CFD Simulation of Devolatilization of Biomass with Shrinkage Effect

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 105 (2017) 505 – 510

The 8th International Conference on Applied Energy – ICAE2016

CFD simulation of devolatilization of biomass with shrinkage effect Tian Lia*, Xiaoke Kub, Terese Løvåsa a

Department of Energy and Process Engineering, Norwegian University of Science and Technology, Kolbjørn Hejes vei 1b, 7491 Trondheim, Norway b Department of Mechanics, Zhejiang University, Hangzhou, 310027 China

Abstract In the present work, a computational fluid dynamics (CFD) model is used to assess devolatilization of biomass under high-temperature condition. The CFD code is based on a multiscale Eulerian−Lagrangian solver previously developed in the framework of OpenFOAM. A particle shrinkage model proposed by Colomba Di Blasi is implemented in this study to better calculate size evaluation of biomass particle. In addition, a two-step devolatilization model is added into the solver. The CFD model is validated against experimental data of rapid devolatilization of biomass in an electric heated drop tube reactor. Compared to the constant volume model and the simplified constant density shrinkage model, the applied model can better reflect changes of particle size at both 750 °C and 950 °C. In particular, the model gives very good prediction on the terminal particle size with the model constants α=0.3, β=0, and γ=0.3. The calculated composition of pyrolysis gas is fairly close to the experimental data as well. © Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ©2017 2016The The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. Keywords: CFD; biomass; devolatilization; Eulerian-Lagrangian approach; shrinkage

1. Introduction The devolatilization process is a crucial step during thermal-chemical conversion of solid fuels (pyrolysis, gasification, and combustion); in particular, for biomass which typically has a high volatile matter content. Because of the disintegration of solid contents and the release of volatiles, the size of the biomass particle reduces significantly during the devolatilization process [1,2]. However, in most of the reported CFD simulation on high-temperature thermal-chemical conversion of biomass, the shrinkage

* Corresponding author. Tel.: +47-735-92696. E-mail address: [email protected].

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.348

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effect is usually ignored or only simply described using a constant density assumption. Our previous study showed that biomass pyrolysis in a high-temperature entrained-flow reactor could not be accurately predicted by the constant diameter model, nor a commonly used constant density model [3]. In the present work, a comprehensive particle shrinkage model is implemented in the Eulerian-Lagrangian CFD model developed within the OpenFOAM framework to overcome this. The size evolution of particles during devolatilization is investigated in different conditions. In addition, the predicted composition of pyrolysis gas is compared with the experimental data. 2. Mathematical modelling In the present 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. Additional details concerning the model implementation have been documented in a previous publication [4]. In the present study, instead of the k-ε turbulent model proposed in the original CFD model, a laminar flow solver is used to match the corresponding experimental condition. As a result, the chemical source term is calculated directly using the Arrhenius expressions without effects of turbulence fluctuations. The current section will focus on the description of the detailed shrinkage model and the two-stage devolatilization model for the discrete particles. 2.1. Particle structural evolution model The biomass shrinkage model employed in the current study was proposed by Di Blasi for biomass pyrolysis [5]. The model details the time evolution of solid-phase volume (Vs), volume occupied by the pores (Vg), and total particle volume (V). Three independent shrinkage factors α, β, and γ are defined in this model. They vary from 0 for total disintegration of the particle to 1 for no shrinkage. The model is described as follows: Vs Vw 0

Vg V

Mw DMc  M w0 M w0



§ Mw M · Vgi  ¨ 1  w ¸ J Vgi  E Vw0  Vs  M w0 © M w0 ¹ Vs  Vg

(1)

(2)

(3)

where Vw0 is the initial effective solid volume, Vgi is the initial volume occupied by the pores, Mw, Mw0, and Mc are the current wood mass, initial wood mass, and current char mass in the biomass particle. This model has been validated using biomass temperature data [5]. However, a direct comparison of modelled particle sizes and experimental measurements is scarce, which is the focus in the present study. Here, also the constant diameter model and the constant density model are investigated for comparison reason. 2.2. Devolatilization model As shown in Fig. 1, biomass devolatilization is modelled through a primary and a secondary stage. In the primary stage, the decomposition products of biomass are grouped as gas-1, tar and char according to

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the Reaction 1, 2, and 3. The gas-1 is a mixture of light gases including CH4, CO, CO2, and H2O [6]. The char contains only carbon in the present study. The tar is modeled as cresol which is one of the most abundant components in wood creosote [7]. The tar may also decompose into light gases through the secondary Reaction 4. The gas-2 is a mixture of CO2, CO, H2, CH4, C2H4, and C2H6 [6]. The heat of the primary devolatilization is assumed to be -418 kJ/mol [8], whereas, the heat of the secondary stage of devolatilization is computed through molecular thermodynamic data [9]. Reaction 1

biomass (wood)

Reaction 2

gas-1 Reaction 4

tar

tar

gas-2

Reaction 3

char Fig. 1. Illustration of the devolatilization model

3. Simulation setup A three-dimensional computational domain (145 cm in length and 7.5 cm in diameter) of the drop tube reactor (DTR) used for experimental validation in this study [1] is discretized by fully hexahedral cells. The schematic of the DTR and the corresponding boundary conditions are showed in Fig. 2. The wall temperatures and gas flow rates are configured according to the experimental conditions. It is worth noticing that in order to keep the same gas velocity, the total nitrogen flow rates were set to be different, which are 18.8 L/min for the 800 °C case and 16.5 L/min for the 950 °C case. Selected input parameters for the discrete phase modelling are listed in Table 2. The wood particles are modelled as a mixture of wood, moisture, and ash according to the proximate analyse reported in the experiments. Two size groups of particles are studied in this study with average equivalent diameter 690μm and 790μm respectively. Both size group follow a Gaussian distribution with the standard deviations as shown in Table 2. A conenozzle-injection model is used to simulate the effect of the dispersion dome used in the experiments.

Fig. 2. (a) The schematic diagram of the DTR [1] (b) The boundary conditions of CFD simulation

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Tian Li et al. / Energy Procedia 105 (2017) 505 – 510 Table 1. Discrete phase parameters Particle composition (wt. %) Wood Moisture Ash 92.73

6.9

0.37

Particle diameter (μm) Mean (standard deviation) 690 (±30) 790 (±40)

Particle mass flow rate (g/min)

Particle density (kg/m3)

0.5

710

All the chemical reactions are calculated based on the well-known Arrhenius equation. These, together with the kinetic constants, are shown in Table 2 along with the corresponding references. Each of the calculated case runs for 10 s in physical time. Results are sampled at every second for the discrete phase and at every calculation time step for the gas phase. Table 2. Chemical reactions and kinetic constants

No.

Reaction

1

biomass → gas-1 gas-1 (wt. %): CH4 1.5, CO 12.0 , CO2 25.5 , and H2O 61.0 2 biomass → tar 3 biomass → char 4 tar → gas-2 gas-2 (wt. %): CO 2 21.3, CO 58.6 , H2 1.8, CH4 9.4, C2H4 8.3, and C2H6 0.6 5 CH4 + H2O → CO + 3H2 6 CO + H2O → CO2 + H2 7 CO2 + H2 → CO + H2O * The unit of pre-exponential factor A depends on the reaction.

Arrhenius equation parameters Pre-exponential Activation factor A* energy Ea (kJ/mol)

Ref.

1.11×1011

177

[6,10]

9.28×109 3.05×107

149 125

[10] [10]

9.55×104

93.3

[6]

8

126 12.6 46.6

[11] [12] [12]

3.00×10 2.78×103 9.59×104

4. Result and discussion Fig. 3 shows predicted size evolutions of biomass particles based on the three particle shrinkage models (constant density, constant diameter, and detailed shrinkage) together with experimental data [1]. After the biomass particles are heated up by the surrounding gases and wall radiation, they start to release moisture. The particle diameter is modelled to remain unchanged in this stage. The end of drying process is followed by the release of volatiles. In the CFD simulations, the particle diameter evolves with different shrinkage models when the particles undergo devolatilization. Because of the variances in initial sizes, the biomass particles shrink at different rates. As a result, the size distribution of the particles spreads out (excluding the constant diameter model). After all the volatiles are released, the size of particles becomes constant and follows a relatively narrow distribution, which can be most clearly seen in Fig. 3 (c). The experimental data confirms shrinkage of the biomass particles during devolatilization, thus, suggesting that the constant diameter model is not suitable for simulating this process. However, the extent of the particle size reduction for the constant density model is far below the measured result. The implemented Di Blasi model shows relatively good agreement with the experiential data, especially in the case of the given biomass with α=0.3, β=0, and γ=0.3. The discrepancies might result from the constant diameter assumption during the drying process. In addition, the slightly unmatched devolatilization kinetics and the homogenous Lagrangian particle assumption might also create errors. Apart from the diameter evolution, it is also worth investigating the composition of pyrolysis gas. Therefore, comparisons have been made between the calculated gas composition and the experimental data, as shown in Fig. 4. For simplification purpose, only the best fitted Di Blasi model with α=0.3, β=0, and γ=0.3 is presented. In Fig. 4, the gas composition is illustrated as the molar faction in the dry gas

Tian Li et al. / Energy Procedia 105 (2017) 505 – 510

excluding H2O and tar. It should be noted that both C2H2 and C6H6 are not included in the devolatilization model. In general, the gas composition is well predicted, especially for the major syngas compositions CO and H2 at 800 °C. The larger divergence at 950 °C might be associated with the kinetic constants used in this study, which was derived from pyrolysis experiment at a low heating rate with different wood. However, both particle size distributions the major species are predicted equally well indicating that the final steps of devolatilization is captured by the detailed shrinkage model.

Fig. 3. Size evolution of biomass particles based on three shrinkage models compared to experimental data for two different initial particle size distributions as well as two different reactor temperatures

Fig. 4. Gas composition at the residence length of 0.9 m

5. Conclusion In the present study, a detailed shrinkage model and a two-stage devolatilization process are implemented in the Eulerian−Lagrangian CFD model previously developed for biomass thermal conversion. Numerical simulations of submillimetre biomass particles subjected to a high-temperature DTR has been used to examine the model performance. It is found that the applied shrinkage model with

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α=0.3, β=0, and γ=0.3 can well predict the shrinkage level of biomass particles after the devolatilization. In addition, the two-stage devolatilization model produces satisfactory composition of pyrolysis gas. 6. Copyright Authors keep full copyright over papers published in Energy Procedia Acknowledgements This publication has been funded by CenBio–Norwegian Bioenergy Innovation Centre. CenBio is cofunded by the Research Council of Norway (193817/E20) under the FME scheme and the research and industry partners. References [1] Chen L, Dupont C, Salvador S, Grateau M, Boissonnet G, Schweich D. Experimental study on fast pyrolysis of free-falling millimetric biomass particles between 800°C and 1000°C. Fuel 2013;106:61–6. [2] Li T, Geier M, Wang L, Ku X, Güell BM, Løvås T, Shaddix CR. Effect of Torrefaction on Physical Properties and Conversion Behavior of High Heating Rate Char of Forest Residue. Energy & Fuels 2015;29:177–84. [3] Ku X, Li T, Løvås T. Effects of Particle Shrinkage and Devolatilization Models on High-Temperature Biomass Pyrolysis and Gasification. Energy & Fuels 2015;29:5127–35. [4] Ku X, Li T, Løvås T. Eulerian–Lagrangian Simulation of Biomass Gasification Behavior in a High-Temperature EntrainedFlow Reactor. Energy & Fuels 2014;28:5184–96. [5] Di Blasi C. Heat, momentum and mass transport through a shrinking biomass particle exposed to thermal radiation. Chem Eng Sci 1996;51:1121–32. [6] Boroson ML, Howard JB, Longwell JP, Peters WA. Product yields and kinetics from the vapor phase cracking of wood pyrolysis tars. AIChE J 1989;35:120–8. [7] Lee KG, Lee SE, Takeoka GR, Kim JH, Park BS. Antioxidant activity and characterization of volatile constituent s of beechwood creosote. J Sci Food Agric 2005;85:1580–6. [8] Chan W-CR, Kelbon M, Krieger BB. Modelling and experimental verification of physical and chemical processes during pyrolysis of a large biomass particle. Fuel 1985;64:1505–13. [9] Smith GP, Golden DM, Frenklach M, Moriarty NW, Eiteneer B, Goldenberg M, Bowman CT, Hanson KH, Song S, Gardiner WC, Lissianski Jr VV, Qin Z. GRI-MECH 3.0 n.d. http://www.me.berkeley.edu/gri_mech/. [10] Wagenaar B, Prins W, Swaaij W. Flash pyrolysis kinetics of pine wood. Fuel Process Technol 1993;36:291–8. [11] Jones WP, Lindstedt RP. Global reaction schemes for hydrocarbon combustion. Combust Flame 1988;73:233–49. [12] Gómez-Barea A, Leckner B. Modeling of biomass gasification in fluidized bed. Prog Energy Combust Sci 2010;36:444– 509.

Biography The corresponding author of this paper Dr. Tian Li is now working as a Postdoc researcher in the Department of Energy and Process Engineering at Norwegian University of Science and Technology. Main research interests of Dr. Tian Li are thermochemical conversion of biomass and numerical modelling of multiphase reacting flow.