CFD simulation of combustible solid waste pyrolysis in a fluidized bed reactor

Journal Pre-proof CFD simulation of combustible solid waste pyrolysis in a fluidized bed reactor

Kuan Ding, Qingang Xiong, Zhaoping Zhong, Daoxu Zhong, Yaning Zhang PII:

S0032-5910(19)31111-8

DOI:

https://doi.org/10.1016/j.powtec.2019.12.011

Reference:

PTEC 15017

To appear in:

Powder Technology

Received date:

19 January 2019

Revised date:

29 October 2019

Accepted date:

6 December 2019

Please cite this article as: K. Ding, Q. Xiong, Z. Zhong, et al., CFD simulation of combustible solid waste pyrolysis in a fluidized bed reactor, Powder Technology(2019), https://doi.org/10.1016/j.powtec.2019.12.011

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© 2019 Published by Elsevier.

Journal Pre-proof

CFD simulation of combustible solid waste pyrolysis in a fluidized bed reactor Kuan Ding

a, b, #

, Qingang Xiong

c, d, #

, Zhaoping Zhong

Zhang a

b, *

, Daoxu Zhong e, Yaning

d, *

College of Materials Science and Engineering, Nanjing Forestry University, Nanjing

Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,

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b

f

210037, China

pr

School of Energy and Environment, Southeast University, Nanjing, 210096, China IT Innovation Center, General Motors, Warren, MI 48092, USA

d

School of Energy Science and Engineering, Harbin Institute of Technology, Harbin,

e-

c

China *

Corresponding

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Jiangsu Provincial Academy of Environmental Science, Nanjing, Jiangsu 210036,

authors.

Email address:

rn

e

Pr

China

#

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[email protected] (Y. Zhang).

These authors contributed equally to this work.

1

[email protected]

(Z.

Zhong);

Journal Pre-proof Abstract Computational fluid dynamics (CFD) simulation of combustible solid waste (CSW) pyrolysis in a fluidized bed reactor with feeding rate of 5 kg/h was performed in this study. The multi-phase flow was simulated using the Euler-Euler method, and a multi-component and multi-step reaction method was adopted to describe the pyrolysis process of CSW. The user-defined functions (UDF) of heterogeneous reactions were programmed and coupled with the computational fluid dynamics (CFD) software. The simulation methods used were validated by comparing the simulated

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temperatures and product yields with the experimental results. Simulation results also indicated that the flow regime in the fluidized bed turned into stabilized fluidization

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gradually over pyrolysis time, and the mass flow rates of pyrolytic products at the

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outlet fluctuated within the range of ±10%. Pyrolysis temperature had the greatest

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effect on the product yield, while initial bed height had the most significant influence on product fluctuations. The CFD simulation results presented in this study provide

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valuable reference for the design and optimization of waste pyrolysis process.

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1. Introduction

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Keywords: CFD simulation; combustible solid waste; pyrolysis; fluidized bed reactor

Computational fluid dynamics (CFD) has been a powerful tool for process design and system optimization. It combines multiple subjects like modern fluid dynamics, mathematical theories in partial differential equations, numerical technologies and computer science. With computers as tools and discrete mathematics as methods, CFD builds computational models for all kinds of fluid dynamic problems, analyzes those using numerical methods, and solves practical problems related to fluid flow, heat transfer, mass transfer and reactions. In most studies on CFD simulation of pyrolysis process, biomass materials have attracted much attention. Meanwhile, due to the advantages of mixing, heat transfer 2

Journal Pre-proof and mass transfer of fluidized bed, many simulating researches have been carried out in this kind of pyrolysis reactor. In this regard, the effects of various reaction conditions on product yields have been extensively investigated, including reaction temperature(Lee et al., 2015), particle size of feedstock (Sharma et al., 2015), particle size of bed material (Xiong et al., 2013a), reaction atmosphere (Mellin et al., 2014; Mellin et al., 2015) and inlet velocity (Xue et al., 2012). During the simulation of the gas-solid two-phase flow, according to the different

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treating methods on solid phase, Euler-Lagrange method and Euler-Euler method are the two major approaches. In the Euler- Lagrange method, gas phase is regarded as

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continuous phase, while the solid particles are simulated as discrete phase. The

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continuous phase was processed in the Eulerian coordinate system, while movement

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of every particle is tracked in the Largrangian coordinate system. When applied to the simulation of biomass pyrolysis, it is advantageous in tracing the entire pyrolysis

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process of every particle. Therefore, this method is theoretically closer to the actual

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movement. Papadikis et al. (Papadikis et al., 2008; Papadikis et al., 2009a) modeled

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the pyrolysis of biomass in a 150 g/h fluidized bed using Euler-Lagrange method. The results indicated that the drag force of carrier gas to particles is the principal factor affecting the particle motion. The effects of virtual mass force became more obvious in area with lower concentration of bed material. Afterwards, the authors simulated the pyrolysis process of single biomass particle. They chose the two-step and semiglobal models of biomass pyrolysis to predict the residence time of vapor and biomass particle, and the properties of the pyrolyzing particle. Papadikis et al. (2009b) also used the same method to simulate the pyrolysis of single biomass particle in an airflow entrained bed. Results included the moving path of particle, the changes of product concentration, the radial temperature and density distribution of the particle, 3

Journal Pre-proof and yields of pyrolytic products were obtained. Recently, a discrete hard sphere particle model (DPM) was used to simulate the effects of of gravities, non-spherical particle method and the restitution coefficient on the particle behaviors by Wang et al. (Wang et al., 2015; Wang et al., 2016). The results obtained also agreed with the experimental ones. Although the Euler-Lagrange method is closer to actual situation, when the particle number of biomass increases to a very high level (e.g. >106 ), a lot of

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computational resources are required and the computing time is critically prolonged. In addition, when the volume fraction of biomass in the fluidized bed exceeds 10%,

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the Lagrange method is no longer applicable. In these cases, Euler-Euler method is

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more satisfactory, which takes no account of the moving state of each biomass particle.

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Instead, all the biomass particles are simulated as fluid. When the number of biomass particles exceeds a certain amount, more accurate simulating results, reduced

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computer resources and shorter computing time can be expected by using the

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Euler-Euler method. He et al. (2004) simulated the gas-particle flow in a bubbling

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fluidized bed without and with immersed tubes. The results could explain the movement of bubbles, the flow regime of particle and the possible erosion. Xue et al. (2011) built the CFD models of continuous fast pyrolysis of biomass in a fluidized bed using the Euler-Euler multi-phase model. Biomass was considered as the composite of cellulose, hemicellulose and lignin at different ratios. Pyrolysis of biomass was described using a multi-component and multi-step kinetics model. Meanwhile, the changes in porosity of biomass were also taken into consideration. The established model was used to simulate the pyrolysis process of cellulose and sugarcane bagasse. The key parameters, like flow rates of pyrolytic products at the outlet, steady flow state in the bed, temperature distribution, and particle density, were 4

Journal Pre-proof simulated in detail versus pyrolysis time. The physical simulating time was 200 s, which reached the steady fluidized state for the first time. The yields of three phase products were predicted under the steady state. The authors declared that the model could be used to predict the key features during the fast pyrolysis of any biomass with known contents of three major components in the fluidized bed. Thereafter, Xue et al. (2012) simulated the effects of conditions on the pyrolysis of cellulose and red oak using the same model. Results indicated a good agreement between the simulated and

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experimental yields at the outlet. However, the elutriation of unreacted biomass (especially the smaller particles) was overestimated by the model, resulting in lower

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bio-oil yields. In order to improve the precision, the particle size distribution should

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be taken into consideration. On the basis of the above model, Xiong et al. devoted

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much effort in optimizing this model, and a main framework which can be incorporated with sub-models had been developed. Using this framework, the effects

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of multiple conditions, including position of feeder and superficial gas velocity

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(Xiong et al., 2013b), drag force models and heat transfer coefficients (Xiong & Kong,

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2014), volatilizing models (Xiong et al., 2014), shape of screw feeder (Xiong et al., 2015), and operating parameters of fluidized bed (fluidized gas velocity, initial bed height and particle size of bed material) (Xiong et al., 2016), had been investigated broadly. The authors claimed that the simulated results agreed well with the experimental ones. From the literature review, although the CFD simulation of biomass pyrolysis process have been commonly investigated, there are hardly any literature reports on modeling of combustible solid waste (CSW). This is not conducive to the application of CSW pyrolysis process. On the other hand, the CSW components are much more complex than biomass, therefore the pyrolysis process of CSW is more complicated. 5

Journal Pre-proof In this work, the pyrolysis of CSW in a fluidized bed was simulated based on our previous experimental operations (Ding et al., 2016). A multi-component, multi-step devolatilization scheme was adopted to describe the pyrolysis process. Important features, including temperature distribution, flow regime and product distribution, were modeled using a 2D model. Afterwards, the effects of key parameters, such as pyrolytic temperature, superficial gas velocity and initial bed height, on the product yields and their fluctuations were examined. This study provides a simple and reliable

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way to understand the pyrolysis performance of CSW in a fluidized bed.

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2. Simulating models 2.1 Basic models

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Continuity equation was calculated using the Euler-Euler model with mass

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exchange between solid and gas phases. The drag force and gravity were counted in the momentum equation. Standard k-ε two equation model was used to calculate

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turbulent flow. Solid pressure, radial distribution function, shearing stress, bulk

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viscosity, and particle temperature were co nsidered in the particle kinetic theory.

consideration.

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Meanwhile, the heat transfer between the gas and solid phase was also taken into

2.2 Pyrolysis models of CSW From the experimental setup (Ding et al., 2016), CSW was principally made up of biomass, waste food, waste paper, and waste plastics. Among them, biomass, waste food and waste paper consisted mainly cellulose, hemicellulose and lignin fractions, and were referred to as lignocellulose. Waste plastics were consisted of polyethylene (PE), polyethylene terephthalate (PET) and polystyrene (PS). For lignocellulosic compounds in CSW (biomass, waste food and waste paper), the multi-component, multi-step pyrolysis scheme proposed by Miller and Bellan 6

Journal Pre-proof (1997) was adopted in this simulation. These compounds could be represented by the contents of three major components: Lignocellulose = α Cellulose + β Hemicellulose + γ Lignin

(1)

Where α, β and γ stand for the mass percentage of each component. The kinetic scheme of cellulose pyrolysis was schemed in Fig. 1, in which cellulose was first converted to active cellulose through a first-order reaction (K 1 ). The active cellulose was further decomposed to tar and char via two competitive reactions (K 2 and K 3 ).

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Then the produced tar went through secondary cracking to produce gas (K 4 ). In this research, this model was also applied to the pyrolysis of hemicellulose and lignin.

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For waste plastics, the pyrolysis kinetic model was simpler. Results from

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experimental researches on pyrolysis characteristics of plastics revealed that the

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pyrolysis of plastics was proceeded in one single step (Encinar & Gonzalez, 2008). Therefore, in this research, the pyrolysis of plastics was described with a one-step

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model, as shown in Fig. 2. According to the literature reports (Cepeliogullar & Putun,

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2013; Liu et al., 2000; Marcilla et al., 2009), the dominant products from pyrolysis of

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plastics were tar and gas, while the yield of char was negligible. Hence, char product was ignored when simulating the pyrolysis of plastics in this research. All the reactions were calculated using Arrhenius equation. The kinetic parameters were listed in Table 1, in which Y and Y1 with subscripts represented the corresponding yields. 2.3 Physical properties of materials The physical parameters of all the materials were listed in Table 2 (Miller & Bellan, 1997). The reaction heat of biomass components referred to Koufopanos et al. (1991). In detail, heat adsorption of reaction K 1 , K2 , K 3 and K 4 was Δh1 = 0, Δh2 = 255 kJ/kg, Δh3 = -20 kJ/kg, and Δh4 = -42 kJ/kg, respectively. The negative symbol 7

Journal Pre-proof represented heat desorption. The reaction heat adsorption of PE, PET and PS were Δh5PE = 975 kJ/kg, Δh5PET = 217 kJ/kg and Δh5PS = 855 kJ/kg, respectively (Cafiero et al., 2015). 2.4 Chemical reaction rate equation In this simulation study, fluidized gas (N2 ) and pyrolytic gas were phase 1 (gas phase, g), and bed material (silica) and CSW feedstock were phase 2 (solid phase, s 1 )

f

and phase 3 (solid phase, s2 ), respectively. All the components were numbered and

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shown in Table 3.

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Chemical reaction rate equations of all the components were listed in Table 4.

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Among which, dm i/dt represents the reaction rates of material i, mi represents the mass of material i, and k i represents the reaction rate constant. The heterogeneous reaction

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rates were coded using the user-defined function and coupled with the major reaction

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model.

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3.1 Physical model

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3. Physical model, boundary conditions and initial conditions

Since it had been verified that simulated results from 2D model and 3D model are consistent with each other (Xiong et al., 2016), a 2D model was established in this research to save computing time. The simplified physical model was shown in Fig. 3. The fluidized gas entered the bed from the bottom, while the pyrolytic tar, gas and carrier gas exited the reactor from the top. The computing domain was gridded using quadrilateral mesh with a size of 5 mm × 5 mm. The total mesh number was 17 600. The gas inlet and the feedstock inlet were set as velocity inlet, while the outlet was set as pressure outlet.

8

Journal Pre-proof 3.2 Boundary conditions and initial conditions The pressure of the outlet was set as atmosphere pressure. The wall function to solid phase was non-slip surface condition, while that to gas phase was standard wall function. In order to simulate the effects of heating jacket on the fluidized bed, the bottom section of the reactor with a height of 1 000 mm was set the sa me temperature as the pyrolytic temperature. The other section of wall was adiabatic. Unsteady fluidized flow was simulated with a time step of 0.005 s.

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The proportion of each component in the CSW was shown in Table 5. The feeding rate of CSW was 1.64 kg/h. Particle sizes of CSW and bed material were 1

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conditions were listed in Table 6.

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mm and 0.35 mm, respectively. N 2 was used as fluidized gas. The simulated

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The velocity of all the phases was initially set at 0. To avoid divergence, the initial temperatures of gas phase and bed material were set at 100 K lower than the

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beginning of the simulation.

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pyrolytic temperature. CSW and fluidized gas were introduced into the bed at the

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In this research, simulation was carried out in the commercial numerical computation software Fluent, and the physical modeling time was 150 s so as to eliminate the effects of initialization conditions and to achieve a steady fluidized state.

4. Results and discussion 4.1 Simulating results at the typical condition 4.1.1

Temperature distribution in bed and product flow rates at outlet The case PY-3 was chosen for analysis as the typical run. During the whole

simulating process, the average temperature of the gas phase and the transient mass flow rates of pyrolytic products at the outlet were recorded at each time step, with the data shown in Fig. 4. At the beginning of the simulation, temperature at the outlet (Tout ) 9

Journal Pre-proof remained the initialized level (723 K), and started to increase at t = 6.15 s. At t = 19.65 s, Tout reached 800 K, and then rose slowly to a relatively stable level of ~820 K at t = 37.40 s. A final average Tout of ~823 K was attained at 56.65 s. Fig. 5 shows the temperature distribution of gas phase in the fluidized bed at different time t. To express the whole pyrolysis zone more clearly, the reactor was separated into two parts, namely lower part and upper part, from the middle position (h = 2.2 m). At t = 6.15 s (Fig. 5(a)), the average Tout kept at the initialized level of 723 K. As the

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high-temperature fluidizing gas went up from bottom area to higher area, Tout was about to be affected. At t = 19.65 s (Fig. 5(b)), Tout reached 800 K, and the

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temperature distribution was still influenced by the initialization conditions. At t =

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37.40 s (Fig. 5(c)), Tout was 820 K, while the difference between the highest

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temperature and the lowest temperature in the bed was within 5 K. It is indicated that the temperature has fully developed and is not affected by the initialized temperature

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any more. At t = 56.65 s (Fig. 5(d)), the temperature was evenly distributed, indicating

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a stable pyrolysis condition in the bed. Temperature at partial area exceeded 823 K,

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which was due to the heat liberation from secondary reaction of pyrolytic oil. From Fig. 5(c) and (d), it can be concluded that when the pyrolysis is on the stable condition, the effects of adding raw material on gas phase temperature are negligible. The possible reasons include: 1) The feedstock is fed into the dense region in a slow speed and then mixed with the hot bed material quickly. The heat for pyrolysis is adsorbed from the whole bed material area, therefore the pyrolysis process has minimum influence on the temperature distribution. 2) The high-temperature N 2 is introduced into the fluidized bed continuously and provide energy for pyrolysis reactions. Correspondingly, the adsorbed 10

Journal Pre-proof heat by pyrolysis process is supplied by hot N 2 . 3) The heating wall also transfer energy to bed material, providing heat for pyrolysis reactions. In summary, the temperature field is barely affected by the physical heat transferring and reaction heat adsorption of the feedstock, which is beneficial to controlling the pyrolysis condition and proceeding the pyrolysis reactions under stabilized temperature.

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Fig. 4 also shows the mass flow rates Fp (Ftar and Fgas) of pyrolytic products (tar and gas) at the outlet of the reactor over pyrolysis time t. Since the flow regime at the

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dense region of the reactor was very complicated bubbling flow state, flow rates at the

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outlet was fluctuating at a certain range rather than a constant value. The overall trend

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of Fp was somehow similar to that of temperature, whereas a time delay was observed. Fp kept at 0 before t = 10 s, and then went up with the rise of bed temperature. The

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pyrolysis reactions became active, and subsequently Fp increased. The variation range

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of Fp was not stabilized within a relatively stable range until t = 73 s (estimated),

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suggesting a long period of stabilizing process for Fp . It could be found from Fig. 4 that the rates of changes in product yields after 73 s were significantly lower than that before. Therefore, a relatively stable flow regime was achieved when t = 73~150 s. Within this period, Ftar varied in the range of 0.228 - 0.402 g/s with an average value of 0.300 g/s, while Fgas changed within 0.060 - 0.107 g/s with an average value of 0.080 g/s. To further understand the relationship between the average flow rates at the outlet and the pyrolysis time t, the average flow rates F p during a certain time period and their standard deviations (SD) σp were proposed. In particular, a certain time period of 10 s was set, and then the average flow rates F p and the standard 11

Journal Pre-proof deviations σp were calculated within this time period. As a result, a F p and a σp were obtained every 10 s for each pyrolytic product throughout the pyrolysis process. Results obtained are expressed in Fig. 6. As shown, F p increased with t before t = 80 s, while kept relatively stable within t = 50 - 150 s. σp stood a relatively high level before t = 20 s, while kept basically stable within 10% of F p after t = 40 s. It indicated an intense fluctuation of F p at the initial stage of pyrolysis, whereas the

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fluctuation was restrained within ±10% after the relatively stable fluidized flow regime was achieved.

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From the above discussion, the relatively stable flow regime, gas phase

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temperature and product yields could be obtained after t = 80 s. Therefore, in the

4.1.2

Flow regime in the bed

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following analysis on the steady flow state, data was chosen at t > 80 s.

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Flow regime of case PY-3 was represented using the volume fraction of bed

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material in the bed at t = 80.15 - 81.65 s time period. Fig. 7 shows the flow regime in

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the bottom half region of the bed. It indicates that the flow regime in the bed is typical bubbling fluidized state. The bubbles are formed by the joint effects of fluidized gas and the pyrolytic vapors of CSW. Small bubbles begin to form at the bottom of the bed (on air distributor), and then grow larger as they rose up against the wall. When these bubbles go up to a certain height, they grow to the biggest and occupy most of the space in the middle of the bed. They keep rising until break. A bubble emerged at t = 80.15 s continues going up and break at t = 81.40 s, suggesting a 1.25 s lifetime of each bubble. The process of formation-developing-break of bubbles causes the periodic fluctuation of bed height of dense region. The bed height goes up as the expansion of bubbles to the highest (~0.8 m) right before the break of the bubbles, and then goes back to the lowest level (~0.68 m). The life cycle of fluctuation of the bed 12

Journal Pre-proof height is ~1.0 s. 4.1.3

Product distribution in the bed Product distribution at t = 80.15 - 81.65 s is shown in Fig. 8. As shown,

distributions of tar and gas were very identical to each other at the same time. The distribution of mass fraction of pyrolytic products varied periodically with pyrolysis time t. At t = 80.40 s, CSW was added into the dense region and heated continuously by fluidized gas and bed material. When the temperature of CSW exceeded the

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degradation temperature, they started to pyrolyze. The produced tar and gas gathered near the feeding inlet and formed a local high-concentration area. At t = 80.65 s, the

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high-concentration area moved up along the wall. At t = 80.90 s, CSW feedstock

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continued degrading, and the pyrolytic products expanded the high-concentration area.

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During the time period of t = 81.15 - 81.40 s, the high-concentration area was pushed out of the bed material layer by the broken bubble and went into the dilute zone. The

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pyrolysis reactions had basically completed, therefore the concentration of products

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started to decrease due to diffusion. At t = 81.40 s, another new high-concentration

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area formed again at the feeding inlet, while the previous one disappeared gradually. Correspondingly,

a

new

cycle

of

formatting-expanding-disappearing

of

high-concentration area started. The period of this cycle lasted for 1 s, which corresponded to the lifetime of a bubble in the dense phase. The periodically fluctuations of pyrolytic products in the bed resulted in the variation of products at the outlet. 4.2 Effects of conditions on the pyrolytic products In this research, the yields of pyrolytic products Yp (Ytar and Ygas) were calculated using the following equation:

13

Journal Pre-proof

Yp

 

n j 1

Fpj t j

(2)

mfeed

Where Δt j represents the j th time step (in this research Δt j= 0.005 s, j = 1 - n), n represents the total time steps, Fpj represents the mass flow rates (g/s) of pyrolytic product p at the outlet in the j th time step, and mfeed represents the accumulated feeding amount in the total time steps. The yield of pyrolytic char Ychar was determined by difference.

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Besides product yields, in the steady fluidized stage, relative standard deviation

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(RSD) SFp (SFtar and SFgas,) was also proposed to describe the variation of Fp , which was defined as

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SFp   p / Fp

(3)

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Where Fp (including Ftar and Fgas ) and σp are the average flow rates and standard

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deviation of product p, respectively. Obviously, a smaller SFp indicates a relatively

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high average flow rate or a relatively low variation of flow rate, which are favorable for optimizing the conditions of the reactor. Effects of pyrolytic temperature T

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4.2.1

As indicated in Fig. 9(a), the pyrolytic temperature shows a great effect on the product yields. As pyrolytic temperature increased from 723 K to 773 K, yield of tar increased from 68.5% to 73.1%, while decreased gradually to 61.0% with further rise of pyrolytic temperature. On the contrary, when pyrolytic temperature increased from 723 K to 773 K, yield of gas decreased from 12.2% to 9.0%, and turned to increase to 24.1% with further increase of pyrolytic temperature to 923 K. Yield of char decreased along with increasing pyrolytic temperature from 19.3% (T = 723 K) to 14.9% (T = 923 K). To understand the effects of secondary cracking, the reaction rates of secondary 14

Journal Pre-proof cracking of tar at t = 130.90 s were calculated at different pyrolytic temperatures, with the results illustrated in Fig. 9(b). The fluidized bed was evenly separated to four parts along the height. From Fig. 9(a), the yield of char was relatively high when T = 723 K. The kinetic models of CSW pyrolysis (in section 2.2) revealed that the production of char and gas took place in the same reaction step. Considering the reaction rate of tar cracking at T = 723 K was relatively low (shown in Fig. 9(b)), the gas product was principally derived from the primary pyrolysis of CSW feedstock. With the increase in

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pyrolytic temperature from 723 K to 773 K, the pyrolysis rate of CSW increased and the production of tar was more competitive. Therefore, the yield of tar increased while

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that of gas decreased. The reaction rate of tar cracking was still low at this

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temperature. The decreasing yield of char as pyrolytic temperature increased from 773

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K to 923 K, implies that reaction K 2 , competing with the production of char (K3 ), are favored, which seems to promote the production of tar. However, yield of tar did not

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increase, while that of gas kept increasing. It could be explained by the secondary

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cracking of tar. Fig. 9(b) displayed the rate of secondary cracking, in which the reactor

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was divided evenly into four parts (part I to IV from the bottom to the top). Part I represented mainly the dense phase zone, while the other parts were diluted phase zones. As shown, when T ≥ 823K, the reaction rate of tar cracking increased exponentially with the rising pyrolytic temperature. Accordingly, the production of gas from tar cracking is strengthened. In addition, the secondary cracking of tar took place majorly at part II to part IV, while a relatively low reaction rate was observed at part I. It is demonstrated that the secondary cracking was much more active in the diluted zones. Dashed lines in Fig. 9(a) illustrates the fluctuations of pyrolytic products at the outlet over pyrolytic temperature. As shown, SFtar and SFgas were the lowest at T = 773 15

Journal Pre-proof K, and they were identical to each other. It suggests that the minimum fluctuations can be obtained at T = 773 K. Therefore, taking both the product yields and their fluctuations into consideration, 773 K is the optimum pyrolytic temperature. When T = 723 K, SFp was higher than that at T = 773 K, and SFtar was larger than SFgas. It was indicated that at a lower pyrolytic temperature, the fluctuations of tar-producing reactions were larger than those of gas-producing ones. As pyrolytic temperature rose from 773 K to 923 K, SFp increased first and then decreased at 923 K. The largest SFp

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was obtained at T = 873 K, indicating the most unstable release of pyrolytic products. Besides, it could also be recognized that SFtar < SFgas when T ≥ 823 K, revealing that

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the fluctuations of gas-producing reactions are higher than those of tar-producing

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ones.

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From the above discussion, T = 773 K is considered as the optimum pyrolytic temperature in terms of maximizing the tar yield and minimizing its fluctuation. Effects of superficial gas velocity v in

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4.2.2

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As shown in Fig. 10, with increasing v in from 0.320 m/s to 0.640 m/s, yield of tar

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increased form 61.5% to 72.8%, while those of gas and char decreased from 21.5% and 17.0% to 11.5% and 15.7%, respectively. It is proved that the effects of v in are more significant on yields of tar and gas than on that of char. The reason is due to that with the increase in v in , residence time of tar in the bed is shortened, and thus the secondary cracking of tar is weakened. As a result, higher amount of tar flows out from the outlet rather than cracks into char and gas, leading to the increase in tar yield. Correspondingly, yield of gas decreases as the secondary cracking of tar is weakened. On the other hand, SFp kept increasing with the raising v in , and changes in SFtar and SFgas were basically identical. This result agrees with Bi (2007). The reason lies in the fact that as v in increases, higher momentum is introduced into the pyrolytic bed. 16

Journal Pre-proof Subsequently, the bubbles in the dense region become larger, which enhances the pressure fluctuation in the bed. Therefore, the flow rates of pyrolytic products at the outlet become more unstable. From the above analysis, to increase the tar yield, the superficial gas velocity should be handled appropriately. Excessive high gas velocity will increase the energy consumption of fan, therefore a balance between the benefits (increase in tar yield) and costs (increase in energy consumption) should be considered.

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Effects of initial bed height hini

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4.2.3

Fig. 11 illustrates the product yields and their RSD versus initial bed height hini.

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As shown, the effects of hini on the product yields were negligible. Specifically, as hini

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increased from 0.4 m to 0.7 m, Ytar increased slightly from 65.4% to 67.3%, Ygas

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decreased marginally from 17.8% to 15.8%, and Ychar changed negligibly within 16.1% - 16.9%. By increasing hini, residence time of CSW in the dense region is extended,

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which makes the pyrolysis more complete. From the discussion in section 4.2.1, the

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secondary cracking of tar at T = 823 K is relatively slow, therefore Ytar tend to

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increase with the extended residence time of feedstock in the dense region. However, effects of increasing hini is limited, and the pyrolysis reactions are principally controlled by pyrolytic temperature. Therefore, hini shows insignificant effects on the yields of pyrolytic products.

Although hini showed a slight effect on product yields, it made a great contribution to their RSD SFp . As shown in Fig. 11, SFp decreased slightly as hini increased from 0.4 m to 0.5 m, then turned to a significant increase when further increasing hini. The increase of hini results in an increase in mass of bed material, and more energy of fluidized gas will be consumed to maintain the fluidized state. Correspondingly, the variation of pressure in the bed will increase, enhancing the 17

Journal Pre-proof fluctuation of flow rate of pyrolytic products at the outlet. Moreover, the increase in hini also makes it easier to form larger bubbles, and the burst of the larger babbles will enhance the fluctuation in the dense region. From the above analysis, to avoid exceeding fluctuation in the bed mater ial and decrease the energy loss of fluidized gas, 0.4 - 0.5 m are the optimum initial bed heights in the investigated reactor. 4.3 Comparison between the simulated and the experimental results

oo

f

In order to verify the reliability of the simulation, several key results at case PY-3 were compared with those obtained from experiments in the same reactor and

pr

conditions (Ding et al., 2016). The results were listed in Table 7, in which

e-

experimental temperatures of dense region and diluted region were measured by

Pr

thermocouples at positions of h = 0.4 m and h = 2.2 m, respectively. For the simulated results, temperatures at the same positions as the experimental ones were recorded

al

during the steady state period. In the experimental runs, temperature in the reactor

rn

gradually decreased following the gas flow direction, with a 25 K of temperature drop

Jo u

detected from the dense region to the outlet of the reactor. There are two major reasons related to the temperature drop: one is the physical heat adsorption and reaction heat consumption, and the other is heat loss from the reactor to the ambient. By contrast, in the simulation process, since the heat adsorption has a negligible effect on the temperature distribution (see section 4.1.1) and the wall is adiabatic, temperature changes slightly at different areas in the reactor. Nevertheless, the relative deviations between experimental and simulated temperatures at different zones in the reactor were within 5%, indicating the similar temperature distributions under experimental and simulated scenarios. Fig. 12 compares the product yields from experimental and simulated runs at 18

Journal Pre-proof different pyrolytic temperatures. It could be observed that the simulated product yields were somewhat different from the experimental ones. To be specific, simulated yields of tar were 11.4% - 19.0% higher than experimental ones. Simulated char yields were 2.2% - 5.2% lower than those from experiments, and simulated gas yields were mostly lower than experimental results within a range of (-2.9%) - 1.6%. Since the char yields from simulation were lower than those from experiments, it could be presumed that the pyrolysis of CSW was more complete under simulation

oo

1)

f

case. The possible reasons include:

In the experimental research, heat transferring from external surface to center of

pr

the feedstock particle requires time, owing to the heat resistance of the feedstock.

e-

Therefore, there is a temperature gradient along the radial direction of the

Pr

particle, which slows down the pyrolysis rate. As a result, some feedstock are not fully decomposed and are converted to char. On the contrary, during the

al

simulation research, the heat resistance is not considered due to the

rn

simplification of the models. In other words, the temperature of each feedstock

Jo u

particle is homogeneous from the interior zone to the external surface. Therefore, the feedstocks are pyrolyzed more completely, producing lower yields of char. 2)

In the experimental research, mass transferring from center to external surface of the feedstock particle requires time, owing to the mass transfer resistance of the feedstock. Therefore, there is a concentration gradient along the radial direction of the particle, which slows down the release of pyrolytic products. As a result, some feedstocks are not fully decomposed and are converted to char. On the contrary, during the simulation research, the mass transfer resistance is not considered due to the simplification of the models. In other words, the product pyrolytic products can be released easily from the interior zone to the external 19

Journal Pre-proof surface. Therefore, the feedstocks are pyrolyzed more completely, producing lower yields of char. 3)

In the experimental research, as the CSW particles flow in the reactor, they will continuously collide and rub with themselves as well as other solid particles like bed material and char. Their particle sizes and mass are decreasing. Some light CSW particles are entrained out of the reactor by the fluidized gas and get into the cyclone separator. In this process, some of the entrained CSW particles have

oo

f

not been fully pyrolyzed. Conversely, in the simulation research, CSW are considered as a kind of fluid, therefore they do not change in size and keep

pr

reacting in the bed until fully decomposed rather than be carried out by fluidized

e-

gas. As a result, the simulated yields of char are lower than the experimental

Pr

ones.

Regarding the reasons to explain the difference between the simulated and

As aforementioned, there is no restrictions of heat and mass transferring, thus the

rn

1)

al

experimental yields of tar and gas, they can be concluded as follows:

2)

Jo u

CSW are pyrolyzed more completely and more tar is produced. In the simulating research, the product yields are calculated at the outlet of the reactor. The yields of tar are determined without condensation. While in the experimental research, the yields of tar are measured after condensation. Tar comes out from the outlet, and then gets into several condensers through pipes. The flow time of tar is much longer, which enables the secondary cracking in these pipes. As a result, part of the tar is converted to gas. It also explains that the gas yields from experiments are higher than those from simulation at relatively higher temperatures (823 K and 923 K). 3)

In the experimental research, there are some losses during the collection of tar, 20

Journal Pre-proof such as incomplete condensation loss and pipe condensation loss. Whereas in the simulating research, there is no such losses, so the tar yields are higher. Although the simulated yields are different from the experimental results, the trends of pyrolytic products change with pyrolytic temperature are basically identical for simulation and experiments. In general, both simulated and experimental results indicate that as the pyrolytic temperature goes up, the tar yield decreases roughly, the gas yield increases gradually, and the char yield decreases continuously. Therefore,

oo

f

the simulation results could reflect the overall trend in product yield.

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5. Conclusions

In this paper, the numerical models of pyrolysis process of CSW in a fluidized

e-

bed was established, and the simulation was carried out using the Fluent software. The

Pr

temperature distribution, flow regime and the product distribution in the pyrolysis bed were investigated. Meanwhile, the effects of pyrolytic temperature, superficial gas

al

velocity and initial bed height on the product yields and fluctuations were explored.

rn

Finally, the simulated results were compared with the experimental ones. The

1.

Jo u

following conclusions could be drawn. Under a typical pyrolysis condition (case PY-3), gas temperature at the outlet reached steady at t = 37.40 s, where the temperature of gas phase was influenced negligibly by the CSW feedstock. The mass flow rates of pyrolytic products at the outlet were not constant; instead they fluctuated with pyrolysis time within ±10% of the average values after reaching the steady fluidized state. A stabilized bubble fluidized flow was observed in the bed, with a bubble lifetime of 1.25 s and a variation cycle of bed height of 1 s. Concentration distribution of pyrolytic products changed periodically in the fluidized bed. Each local high-concentration zone of pyrolytic products lasted for 1 s. 21

Journal Pre-proof 2.

Pyrolytic temperature showed the most obvious effects on product yields. As pyrolytic temperature increased, tar yield increased first and then decreased after 773 K, while gas yield exhibited an opposite trend. At higher pyrolytic temperatures (T ≥ 773 K), the secondary cracking of tar becomes active, especially at the diluted zone. The secondary cracking contributed to the decrease in tar yield. Considering the joint benefits of the highest yield and the lowest fluctuation, 773 K was the optimum temperature for producing tar. The

oo

f

increase in superficial gas velocity shortened the residence time of tar in the reactor, thus increased the tar yield and decreased the gas yield. The char yield

pr

was not apparently affected, while the fluctuations of flow rates of pyrolytic

e-

products increased. The initial bed height showed little influence on product

Pr

yields, whereas the fluctuations of pyrolytic products were enhanced significantly by the increased initial bed height. Comparing the simulated results with the experimental ones, the temperature

al

3.

rn

distributions were identical, while the product yields differed from each other.

Jo u

The internal heat resistance, mass transfer resistance, and the changes in particle size contributed to the difference. Even though, the simulated and experimental trends in product yield variation as the increased pyrolytic temperature were similar, indicating a valuable referential value of simulated results. To sum up, CFD modeling, as a low-cost and high-efficient method, provides reliable reference for process design and optimization.

Acknowledgement This paper is sponsored by the National Natural Science Fund Program of China (51276040).

22

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References Bi, H.T.T. 2007. A critical review of the complex pressure fluctuation phenomenon in gas-solids fluidized beds. Chemical Engineering Science, 62(13), 3473-3493. Cafiero, L., Fabbri, D., Trinca, E., Tuffi, R., Ciprioti, S.V. 2015. Thermal and spectroscopic (TG/DSC-FTIR) characterization of mixed plastics for materials and energy recovery under pyrolytic conditions. Journal Of Thermal Analysis And

f

Calorimetry, 121(3), 1111-1119.

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Cepeliogullar, O., Putun, A.E. 2013. Utilization of two different types of plastic

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wastes from daily and industrial life. Journal of Selcuk University Natural and Applied Science, 694-706.

e-

Ding, K., Zhong, Z.P., Zhong, D.X., Zhang, B., Qian, X.X. 2016. Pyrolysis of

Pr

municipal solid waste in a fluidized bed for producing valuable pyrolytic oils. Clean Technologies and Environmental Policy, 18(4), 1111-1121.

al

Encinar, J.M., Gonzalez, J.F. 2008. Pyrolysis of synthetic polymers and plastic

rn

wastes. Kinetic study. Fuel Processing Technology, 89(7), 678-686.

Jo u

He, Y., Lu, H., Sun, Q., Yang, L., Zhao, Y., Dimitri, G., Jacques, B. 2004. Hydrodynamics of gas–solid flow around immersed tubes in bubbling fluidized beds. Powder Technology, 145(2), 88-105. Koufopanos, C.A., Papayannakos, N., Maschio,

G., Lucchesi, A. 1991.

MODELING OF THE PYROLYSIS OF BIOMASS PARTICLES - STUDIES ON KINETICS, THERMAL AND HEAT-TRANSFER EFFECTS. Canadian Journal of Chemical Engineering, 69(4), 907-915. Lee, Y.R., Choi, H.S., Park, H.C., Lee, J.E. 2015. A numerical study on biomass fast pyrolysis process: A comparison between full lumped modeling and hybrid modeling combined with CFD. Computers & Chemical Engineering, 82, 202-215. 23

Journal Pre-proof Liu, Y., Qian, J., Wang, J. 2000. Pyrolysis of polystyrene waste in a fluidized-bed reactor to obtain styrene monomer and gasoline fraction. Fuel Processing Technology, 63(1), 45-55. Marcilla, A., Beltrán, M.I., Navarro, R. 2009. Thermal and catalytic pyrolysis of polyethylene over HZSM5 and HUSY zeolites in a batch reactor under dynamic conditions. Applied Catalysis B: Environmental, 86(1–2), 78-86. Mellin, P., Kantarelis, E., Zhou, C., Yang, W. 2014. Simulation of Bed Dynamics

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and Primary Products from Fast Pyrolysis of Biomass: Steam Compared to Nitrogen as a Fluidizing Agent. Industrial & Engineering Chemistry Research, 53(30),

pr

12129-12142.

e-

Mellin, P., Yu, X., Yang, W., Blasiak, W. 2015. Influence of Reaction Atmosphere

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(H2O, N2, H2, CO2, CO) on Fluidized-Bed Fast Pyrolysis of Biomass Using Detailed Tar Vapor Chemistry in Computational Fluid Dynamics. Industrial & Engineering

al

Chemistry Research, 54(33), 8344-8355.

rn

Miller, R.S., Bellan, J. 1997. A Generalized Biomass Pyrolysis Model Based on

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Superimposed Cellulose, Hemicelluloseand Liqnin Kinetics. Combustion Science and Technology, 126(1-6), 97-137. Papadikis, K., Bridgwater, A.V., Gu, S. 2008. CFD modelling of the fast pyrolysis of biomass in fluidised bed reactors, Part A: Eulerian computation of momentum transport in bubbling fluidised beds. Chemical Engineering Science, 63(16), 4218-4227. Papadikis, K., Gu, S., Bridgwater, A.V. 2009a. CFD modelling of the fast pyrolysis of biomass in fluidised bed reactors. Part B: Heat, momentum and mass transport in bubbling fluidised beds. Chemical Engineering Science, 64(5), 1036-1045. Papadikis, K., Gu, S., Bridgwater, A.V., Gerhauser, H. 2009b. Application of CFD 24

Journal Pre-proof to model fast pyrolysis of biomass. Fuel Processing Technology, 90(4), 504-512. Sharma, A., Wang, S., Pareek, V., Yang, H., Zhang, D. 2015. Multi- fluid reactive modeling of fluidized bed pyrolysis process. Chemical Engineering Science, 123, 311-321. Wang, T., He, Y., Kim, D.R. 2015. Granular temperature and rotational characteristic analysis of a gas–solid bubbling fluidized bed under different gravities using discrete hard sphere model. Powder Technology, 271, 35-48.

oo

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Wang, T., He, Y., Tang, T., Zhao, Y. 2016. Numerical investigation on particle behavior in a bubbling fluidized bed with non-spherical particles using discrete hard

pr

sphere method. Powder Technology, 301, 927-939.

e-

Xiong, Q., Aramideh, S., Kong, S.-C. 2013a. Modeling Effects of Operating

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Conditions on Biomass Fast Pyrolysis in Bubbling Fluidized Bed Reactors. Energy & Fuels, 27(10), 5948-5956.

al

Xiong, Q., Kong, S.-C. 2014. Modeling effects of interphase transport coefficients

rn

on biomass pyrolysis in fluidized beds. Powder Technology, 262, 96-105.

Jo u

Xiong, Q., Kong, S.-C., Passalacqua, A. 2013b. Development of a generalized numerical framework for simulating biomass fast pyrolysis in fluidized-bed reactors. Chemical Engineering Science, 99, 305-313. Xiong, Q., Xu, F., Ramirez, E., Pannala, S., Daw, C.S. 2016. Modeling the impact of bubbling bed hydrodynamics on tar yield and its fluctuations during biomass fast pyrolysis. Fuel, 164, 11-17. Xiong, Q.G., Aramideh, S., Kong, S.C. 2014. Assessment of Devolatilization Schemes in Predicting Product Yields of Biomass Fast Pyrolysis. Environmental Progress & Sustainable Energy, 33(3), 756-761. Xiong, Q.G., Aramideh, S., Passalacqua, A., Kong, S.C. 2015. Characterizing 25

Journal Pre-proof Effects of the Shape of Screw Conveyors in Gas-Solid Fluidized Beds Using Advanced Numerical Models. Journal Of Heat Transfer-Transactions Of the Asme, 137(6), 7. Xue, Q., Dalluge, D., Heindel, T.J., Fox, R.O., Brown, R.C. 2012. Experimental validation and CFD modeling study of biomass fast pyrolysis in fluidized-bed reactors. Fuel, 97, 757-769. Xue, Q., Heindel, T.J., Fox, R.O. 2011. A CFD model for biomass fast pyrolysis in

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fluidized-bed reactors. Chemical Engineering Science, 66(11), 2440-2452.

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Captions

Pr

Fig. 2 Kinetic model of plastic pyrolysis

e-

Fig. 1 Kinetic model of lignocellulosic compounds pyrolysis

Table 1 Kinetic parameters of CSW pyrolysis

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Table 2 Physical parameters of all the materials

rn

Table 3 Component numbers in the simulation

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Table 4 Chemical reaction rate equations of all the components Fig. 3 Physical model of the fluidized bed Table 5 Components of lignocellulose and plastics in CSW Table 6 Pyrolysis conditions used in the simulation Fig. 4 Average temperature and flow rates of pyrolytic products at the outlet over pyrolysis time t (case PY-3) Fig. 5 Temperature (K) distribution in the bed at different pyrolysis time t (case PY-3): (a) t = 6.15 s; (b) t = 19.65 s; (c) t = 37.40 s; (d) t = 56.65 s Fig. 6 The average flow rates of pyrolytic products and their standard deviations (case PY-3) 26

Journal Pre-proof Fig. 7 Volume fraction of bed material at different t (case PY-3) Fig. 8 Mass fraction of pyrolytic products (case PY-3): (a) tar; (b) gas Fig. 9 Effects of pyrolytic temperature T on (a) product yields Yp and their RSD SFp and (b) reaction rate of secondary cracking k 4 at t = 130.90 s. (other conditions: v in = 0.426 m/s，hini = 0.5 m) Fig. 10 Effects of superficial gas velocity v in on product yields Yp and their RSD SFp

f

(other conditions: T = 823 K, hini = 0.5 m)

oo

Fig. 11 Effects of initial bed height hini on product yields Yp and their RSD SFp (other

pr

conditions: T = 823 K, v in = 0.426 m/s)

Table 7 Comparison of temperatures in the major areas of the reactor from simulation

e-

(case PY-3) and experiments

Jo u

rn

al

Pr

Fig. 12 Comparison of product yields from experimental and simulated results

27

Journal Pre-proof

Lignocellulose (s) (cellulose, hemicellulose, lignin)

K2 K1

Tar (g)

K4

Gas (g)

Active compounds (s) K3

Y Char (s) + (1-Y) Gas (g)

Jo u

rn

al

Pr

e-

pr

oo

f

Fig. 13 Kinetic model of lignocellulosic compounds pyrolysis

28

Journal Pre-proof Plastics (s)

K5

Y1 Tar (g) + (1-Y1) Gas (g)

(PE, PET, PS)

Jo u

rn

al

Pr

e-

pr

oo

f

Fig. 14 Kinetic model of plastic pyrolysis

29

Journal Pre-proof

4400

Outlet

Φ31

500

Feedstock

oo

f

Φ100

Fluidized gas

Jo u

rn

al

Pr

e-

pr

Fig. 15 Physical model of the fluidized bed

30

Journal Pre-proof

0.4

800 0.3 780 0.2

Outlet temperature Flow rate of tar Flow rate of gas

760

0.1 740

720

Flow rate at the outlet (g/s)

Outlet temperature (K)

820

0.0 0

20

40

60

80

100

120

140

Pyrolysis time t (s)

Fig. 16 Average temperature and flow rates of pyrolytic products at the outlet over

Jo u

rn

al

Pr

e-

pr

oo

f

pyrolysis time t (case PY-3)

31

al

Pr

e-

pr

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f

Journal Pre-proof

rn

Fig. 17 Temperature (K) distribution in the bed at different pyrolysis time t (case

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PY-3): (a) t = 6.15 s; (b) t = 19.65 s; (c) t = 37.40 s; (d) t = 56.65 s

32

Journal Pre-proof 0.4

0.04

Gas flow rate SD of gas flow rate

0.3

0.03

0.2

0.02

0.1

0.01

0.0 0

20

40

60

80

100

120

140

SD of flow rate (g/s)

Average flow rate (g/s)

Tar flow rate SD of tar flow rate

0.00 160

f

Pyrolysis time t (s)

Jo u

rn

al

Pr

e-

pr

(case PY-3)

oo

Fig. 18 The average flow rates of pyrolytic products and their standard deviations

33

Journal Pre-proof

Jo u

rn

al

Pr

e-

pr

oo

f

Fig. 19 Volume fraction of bed material at different t (case PY-3)

34

Journal Pre-proof

Jo u

rn

al

Pr

e-

pr

oo

f

Fig. 20 Mass fraction of pyrolytic products (case PY-3): (a) tar; (b) gas

35

Journal Pre-proof 10

Ytar

Ygas

SFtar

SFgas

Ychar 9

Product yield Yp (%)

80

8 60 7 40 6 20

5

0

RSD of product yield SFp (%)

(a)

Secondary cracking rate 4k (mol·m-3·s-1)

100

773

823

873

Part Part Part Part

I II III IV

0.1

0.01

4 723

(b) 1

723

923

773

823

873

923

Pyrolytic temperature T (K)

Pyrolytic temperature T (K)

Fig. 21 Effects of pyrolytic temperature T on (a) product yields Yp and their RSD SFp

Jo u

rn

al

Pr

e-

pr

oo

0.426 m/s，hini = 0.5 m)

f

and (b) reaction rate of secondary cracking k 4 at t = 130.90 s. (other conditions: v in =

36

Journal Pre-proof 100

11

Ygas SFgas

Ychar

80

10

60

9

40

8

20

7

0

RSD of product yield SFp (%)

Product yield Yp (%)

Ytar SFtar

6 0.320

0.426

0.533

0.640

Superficial velocity Vin (m/s)

Fig. 22 Effects of superficial gas velocity v in on product yields Yp and their RSD SFp

Jo u

rn

al

Pr

e-

pr

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f

(other conditions: T = 823 K, hini = 0.5 m)

37

Journal Pre-proof 25

Ytar SFtar

Ygas SFgas

Ychar

Product yield Yp (%)

20 60 15

40 10

20

5

0

RSD of product yield SFp (%)

80

0 0.4

0.5

0.6

0.7

Initial bed height hini (m)

Fig. 23 Effects of initial bed height hini on product yields Yp and their RSD SFp (other

Jo u

rn

al

Pr

e-

pr

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conditions: T = 823 K, v in = 0.426 m/s)

38

Journal Pre-proof 80

723K-Exp 723K-Simu 823K-Exp 823K-Simu 923K-Exp 923K-Simu

Product yield (%)

60

40

20

0 Tar

Gas

Char

Pyrolytic product

Jo u

rn

al

Pr

e-

pr

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f

Fig. 24 Comparison of product yields from experimental and simulated results

39

Journal Pre-proof

Component

Reaction

Y

A /s-1

E /MJ·kmol-1

Reference

Cellulose

k 1c

-

2.80 × 1019

242.4

(Miller & Bellan, 1997)

k 2c

-

3.28 × 1014

196.5

(Miller & Bellan, 1997)

k 3c

Yc=0.35

1.30 × 1010

150.5

(Miller & Bellan, 1997)

k 1h

-

2.10 × 1016

186.7

(Miller & Bellan, 1997)

k 2h

-

8.75 × 1015

202.4

(Miller & Bellan, 1997)

k 3h

Yh =0.60

2.60 × 1011

145.7

(Miller & Bellan, 1997)

k 1l

-

9.60 × 108

107.6

(Miller & Bellan, 1997)

k 2l

-

1.50 × 109

143.8

(Miller & Bellan, 1997)

k 3l

Yl=0.75

e-

Table 8 Kinetic parameters of CSW pyrolysis

7.70 × 106

111.4

(Miller & Bellan, 1997)

Tar

k4

-

4.25 × 106

108.0

(Miller & Bellan, 1997)

PE

k 5pe

2.30 × 1018

285.7

(Encinar & Gonzalez, 2008)

PET

k 5pet

3.85 × 109

161.2

(Encinar & Gonzalez, 2008)

1.61 × 108

136.6

(Encinar & Gonzalez, 2008)

PS

k 5ps

pr

Pr

al rn

Y1pe=0.6

Y1pet=0.667

Jo u

Lignin

oo

f

Hemicellulose

Y1ps=0.938

40

Journal Pre-proof Table 9 Physical parameters of all the materials Apparent density ρ /kg·m-3

Porosity ε

Specific heat capacity C /J·kg-1 ·K-1

Thermal conductivity λ /W·m-1 ·K-1

Viscosity μ /kg·m-1 ·s-1

Molecular weight M /kg·kgmol-1

Lignocellulose

650

0.70

2300

0.1256

-

-

PE

920

-

2100

0.335

-

-

PET

1330

-

2100

0.335

-

-

PS

1050

-

2100

0.335

-

-

Tar

Ideal gas

-

2500

0.02557

3.0×10-5

100

Char

350

0.85

1100

0.0837

-

-

Gas

Ideal gas

-

1100

0.02557

3.0×10-5

30

Bed material

2650

0.40

800

10

-

-

Jo u

rn

al

Pr

e-

pr

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f

Material

41

Journal Pre-proof Table 10 Component numbers in the simulation Materials (phase)

No.

Materials (phase)

No.

1

PET (s2 )

8

Hemicellulose (s2 )

2

PS (s2 )

9

Lignin (s2 )

3

Tar (g)

10

Active cellulose (s2 )

4

Gas (g)

11

Active hemicellulose (s2 )

5

Char (s2 )

12

Active lignin (s2 )

6

Bed material (s1 )

13

PE (s2 )

7

N2 (g)

14

Jo u

rn

al

Pr

e-

pr

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f

Cellulose (s2 )

42

Journal Pre-proof Table 11 Chemical reaction rate equations of all the components Material No.

Material No.

Reaction rate equation

Reaction rate equation

dm1  k1c m1 dt

2

dm2  k1h m2 dt

3

dm3  k1l m3 dt

4

dm4  k1c m1   k2c  k3c  m4 dt

5

dm5  k1h m2   k2 h  k3h  m5 dt

6

dm6  k1l m3   k2l  k3l  m6 dt

7

dm7  k5 pe m7 dt

8

dm8  k5 pet m8 dt

9

dm9  k5 ps m9 dt

oo

f

1

dm10  k2c m4  k2 h m5  k2l m6  k4 m10  Y1 pe k5 pe m7  Y1 pet k5 pet m8  Y1 ps k5 ps m9 dt

11

dm11  1  Yc  k3c m4  1  Yh  k3h m5  1  Yl  k3l m6  1  Y1 pe  k5 pe m7 dt

pr

10

rn

al

Pr

dm12  Yc k3c m4  Yh k3h m5  Yl k3l m6 dt

Jo u

12

e-

 1  Y1 pet  k5 pet m8  1  Y1 ps  k5 ps m9  k4 m10

43

Journal Pre-proof Table 12 Components of lignocellulose and plastics in CSW Biomass components /wt%

Plastic components /wt%

Hemicellulose

Lignin

PE

PET

PS

33.95

29.81

13.23

5.75

5.75

11.50

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Pr

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pr

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Cellulose

44

Journal Pre-proof Table 13 Pyrolysis conditions used in the simulation Initial bed height

Pyrolytic temperature

Superficial velocity

T /K

v in /m·s-1

PY-1

723

0.426

0.5

PY-2

773

0.426

PY-3

823

PY-4 PY-5

Superficial velocity

T /K

v in /m·s-1

PY-6

823

0.320

0.5

0.5

PY-7

823

0.533

0.5

0.426

0.5

PY-8

823

0.640

0.5

873

0.426

0.5

PY-9

823

0.426

0.4

923

0.426

0.5

PY-10

823

0.426

0.6

PY-11

823

0.426

0.7

Case #

rn

al

Pr

e-

pr

oo

f

hini /m

Jo u

Case #

Initial bed height

Pyrolytic temperature

45

hini /m

Journal Pre-proof Table 14 Comparison of temperatures in the major areas of the reactor from simulation (case PY-3) and experiments Experimental /K

Simulated /K

STD

Dense region

813.59

823.26

1.19%

Diluted region

802.81

822.73

2.48%

Outlet

787.70

822.69

4.44%

Jo u

rn

al

Pr

e-

pr

oo

f

Temperature

46

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.

Jo u

rn

al

Pr

e-

pr

oo

f

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

47

Journal Pre-proof The average flow rates of pyrolysis products and their standard deviations over pyrolysis time. 0.4

0.04

0.3

0.03

0.2

0.02

0.1

0.01

0.0 20

40

60

80

100

120

140

0.00 160

al

Pr

e-

pr

oo

f

Pyrolysis time t (s)

rn

0

SD of flow rate (g/s)

Gas flow rate SD of gas flow rate

Jo u

Average flow rate (g/s)

Tar flow rate SD of tar flow rate

48

Journal Pre-proof

Jo u

rn

al

Pr

e-

pr

oo

f

 The pyrolysis of combustible solid waste in a fluidized bed was simulated.  A multi-component model was used to present the raw material.  The multi-step pyrolysis model was adopted to describe the pyrolysis reactions.  The instantaneous flow regime and the mass flow rates of products were obtained.  The effects of parameters in the fluidized bed were also analyzed.

49

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12