CFD-DEM Modeling of Sewage Sludge Flow

in a Vertical Falling Pyrolysis Reactor

Shaoheng Ge1, Dezhen Chen1, Jun Yuan1, Lijie Yin1,Zhenfei Mei1

Abstract

Coupled computational fluid dynamics and discrete element method (CFD-DEM) is carried out to

investigate the flow and heat transfer characteristics of sewage sludge (SS) particles in a vertical falling

pyrolysis reactor. In the pyrolysis reactor there was a high temperature flue gas flow fed in from the same inlet where the sewage sludge particle was fed. The temperature and mass flow rate of the flue gas were

changed during simulation; and the influence of the deformation of particles, the wall temperature and

the reactor diameter on the temperature distribution and heat transfer characteristics of the SS particles were comprehensively explored. Considering the mass loss during the pyrolysis, the relationship between

the mass change of the particle and temperature was previously obtained by TGA experiment, and by

TGA curve the function between the particle diameter and temperature was fitted based on the assumption that density of the SS particle did not change during pyrolysis. The models for heat transfer

were verified by comparing the experimental data and the results of CFD-DEM modeling. The simulation

results indicated that the flow of the sewage sludge particles was close to a plug flow. Additionally, convective heat transfer between the particles and gas phase played a leading role for warming up SS

particles. Furthermore, the inlet temperature and mass flow rate of the flue gas have an obvious effect on

the heat transfer process. However, the influences of the particle deformation, the wall temperature and the reactor on the heat transfer process were relatively small.

Keywords: CFD-DEM, heat transfer, vertical falling reactor, sewage sludge particle

1. INTRODUCTION

Sewage sludge is the main by-product of sewage treatment plants, which is an extremely complex

heterogeneous body composed of organic debris, bacterial cells, inorganic particles, colloids, etc. The

contents of moisture and organic are both high in sewage sludge, which makes it easy to rot and stink. The

sludge pyrolysis technology uses thermochemical effects to convert organic matter into hydrocarbons in an

anaerobic environment. The final products are pyrolysis water, tar, pyrolysis gas and semi-coke. In addition

to realizing the harmlessness, resource utilization is achieved. Compared with incineration, the pyrolysis of

sewage sludge discharges less dioxin, and the heavy metals contained in the sludge are basically fixed in the

solid residue (coke), which is a promising sludge treatment technology[1, 2].

The choice of pyrolyzer in the pyrolysis process plays an important role. On the one hand, it affects the

efficiency of energy utilization , which is directly related to economic benefits. On the other hand, for certain

specific solid wastes, considering their physical and chemical characteristics, only the customized pyrolyzer

can meet the requirements. The reported waste pyrolyzers include fixed bed pyrolyzer, rotary kiln pyrolyzer,

1 Corresponding author: Thermal and Environmental Engineering Institute, School of Mechanical Engineering, Tongji

University, 1239 Siping Road, Shanghai 200092, China chendezhen@tongji.edu.cn

EurAsia Waste Management Symposium, 26-28 October 2020, İstanbul/Türkiye

fluidized bed pyrolyzer, etc. [3]. This paper proposes a new type of pyrolyzer, namely a vertical drop reactor,

which falls by itself under the action of gravity without external force and is easy to seal. The heat transfer

surface can be arranged flexibly. At the same time, the hot flue gas is used to directly heat the sludge, which

greatly improves heat exchange performance, enhances production efficiency, and reduces equipment size.

And the high temperature of the flue gas will cause the tar to crack, reducing the tar content in the pyrolysis

gas. The water vapor in the flue gas can reform the tar in the volatile matter to increase the calorific value of

the pyrolysis gas [4], and the water vapor also has an important influence on the semi-coke [5].

The pyrolysis process of sludge in the vertical falling reactor is a dense gas-solid reaction system. Numerical

simulation method has become an important method for studying gas-solid two-phase flow in complex and

dense gas-solid system. It can effectively make up for the deficiencies of existing test conditions and test

methods. There are two main numerical simulation methods for simulating dense gas-solid reaction systems:

Euler-Euler

Method and Euler-Lagrangian method. The latter treats fluid as a continuous medium and particle phase as a

discrete medium to deal with issues such as particle dynamics, trajectories, heat and mass transfer. A typical

model is a CFD-DEM model based on the Discrete element method (DEM). In the early stage double Euler

method was adopted in the simulation of fluidized bed. Because particles are regarded as pseudo-fluid, the

information at the particle level cannot be obtained. By means of Euler-Lagrangian the motion trajectory of

each particle is tracked, and in the fluidized system rich information of force and motion on the particle scale

is given [6]. The CFD-DEM method is an Euler-Lagrangian method. A soft sphere model is employed in

dealing with the interaction between particles. The search for particle collisions consumes computational

resources, and the consumption of computational resources for coupling heat transfer, mass transfer, and

chemical reaction will be extremely huge. In the numerical simulation of the moving bed thermal conversion

process, there are few studies using the CFD-DEM method. the CFD-DEM method is widely used in

studying fluidized beds, but they are mainly concentrated in two-dimensional and quasi-three-dimensional

spaces, and the calculated particles are mostly on the order of thousands [7].

In addition, particle shrinkage not only affects the pyrolysis, but also affects the particle's trajectory when it

leaves the reactor [8]. Due to the complex pyrolysis process of sludge particles, there are few simulations of

this pyrolysis process, and most of them are single particles. Liu Xiuru [9] used MATLAB programming to

study the heating process of a single sludge particle in a fluidized bed and analyzed the influence of bed

temperature, particle size and other factors on the heating process of individual sludge particles. Jing

Liangjing [10], Zhang Yu [11], Meng Qingmin et al. [12], Qi Guoli et al. [13] established a pyrolysis model

of single biomass particles, and studied the influence of external temperature and particle size on the internal

heat transfer. These studies all assume that the sludge particles are uniform spheres, and do not consider the

collision and heat transfer between the particles, nor the changes in particle volume during the pyrolysis

process. Mohseni et al. [14] assumed that the particles were cylindrical and used a shrinking model to study

the physical and chemical reaction processes of biomass particles in the pyrolysis and combustion processes.

During the reaction, only the radial shrinkage of the particles was considered and the particle length remains

unchanged, which is a one-dimensional model.

Based on the CFD-DEM model, the convective heat transfer between flue gas and sludge particles, the heat

conduction between the reactor wall and the particles, the heat conduction between the particles, and the

particle size evolution during the pyrolysis process are considered in simulating pyrolysis process of sludge

particles in vertical falling reactor. Besides, the trajectories of different particles are tracked, and the

temperature distribution and heat transfer characteristics of the particles are analyzed in the reactor, which

provide a certain basis for the design of the vertical falling reactor.

2. MODEL DESCRIPTION

Based on the CFD-DEM method, the Euler method in used in the gas phase, which is described by the

Navier-Stokes method, and the DEM method is adopted in the particle phase to track each particle in the

system. For each particle, the contact force of the surrounding particles, the particle's gravity, the drag force

and pressure gradient force of the gas are considered. Due to the complication of the pyrolysis process of

sludge particles, this research mainly focus on the movement and heat transfer characteristics of particles in

the vertical falling reactor. Therefore, in the model the following assumptions are made:

1) The sludge particles are spherical and dry particles;

2) The temperature gradient inside the sludge particles is not considered;

3) During the pyrolysis of sludge particles, the particle size is uniformly shrunk;

CFD-DEM Modeling of Sewage Sludge Flow in a Vertical Falling Pyrolysis Reactor

Shaoheng Ge, Dezhen Chen, Jun Yuan, Lijie Yin,Zhenfei Mei

2.1. Governing Equation for Gas Phase Layout

For the gas-solid flow simulations in dense phase systems, the flow type of gas phase is assumed to be

laminar [15]. Besides, in this paper the Reynolds number of the working conditions is small, so the laminar

flow model is chosen.

The conservation equations of mass and momentum for the continuous phase can be written as:

g

g g 0t

u

(1)

g g

g g g gp gpt

uu u f g

(2)

where ρg, ug, and p are the gas density, inlet velocity, and pressure, respectively. gp ,1

vk

g ii= f V

f

is the volumetric gas-particle interaction force and 11

vk

iiV V

is the local voidage in a CFD

cell. Additionally, the gas phase stress tensor τ is given by:

g g g g g k2 3

u u u-1

(3)

where μg and δk are, respectively, the gas viscosity and Kronecker delta. kv is the total number of particles in

a current computational cell of volume ∆V, and Vi is the volume of particle i (or part of the volume if the

particle is not fully in a certain cell).

Correspondingly, the energy equation for heat transfer of the gas phase can be expressed as:

g

g g , ,

1

vkg g

g g g g g i g wall

i

c Tc T c T Q Q

t

u (4)

where cg is specific heat capacity of gas, Γ is the gas thermal diffusivity (defined as μg/σT) and σT is the

turbulence Prandtl number, which is set to 1.0 [16]. Qg,i is the heat flux between gas and particle i which

locates in a certain computational cell, and Qg,wall is the transitive fluid-wall heat flux.

2.2. Governing Equations for Solid Phase

The particles motion can be described by DEM, originally proposed by Cundall and Strack [17], meanwhile

the application has obtained the extensive promotion. At any given time t, the translational and rotational

motions of individual particle i are solved by the Newton's second law as:

, , ,i

i g i e ij d ij ij

dm m

dt

vf f f g (5)

, , ,i

i t ij r ij n ijj

dI

dt T T T

(6)

where mi, Ii, vi, and ωi are the mass, moment of inertia, linear and angular velocity of individual particle i,

respectively. mig and fg,i are the gravitational force and fluid-particle interaction force (here, only the fluid

drag force acted on individual particle i is considered). Moreover, the vector sum of elastic force fe,ij and

viscous damping force fd,ij are used to characterize the inter-particle forces. Note that these interparticle

forces can be resolved into the normal and tangential components at a contact point [16]. The torque acting

on particle i due to particle j includes: Tt,ij which is generated by the tangential force and causes particle i to

rotate, and Tr,ij which, commonly known as the rolling friction torque, is generated by asymmetric normal

contact force and slows down the relative rotation between contacting particles . Additionally, Tn,ij should be

generated when the normal force does not pass through the particle center.

Correspondingly, the energy conservation equation of the individual particle i can be described as [18]:

EurAsia Waste Management Symposium, 26-28 October 2020, İstanbul/Türkiye

, , , ,i

i p i i j i g i radj

dTmc Q Q Q

dt (7)

where cp,i and Ti are specific heat capacity and temperature of particle i; Qi,j is the heat flux between particles

i and j by conduction; Qi,g is the heat flux by convection between particle i and its local surrounding gas; and

Qi,rad is the heat flux between particle i and its surrounding environment by radiation.

2.3. Heat Transfer Models

As for the energy balance between Equations (4) and (7), there normally includes three paths: convection

(gas-particle or gas-wall), conduction (particle-particle or particle-wall) and radiation (particle-surroundings)

[16]. In the current work, the both radiation heat transfers are ignored due to low emissivity (for gas) [19] and

low temperature (for particle) [20]. The rest two models of heat transfer mechanisms are briefly described in

the following parts.

2.3.1 Convective Heat Transfer

The heat flux of convective transfer Qi,g between particle i and the surrounding gas can be calculated as

follows:

, , ,i g i conv i g i iQ h A T T (8)

where Ai and Tg,i are the particle surface area and the gas temperature, respectively. The heat transfer

coefficient 2

, ,6 1i conv g p ih k Nu d is associated with the Nusselt number (Nu), which is a function

of particle Reynolds number and gas Prandtl number[21], given as follows :

1/2 1 32 0.6i iNu = Re Pr (9)

where kg is the gas thermal conductivity, dp,i is the diameter of particle i, and the gas Prandtl number is

defined asg g gPr c k

, for simplicity, which is assumed as a constant number 0.74 for this work.

The heat transfer Qg,wall between gas and near wall can be determined by:

, , ,g wall g wall g wall wall gQ h A T T (10)

where hg,wall and Ag,wall the heat transfer coefficient and contact area between gas and wall.

2.3.2 Conductive Heat Transfer

For the heat conduction among the solid particles, the thermal conductive model suggested by Chaudhuri et al.

[22] is adopted in this paper. As is shown in Fig.1 the heat is conducted through the overlapping surface. The

mechanism of direct heat conduction between particles and walls is similar to that between particles and not

described in this paper. The heat flux Qi,j between two particles i and j in contact is simulated using a linear

model, which is defined as:

, ,i j i j j iQ h T T (11)

where Ti and Tj are the temperatures of the contact particles and the inter-particles thermal conductance hi,j is

1* 3

, *

32

4

ni j p

Rh k

E

f (12)

in which kp and fn are the thermal conductivity of the particle and the normal force, R* and E* are the

geometric mean of the contact particles radius and the effective Young’s modulus for the two particles,

respectively. After all the heat fluxes are calculated, the temperature change of each particle over time is

updated explicitly using:

CFD-DEM Modeling of Sewage Sludge Flow in a Vertical Falling Pyrolysis Reactor

Shaoheng Ge, Dezhen Chen, Jun Yuan, Lijie Yin,Zhenfei Mei

i i

p p p

dT Q

dt c V (13)

where Qi and ρpcpVp are the sum of all heat fluxes and the thermal capacity of involving particle i. As a result,

Equations (8)-(13) can be used to predict the evolution of each particle’s temperature for a flowing granular

system.

Figure 1. Heat conduction model between particles

2.4. Particle Deforming Model

In this simulation, it is assumed that the density of the particles remains unchanged, and the particle size

gradually decreases as the mass decreases. Through the thermogravimetric experiment, the relationship

between the mass change of the particles and the temperature is obtained, and then the relationship between

the particle size and temperature is fitted. Calculated by chemical reaction kinetics, the radius evolution with

temperature is given as follows:

3

/2)

1/

1(-1

RTEEeART (14)

%)1/()1(3

0

3

endnew W

R

R (15)

3/13

0 ]%)1(1[ RWR endnew (16)

Where is the conversion rate of sludge particles (%).A,R and T are the former factor (1/min),universal gas

constant ()/(10314.8 3- kmolkJ

) and the particle temperature, respectively. β is the heating rate (℃/min) and E is the activation energy (kJ).R0 and Rnew are the initial particle size and the changing particle size

during the reaction process, respectively. Wend% represents the weight loss rate of the sample at the end of the

pyrolysis experiment.

Figure 2 shows the TG curve of a dry sludge particle with the mass of 7.4 mg and size of 2.16 mm at a

heating rate of 7°C/min and a final reaction temperature of 650°C. It can be seen from the figure that the

simulation results are consistent with the experimental results.

Figure 2. TG curve of a dry sludge particle

EurAsia Waste Management Symposium, 26-28 October 2020, İstanbul/Türkiye

3. SIMULATION CONDITIONS

The object simulated in this paper is a vertical falling reactor and the model is shown in Figure3, which is

170mm long and 60mm in diameter. In order to save computing resources, the selected length is shorter, and

the selected particles fall from the top, moving down and flow out from the bottom. The hot flue gas at the

inlet also flows in at the top and flows out at the bottom, and the flow grid size is 2.5mm. The particles, fluids

and other parameters are listed in Table 1.

Figure 3. Schematic diagram of the vertical falling reactor

Table 1. Simulation parameters and working conditions

Calculation conditions or parameters Value

Reactor diameter/mm 60,80,100

Reactor wall temperature/K 823,948,1073 Particle diameter/mm 0.7,0.8,0.9

Particle density/kg·m-3 1388 Particle initial temperature/K 284.4

Particle mass flow/g·s-1 1.7

Particle thermal conductivity/W·m-1·K-1 0.58 Particle specific heat capacity/J·kg-1·K-1 840

Coefficient of elastic recovery 0.75

Coefficient of static friction 0.2 Rolling friction coefficient 0.05

Poisson's ratio 0.42

Shear modulus/Pa 1.1×107 Initial gas temperature/K 823,948,1073

Gas inlet velocity/m·s-1 0.1、0.5、1

Gas phase time step/s 8×10-6 Particle phase time step/s 4×10-5

4. RESULTS AND DISCUSSION

4.1. Model Verification

In order to verify the correctness of the CFD-DEM and heat transfer sub-models used in this paper, the results

calculated using the model in this paper are compared with the experimental results of gas-solid heat transfer

in a bubbling bed made by Patil [23]. The experimental data are mainly used to verify the applicability of the

heat transfer model in the complex dense phase system. The kinematic parameters and thermophysical

parameters (for example, coefficient of restitution, viscosity and thermal conductivity, etc.) of the particles

are set according to the experiment, as shown in Table 2.

Table 2. Settings of quasi-two-dimensional bubbling bed verification case

Parameters Values

Dimensions, x y z 8.0cm×1.5cm×25cm

Girds, Nx Ny Nz 32×6×100(Grid sieze 2.5mm) Mass, m 75g(57296)

Particle diameter, dp 0.8mm,1.0mm,1.2mm

Gas phase velocity, Ubg 1.2m/s,1.54m/s,1.71m/s

Gas phase time step,△tg 5.0×10-5s

Particle phase time step,△ts 8.0×10-7s

Figure 4 shows the schematic diagram of the quasi-two-dimensional bubble bed geometry model used in the

experiment of Patil et al. [23],whose length, thickness and height are 80mm, 15mm and 250mm,respectively.

CFD-DEM Modeling of Sewage Sludge Flow in a Vertical Falling Pyrolysis Reactor

Shaoheng Ge, Dezhen Chen, Jun Yuan, Lijie Yin,Zhenfei Mei

For the working condition with 75 g particles, the number of particles is 57296. The particles with a

temperature of 363.15 K were initially accumulated in the lower part of the bed, and then the fluidizing gas

with a temperature of 293.15 K was introduced. For the velocity boundary condition, the inlet is set to a

uniform velocity and the wall surface is no slip; for the pressure boundary condition, the outlet is set to the

ambient atmospheric pressure. The specific research conditions are listed in Table 2. According to the

experiment of Patil et al. [23], three groups of working conditions of 1.20 m/s, 1.54 m/s and 1.71 m/s were

studied for the influence of speed. The calculation area is divided into grids of 32 × 6 × 100, and the grid size

is about 2.5 mm, which is about three times the particle size. Such a mesh and particle size ratio meets the

requirements of traditional CFD-DEM calculations. The fluid phase and solid phase time steps are set to 1.0 ×

10-4 s and 1.0 × 10-6 s, respectively. Each calculation example calculates for 10 s, and the last 5 s is

statistically averaged.

Figure. 4 Schematic diagram of the quasi-two-dimensional bubble bed geometry model

Figure 5 quantitatively compares the evolution of the average particle temperature in the bubbling bed

between the experiment and the simulation under different working conditions. As shown in Fig.5, increasing

the fluid velocity will enhance the convective heat transfer rate of each particle, resulting in the fast decrease

of the temperature of the particles in the bed. In addition, the higher fluid speed makes the particle movement

more violent, and the particle collision frequency increases, so that the particle-particle heat conduction and

the particle-fluid-particle heat conduction also rise up correspondingly, taking away more heat from this

system. Therefore, the particles cool down faster. In short, the simulation results are in good agreement with

the experimental values. The heat transfer model in this article is suitable for densely packed two phase heat

transfer.

Figure 5. Comparison of average particle temperature evolution in bubbling bed

4.2. Temperature Distribution and Evolution During Particle Movement

Figure 6.a illustrates the temperature distribution of the particles in the reactor. The particles of normal

temperature fall into the reactor and come into contact with the hot flue gas immediately, thus leading to the

EurAsia Waste Management Symposium, 26-28 October 2020, İstanbul/Türkiye

rapid rise of temperature. As the particle bed moves down, they continue to be heated by the hot flue gas and

the wall, but the heating rate slows down. The temperature of the exiting particles is relatively uniform,

whereas the temperature near the wall is slightly higher compared to that at the centre. The temperature

distribution of the gas phase is exhibited in Figure 6.b. After the gas enters the reactor, it exchanges heat with

the particles and rapidly cools down. At the same time, the gas is heated by the wall, forming a temperature

distribution with high boundary temperature and low center temperature. Figure 6.c demonstrates the pressure

change in the fluid flow process. From entering to exiting the bed, the fluid pressure drop is 1329Pa/m,

indicating that this type of reactor has relatively high resistance and should not be too long in the design.

(a)

(b)

(c)

Figure 6. Particle phase temperature distribution (a), gas phase temperature distribution(b), gas pressure distribution(c)

Figure 7 shows the flow state of the particles in the reactor. In the initial stage, the flow state of the particles

on the bed is basically a horizontal push flow. Under the action of gravity, the particles flow downward, and

the shape of the surface of the moving bed remains basically unchanged. After a certain height, the velocity

difference between the particles in the center and the particles near the wall becomes larger and larger, and

the particle flow gradually changes to a convergent flow.

1s

3s

Figure 7. The flow regime of particles in the reactor at different times

4.3. Influence of Different Heat Transfer Methods

The temperature and heat flow changes of the particles close to the wall and the centre are demonstrated in

Figure 8. The temperature of the two kinds of particles rises rapidly during the initial time, and the particles

near wall slowly rise to stability. After the temperature of the particles at the center stabilizes, there will be a

certain increase in the final time for the particles at the centre. The reason is that the flue gas heats the

particles in the initial stage and then cools down, but it is heated up by the wall surface hereafter, which in

turn transfers heat to the particles. It can also be seen in the figure that for both types of particles, convective

heat transfer dominates, but for particles on the wall, after a certain period of time, the amount of heat transfer

on the wall exceeds the convective heat transfer, which is mainly due to the decrease of the flue gas

temperature and constant of the wall temperature. Whereas，the total amount of convective heat transfer is

much larger than the wall heat conduction. As to particles at the center, including convective heat transfer and

inter-particle heat transfer, it can be observed that in a relatively short time the heat transfer between particles

is negligible.

CFD-DEM Modeling of Sewage Sludge Flow in a Vertical Falling Pyrolysis Reactor

Shaoheng Ge, Dezhen Chen, Jun Yuan, Lijie Yin,Zhenfei Mei

(a) Particles near the wall

(b) Particles nwar the center

Figure 8. Evolution of particle temperature and heat flow at different positions

4.4. Influence of Gas Inlet Temperature on the Heat Transfer

The temperature evolution of the particles after entering the reactor at different inlet flue gas temperatures is

illustrated in Figure 9. As the inlet flue gas temperature goes up, the temperature of the particles raises

significantly, and so does the final outlet temperature. Due to the increase in temperature difference, it shows

that the gas inlet temperature has a great influence on the entire heat exchange process. Through comparison,

when the flue gas inlet temperature is 1073K, the average temperature of the outlet particles is 790K, which

reaches the temperature required for normal pyrolysis.

Figure 9. Evolution of particle temperature at different gas inlet temperatures

(Particle size 0.8mm, wall temperature 823K, gas inlet velocity 1m/s, reactor tube diameter 60mm)

4.5. Influence of Flue Gas Particle Flow Ratio on the Heat Transfer

The flow ratio of hot flue gas to particles is also an important factor affecting the heat exchange process.

Figure 10 compares the temperature evolution of the particles under three different flow ratios. With the

increase of the flue gas particle flow rate, increasing rate of temperature and the outlet temperature of the

particles are enhanced. In the condition of same reactor tube diameters, the increase of flue gas flow rate will

lead to the growth of gas flow velocity. The gas-solid phase velocity difference enhances the heat transfer

intensity and the temperature rise speeds up, so the outlet temperature also increases accordingly.

EurAsia Waste Management Symposium, 26-28 October 2020, İstanbul/Türkiye

Figure 10. Evolution of particle temperature under different flue gas particle flow ratios (Particle size 0.8mm, gas inlet temperature 823K, wall temperature 823K, reactor tube diameter 60mm)

4.6. Effect of Particle Size and Particle Size Changes on the Heat Transfer

The left picture of Figure 11 examines the effect of particle size on the heat transfer process. It can be seen

that the smaller the particle size, the faster the temperature rise of the particles. The reason is that with the

particle size decreasing, the bed porosity and the gas flow velocity will increase correspondingly, which

strengthen the heat transfer. At the same time, the total contact area will benefits from the decrease of the

particle size, and according to equation (11) the heat conduction between the particles is positively correlated

with the contact area, which strengthens the heat conduction. But as a whole, the size impact on the heat

exchange process is not so obvious when the particle size range does not change too much. The right figure of

Figure 11 shows the influence of the particle size variation on the heat transfer process during the pyrolysis

process. Compared with the constant particle size, the heat transfer effect of the particles is better if the

particle size changes are considered. This is also due to the influence of particle size change on porosity and

contact area.

Figure 11. Evolution of particle temperature with different particle sizes and changes in particle size

(Wall temperature 823K, gas inlet temperature 1073K, gas inlet velocity 1m/s, reactor tube diameter 60mm)

4.7. Influence of Wall Temperature on the Heat Transfer

In Figure 12, the temperature rise rate of the particles at the initial time is almost the same under different

wall temperatures. This reason lies in that the temperature difference between the flue gas and the particles is

huge at the beginning, and the convective heat transfer occupies an absolute dominant position and the

influence of the wall temperature can be ignored. However, as the particles moving downward, the influence

of the wall temperature begins to appear. The temperature of the particles near the wall with high temperature

maintains a relatively high temperature rise rate, mainly because the high temperature wall continues to heat

the gas nearby and the gas can maintain a higher temperature. At the same time, the heat transferred from the

high temperature wall is also relatively high, so the wall temperature affects mainly after the particles after

entering the reactor for a period of time.

CFD-DEM Modeling of Sewage Sludge Flow in a Vertical Falling Pyrolysis Reactor

Shaoheng Ge, Dezhen Chen, Jun Yuan, Lijie Yin,Zhenfei Mei

Figure 12. Evolution of particle temperature at different wall temperatures (Particle size 0.8mm, gas inlet temperature 823K, gas inlet velocity 1m/s, reactor tube diameter 60mm)

4.8. Influence of Reactor Tube Diameter on the Heat Transfer

The left picture of Figure 13 shows the evolution of particle temperature under different tube diameters. The

temperature difference between 60mm and 80mm pipe diameter is great, but with the diameter increasing the

difference in temperature to is very small. The right figure of Figure 13 gives the proportion of the heat

conduction to the total heat transfer of the three pipe diameters and the heat conduction of the 60mm pipe

diameter is relatively large. In this condition particles near the wall account for a high percentage, and the

wall temperature has a greater influence on the gas, which will affect the particle temperature in return, so the

particle temperature is relatively higher in the case of small pipe diameters. When the pipe diameter increases,

the influence of the wall surface temperature gradually decreases, and there exists a critical value for the

influence of the pipe diameter, which is 60mm in this simulation condition.

Figure 13. Particle temperature evolution and wall heat conduction ratio under different reactor inner diameters

(Particle size 0.8mm, gas inlet temperature 823K, wall temperature 823K, gas inlet velocity 1m/s)

5. CONCLUSION

Based on the Euler-Lagrangian method, the solid phase adopts the DEM method, and the heat transfer

between particles, particles and wall, particles and gas phase, and gas phase and wall surfaces are considered.

The applicability of the model is first verified, and the influences of particle diameter and reactor size, etc. on

the heat transfer process of a vertical falling reactor are studied. The main conclusions are as follows:

1) The temperature of the particles in the reactor is high at the boundary and low at the center. The

temperature of the particles flowing out of the reaction is relatively uniform; and the pressure drop of the

fluid in the reactor is relatively high, which is 1329 Pa/m.

2) After the particles enter the reactor, the temperature rises rapidly and then slows down. Convective heat

transfer dominates the process of heating the particles, but the temperature of the wall also affects the

convective heat transfer to a certain extent.

EurAsia Waste Management Symposium, 26-28 October 2020, İstanbul/Türkiye

3) The temperature and flow rate of the inlet gas of the reactor have a greater impact on the heat exchange

process, followed by the wall temperature, and particle size have less impact. The impact of the reactor tube

diameter has a critical value.

ACKNOWLEDGMENT

This work was financially supported by the National Natural Science Foundation of China (Granted No.

51776141).

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BIOGRAPHY

Shaoheng Ge studies as a PHD candidate at Thermal and Environmental Engineering Institute, School of Mechanical Engineering, Tongji University

Ge received his BSc in Material Engineering in 2016 from Xiamen University, Xiamen, China, and

his MSc in Civil Engineering in 2009from Tongji University, Shanghai, China.

He may be contacted at 1810229@tongji.edu.cn or 463855212@qq.com.