Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant

Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant

Accepted Manuscript Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant Eli...

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Accepted Manuscript Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant

Eliseu Monteiro, Tamer M. Ismail, Ana Ramos, M. Abd El-Salam, Paulo Brito, Abel Rouboa PII:

S0360-5442(17)31805-4

DOI:

10.1016/j.energy.2017.10.100

Reference:

EGY 11744

To appear in:

Energy

Received Date:

06 March 2017

Revised Date:

14 September 2017

Accepted Date:

22 October 2017

Please cite this article as: Eliseu Monteiro, Tamer M. Ismail, Ana Ramos, M. Abd El-Salam, Paulo Brito, Abel Rouboa, Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant, Energy (2017), doi: 10.1016/j.energy.2017.10.100

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ACCEPTED MANUSCRIPT Highlights Experimental evaluation of Portuguese peach stone gasification was conducted. A homemade comprehensive CFD model for biomass gasification is proposed. The model is capable of predicting the syngas composition under different conditions. The effect of moisture, ER and SBR on peach stone gasification are studied.

ACCEPTED MANUSCRIPT Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant

1 2 3 4 5 6 7 8 9 10 11 12 13

Abstract

14

Among the renewable energies available, biomass constitutes an auspicious option, due

15

to its environmental-friendly character allied to its significant energy supply. As a path

16

to maximize biomass energy efficiency, gasification has been reported as an adequate

17

technology. Numerical models that can predict and optimize the experimental

18

conditions as well as the equipment design for biomass gasification are imperative,

19

towards a cost-saving and sustainable performance. This work shows the experimental

20

and numerical results of thermal gasification of Portuguese peach stone. Assays were

21

performed using a thermal gasification pilot plant with a bubbling fluidized bed at

22

temperatures ranging from 750β—¦ C to 850β—¦ C with mass flow rates of 30 kg/h to 60 kg/h.

23

A homemade comprehensive two-dimensional CFD model is proposed to optimize the

24

operating conditions of the biomass gasification process. The numerical model results

25

were compared with experimental data and good agreement was found. A parametric

26

study was performed in order to understand the influence of moisture content, steam to

27

biomass ratio and equivalence ratio in the composition of the producer gas. The results

28

of the study showed a negative impact of moisture and equivalence ratio over

29

conversion efficiency and producer gas quality, and a positive impact for steam to

30

biomass ratio which promotes higher calorific values and overall efficiency for the

31

process.

Eliseu Monteiro1,2*, Tamer M. Ismail3, Ana Ramos1, M. Abd El-Salam4, Paulo Brito2, Abel Rouboa1,5 1CIENER-INEGI,

2

Faculty of Engineering of the University of Porto, Portugal C3i – Interdisciplinary Center for Research and Innovation, Polytechnic Institute of Portalegre, Portugal 3Mechanical Engineering Department, Suez Canal University, Ismailia, Egypt 4Department of Basic Science, Cairo University, Giza, Egypt 5MEAM Department, University of Pennsylvania, PA 19020, Philadelphia, USA

1

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Keywords: Experimental biomass gasification; gasification modeling; gasification simulation

3 4

Nomenclature

5

Ac

pre-exponential factor for char burning rate

kg m-2 s-1 Pa-1K-1

6

As

particle surface area

m2

7

Av

pre-exponential factor for devolatilization rate

s-1

8

C

gas species concentration

mol/m3

9

𝐢𝐷

drag coefficient

10

Cmix

mixing rate constant

11

Cp

specific heat capacity

J/kg K

12

Cw,g

moisture concentration in the gas phase

kg/mΒ³

13

Cw,s

moisture concentration at the solid phase

kg/mΒ³

14

D

mass diffusion coefficient

mΒ²/s

15

dp

particle diameter

m

16

E

activation energy

J/mol

17

Eb

blackbody emission of the gas

W

18

e

coefficient of restitution for particle collisions

19

go

radial distribution function

20

Hevp

evaporation heat of the solid material

J/kg

21

hf

enthalpy of formation

J/kg

22

hrs

radiation heat transfer coefficient

W m-2 K-1

23

hrv

effective radiation heat transfer coefficient of the voids

W m-2 K-1

24

hs

convective mass transfer coefficient between solid and gas

m/s

2

ACCEPTED MANUSCRIPT 1

hs'

convective heat transfer coefficient between solid and gas

W m-2 K-1

2

I

radiative intensity

W

3

K

turbulent kinetic energy

mΒ²s-2

4

kd

diffusion rate

kg/mΒ² s Pa

5

kf

thermal conductivity of the fluid

W/m K

6

ks

thermal conductivity of the solid

W/m K

7

keff

effective thermal conductivity

W/m K

8

keff,0

thermal conductivity for no fluid flow

W/m K

9

ls

equivalent thickness of a solid layer

m

10

M

molecular weight

kg/mol

11

PO2

partial pressure of oxygen

Pa

12

Qcr

heat absorbed by the solid

W

13

qr

radiative flux density

W

14

R

universal gas constant

J/mol K

15

Revp

moisture evaporation rate

kg/s

16

Rc

char consumption rate

kg/s

17

Re

Reynolds number

18

Rv

volatiles evolution rate

19

Sc

Schmidt number

20

Sh

Sherwood number

21

𝑆Φ

Source term

22

T

temperature

K

23

t

time

s

kg/s

3

ACCEPTED MANUSCRIPT 1

Yv

mass fraction of volatile matter

2

U

instantaneous velocity

m/s

3

V

volume of particle

mΒ³

4

Greek Letters

5



absorption coefficient

6

Ξ²

gas-solid interphase drag coefficient

7

Ο•

void fraction in bed

8

𝛾𝑠

dissipation of fluctuating energy

9

Ξ“Ξ˜

diffusion energy

10



dynamic viscosity

kg/m s

11

πœ€

dissipation rate of turbulent kinetic energy

m-2s-3

12

Ο΅

emissivity

13

Οƒp

scattering coefficient

14

Ξ΄

Stephan-Boltzmann constant

W/mΒ² K4

15



density

kg/mΒ³

16

πœ†π‘”

thermal dispersion coefficient

17

πœ‘π‘ 

energy exchange between the gas and solid

18

πœ†π‘šπ‘–π‘₯ effective dispersion coefficient

19

Ξ¦

dependent variable

20

Θs

particle phase pseudo-temperature

21

ΞΆ

random number that obeys the Gauss distribution

22

Ο„s

stress tensor

W/m3

mΒ²/sΒ²

Pa 4

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Subscripts

2

b

bulk

3

c

char burnout

4

eff

Effective

5

f

Fluid

6

g

Gas

7

p

Particle

8

s

Solid

9

sb

solid bulk

10

sg

solid to gas

11

T

Total

12

v

volatile matter

13 14

1. Introduction

15

Biofuels are known to soften the fading of non-renewable fuels like coal, constituting a

16

greener alternative for energy production and replacing the use of fossil fuels that,

17

besides being finite, are also more pollutant [1,2]. Among various renewable sources,

18

biomass is a broadly produced material showing enhanced conversion potential by a

19

handful of established processes [3-5]. Gasification is one of the most valuable

20

techniques as it produces a versatile synthetic gas (syngas) capable of furnishing

21

worldwide desirable assets such as heat, power and chemicals [3,6,7].

22

Biomass concerns all the organic material derived from plants or animals, its

23

gasification featuring environmental advantages like carbon-neutral technologies 5

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(meaning that the CO2 produced in the conversion processes is further available for a

2

new photosynthesis succession, leading to the greenhouse effect decline) as well as

3

lower gaseous pollutant emissions reducing acid rain formation [1,5,8]. Either by

4

natural processes like plant and animal living cycles or as a result of human activities,

5

biomass is available on a renewable basis, contributing to a total of 62 countries in the

6

world already producing electricity from it. In fact, by 2008 Economic Co-operation and

7

Development (OECD) reported USA as the leading producer (26% share), Germany as

8

the second (15%), Brazil and Japan sharing the third position (both with 7% production)

9

[9].

10

Amid the various technologies with potential to convert biomass, gasification features

11

the most promising technique for solid residues conversion, at the same time complying

12

with the international policies that rule noxious emissions into the environment and

13

granting high conversion efficiencies [1,2, 10]. Its main advantage relies on the severely

14

higher temperatures applied, which grant a superior-quality cleaner syngas, due to the

15

incisive removal of undesired contaminants. Indeed, the high efficiencies afforded by

16

gasification support its notoriety [11] taking into account the increasing environmental

17

restrictions imposed by governments and international agencies. Therefore, gasification

18

is seen as a prominent and viable technology providing a cleaner alternative solution for

19

waste treatment with energy recovery [12, 13]. The main advantages gasification

20

depicts over traditional combustion are especially related to the final use that syngas can

21

be given, different applications being possible. At the cost of only a fraction of the

22

stoichiometric amount of oxygen when compared to other methods, gasification enables

23

the formation of dioxins, SO2 and NOx to be controlled and reduced [14]. In a recent

24

review of biomass gasification [15] its technological developments, their dissemination

6

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and economic evaluation were discussed, highlighting all the advantages that this

2

conversion technique affords.

3

Gasification comprises four main steps, aiming to enhance heating value and energy

4

density, removing components like water, nitrogen, oxygen and others (as elemental

5

sulfur, that represent a valuable by-product for the process) [8]. The first step is biomass

6

drying/heating, which is accomplished by water vaporization at low temperatures.

7

Following, pyrolysis/devolatilization at temperatures between 150ΒΊC and 400ΒΊC

8

produces liquid and gaseous fractions along with tar. Then oxidation takes place by the

9

supply of a gasification agent that gives rise to a gaseous mixture of CO, CO2 and H2O

10

and regenerates the energy needed for other steps of the process. Lastly, this mixture is

11

reduced being char converted into CO, CH4 and H2 yielding the final product (syngas),

12

which will also contain trace amounts of some other hydrocarbons, inert gases and

13

contaminants, together with ashes and tars formed in gasification phase [3,8].

14

Gasification can be held in several different types of gasifiers [5,8,16], the choice of a

15

particular equipment being supported by a set of specific variables from feedstock

16

properties to the desired syngas features [17,18]. In the specific case of biomass

17

gasification, fluidized beds depict the best performance since this kind of equipment

18

allows a wider particle size range, which is essential for this type of residues [6, 18-21].

19

Besides, fluidized beds are advantageous for their relatively low cost, ease of

20

construction and operation, scale-up potential, as well as the high efficiencies attained

21

[16].

22

There are numerous studies on biomass gasification in fluidized beds, namely

23

resourcing to microalgae [22], forestry and agro-industrial residues [20, 23-27], sludge

24

[28, 29] and wood wastes [6, 30, 31] among others [32, 33]. Within the agro-industrial 7

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residues, various types of kernel have already motivated some research considering

2

several thermochemical conversion techniques [34-36]. Peach stones are among them

3

depicting a prominent biomass source, easily available in the mediterranean region, with

4

good energetic content already studied as an alternative to fossil fuels with encouraging

5

results [36, 37]. From the major advantages accomplished by the thermochemical

6

conversion of peach stone, the emission of less noxious flue gases, the higher

7

efficiencies and higher heating values achieved when compared to coal or other biomass

8

species are highlighted [36, 37].

9

Numerical modeling can aid at maximizing biomass gasification potential, once this

10

fuel and technique are complex inputs and several parameters can influence the final

11

outcome [23]. This way, modeling serves as a tool to deepen the knowledge of the

12

process making use of simulations that can help to predict and optimize the

13

development of the experimental runs, as well as setting the best conditions on the

14

design and scaling of the gasifier and the overall experimental performance [4, 6, 38,

15

39]. For this purpose, diverse models dealing with thermodynamic equilibrium, kinetics,

16

mass and heat transfer may be developed with the possibility of each being designed for

17

the specific goal and type of biomass in order to better adjust to the desired conditions

18

[17, 40]. Computational fluid dynamic (CFD) codes are one of the most used methods

19

as they are further committed in finding solutions of conservation of mass, momentum,

20

energy, hydrodynamics and turbulence. In the case of fluidized bed reactors, these codes

21

play an essential role as they provide qualitative and quantitative information even for

22

more complicated regions of the reactor, effectively fine-tuning the operational

23

conditions and syngas composition [8]. CFD modeling engages advanced numerical

24

methods on solid or gas phase description, as well as for the mixing of the phases,

25

applying equations and complex parameters [8, 41, 42]. After validating the model, 8

ACCEPTED MANUSCRIPT 1

experimental data can be predicted and working conditions can be adequately changed

2

according to those predictions, if necessary. Puig-Arnavat et al. [43] analyzed several

3

biomass gasification models, describing their advantages and limitations, special

4

attention being given to the difference between kinetic and equilibrium models. The

5

authors state that while the first are able to predict the progress and syngas composition

6

along the reactor, the later point the maximum yields of specific products within the

7

reacting system. Therefore, the last ones are less computational-demanding but with the

8

shortcoming of providing low accuracy results in some cases.

9

Gerber et al. [40] present an Eulerian multiphase 2-D model for wood gasification in a

10

fluidized bed constituted of char. Operational and modeling parameters were explored

11

permitting the comparison of two different models. This way, the authors concluded

12

that some modeling parameters highly influenced syngas composition, while the

13

operational conditions had little effect on this output but presented a strong influence in

14

the tar content. Char was suggested to act as catalyst reducing tar yields, which is a big

15

advantage for the studied system once tar production is one of the limiting factors in

16

fluidized beds. Gao et al. [44] also aimed at reducing tar contents in the gasification of

17

sawdust in order to attain a high-quality syngas. With this purpose, they studied the

18

effect of air to biomass on the final characteristics of the producer gas, through the

19

development of a detailed CFD model for a cyclone gasifier. Although the model under

20

predicted CO and CO2 concentrations, high carbon conversions as well as good

21

agreement with temperature and syngas yield profiles were attained. Janajreh and Shrah

22

[45] investigated the downdraft gasification of wood chips with CFD simulations,

23

modeling the Lagrangian particle coupled evolution at several different locations inside

24

the gasifier. Reactivity was found to be heavily influenced by biomass heating value

25

and moisture content. Heterogeneous temperature distribution along the reactor was 9

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defined as a drawback for small-scale gasifiers, also contributing to efficiency loss and

2

to the complexity of the flow inside the gasifier. An interesting input given by the

3

authors was to offset biomass particle size with specific kinetic data using the discrete

4

phase method, which proved to be a suitable assumption as numerical and experimental

5

values matched. Ku et al. [46] settled a multiscale Eulerian-Lagrangian CFD model to

6

assess biomass gasification, resourcing also to the interaction between continuous gas

7

phase and discrete particles. Operating parameters such as temperature, steam/carbon

8

molar ratio, excess air ratio, biomass type and particle size were evaluated, to

9

demonstrate the accuracy of the proposed model. The authors concluded that raising

10

temperatures and steam/carbon molar ratio enhanced syngas quality, while increasing

11

the excess air ratio promoted a drop on syngas quality. Different biomass types resulted

12

in distinct yields for some syngas components. Still, these differences were not

13

significant which enabled one type of biomass to replace the other, in this particular

14

experiment case. They also verified that, for increasing particle sizes both syngas

15

quality and carbon conversion efficiency was reduced. More recently, Monteiro et al.

16

[47] assessed the production of syngas from the gasification of miscanthus within a 2-D

17

CDF model, using an Eulerian-Eulerian approach to describe mass exchange,

18

momentum and energy for solid and gaseous phases. Numerical results were compared

19

to the experimental ones achieved with a semi-industrial gasification plant, and good

20

agreement for the syngas composition was obtained. Parameters such as equivalence

21

ratio, temperature and steam to biomass ratio were evaluated, monitoring the producer

22

gas composition and yield. Raising ER promoted a decrease in syngas quality as well as

23

its heating value, although carbon conversion efficiency and tar reduction were

24

enhanced. Oppositely, temperature increase has shown to favor syngas quality, heating

10

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value and efficiency. Regarding SBR, a positive relation with H2 content and tar

2

reduction was found, while negatively affecting the heating value.

3

This work reports experimental and numerical studies on an autothermal bubbling

4

fluidized bed gasification pilot plant of Portuguese peach stone. A comprehensive

5

homemade two-dimensional unsteady state CFD model is proposed in order to comply

6

with the particular needs of the fluidized bed gasification that are not readily available

7

by the computational packages such as the volatiles treated as a non-conventional

8

material just like biomass and the flexibility to deal with different biomass types. The

9

model is used to perform a parametric study as a tool to understand the influence of

10

moisture content, steam to biomass ratio and equivalence ratio in the gasification

11

process performances.

12

2. Physical model

13

The semi-industrial biomass gasification pilot plant used main components from the left

14

to the right in the Fig.1 are:

15

– Biomass feeding system with two storage tanks in series feeding the reactor through a

16

worm screw of controllable velocity;

17

– Bubbling fluidized bed reactor whose geometry is depicted in Fig. 2. This reactor has

18

three thermocouples installed along the vertical in order to monitor the gasification

19

temperature. The gasification pilot plant works at negative pressure settled by a vacuum

20

pump;

21

– Gas cooling system composed by two co-current heat exchangers. The first heat

22

exchanger cools down the producer gas to around 300ΒΊC and preheats the air feed

11

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entering the reactor. The second heat exchanger cools down the producer gas to around

2

150ΒΊC per forced air flow;

3

– Cellulosic bag filter for the removal of ashes and unconverted carbon particles;

4

– Tube heat exchanger for the removal of liquid condensates by cooling the producer

5

gas to room temperature.

6

The main component is the bubbling fluidized bed which is constituted by a reaction

7

chamber of 0.50 m internal diameter and 4.15 m height internally coated with ceramic

8

refractory material as shown in Fig. 2. The bottom bed of the reactor has a static height

9

of 0.15 m and is composed by dolomite (calcium magnesium carbonate CaMg (CO3)2,

10

with a solid density of about 2800 kg/m3 and particle size 0.3-0.7 mm; 80 kg of

11

dolomite composed the bottom bed. Preheated atmospheric air at 350 K was used as

12

gasification agent, fed through a distributor plate. The biomass is fed at the bed surface,

13

0.50 m above the distributor plate, by means of a screw feeder.

14

The start-up of the reactor until an operating bed temperature of around 400 ΒΊC was

15

done by a propane burner and after with coal to around 800ΒΊC. After reaching a bed

16

temperature of around 800 ΒΊC, the biomass feeding was started and the coal was

17

switched off. The biomass partial combustion allows the delivery of the necessary heat

18

to achieve the desired reactor temperature and the equivalence ratio was controlled by

19

adjusting the biomass feeding rate and the atmospheric air flow rate. Then, the bubbling

20

fluidized bed gasifier was operated under autothermal and steady-state conditions.

21

The reactor was operated at atmospheric pressure and in bubbling regime according to

22

Geldart [48] classification, with superficial gas velocity of around 0.25 m/s according to

23

Ergun equation [49]. 12

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The fuel used in the gasification experiments was Portuguese peach stone. This biomass

2

was characterized in terms of properties with interest for the gasification process and

3

modeling (proximate and ultimate analysis, heating value and empirical chemical

4

formula), as shown in Table 1.

5

The operating conditions of the gasifier were characterized by the biomass feed rate, air

6

feed rate, equivalence ratio, temperature along the reactor and gas composition at the

7

exit. Table 2 shows information about the operating conditions.

8

The equivalence ratio (ER) was calculated as the ratio between the actual air added to

9

the reactor and the stoichiometric air for the biomass. The stoichiometric air was

10

estimated based upon a mass balance considering the ultimate analysis of the biomass

11

expressed in Table 2 and the amount of air needed to complete oxidation of the

12

biomass. Taking the biomass feed rate and air feed rate in account, the equivalence ratio

13

values used were between 0.29 and 0.45 (Table 2).

14

The producer gas was sampled at the exit of the condenser by means of Tedlar bags

15

every time the gasification plant has reached a stationary state in terms of temperature

16

variation in the reactor in the range of 10ΒΊC. Producer gas analysis was performed in a

17

Varian 450-GC gas chromatograph with two thermal conductivity detectors that allow

18

the detection of H2, CO, CO2, CH4, O2, N2, C2H2, C2H4 and C2H6 using helium and

19

nitrogen as carrier gases. Table 2 shows the analysis of the experimental conditions and

20

the producer gas.

21

The lower heating value (LHV) of the dry gas was estimated based on the relative

22

percentage of fuel gases components present and respective LHV at reference

23

conditions of those fuel gases components. The LHV of the dry gas was found between

13

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4.1 and 5.4 MJ/Nm3, with the higher values found for equivalence ratios between 0.29

2

and 0.36 with average bed temperatures in the range 750ΒΊC to 800ΒΊC.

3

Regarding the syngas yield, in this work it was estimated according to the relation

4

between the dry gas volumetric flow rate and the biomass (dry basis) mass flow rate.

5

The dry gas volumetric flow rate was estimated based on a mass balance of the N2 in the

6

reactor, including the N2 entering with gasification agent and in the biomass fuel, and

7

the N2 exiting in the produced gas. It was assumed that at the operating temperature, the

8

N2 in the gasification agent was not converted, and exits as N2, and as approximation,

9

that all the nitrogen in the fuel exits in the produced gas as N2. The syngas yield values

10

obtained for the gasification experiments were between 1.5 and 2.2 Nm3gas/kg biomass.

11

The cold gas efficiency was calculated as the ratio between the chemical energy in the

12

produced gas and the chemical energy in the biomass fed [8]. Values of cold gas

13

efficiency between 47.9 and 67.3% were calculated for the experimental conditions

14

used. In-depth analysis on the gasification plant can be found elsewhere [50].

15

3. Mathematical model

16

Computational fluid dynamics (CFD) is a field of knowledge and techniques used to

17

solve mathematical models of fluid flow, heat transfer and associated phenomena such

18

as chemical reactions. The advance of computer capacity allowed the CFD models to

19

have a remarkable impact on reactor design and performance optimization [51].

20

In the case of single-phase reacting flows it is already possible to move directly from

21

the lab-scale to the plant-scale design by using numerical simulations. However, in the

22

case of multiphase reacting flows there are significant research issues that must be

23

resolved before they can be considered reliable for reactor design and optimization [52]. 14

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Bubbling fluidized-bed gasification as a multiphase reaction flow comprises nonlinear

2

coupling of hydrodynamics, heat and mass transfer, and chemical reactions [53]. The

3

accurate modeling of the transport of mass and heat in multiphase flows is fundamental

4

to the correct design and performance optimization of biomass gasifiers.

5

Biomass gasification involves a number of fundamental processes, which can be

6

divided into four phases: drying, pyrolysis, gasification and combustion [4, 8].

7

3.1 Drying model

8

Drying consists in the evaporation of the moisture contained in the biomass. The

9

amount of heat required is proportional to the biomass moisture content, which can vary

10

considerably. In the autothermal gasification, the heat required is obtained from the

11

other stages of the gasification process. In the drying stage the biomass does not

12

experience any kind of decomposition. However, at temperatures above 100ΒΊC, the

13

biomass moisture is removed and converted into steam. Therefore, the rate of moisture

14

release (Revp) from solid biomass is modeled as expressed in Table 3.

15

The calculation of the mass transfer rate implies the determination of the moisture

16

concentrations Cw,s, Cw,g in the solid phase and gas phase, respectively. This is done by

17

assuming the vapor as an ideal gas.

18

The moisture concentration at the surface can then be calculated from the vapor

19

pressure. Pw,s(Ts) is the vapor pressure corresponding to the equilibrium line at

20

temperature Ts. By equilibrium reasons at the interface between the gas and the liquid,

21

the temperature of the vapor is equal to the surface temperature, Ts. Likewise, the

22

moisture concentration in the gas phase Cw,g can be expressed in terms of Tg. The

15

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convective mass (hs) and heat (h’s) transfer coefficients can be calculated from the

2

Sherwood (Sh) and Nusselt (Nu) numbers.

3

3.2 Pyrolysis model

4

Pyrolysis is a thermal decomposition of the biomass taking place in the absence of

5

oxidizing agents and at moderate temperatures. From the pyrolysis process a variety of

6

chemical species are formed, which can be divided into three categories: volatiles, tars

7

and char. Volatile components are a mixture of gases mainly comprised of CO, H2,

8

CO2, H2O and light hydrocarbons. Tars are a complex mixture of condensable

9

hydrocarbons assumed to be mainly aromatic [56]. Char is a solid residue containing

10

mostly carbon but also some volatiles and ash. The gas species experience

11

homogeneous gas phase reactions forming H2, CO, CO2 and H2O, which afterwards

12

gasify and combust the char.

13

The pyrolysis phenomenon has been described by various approaches: one step global

14

models (primary tar), three parallel primary reactions and one step global model with

15

generation of secondary tar [57]. Park et al. [58] reported that accounting for the

16

secondary reactions is of minor importance. Haseli et al. [57] confirms such

17

achievement through a comparative study concluding that the three parallel reactions

18

model is sufficient to achieve a reasonable agreement between the predictions and

19

pyrolysis experiments, thus any inclusion of secondary reactions (tar cracking) slightly

20

affects the final results. Nevertheless, in this model a one step global model with

21

secondary tar generation was adopted as expressed in the Table 4 in order to increase

22

the comprehensiveness of the proposed model.

23

The reaction parameters for pyrolysis model are given in Table 5.

16

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3.3 Heterogeneous gasification reactions

4

In the pyrolysis phase, char is generated that is further converted in heterogeneous

5

gasification reactions. The gases released during pyrolysis phase provide various

6

oxidizing agents such as O2, H2O and CO2 for the heterogeneous gasification reactions.

7

Therefore, gasification can be distinguished by three possibilities with different product

8

gas compositions. These are oxygen gasification, steam gasification and carbon dioxide

9

gasification as expressed in Table 6. The Kinetic/Diffusion Surface Reaction Model was

10

implemented to include both diffusion and kinetic effects on the heterogeneous

11

reactions.

12

Where Rc is the char consumption rate, Kd is the diffusion rate and Kr is the Arrhenius

13

kinetic rate.

14

3.4 Homogeneous gas reactions

15

Homogeneous reactions are reactions that include only reactants and products in the

16

same phase. In gasification, these are reactions in the gas phase. The homogeneous

17

gasification reactions rate coefficients are based on the Arrhenius law and are given in

18

Table 7.

19

In the above equations 𝐢𝐢𝑂; 𝐢𝐻2𝑂;𝐢𝐢𝐻 π‘Žπ‘›π‘‘ 𝐢𝐻 are the gas species concentrations. The

20

actual reaction rates of volatile species are obtained as:

21

𝑅 = π‘šπ‘–π‘›[π‘…π‘˜π‘–π‘›,π‘…π‘šπ‘–π‘₯]

4

2

(1)

17

ACCEPTED MANUSCRIPT 1

The mixing rate inside the bed is understood to be proportional to the pressure drop

2

throughout the bed. According to the Ergun equation [49], the mixing rate can be

3

expressed as [65]:

4

π‘…π‘šπ‘–π‘₯ = πΆπ‘šπ‘–π‘₯πœŒπ‘” 150

{

𝐷𝑔(1 β€’ πœ™)2/3 𝑑𝑝2πœ™

+ 1.75

} Γ— π‘šπ‘–π‘›{

𝑒𝑔(1 β€’ πœ™)1/3 π‘‘π‘πœ™

}

𝐢𝑓𝑒𝑒𝑙 𝐢𝑂2 𝑆𝑓𝑒𝑒𝑙 , 𝑆𝑂

2

(2)

5

Where the mass diffusion coefficient (𝐷𝑔) is obtained as follows [66]:

6

𝐷𝑔 = 1.5 Γ— 10 β€’ 5

7

3.5 Transport equations for gas and solid phases

8

3.5.1 Mass conservation

9

The biomass feed changes from solid phase into gas phase by reacting with the

10

oxidizing agents. Therefore, the continuity equations are expressed for both phases and

11

are given in Table 8.

12

πœ™ is the void fraction in the bed, V0 is the initial volume, π‘Ž1, π‘Ž2 and π‘Ž3 are coefficients

13

equal to 1 or 0 according, respectively, to the appearance of moisture (Revp),

14

devolatilization (Rv), and char combustion (Rc) included in the source term 𝑆𝑠𝑔.

15

3.5.2 Momentum conservation

16

The momentum equations are presented for the gas and solid phases. The model follows

17

an Eulerian-Eulerian approach, which allows a realistic description of the time

18

dependent processes in reacting fluidized beds. Compared to Euler-Lagrange, the Euler–

19

Euler model is computationally less demanding because it assumes particles as

20

continuum instead of tracking each individual particle and allow the calculation of

[

𝑇𝑔 + 273.15 298

]

1.5 1.41

(3)

πœ™

18

ACCEPTED MANUSCRIPT 1

larger reactors [69]. The Gidaspow [70] drag model is implemented to calculate the gas-

2

solid momentum exchange coefficient.

3

The turbulence model of the gas phase is of major importance in a fluidized bed

4

gasification scenario. A standard k-Ξ΅ model is implemented as turbulence model, since it

5

is the most appropriate model when turbulence transfer between phases plays an

6

important role as is the case of fluidized beds. Closure laws are needed for the particle

7

collision pressure and the granular flow based on the kinetic theory is implemented.

8

(Table 9).

9

3.5.3 Granular temperature model

10 11

The granular temperature of the solid phase is proportional to the kinetic energy of the

12

particles translation and collision. The thermodynamic temperature is a quantum of the

13

fluctuating energy of the molecules; the granular temperature expresses the macroscopic

14

kinetic energy of the random particle motion [73]. The granular temperature model is

15

implemented in order to describe the viscous forces and the solid pressure of the particle

16

phase as expressed in Table 10.

17 18

The term ( β€’ 𝑝𝑠𝐼 + πœπ‘ ) :βˆ‡π‘’π‘  expresses the generation of energy by the solid stress tensor.

19

3.5.4 Energy conservation

20

To describe the energy conservation the following energy conservation equations must

21

be solved (Table 11).

22

qr represent the radiative flux, assuming the bed as a gray optically medium

23

characterized by the extinction coefficient (K) and the emissivity (Ο΅). 19

ACCEPTED MANUSCRIPT 1

3.5.6 Species conservation

2

To describe the species conservation the following species conservation equations must

3

be solved (Table 12).

4

π‘Œπ‘–π‘” is the mass fraction of the considered chemical species; π‘Œπ‘–π‘  is the mass fraction of

5

the particles such as moisture, volatile, fixed carbon and ash. The source term of the

6

species conservation equation for gas and solid phases is calculated individually for

7

each species and particles.

8

3.6 Numerical procedure

9

The finite volume method is used for the discretization of the geometrical domain and

10

the partial differential equation set. A staggered grid is used instead of a collocated one

11

in order to properly represent the influence of the pressure in the discretized momentum

12

equations. Therefore, a staggered grid is implemented for the velocity components. The

13

scalar variables are calculated at nodal points and the velocity components on staggered

14

grids centered on the cell faces. This also helps to avoid some convergence problems

15

and oscillations in velocity and pressure fields. The problems associated with the non-

16

linearities in the equation set and the pressure-velocity linkage is solved by adopting the

17

semi-implicit method for pressure-linked equations algorithm (SIMPLE). From the

18

discretization procedure results a set of algebraic equations that is solved using the

19

point-by-point iterative method of Gauss-Seidel in combination with the algebraic

20

multi-grid solver.

21

The accuracy and convergence of the decomposition of the differential equation set

22

depends to a large extent on whether the boundary information can be given correctly.

23

The boundary conditions of the proposed model are given in Table 13. 20

ACCEPTED MANUSCRIPT 1

The boundary conditions for mass, species, and enthalpy comprise biomass feed points,

2

gas inlet, and non-permeability of the reactor walls. The boundary conditions for the

3

momentum are the velocities of gas and particles at the boundaries. It is assumed that

4

the gasifying agent flow at uniform velocity and that only the gas phase is allowed to

5

outflow the reactor. The no-slip condition is applied to the upright gas velocity

6

component at the side walls. The horizontal components of the gas and particle

7

velocities are set to zero. The outlet pressure is set equal to the normal atmospheric

8

pressure.

9

The boundary condition is closely related to the shape of the mesh, so the shape of the

10

mesh largely determines the computational solution accuracy and convergence. A grid

11

independence test is carried out to reduce the computational time as well as to build up

12

confidence level. Three meshes for the fluidized bed gasifier were created with the

13

present code. These meshes were utilized for sensitivity studies to determine whether

14

mesh size would affect the gasification metrics. The information regarding the meshes

15

characteristics is presented in Table 14.

16

Molar fraction compositions for H2, CO, CH4 and CO2 at gasifier outlet are compared

17

for the described meshes as shown in Fig. 3.

18

The simulation results for the various meshes are found to be in an increasing agreement

19

with each other. As can be seen, the mesh with number of cells exceeding 200,000 cells

20

reveal a small variation in parameter convergence less than 1%. Consequently, the

21

medium grid system having 200,000 cells was chosen for the rest of the numerical

22

analysis under different operation condition.

21

ACCEPTED MANUSCRIPT 1

The computational effort of a numerical solution grows almost linearly with the size of

2

the problem which mainly depends on the complexity of the equation set to solve and

3

the size of the grid. The geometrical domain of the bed is divided into 200,000 small

4

cells. The time step is 10-4 s and the gasification time was resolved by 18000 time steps.

5

In such a complex model, it is sometimes difficult to define a good initial condition. For

6

this reason, the process was first simulated considering only flow and non-reacting heat

7

transfer (also known as β€œcold flow”) and after reaching conversion, reactive multiphase

8

flow was added.

9

4. Results and discussion

10

4.1. Syngas composition

11

In order to validate the proposed mathematical and simulation model, the numerical

12

results were compared with the experimental data collected from the gasification pilot

13

plant already described. Figure 4 shows a comparison between the numerical results

14

predicted by the proposed model and the experimental data under the operation

15

conditions described in Table 2.

16

From the analysis of the Fig. 4, a good agreement between the experimental and

17

numerical results is found even for different operating conditions.

18

From the gasification process results the formation of three fractions: syngas, ashes (and

19

eventually char) and condensates. The syngas is the most important fraction, accounting

20

for more than 70 % and comprises light gases, notably CO, H2, CH4, CO2 and N2.

21

Figure 5 shows the mole fraction contours of the main syngas fractions.

22

ACCEPTED MANUSCRIPT 1

The O2 and CO2 contours show an opposite profile; one is maximum where the other is

2

minimal. This can be seen observing the bottom of the reactor, where the O2 quantity is

3

more pronounced due to the proximity of the air inlet, while CO2 is in minimal amounts.

4

Its quantity is raised along the reactor, due to the oxidation reactions taking place,

5

which partially burn the volatiles and producing more CO2. Logically, at the top of the

6

reactor CO2 is present in a great extension whereas O2 and CO shows their minimal

7

amounts, due to the oxidation reactions. Similar results have been reported in the

8

literature [19, 77].

9

These numerical contours also depicts analogous profiles for H2, CH4 and C2H4

10

(ethylene) although with different intensities. These components present their maximum

11

yields near the biomass inlet. Especially in the case of H2, the lower temperatures found

12

at reactor greater heights promote primary water-gas and steam-methane reforming

13

reactions enhancing its production [77, 78]. Concerning CH4 and C2H4, they tend to

14

decompose at higher temperatures which normally also means a decrease in tar content

15

[19], as we were able to find.

16

4.2. Effect of process parameters on gasification

17

4.2.1 Moisture content

18

The moisture content of biomass considerably affects gasification, because it is

19

vaporized inside the reactor, absorbing heat and reducing the temperature, while the

20

produced steam is able to react with other compounds present within the various

21

reactions during all stages of the gasification process [79]. Figure 6 shows the effect of

22

moisture content on each of the syngas components.

23

ACCEPTED MANUSCRIPT 1

As can be seen, higher moisture contents promote an accentuated decrease in the CO

2

yield, as well as a small increase for H2, more pronounced in the case of CO2 and a

3

slight decrease in the case of light hydrocarbons. This has been reported in some other

4

published works [19] and can be explained based on the fact that when more moisture is

5

present, CO levels decrease through their consumption in the water-gas shift reaction,

6

which subsequently raises CO2 and H2 contents [2]. Referring to the hydrocarbons, for

7

higher biomass moisture contents, they tend to crack incompletely due to the

8

temperature reduction [2].

9

In what regards the influence of moisture content in the efficiency of the overall process

10

of gasification, cold gas efficiency (CGE), carbon conversion efficiency (CCE) and low

11

heating value (LHV) are good parameters to take into account. Figure 7 shows these

12

trends and also depicts tar behavior for different moisture contents.

13

From Fig. 7 it can be seen that higher moisture contents on biomass lead to enhanced

14

CCE and tar yields, as well as decreased CGE and LHV. Regarding the increase of tar

15

content with moisture, this is related to the inherent drop of temperature inside the

16

reactor which difficult tar cracking before syngas is released [2]. Concerning CGE, the

17

presented behavior was similarly reported elsewhere and associated not only to the

18

difficulty of the system to reach the required temperature for gasification due to the high

19

energy demanding conditions (less energy being available to ensure the endothermic

20

reactions), but also to the dilution effect of syngas with water and CO2 [79]. The

21

progressive decrease of LHV with moisture increase is associated to the loss of energy

22

suffered by the system as a result of the reduced CO, CH4 and hydrocarbon contents

23

which are consumed in the steam reforming reactions, lowering the energy conversion

24

efficiency as reported for other feedstocks [80].

24

ACCEPTED MANUSCRIPT 1

4.2.2. Steam-to-biomass ratio

2

The steam-to-biomass ratio (SBR) is defined as the relation between the steam flow rate

3

and the biomass flow rate fed into the gasifier and is a key process parameter involved

4

in steam gasification. Figure 8 details the syngas composition for some SBR values.

5

The abrupt decrease in CO content, associated with the increase of H2 and CO2 for

6

higher SBR is explained by the raise of partial pressure inside the reactor, promoted by

7

the steam injection, as stated by other researchers [26, 30, 81]. The steam injection

8

favors the water gas, water gas shift and steam reforming reactions which, besides the

9

described effects also endorse hydrocarbon breakdown as reported by some authors [30,

10

81].

11

As with the moisture content, SBR influence on CGE, CCE, LHV and tar yield was also

12

investigated and is shown in Fig. 9.

13

SBR increase leads to higher CGE, CCE and LHV accompanied by lower tar yields.

14

This is due to the steam reforming reactions that convert H2O available from the

15

gasifying agent into H2, which is consistent with Fig. 8 and with reports from other

16

authors [82,83]. In what regards tar contents in the produced syngas, they decrease for

17

higher SBR once their decomposition is favored improving syngas quality as proved by

18

high CGE, CCE and LHV.

19

3.2.3 Equivalence ratio

20

Equivalence ratio (ER) is the ratio of the actual air to fuel ratio to the stoichiometric air

21

to fuel ratio, and this measure is very important as it permits to distinguish between

22

gasification and combustion. Gasification presents ER values below 1, the optimum

25

ACCEPTED MANUSCRIPT 1

range for biomass lying between 0.2 and 0.4, depending on other operation conditions

2

[3, 6]. Figure 10 shows syngas composition for growing ER values.

3 4

From Fig. 10 it can be seen growing contents of H2 and CO until a peak is reached.

5

Afterwards, they start to decrease returning to a value slightly below the initial one.

6

These two components are negatively affected by the oxidation reactions occurring in a

7

progressively more oxygenated atmosphere [84]. The increase in the ER means that

8

more air is added to the gasifier favoring the oxidation reactions. An ER value lower

9

than 0.2 results in various problems, including incomplete gasification, excessive char

10

formation, and a low heating value of the syngas. An ER higher than 0.4 results in

11

excessive formation of combustion products, such as CO2 and H2O, at the expense of H2

12

and CO [8]. For these reasons an optimal ER value should appear in the ER window of

13

0.2-0.4 for biomass gasification. This situation is observed in the Fig. 10, where the H2

14

and CO contents are increasing up to a certain ER and then decrease assuming an

15

elbow-shape function.

16

Regarding CO2, an opposite behavior was registered, with initial values decreasing

17

nearly until the same peak region, from where they start to raise reaching similar

18

contents of the initial ones. This can be explained based on the thermal cracking of the

19

volatiles, once in the presence of more oxygen, more CO2 will be produced at the

20

expense of CO [84].These trends were also found by other researchers for different

21

feedstocks and with distinct peak values [85]. Regarding the hydrocarbons, their

22

contents shows lower decreases within the interval of ER values under study, due to the

23

oxidation reactions favored with the presence of more oxygen and, in some extent, to

24

the steam reforming reactions [84].

26

ACCEPTED MANUSCRIPT 1

The influence of ER on tar content, lower heating value, cold gas efficiency and carbon

2

conversion efficiency was investigated in the referred interval and is shown in Fig. 11 at

3

constant temperature.

4

As can be seen, ER increase means a general trend of improvement of CGE and CCE

5

whereas LHV and tar yield diminish. This is caused by the extra air introduced in the

6

gasifier. This effect is more visible in the case of LHV, which seems to start increasing

7

but, at an ER peak value it begins to drop due to the diluting effect of N2 and also to the

8

progressive reduction of H2 and CO contents, together with the CO2 augment, as seen in

9

Fig.10. As there are so many variables to balance, the maximum LHV can only be

10

attained through a compromise range for ER, forming an asymmetric curve as depicted

11

in Fig. 10. Cold-gas efficiency (CGE) is the energy input over the potential energy

12

output based on the LHV of both the solid fuel and the product gas, strongly depending

13

on the LHV [8]. Therefore, CGE also presents an elbow-shape function. These

14

tendencies in the calorific value parameters are in agreement with other reported works

15

[85].

16

The increase in the ER favors the oxidation reactions that being exothermic promote the

17

increase of the reactor temperature. The increase of the temperature favors the steam

18

reforming reactions, which in turns promote the carbon conversion efficiency as shown

19

in Fig. 11. This also leads to the increase of gas yield, which improves the tar

20

decomposition due to the reforming and cracking reactions [86], which is also seen in

21

Fig. 11.

22

5. Conclusions

27

ACCEPTED MANUSCRIPT 1

In the experimental work, peach stones were used in the autothermal pilot scale

2

bubbling fluidized-bed gasifier as a feedstock. The result showed the final gas

3

compositions about 51.1 - 53.5% of N2, 13.1-16.7% of CO2, 12.4 -18.1% of CO, 7.7-

4

11.5% of H2, 2.8-4.2% of CH4, and 0.3-1.1% of C2H4 for equivalence ratios in the range

5

0.29-0.45 and gasification temperature from 750 ΒΊC to 850ΒΊC.

6

The LHV of the dry gas was found between 4.1 and 5.4 MJ/Nm3, with the higher values

7

found for equivalence ratios between 0.29 and 0.36 with average bed temperatures in

8

the range 750ΒΊC to 800ΒΊC. The specific dry gas production was between 1.5 and 2.2

9

Nm3/kg biomass db and the cold gas efficiency between 47.9 and 67.3%.

10

A comprehensive homemade two-dimensional unsteady state CFD model was proposed

11

to investigate the effects of moisture content, steam to biomass ratio and equivalence

12

ratio in the process performances such as (1) producer gas composition, (2) producer

13

gas LHV, (3) carbon conversion efficiency, (4) cold gas efficiency, and (5) tar yields.

14

Obtained results show good agreement between experimental and numerical runs,

15

which suggests that the homemade proposed model is capable of predicting the

16

producer gas composition as well as the trends for the main conditions governing the

17

gasification process for the selected feedstock.

18

By parametric study, the five process performances were evaluated for the pilot

19

gasification plant with respect to ER, SBR and biomass moisture. The results of the

20

study showed a negative impact of moisture and equivalence ratio over conversion

21

efficiency and producer gas quality, and a positive impact for steam to biomass ratio

22

which promotes higher calorific values and overall efficiency for the process.

23

Acknowledgments

28

ACCEPTED MANUSCRIPT 1

Ana Ramos thanks the Portuguese Foundation for Science and Technology for her PhD

2

grant [SFRH/BD/110787/2015].

3

4

5

6

7

8

9

10

11

12 13

14

15

16

17

18 29

ACCEPTED MANUSCRIPT 1

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Portuguese municipal solid wastes. Int J Hydrogen Energy 2016; 41: 10619-30.

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6

review. Appl Energ 2013; 111: 129-41.

7

8

9

10

11

12

13

14

15

16

17

18

40

ACCEPTED MANUSCRIPT 1

2

(f) (d) (a)

(e)

(c)

(g)

(b) (k)

(j)

(i)

(h)

3 4 5 6

Figure 1 - Schematic of the biomass gasification pilot plant. (a) Feed system; (b) Bubbling fluidized bed gasifier; (c) Heat exchangers; (d) Bag filter; (e) Condenser; (f) Flare; (g) Condensate storage tank; (h) Vacuum pump; (i) Air compressor; (j) Air fan; (k) Air fan.

7

41

ACCEPTED MANUSCRIPT

1

1.0 m

2 0.2 m

Refractory

3

Reactor

4

Syngas

Steel wall

5

0.5 m

6 4.15 m

7 8 9 10 11

13

Biomass

0.2 m

12

14 15 1 2

Air flow

Figure 2- Bubbling fluidized bed reactor scheme

3

4

42

ACCEPTED MANUSCRIPT 22

Coarse

20

Medium

Fine

18

Molar Fraction (%)

16 14 12 10 8 6 4 2 0

1 2

H2

CO

CH4

CO2

Figure 3 - Grid independency study for the described meshes

3

4

5

6

7

8

9

10

43

ACCEPTED MANUSCRIPT

Syngas fraction (% vol. dry basis)

60 50

Experimental work

40

Theoretical Model

a)

30 20 10 0 H2

1

CO

CH4

CO2

N2

CO2

N2

CO2

N2

Syngas fraction (% vol. dry basis)

60 50

Experimental work

40

Theoretical Model

b)

30 20 10 0 H2

2 3 4

Syngas fraction (% vol. dry basis)

60 50 40

CO

Experimental work

CH4

c)

Theoretical Model

30 20 10 0

5 6 7 8

H2

CO

CH4

Figure 4 - Experimental and predicted syngas compositions of peach stone from table 2 a) run# 1 b) run# 4 and c) run# 7 44

ACCEPTED MANUSCRIPT

1 2 3

4 5 6 7 8

O2

CO

CH4

CO2

C2H4

H2

Tar

Figure 5 - Numerical contours for syngas composition produced within the fluidized bed gasifier for peach stone under operating conditions of run# 5

9

10

11

12

13

45

ACCEPTED MANUSCRIPT

25

Syngas composition (%)

CH4

C2H4

C2H2

20

15

10

5

0 5

1 2 3 4

10

15

20 25 Moisture content (%)

30

35

40

Figure 6 - Effect of moisture content on the produced syngas for peach stone under the operating conditions of run# 5

5

6

7

8

9

10

11

12

13

14 46

7

Efficiency ( %)

100

LHV (MJ/Nm3), Tar yield (g/Nm3)

ACCEPTED MANUSCRIPT

6

80

5

60

4 3

40

2 20

CGE CCE Moisture content (%)

1

LHV

Tar

Moisture content (%) 0

0 0

1 2 3

10

20

30

40

Figure 7 - Effect of moisture content on CGE, CCE, LHV and tar yield for peach stone under the operating conditions of run# 5

4

5

6

7

8

9

10

11

12

13

47

ACCEPTED MANUSCRIPT 1

25 H2

CO

CO2

Syngas composition (%)

20

15

10

5

0 0

1

2 Steam biomass ratio

3

4

2 3

Figure 8 - Effect of steam biomass ratio on the produced syngas for peach stone under the operating

4

conditions of run# 5

5

6

7

8

9

10

11

12

48

Efficiency ( %)

100

LHV (MJ/Nm3), Tar yield (g/Nm3)

ACCEPTED MANUSCRIPT

7 6

80

5 60

4 3

40

2 20

Steam biomass ratio

1

CGE LHV

Steam biomass ratio 0

CCE Tar

0 0

1

2

3

4

1 2 3

Figure 9 - Effect of steam biomass ratio on CGE, CCE, LHV and tar yield for peach stone under the operating conditions of run# 5

4

5

6

7

8

9

10

11

12

13 49

ACCEPTED MANUSCRIPT 1

2

Syngas composition (%)

25

C2H2 CO2

H2 CH4

CO C2H4

20

15

10

5

0 0.15

0.2

0.25

ER

0.3

0.35

0.4

3 4 5

Figure 10- Effect of equivalence ratio on the produced syngas for peach stone under the operating conditions of temperature 800Β°C, admission biomass 45 kg/h and moisture content 10%

6

7

8

9

10

11

12

13 50

ACCEPTED MANUSCRIPT 1

Efficiency ( %)

100

LHV (MJ/Nm3), Tar yield (g/Nm3)

2

6 5

80

4 60 3 40 2 20

1

ER CGE

0

CCE

ER

0 0.15

0.2

0.25

LHV

0.3

Tar

0.35

0.4

3 4 5

Figure 11 - Effect of equivalence ratio on CGE, CCE, LHV and tar yield for peach stone under the operating conditions of temperature 800Β°C, admission biomass 45 kg/h and moisture content 10%

6

7

8

9

10

11

12

13 51

ACCEPTED MANUSCRIPT 1 2

Table 1 - Proximate and ultimate analysis of peach stone Biomass proprieties

Peach stone

Proximate analysis (wt % wet basis) Volatile Fixed carbon Ash Moisture

63.0 29.0 1.0 7.0

Ultimate analysis (wt % dry and ash free basis) N C H O

4.9 41.0 5.7 48.4

S

0.0

Density (kg/m3)

480

Higher heating value (MJ/kg)

18.8

Empirical formula

CH1.668O0.885N0.102

3 4

5

6

7

8

9

10

11

12

13

52

ACCEPTED MANUSCRIPT 1 2

Table 2. Experimental operating conditions and producer gas analysis for peach stone Experimental conditions

Peach Stone Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9

Temperature of gasification (ΒΊC) Biomass feed rate (kg/h) Air feed rate (Nm3/h)

750 30 45

750 45 55

750 60 87

800 30 49

800 45 57

800 60 91

850 30 57

850 45 60

850 60 93

Equivalence ratio

0.36

0.29

0.35

0.39

0.3

0.36

0.45

0.32

0.37

H2 CO CH4 CO2

8.3 18.2 3.7 13.1

8.5 15.7 4.1 15.1

10.3 12.4 3.2 16.7

8.2 16.5 3.6 14.7

7.4 14.9 4.2 15.2

11.5 14 2.8 15.9

8.1 14.1 2.9 16

7.7 12.9 3.7 16.5

11 13.7 3.1 16.0

N2 O2 C2H2

51.4 3.9 0.2

51.1 4.1 0.1

52.9 3.9 0.1

52 3.8 0.1

52.2 4.8 0.1

51.7 3.6 0.1

53.2 3.1 0.1

53.5 4.6 0.2

51.9 4.1 0.1

C2H4

1.1

1.1

0.5

1.0

1.1

0.4

0.3

0.9

0.3

C2H6

0.1

0.2

0.0

0.1

0.1

0.0

0.0

0.0

0.0

5.4

5.2

4.2

5.0

5.0

4.3

4.1

4.4

4.3

1.8

1.5

1.7

2.0

1.5

1.9

2.2

1.6

1.9

67.4

54.2

49.5

67.2

52.3

54.5

60.0

47.9

54.8

Producer gas fraction (%vol. db)

Gas LHV (MJ/Nm3) Gas Yield

3

(Nm3/kg

biomass)

Cold gas efficiency (%)

4

5

6

7

8

9

10

11

12 53

ACCEPTED MANUSCRIPT 1

Table 3 - Drying model

Ref.

𝑅𝑒𝑣𝑝 = π΄π‘ β„Žπ‘ (𝐢𝑀,𝑠 β€’ 𝐢𝑀,𝑔) 𝑇𝑠 < 100℃ 𝑇𝑠 = 100℃

(β„Žπ‘ , (𝑇𝑔 β€’ 𝑇𝑠) + πœ–π›Ώ(π‘‡βˆž4 β€’ 𝑇4𝑠)) 𝐢𝑀,𝑠 =

[54] [54]

𝑃𝑀,𝑠(𝑇𝑠)

[54]

𝑅𝑇

( 𝑃 (𝑇) = 10

π‘„π‘π‘Ÿ

𝑅𝑒𝑣𝑝 = 𝐻 𝑒𝑣𝑝 π‘„π‘π‘Ÿ = 𝐴𝑠

0.622 +

7.5𝑇 +𝑇 238

)

[55]

𝑀

[55]

1 3

π‘†β„Ž = 2 + 1.1𝑆𝑐 𝑅𝑒0.6 1 3

𝑁𝑒 = 2 + 1.1π‘ƒπ‘Ÿ 𝑅𝑒

[55] 0.6

2

3

4

5

6

7

8

9

10

11 54

ACCEPTED MANUSCRIPT 1

2 3

Table 4 - Chemical reactions [59] 𝐢π‘₯π»π‘¦π‘‚π‘§β†’π‘β„Žπ‘Žπ‘Ÿ + π‘£π‘œπ‘™π‘Žπ‘‘π‘–π‘™π‘’ π‘”π‘Žπ‘ π‘’π‘  (𝐢𝑂2 + 𝐻2𝑂 + 𝐢𝐻4 + 𝐻2 + 𝐢𝑂) + π‘π‘Ÿπ‘–π‘šπ‘Žπ‘Ÿπ‘¦ π‘‘π‘Žπ‘Ÿ π‘ƒπ‘Ÿπ‘–π‘šπ‘Žπ‘Ÿπ‘¦ π‘‘π‘Žπ‘Ÿβ†’π‘£π‘œπ‘™π‘Žπ‘‘π‘–π‘™π‘’π‘  + π‘ π‘’π‘π‘œπ‘›π‘‘π‘Žπ‘Ÿπ‘¦ π‘‘π‘Žπ‘Ÿ Volatile matter in solid rate (kg/s): π‘‘π‘Œπ‘£ 𝐸𝑣 𝑅𝑣 =β€’ πœŒπ‘ π‘ = πœŒπ‘ π‘π‘Œπ‘£π΄π‘£π‘’π‘₯𝑝 β€’ 𝑑𝑑 𝑅𝑇

( )

𝐴𝑣 = 3.63 Γ— 104 s β€’ 1 ,

𝐸𝑣 𝑅

= 9340 K

4 5 6 7 8 9 10 11 12 13 14 15 16 17

55

ACCEPTED MANUSCRIPT 1 2 3 4 5

Table 5- Reaction parameters for pyrolysis Reaction

𝐴 (𝑠 β€’ 1)

𝐸 (π‘˜π½π‘šπ‘œπ‘™ β€’ 1)

Heat of reaction (MJ/kg)

Ref.

π‘˜1

1.44 Γ— 104

88.6

-0.42

[60]

π‘˜2

4.13 Γ— 106

112.7

0.42

[60]

7.38 Γ— 105

106.5

0.42

[60]

π‘˜4

4.28 Γ— 106

107.5

0.04

[61]

π‘˜5

1 Γ— 105

107.5

0.04

[61]

π‘π‘–π‘œπ‘šπ‘Žπ‘ π‘ β†’π‘”π‘Žπ‘  π‘π‘–π‘œπ‘šπ‘Žπ‘ π‘ β†’π‘‘π‘Žπ‘Ÿ π‘˜3

π‘π‘–π‘œπ‘šπ‘Žπ‘ π‘ β†’π‘β„Žπ‘Žπ‘Ÿ π‘‘π‘Žπ‘Ÿβ†’π‘”π‘Žπ‘  π‘‘π‘Žπ‘Ÿβ†’π‘β„Žπ‘Žπ‘Ÿ 6

7

8

9

10

11

12

13

14

56

ACCEPTED MANUSCRIPT 1

2

Table 6 - Heterogeneous gasification reactions Heterogeneous reactions [62]:

Kinetic/Diffusion Surface Reaction Model [63]: 𝑃𝑂

𝐢 + 0.5𝑂2→𝐢𝑂

𝐢 + 𝐢𝑂2β†’2𝐢𝑂

𝑅𝑐 =

2

1 1 + πΎπ‘Ÿ 𝐾𝑑

𝑇𝑠 + 𝑇𝑔 5.06 Γ— 10 β€’ 7 𝐾𝑑 = Γ— 𝑑𝑝 2

(

C + H2O→CO + H2

[

πΎπ‘Ÿ = 3.0 Γ— 𝑇𝑠𝑒π‘₯𝑝

3

4

5

6

7

8

9

10

11

12 57

)

]

10300 𝑅𝑇𝑠

0.75

ACCEPTED MANUSCRIPT 1

2

3

Table 7 - Gasification homogeneous reactions Homogeneous reactions [62]:

Reaction rate [64]:

1 𝐻2 + 𝑂2→𝐻2𝑂 2

𝑅𝐻 = 5.159 Γ— 1015𝑒π‘₯𝑝

1 𝐢𝑂 + 𝑂2→𝐢𝑂2 2

𝑅𝐢𝑂 = 1.0 Γ— 1015𝑒π‘₯𝑝

𝐢𝐻4 + 2𝑂2→𝐢𝑂2 + 2𝐻2𝑂

(

2

(

)𝐢

0.5 𝐢𝑂𝐢 𝑂 2

(

)𝑇

𝑅𝐢𝐻 = 3.552 Γ— 1014𝑒π‘₯𝑝

(

𝑅𝑀𝑔 = 2.78 𝑒π‘₯𝑝

4

5

6

7

8

9

10

11

12

58

β€’ 1.5 1.5 𝑔 𝐢𝑂2𝐢𝐻2

β€’ 16000 𝑇𝑔

4

𝐢𝑂 + 𝐻2𝑂↔𝐢𝑂2 + 𝐻2

)𝑇

β€’ 3430 𝑇𝑔

β€’ 15700 𝑇𝑔

)( 𝐢

β€’ 12600 𝑅𝑇𝑔

β€’1 𝑔 𝐢𝑂2𝐢𝐢𝐻4 𝐢𝐢𝑂2𝐢𝐻2

𝐢𝑂𝐢𝐻2𝑂 β€’ 0.0265𝑒π‘₯𝑝(65800/𝑅𝑇𝑔)

)

ACCEPTED MANUSCRIPT 1

2

Table 8 - Mass conservation Gas phase: βˆ‚(πœ™πœŒπ‘”) βˆ‚π‘‘

+ βˆ‡(πœ™πœŒπ‘”π‘’π‘”) = 𝑆𝑠𝑔

Solid phase: βˆ‚((1 β€’ πœ™)πœŒπ‘ ) βˆ‚π‘‘

+ βˆ‡((1 β€’ πœ™)πœŒπ‘ π‘’π‘ ) =β€’ 𝑆𝑠𝑔

Void fraction [67]: πœ™=πœ™βˆ˜

π‘‰βˆ˜ 𝑉

Biomass volume shrinkage [68]:

π‘‰βˆ˜ 𝑉

∘ β€’ π‘…π‘‘π‘Ÿπ‘¦) β€’ π‘Ž2(𝑅 π‘£βˆ˜ β€’ 𝑅𝑣) β€’ π‘Ž3(𝑅 π‘βˆ˜ β€’ 𝑅𝑐) = 1 β€’ π‘Ž1(π‘…π‘‘π‘Ÿπ‘¦

Source term:

𝑆𝑠𝑔 = 𝑅𝑒𝑣𝑝 + 𝑅𝑣 + 𝑅𝑐 3

4

5

6

7

8

9

10

59

ACCEPTED MANUSCRIPT Table 9 - Momentum conservation Gas Phase: βˆ‚(Ο•πœŒπ‘”π‘’π‘”) + βˆ‡(πœ™πœŒπ‘”π‘’π‘”π‘’π‘”) =β€’ Ο•βˆ‡π‘ƒg + πœ™πœŒπ‘”π‘” β€’ Ξ²(𝑒𝑔 β€’ 𝑒𝑠) + βˆ‡πœ™πœπ‘” βˆ‚π‘‘ Gas–solid drag coefficient,   𝛽=

{

150

(1 β€’ πœ™)2πœ‡π‘‡ πœ™π‘‘π‘2

+ 1.75

πœŒπ‘”(1 β€’ πœ™)|𝑒𝑔 β€’ 𝑒𝑠| 𝑑𝑝

πœŒπ‘”(1 β€’ πœ™)|𝑒𝑔 β€’ 𝑒𝑠| 3 πœ™ β€’ 2.65 𝑔 , 4𝐢𝐷 𝑑𝑝

, πœ™ ≀ 0.8

πœ™ > 0.8

where : 𝐢𝐷 =

{

and:

24 𝑅𝑒𝑠

(1 + 015𝑅𝑒0.687 𝑠 ),

𝑅𝑒𝑠 ≀ 1000 0.44, 𝑅𝑒𝑠 > 1000

πœŒπ‘”πœ™|𝑒𝑔 β€’ 𝑒𝑠|𝑑𝑝

𝑅𝑒𝑠 =

πœ‡π‘”

Gas phase stress tensor [71]: 2 πœπ‘” = πœ‡π‘”[βˆ‡π‘’π‘” + βˆ‡π‘’π‘”π‘‡] β€’ πœ‡π‘‡(βˆ‡π‘’π‘”) 3 where: π‘˜2

πœ‡π‘‡ = πœ‡π‘” + πœ‡π‘‘ ; πœ‡π‘‘ = πœŒπ‘”πΆπœ‡ πœ€ ; πΆπœ‡ = 0.09. The governing transport equations for k and ο₯ [72]: βˆ‚ βˆ‚π‘‘

(

πœ‡π‘‘

πœ‡π‘‘

)

)

(πœ™πœŒπ‘”π‘˜) + βˆ‡(πœ™πœŒπ‘”π‘’π‘”π‘˜) = + βˆ‡ πœ™πœŽπ‘˜βˆ‡π‘˜ + πœ™πΊπ‘˜ β€’ πœ™πœŒπ‘”πœ–

(

βˆ‚ βˆ‚π‘‘

(πœ™πœŒπ‘”πœ€) + βˆ‡(πœ™πœŒπ‘”π‘’π‘”πœ€) + βˆ‡ πœ™πœŽπœ€βˆ‡πœ€ + πœ™(πΆπœ€1πΊπ‘˜ β€’ πΆπœ€2πœŒπ‘”πœ–)

Gk represents the generation of turbulence kinetic energy due to the mean velocity gradients [72]: 2 πΊπ‘˜ = πœ‡π‘‘βˆ‡π‘’π‘”.[βˆ‡π‘’π‘” + βˆ‡π‘’π‘”π‘‡] β€’ 3βˆ‡π‘’π‘”(πœ‡π‘‘βˆ‡π‘’π‘” + πœŒπ‘”π‘˜) and: πΆπœ€1= 1.44 ; πΆπœ€2 = 1.92, the turbulent Prandtl numbers for π‘˜ and Ξ΅ are πœŽπ‘˜ = 1 and πœŽπœ€ = 1.3 [71]. Solid phase: βˆ‚((1 β€’ Ο•)πœŒπ‘ π‘’π‘ ) + βˆ‡((1 β€’ πœ™)πœŒπ‘ π‘’π‘ π‘’π‘ ) = βˆ‚π‘‘ =β€’ (1 β€’ πœ™)βˆ‡π‘ƒπ‘  + (1 β€’ πœ™)πœŒπ‘ π‘” β€’ 𝛽(𝑒𝑔 β€’ 𝑒𝑠) + βˆ‡(1 β€’ πœ™)πœπ‘  Stress tensor of the solid phase: 2 πœπ‘  = πœ‡π‘ β€’ 3πœ‡π‘  βˆ‡π‘’π‘  + πœ‡π‘ (βˆ‡π‘’π‘  + 𝑒𝑇𝑠)

(

)

Where [70]: 4 πœ‡π‘ = 3(1 β€’ πœ™)πœŒπ‘ π‘‘π‘π‘”π‘œ; 4

πœ‡π‘  = 5(1 β€’ πœ™)πœŒπ‘ π‘‘π‘π‘”π‘œ(1 + 𝑒)

Ξ˜π‘  Ο€

10πœŒπ‘ π‘‘π‘ πœ‹Ξ˜π‘ 

[

4

+ 96(1 + 𝑒)πœ™π‘” 1 + 5π‘”π‘œ(1 β€’ πœ™)(1 + 𝑒) π‘œ

Solid pressure [72]: 𝑃𝑠 = (1 β€’ πœ™)πœŒπ‘ Ξ˜π‘  + 2(1 + 𝑒)(1 β€’ πœ™)2π‘”π‘œπœŒπ‘ Ξ˜π‘  Where [70]:

[

3

π‘”π‘œ = 5 1 β€’

(

1 3

)]

(1 β€’ πœ™) (1 β€’ πœ™)π‘šπ‘Žπ‘₯

β€’1

Granular temperature: 3 1 , , 2Ξ˜π‘  = 2βŒ©π‘’π‘ π‘’π‘ βŒͺ with: 2π‘˜ 0.5 𝑒𝑠, = 𝜁 3 𝜁 is a Gauss distribution random number 0 ≀ 𝜁 ≀ 1.

(

)

60

]2

(38)

ACCEPTED MANUSCRIPT 1

Table 10 - Granular temperature model [74] βˆ‚ βˆ‚π‘‘

2

(πœŒπ‘ (1 β€’ πœ™)Ξ˜π‘ ) + βˆ‡.(1 β€’ πœ™)πœŒπ‘ π‘’π‘ Ξ˜π‘  = 3[( β€’ 𝑝𝑠𝐼 + πœπ‘ ) :βˆ‡π‘’π‘  + βˆ‡.(Ξ“Ξ˜βˆ‡Ξ˜) β€’ 𝛾𝑠 + 𝐷𝑔𝑠 + πœ‘π‘ ]

𝛾𝑠 = 3(1 β€’ 𝑒2)(1 β€’ πœ™)2πœŒπ‘ π‘”0Θ

(

)

4 Θ β€’ βˆ‡.𝑒𝑠 𝑑𝑝 πœ‹

πœ‘π‘  =β€’ 3π›½Ξ˜

( ) |𝑒

π‘‘π‘πœŒπ‘  18πœ‡π‘”

𝐷𝑔𝑠 = 4 Ξ“Ξ˜ =

2

πœ‹Ξ˜ 𝑑2𝜌 𝑝 𝑠

150πœŒπ‘ π‘‘π‘ πœ‹Ξ˜

|2

𝑔 β€’ 𝑒𝑠

[

]

6 Θ 1 + (1 + 𝑒)𝑔0(1 β€’ πœ™) 2 + 2(1 β€’ πœ™)2πœŒπ‘ π‘‘π‘π‘”0(1 + 𝑒) 384(1 + 𝑒)𝑔0 5 πœ‹

2

3

4

5

6

7

8

9

10

11

12

13 61

ACCEPTED MANUSCRIPT Table 11 - Energy conservation Gas phase: βˆ‚((1 β€’ πœ™)πœŒπ‘”π‘π‘π‘”π‘‡π‘”) βˆ‚π‘‘

+ βˆ‡(πœ™πœŒπ‘”π‘’π‘”π‘π‘π‘”π‘‡π‘”) = βˆ‡(πœ†π‘”.βˆ‡π‘‡π‘”) + π΄π‘ β„Žπ‘ , (𝑇𝑔 β€’ 𝑇𝑠) + 𝑆𝑇

𝑔

Solid phase: βˆ‚((1 β€’ πœ™)πœŒπ‘ π‘π‘π‘ π‘‡π‘ ) βˆ‚π‘‘

+ βˆ‡((1 β€’ πœ™)πœŒπ‘ π‘’π‘ π‘π‘π‘ π‘‡π‘ ) = βˆ‡(π‘˜π‘’π‘“π‘“.βˆ‡π‘‡π‘ ) + (βˆ‡π‘žπ‘Ÿ) β€’ π΄π‘ β„Žπ‘ , (𝑇𝑔 β€’ 𝑇𝑠) + 𝑆𝑇

Radiative flux density [75]: βˆ‡π‘žπ‘Ÿ =β€’

16πœŽπ‘‡2

16πœŽπ‘‡3

𝐾

3𝐾

(βˆ‡π‘‡)2 +

(βˆ‡2𝑇)

Thermal dispersion coefficient πœ†π‘” [76]: πœ†π‘” = π‘˜π‘’π‘“π‘“,0 + 0.5 Γ— 𝑑𝑝 Γ— π‘ˆπ‘” Γ— πœŒπ‘” Γ— 𝐢𝑝𝑔 π‘˜π‘’π‘“π‘“,0 = πœ™(π‘˜π‘“ + β„Žπ‘Ÿπ‘£βˆ†π‘™) +

(1 β€’ πœ™)βˆ†π‘™ π‘˜π‘“

1/( 𝑙 + β„Žπ‘Ÿπ‘ ) + 𝑙𝑠/π‘˜π‘  𝑣

Where: 𝑙𝑠 =

( )

2𝑑𝑝 3

; 𝑙𝑣 = 0.151912βˆ†π‘™

π‘˜π‘“

π‘˜π‘Žπ‘–π‘Ÿ

𝑇𝑠

( )( )𝑛;

β„Žπ‘Ÿπ‘  = 0.1952 Γ— 𝑑𝑝

πœ– 2β€’πœ–

100

(

πœ™(1 β€’ πœ–)

; β„Žπ‘Ÿπ‘£ = 0.1952 1 + 2(1 β€’ πœ™)πœ–

𝑇𝑠

) β€’ 1( )𝑛 100

βˆ†π‘™ = 0.96795 𝑑𝑝(1 β€’ πœ™) β€’ 1/3;

(

π‘˜π‘Žπ‘–π‘Ÿ(𝑇𝑔) = 5.66 Γ— 10 β€’ 5𝑇𝑔 + 1.1 Γ— 10 β€’ 2; 𝑛 = 1.93 + 0.67exp β€’ Source term: 𝑆𝑇𝑔 = β€’ 𝑅𝑒𝑣𝑝 Γ— β„Žπ‘“,𝐢𝑂 𝑀𝐢𝑂

𝑆𝑇𝑠 = β€’ 𝑅𝑒𝑣𝑝 Γ— 𝑀

𝐢𝑂2

Γ— [β„Žπ‘“,𝐢𝑂2 β€’ β„Žπ‘“,𝐢𝑂] Γ—

[

π‘ŒπΆπ‘‚ 2

]

β€’1

1

2

3

4

5

62

(π‘šπ‘”. β€’ 0.39) 0.054

)

𝑠

ACCEPTED MANUSCRIPT Table 12 - Species conservation Gas phase: βˆ‚(πœ™πœŒπ‘”π‘Œπ‘–π‘”) βˆ‚π‘‘

+ βˆ‡(πœ™πœŒπ‘”π‘’π‘”π‘Œπ‘–π‘”) = βˆ‡(π·π‘–π‘”βˆ‡(πœ™πœŒπ‘”π‘Œπ‘–π‘”)) + π‘†π‘Œ

𝑔

Solid phase: βˆ‚((1 β€’ πœ™)πœŒπ‘ π‘Œπ‘–π‘ ) βˆ‚π‘‘

+ βˆ‡((1 β€’ πœ™)πœŒπ‘ π‘’π‘ π‘Œπ‘–π‘ ) = π‘†π‘Œ

𝑠

1

2

3

4

5

6

7

8

9

10

11

12

13

63

ACCEPTED MANUSCRIPT 1

Table 13 - Boundary conditions Inlet

𝑃 = 𝑃𝑖𝑛 ; 𝑇𝑔 = 𝑇𝑖𝑛,𝑔 ; 𝑒 = 𝑒𝑖𝑛 ; π‘Œπ‘– = π‘Œπ‘–, π‘Žπ‘–π‘Ÿ

Outlet βˆ‚π‘‡π‘  βˆ‚π‘¦

βˆ‚π‘’ βˆ‚π‘‡π‘” βˆ‚π‘‡π‘  βˆ‚π‘Œπ‘– βˆ‚π‘ƒ = = = = =0 βˆ‚π‘¦ βˆ‚π‘¦ βˆ‚π‘¦ βˆ‚π‘¦ βˆ‚π‘¦

=0

2

3

4

5

6

7

8

9

10

11

12

13

14

15

64

ACCEPTED MANUSCRIPT 1 2

Table 14 - Mesh characteristics Mesh type

Number of cells

Coarse

100,000

Medium

200,000

Fine

250,000

3

4

65