both the CV and CD models, the char conversion will follow the same mechanism, including the effects of both bulk diffusion and chemical reaction rates. However, the char consumption rate is also dependent upon the particle diameter and particle surface area, which means that particles with different sizes will have a different char consumption rate. For details of the governing mass, momentum, and energy equations for particles and the char consumption equations, including rate constants, the reader is referred to our earlier publication.12 2.2. Devolatilization Models. After moisture evaporation, the biomass starts to devolatilize. The overall decomposition of biomass can be described by the following equilibrium equation, and each product yield is resolved using proximate

(4)

where mdevol is the mass of volatiles remaining in the particle, A = 5.0 × 106 s−1, E = 1.2 × 108 J/kmol, and Tp is the particle temperature. Note that the values of A and E are determined on the basis of the experimental work of Prakash and Karunanithi,29 where the kinetic parameters of the SKRD model for beech wood are provided on the basis of the experimental data. Moreover, the predictive performance and accuracy of the integrated model have been demonstrated by validating its results against a broad range of experimental data of Qin et al.,8 as reported in our earlier publication.12 2.2.2. CRD Model. In the CRD model, the volatiles are released at a constant rate and the devolatilization rate is defined as follows: dmdevol = −A 0mdevol,0 dt

(5)

where mdevol,0 is the initial mass of the volatiles in the fuel particle and the default value of A0 is 12 s−1 based on the work of Silaen and Wang.30 Note that the constant A0 = 12 s−1 is normally used for coal pyrolysis. Considering that there is no corresponding A0 for beech wood in the literature, four other different values of A0 (6, 8, 10, and 14) are tested to check its sensitivity. B

DOI: 10.1021/acs.energyfuels.5b00953 Energy Fuels XXXX, XXX, XXX−XXX

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conditions, which are listed in Table 3, and additional details have been reported in previous studies.8,12

3. SIMULATION SETUP A 3D domain of the laboratory-scale entrained-flow reactor located at the Technical University of Denmark (DTU)8 shown in Figure 1 is

4. RESULTS AND DISCUSSION In the following, the effects of different particle shrinkage and devolatilization models on biomass pyrolysis and gasification performance are studied. For pyrolysis application, the value of the excess air ratio (λ), which is defined as the ratio of actual air input to the stoichiometric air required for complete combustion, is fixed at 0. For gasification cases, λ is fixed at 0.3. To discuss the quantitative differences between the chosen models and the sensitivity of the models under different conditions, it is useful to first present the characteristics of pyrolysis and gasification in the given system in a qualitative manner. 4.1. Pyrolysis and Gasification Phenomena. Figures 2 and 3 show the typical pyrolysis (case P1 in Table 3) and gasification (case G1 in Table 3) phenomena, respectively. Figures 2a and 3a present the simulated temperature contours in the vertical center plane of the reactor. For pyrolysis application (Figure 2a), because of the injection of the cold fuel and the endothermic effects of moisture vaporization, there is clearly a low-temperature region near the solid fuel inlet. However, for the gasification case (Figure 3a), a small hightemperature area appears in the same region and the peak temperature is around 2300 K. The dramatic temperature increase is caused by the partial combustion of volatiles through exothermic reactions. Abani and Ghoniem19 studied a coal-fed entrained-flow gasifier using large-eddy simulations and found a similar peak temperature of 2700 K in the region just below the injector, although their fuel was coal. The temperature distribution further downstream in the reactor is much more homogeneous for both pyrolysis and gasification cases. Panels b-f of Figure 2 and panels b−g of Figure 3 show the distributions of various gas-phase species in the vertical center plane of the reactor. Near the injector, owing to the rapid devolatilization of biomass, high concentrations of H2, CO, CO2, and CH4 can be observed. Afterward, as shown in Figure 3g, the oxygen is quickly consumed by reacting with those volatile gases and does not travel far from the injectors before it is fully depleted. Followed by the char gasification reactions downstream, CO2 and H2O are gradually consumed, whereas concentrations of CO and H2 become higher. Generally, the profiles for species of the gasification case are qualitatively similar to the corresponding results of the pyrolysis case, except that there is no O2 species under pyrolysis conditions. Figures 2g and 3h present the evolution of the char concentration (mass fraction) in biomass particles. Because of the release of volatiles, the mass fraction of char increases just after the injection of biomass for both pyrolysis and gasification cases. However, further downstream, almost all of the char is consumed by gasification agents for gasification conditions (blue color), while a significant amount of char is preserved in the fuel particles for pyrolysis conditions (red color). 4.2. Effect of Particle Shrinkage Models. In this subsection, the effect of particle shrinkage models on biomass pyrolysis and gasification performance is studied. The constant volume (CV) and constant density (CD) models described in subsection 2.1 are compared. Accordingly, 12 different cases are tested (cases P1−P6 and G1−G6 in Table 3), where the S/C ratio is varying from 0 to 1. Figure 4 shows the average particle weight loss along the reactor length for the CV and CD models (cases P1 and P4 in Table 3). A rapid release of volatiles can be

Figure 1. (a) Three-dimensional computational mesh for the DTU high-temperature entrained-flow reactor and (b) top view of the inlets. constructed and discretized by using 281 280 hexahedral cells. A gridsensitivity study of this setup has been carried out and reported in our previous paper.12 The fuel used in the simulations is beech wood. Its properties are configured identically to the fuel used in the experiments at DTU, and its characteristics are summarized in Table 1. The fuel particles are assumed to be spherical with diameters

Table 1. Beech Wood Properties proximate analysis (wt %, as-received basis) moisture ash volatile fixed carbon

9.04 0.61 76.70 13.65

elemental analysis (wt %, daf basis) C H O others

49.9 6.4 43.6 0.1

following a Rosin−Rammler distribution (the median diameter is 310 μm) and are stochastically injected into the reactor at the rate of 50 000 particles per second. In addition, the corresponding composition of volatiles for the biomass is shown in Table 2 based

Table 2. Volatile Compositions for the Beech Wooda

a

component

mass fraction (%)

CH4 H2 CO2 CO

18.2 2.9 41.4 37.5

The moisture, char, and ash are excluded.

on the equilibrium as given by eq 3 and elemental conservation. Note that the real biomass devolatilization process is very complex, and different gas species may release at different stages during the devolatilization process. However, for simplification purposes, we treat the mixture of different gas species as “volatiles”, and the “volatiles” are released at a rate based on eq 4 or 5. After release, the “volatiles” instantaneously decompose into a mixture of CH4, H2, CO2, and CO, and the fraction of each gas species is determined by the data in Table 2. Fuel particles carried by a 10 NL/min room-temperature feeder gas (air for gasification conditions and nitrogen for pyrolysis conditions) enter the reactor from the center inlet. The preheated main gas flow is blown into the reactor from the outer ring inlet. All boundary conditions in the simulations are set to match the experimental C

DOI: 10.1021/acs.energyfuels.5b00953 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 3. Test Cases case

reactor temperature (°C)

S/C

steam/carbon molar ratio (S/C)

P1 P2 P3 P4 P5 P6 P7 P8 P9

1400 1400 1400 1400 1400 1400 1400 1400 1400

0 0.5 1.0 0 0.5 1.0 0 0.5 1.0

0 0 0 0 0 0 0 0 0

steam/carbon molar ratio (S/C)

G1 G2 G3 G4 G5 G6 G7 G8 G9

1400 1400 1400 1400 1400 1400 1400 1400 1400

0 0.5 1.0 0 0.5 1.0 0 0.5 1.0

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

parameter

excess air ratio

biomass feed rate (g/min)

particle residence time (s)

Pyrolysis Cases 12.8 2.27 12.8 1.82 12.8 1.53 12.8 2.18 12.8 1.75 12.8 1.50 12.8 2.37 12.8 1.90 12.8 1.61 Gasification Cases 12.8 1.94 12.8 1.63 12.8 1.42 12.8 1.87 12.8 1.58 12.8 1.37 12.8 2.07 12.8 1.72 12.8 1.49

air/steam flow rates through outer ring inlet (g/min) 0/0 0/4.3 0/8.6 0/0 0/4.3 0/8.6 0/0 0/4.3 0/8.6 6.9/0 6.9/4.3 6.9/8.6 6.9/0 6.9/4.3 6.9/8.6 6.9/0 6.9/4.3 6.9/8.6

particle shrinkage model

devolatilization model

CVa CV CV CDc CD CD CV CV CV

SKRDb SKRD SKRD SKRD SKRD SKRD CRDd,e CRD CRD

CV CV CV CD CD CD CV CV CV

SKRD SKRD SKRD SKRD SKRD SKRD CRD CRD CRD

a

CV denotes constant volume. bSKRD denotes single kinetic rate devolatilization. cCD denotes constant density. dCRD denotes constant rate devolatilization. eFor each CRD case, the default value of A0 in eq 5 is 12; in addition, four other different values of A0 (6, 8, 10, and 14) are also tested to check its sensitivity.

Figure 2. Pyrolysis behavior: (a) temperature contour, (b−f) gas species mass fraction distribution in the vertical center plane of the reactor at t = 10 s, and (g) particle distribution colored with the mass fraction of char in the particle (case P1 in Table 3). The particle shrinkage model is CV, and the devolatilization model is SKRD.

seen for both models, and the devolatilizations for the CV and CD models are finished at z = 0.20 and 0.27, respectively, which means that the CV model predicts a faster devolatiliza-

tion rate than the CD model. When comparing the CV with the CD model, we adopt the same devolatilization model (SKRD). As shown in eq 4, although the devolatilization rate of the D

DOI: 10.1021/acs.energyfuels.5b00953 Energy Fuels XXXX, XXX, XXX−XXX

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Figure 3. Gasification behavior: (a) temperature contour, (b−g) gas species mass fraction distribution in the vertical center plane of the reactor at t = 10 s, and (h) particle distribution colored with the mass fraction of char in the particle (case G1 in Table 3). The particle shrinkage model is CV, and the devolatilization model is SKRD.

which, in turn, affects the particle motion equation, particle energy equation, and char reaction equations. Because the char consumption rate is dependent upon the particle diameter and particle temperature, the particle shrinkage model will exert an influence on the gas production. Figure 5 depicts the species production at the reactor exit as a function of the S/C ratio for the CV and CD models (cases P1−P6 and G1−G6 in Table 3), including the corresponding experimental data of Qin et al.8 The lines denote the numerically simulated results, whereas the corresponding symbols represent the experimental data. It is observed that the CV model predicts higher H2 and CO productions and a lower CO2 production than the CD model for both pyrolysis and gasification conditions. For pyrolysis cases (Figure 5a), in comparison to the experimental data, the CD model gives somewhat better H2 prediction (minimum relative error of ∼3% and maximum relative error of ∼6%) than the CV model (minimum relative error of ∼5% and maximum relative error of ∼15%). In terms of CO prediction, the minimum relative error of the CV model is ∼1% and the maximum relative error is