Predicting the effect of random factors to the combustion of oily solid waste using fluent

The oily solid waste, which was produced from the oil mine exploiting action in Vietnam, was to be burnt in the primary chamber of the two stable chambers incinerator. The combusting gases in the primary chamber were sampled by the Testo 350 and Testo 360 gas analysers. The recorded gas compositions in the primary chamber were to be employed as a material for the prediction in this paper.

Trang 1

Electronics and Automation

L A Kien “Predicting the effect of random factors …”

104

PREDICTING THE EFFECT OF RANDOM FACTORS TO THE

COMBUSTION OF OILY SOLID WASTE USING FLUENT

Le Anh Kien*

Abstract: The oily solid waste, which was produced from the oil mine exploiting

action in Vietnam, was to be burnt in the primary chamber of the two stable

chambers incinerator The combusting gases in the primary chamber were sampled

by the Testo 350 and Testo 360 gas analysers The recorded gas compositions in the

primary chamber were to be employed as a material for the prediction in this paper

The combusting gas properties were hugely varying during the progressing of

incineration This paper discusses the effect of random factors of gases species and

temperature at the inlet to the combustion in the two stable chambers incinerator

The temperature, rate of reaction of CH 4 and CO species were monitored in the

entirely computing domain A mathematical model of the finite volume method

(FVM) was employed to predict the three dimensional reacting flows within these

modified incinerator designs Thek- submodel has been employed for turbulence,

together with a combustion model of Magnussen and Hjertager A Lagrangian

model was used to estimate the residence times for different cases The user-defined

functions (UDFs) were created to define the boundary conditions for the modelling

The FLUENT of version 6.2 was used as a solver

Keywords: Two stable chambers incinerator, Oily solid waste, Incinerator modelling, Fluent

1 INTRODUCTION

Currently, due to the demand of economic development in Vietnam, the export

products, which will bring the foreign currency, were especially concerned The oil

exploiting industry is one of the most important industries in Vietnam For past several

years, the quantity of the exploiting product was continuously increasing This caused the

oily solid waste quantity increasing rapidly Because of the specific properties of this solid

waste, it could not be disposed together with municipal solid waste and industrial solid

waste This caused a difficulty for management and disposal work The thermal

degradation method is recently taken into account as the best method for the disposal of

this type of waste Most of the incinerators that were employed to incinerate oily solid

waste are two stable chambers incinerators

In the general incineration science, many researchers have shown that the combustion

progress of solid waste, biomass was taken place with three main stages The first stage is

the water vaporisation, which released moisture in solid phase The second stage is the

devolatilisation that released volatile matter The third stage is the char burning that

consumed char Depending on the type and properties of material, these stages can occur

succeeding and overlapping together [1-5]

In this study, the first stage was considered as taking place within 5 min after feeding

the oily solid waste to the incinerator However, this stage has not only released most of

moisture but has released vaporisable hydrocarbon in solid waste as well Furthermore,

due to the large amount of mass of solid waste was released shortly, it caused the velocity

of gas phase, which was transformed from solid phase, was high and the combustion

reactions took place very slow and minor This stage consumed about 30% of initial

feeding mass The second stage took place within 15 to 25 min from the first stage This

stage devolatilised about 50% of initial mass to form char and tar The volatilised products

mainly comprised oil in waste and cracking products from solid waste In this stage, owing

Trang 2

to the combustion of volatilised matter, the temperature in the incinerator was increasing The temperature and the devolatilisation of matter in this stage were to be considered as the highest and major The third stage took place within 30 to 50 min from end of the second stage, consumed about 10% of initial mass of solid waste In this stage, the process was the burning up of char and forming ash The chemical reaction was the combustion of char with air to release carbon monoxide (CO) and carbon dioxide (CO2) The ratio of CO and CO2 was depending on the incidence of oxygen [6, 7] In this stage, due to the combustion rate was low and the air supplied was not reducing, the temperature of the incinerator, therefore, was dropping down toward the end of the incineration process

For the above reasons, it has been assumed that the gas products from the oily solid waste combustion were quite different to the other materials Therefore, in order to have a master view about the environmental impacted assessment during the processing of oily solid waste, the mathematical modelling technique have been employed to survey concentration of the pollutants at the outlet of the rig and the technical requirements for the incineration performance Furthermore, due to the properties of incinerated products were varied in a wide range during the combustion taking place, it also affected to the burning efficiency in the secondary chamber Therefore, it influenced to the environmental atmosphere around the stack

The combustion in the two stable chambers incinerator was employed in the research of Jangsawang et al (2005) This research showed the influence of secondary air on the combustion [8] Rogaume et al (2002) presented the effects of different airflows on the formation of pollutants in the incineration on fixed bed reactor [9] There have also been some researchers performed their studies on the changing of condition reaction, fuel properties, or types of initial materials [10-14] They all contributed to the understanding

of combustion in different apparatus, different operating conditions, and different types of material However, there were no studies carried out on the effect of random factors at the inlet in the two stable chambers incinerator whilst the combustion taking place This paper would contribute to the understanding of the effects of species concentration variation and temperature to the prediction in the secondary chamber of the two stable chambers incinerator

2 EXPERIMENT 2.1 Material for modelling and simulation

The research was conducted on the pilot incinerator in Institute for Tropical Technology and Environment (ITE) as shown:

Figure 1.The pilot incinerator (1) Primary chamber;(2) Secondary chamber;

(3) Outlet pipe that the flue gas escaped; (4) Bottom of the incinerator that the primary air entered from; (5) Connected pipe between two chambers

1

4

5

Trang 3

was 5kg The primary air was set to be a constant of 936kg/m

incinerator from the bottom The gases in the prima

chamber via a connected pipe The online gas analyser Testo 360 (Germany) was

conducted to measure the components of incinerated products The probe of the Testo360

was posited at the connected pipe The basis dimensions

capacity of primary chamber was 54 litres The capacity of secondary chamber was 72

litres The dimension of connected pipe and outlet were 20 centimetres The mean values

of temperature and species mass fraction inlet w

2.2

(x,

pressure

temperature,

kinetic energy

concentration, and turbulence are given below

2.2.1 Continuity equation

so

equation)

continuity In the above equation,

this research, the source term is not taken into account

2.2.2 Momentum equations

written in the following form

106

At the primary chamber, oily solid fuel was fed as a batch The capacity for each batch

2.2 Mathematical description of the process

The

,y,z

pressure

temperature,

kinetic energy

The equations for continuity, velocity components, temperature, chemical

The pressure variable is associated with the

so-called

equation)

In Cartesian coordinates, the u

where,

P is the static pressure

ij

The stress tensor

Time

(min)

< 3

3

11

Mean

Mathematical description of the process

The independent variables are the three components of the Cartesian coordinate system,

z) The dependent variables are the velocity

pressure

temperature,

kinetic energy

called

equation)

where,

ij is the stress tensor (described below),

The stress tensor

Time

(min)

< 3

3-10

11-35

Mean

independent variables are the three components of the Cartesian coordinate system,

) The dependent variables are the velocity

pressure P

temperature,

kinetic energy

called pressure

equation) [15]

where,

is the stress tensor (described below),

The stress tensor

Time

(min)

< 3

10

35

Mean

(N/m

temperature, T

kinetic energy

pressure

[15]

The stress tensor

(N/m2

T (K

kinetic energy k (m

t







pressure

[15], which are deduced fr







The stress tensor

2); the species concentration in gas phase,

K); and two characteristics of gas turbulence, namely the turbulence

(m2/s The equations for continuity, velocity components, temperature, chemical

t 



pressure-correction equation, SIMPLE (semi

, which are deduced fr

i

t

u





The stress tensor

Temperature

); the species concentration in gas phase, ); and two characteristics of gas turbulence, namely the turbulence /s2), and its dissipatio

div The pressure variable is associated with the

correction equation, SIMPLE (semi , which are deduced fr





The stress tensor ij is given by:

Table 1.

Temperature

1100

); the species concentration in gas phase, ); and two characteristics of gas turbulence, namely the turbulence ), and its dissipatio

( div  The pressure variable is associated with the

i

x



is given by:

Table 1.

Temperature

(C) 650 1100 900

)

ui

 The pressure variable is associated with the

i

(

is given by:

Table 1

Temperature

C) 650 1100 900

)  The pressure variable is associated with the

iu u



is given by:

Temperature and gas concentrations in primary chamber

Temperature

div The pressure variable is associated with the

continuity In the above equation, ρ

In Cartesian coordinates, the unsteady

j) u

is given by:

Temperature

Dgrad ( div The pressure variable is associated with the

ρ represents the density and u this research, the source term is not taken into account

nsteady

) 

L

CH

1.806 1.5 0.54

); the species concentration in gas phase, ); and two characteristics of gas turbulence, namely the turbulence ), and its dissipation rate,

Dgrad The pressure variable is associated with the

correction equation, SIMPLE (semi , which are deduced from the finite

represents the density and u this research, the source term is not taken into account

nsteady

x

P



A Kien

1.806 1.5 0.54

); the species concentration in gas phase, ); and two characteristics of gas turbulence, namely the turbulence

n rate, The equations for continuity, velocity components, temperature, chemical

Dgrad

correction equation, SIMPLE (semi

om the finite represents the density and u this research, the source term is not taken into account

nsteady-state equations of motion may be conveniently

i

P



Kien

4

1.806 0.54

n rate, The equations for continuity, velocity components, temperature, chemical

)

 The pressure variable is associated with the

state equations of motion may be conveniently

x







Kien “Predicting the effect of random factors

of temperature and species mass fraction inlet were tabulated in Table 1, as:

Mass fraction of species, %

6.405 4.444

5.480

) The dependent variables are the velocity u

n rate, (m The equations for continuity, velocity components, temperature, chemical

The pressure variable is associated with the continuity equation in anticipation of the

j

ij

x



“Predicting the effect of random factors

incinerator from the bottom The gases in the primary chamber flew up to the secondary

ere tabulated in Table 1, as:

Mass fraction of species, % CO

6.405 4.444 5.59

5.480

u i in the ); the species concentration in gas phase, ); and two characteristics of gas turbulence, namely the turbulence

(m3/s The equations for continuity, velocity components, temperature, chemical

continuity equation in anticipation of the correction equation, SIMPLE (semi

om the finite-represents the density and u this research, the source term is not taken into account

ry chamber flew up to the secondary chamber via a connected pipe The online gas analyser Testo 360 (Germany) was

Mass fraction of species, % CO

6.405 4.444 5.59

5.480

in the ); the species concentration in gas phase, ); and two characteristics of gas turbulence, namely the turbulence

/s2)

continuity equation in anticipation of the correction equation, SIMPLE (semi-implicit method for pressure

-difference form of the equation of represents the density and u

was posited at the connected pipe The basis dimensions of the pilot incinerator were: the

in the x

)

continuity equation in anticipation of the implicit method for pressure difference form of the equation of represents the density and u

of the pilot incinerator were: the capacity of primary chamber was 54 litres The capacity of secondary chamber was 72

CO

16.22 20.08 18.67

18.323

x, y

); the species concentration in gas phase, C

); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

continuity equation in anticipation of the implicit method for pressure

difference form of the equation of represents the density and u

Electronics

16.22 20.08 18.67

18.323

y and

C (kmol/m

); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

difference form of the equation of represents the density and u

Electronics

2

16.22 20.08 18.67

18.323

and z

(kmol/m ); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

difference form of the equation of represents the density and ui, the velocity vector In

Electronics

was 5kg The primary air was set to be a constant of 936kg/m2h, and entered the

z directions (m/s); the

(kmol/m ); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

continuity equation in anticipation of the implicit method for pressure difference form of the equation of

, the velocity vector In

Electronics

h, and entered the

O

4.82 1.72 5.35

3.963

directions (m/s); the (kmol/m3); the gas phase ); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

h, and entered the

O 2

4.82 1.72 5.35

3.963

directions (m/s); the ); the gas phase ); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

and Automation

h, and entered the

directions (m/s); the ); the gas phase ); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical

difference form of the equation of

and Automation

h, and entered the

H

10.32 1.03 3.61

4.987

directions (m/s); the ); the gas phase ); and two characteristics of gas turbulence, namely the turbulence The equations for continuity, velocity components, temperature, chemical-species

continuity equation in anticipation of the implicit method for pressure-difference form of the equation of

and Automation

h, and entered the

H 2 O

10.32 1.03 3.61

4.987

directions (m/s); the ); the gas phase ); and two characteristics of gas turbulence, namely the turbulence

species

continuity equation in anticipation of the

-linked difference form of the equation of

and Automation

“Predicting the effect of random factors …”

h, and entered the

Temperature and gas concentrations in primary chamber.

O

10.32 1.03 3.61

4.987

species

(1) continuity equation in anticipation of the

linked difference form of the equation of

(2)

and Automation

”

h, and entered the

species

(1) continuity equation in anticipation of the

linked difference form of the equation of

(2)

Trang 4

























x 3

2 ) u ( x x

u

i i

T i i i

i

2.2.3 Energy equation

An energy balance for the system with turbulence leads to the following equation:































































i h

t

i i

h x

) h u ( x t

) h (





h

i i

Where, Yi is the mass fraction of species j, and



 T

T j p j ref

dT c

where, Tref is 298.15 K

2.2.4 Species transport equations

The species transport equations takes the following generation form:

i i

i

x

J ) Y u ( x t

) Y (





















(7)

where, Ri is the net rate of production of species i by chemical reaction is given by the smaller of the two expressions below:



















i r

R R

i r r i

M a

Y min k A M a







j j j

P P i

i r i

M a

Y k AB M a

where, ai and aj are stoichiometric coefficients for reactant i and product j in reaction r

Mi and Mj are the molecular weight of reactant species i and product species j in

YP is the mass fraction of any product species, P

YR is the mass fraction of a particular reactant, R

A is an empirical constant equal to 4.0

B is an empirical constant equal to 0.5

The mass diffusion is computed as in the following form:

i i

t

t i i

x

Y Sc D J



















here, Di is the diffusion coefficient for species i in the mixture

Sct is the turbulent Schmidt number

t is the turbulent viscosity

Trang 5

of a turbulent velocity scale and a length scale

2.2.5 Turbulence model (k

following transport equations:

turbulent kinetic energy and

2.2.6 Radiation model

medium at position r in the direction s is

direction vector, path length, absorption coefficient, refractive index, scattering

coefficient, Stefan

position r and direction s), local tempe

2.2.6.1 Discrete transfer radiation model (DTRM)

radiation leaving the surface element in a certain range of solid angles c

approximated by a single ray The discrete model has some advantages It is a relatively

simple model The accuracy of the model can be increased by increasing the number of

rays, and it applies to a wide range of optical thickness, as well as some d

The model assumes that all surfaces are diffuse The effect of scattering is not included

The implementation assumes grey radiation, and solving a problem with a large number of

rays is CPU

Stefan

estimated as

108

The turbulent viscosity

where C

The turbulence kinetic energy, k, and its rate of dissipation,

where

The radiative transfer equation (RTE) for an absorbing, emitting, and scattering

where r, S, S’, s, a, n,

coefficient, Stefan

The main assumption of the DTRM (the so

rays is CPU

The equation for the change of radiant i

where a, I, T, and

Stefan

estimated as

where C

where

coefficient, Stefan

rays is CPU

where a, I, T, and

Stefan–Boltzmann constant respectively If a is constant along the ray, then

estimated as

where C

where C

coefficient, Stefan

rays is CPU

where a, I, T, and

Boltzmann constant respectively If a is constant along the ray, then

estimated as



where C is a constant



C1, C

dI

coefficient, Stefan

rays is CPU-intensive

ds

dI

where a, I, T, and

estimated as



t

is a constant



 i x

xi



C2,

ds

r dI

coefficient, Stefan

intensive

 ds

dI

where a, I, T, and





is a constant

 (

(

, C3, turbulent kinetic energy and

ds

) s , r

coefficient, Stefan–

intensive

 a where a, I, T, and

C

is a constant

 uik

ui



, σk

 )

–Boltzmann constant, total radiation intensity (which depends on position r and direction s), local tempe

intensive

 I where a, I, T, and 





2

k

is a constant

 ) k

) 



k and turbulent kinetic energy and

a (

where r, S, S’, s, a, n, 

Boltzmann constant, total radiation intensity (which depends on position r and direction s), local tempe

a.

is gas absorption coefficient, intensity, gas local temperature and Boltzmann constant respectively If a is constant along the ray, then

The turbulent viscosity t



2

2.2.5 Turbulence model (k–





x





and σ turbulent kinetic energy and G





s,  direction vector, path length, absorption coefficient, refractive index, scattering



 T

is gas absorption coefficient, intensity, gas local temperature and Boltzmann constant respectively If a is constant along the ray, then

is obtained by assuming that it is proportional to the product

model)



 i x

xi  





σ represent empirical constants,

Gb the generation of turbulence due to the buoyancy

s).

, I, T, direction vector, path length, absorption coefficient, refractive index, scattering

4

T

model)























represent empirical constants, the generation of turbulence due to the buoyancy

r I ).

, I, T, direction vector, path length, absorption coefficient, refractive index, scattering

model)





 k t

t





) S , r

, I, T,  direction vector, path length, absorption coefficient, refractive index, scattering

Boltzmann constant, total radiation intensity (which depends on position r and direction s), local temperature, phase function and solid angle respectively

L



 i x k

xi  









medium at position r in the direction s is given as:

 )

, and direction vector, path length, absorption coefficient, refractive index, scattering

Boltzmann constant, total radiation intensity (which depends on

rature, phase function and solid angle respectively

A Kien

















given as:

n a

, and  direction vector, path length, absorption coefficient, refractive index, scattering

The equation for the change of radiant intensity, dI, along a path, ds, can be written as:

Kien

 G

C1

given as:

 2 n

’ position vector, direction vector, scattering direction vector, path length, absorption coefficient, refractive index, scattering

ntensity, dI, along a path, ds, can be written as:

Kien “Predicting the effect of random factors

 k G

( k



given as:



 T

’ position vector, direction vector, scattering direction vector, path length, absorption coefficient, refractive index, scattering

The main assumption of the DTRM (the

so-radiation leaving the surface element in a certain range of solid angles c

 G

G ( k





4 T

-called the discrete model) is that the radiation leaving the surface element in a certain range of solid angles c

 h G

k 



 4

called the discrete model) is that the radiation leaving the surface element in a certain range of solid angles c





1 ( 







 4 0

s 4



C



represent empirical constants, G

the generation of turbulence due to the buoyancy





0 r I

Electronics

The turbulence kinetic energy, k, and its rate of dissipation, 

G )

C3

Gk the rate of production of the generation of turbulence due to the buoyancy

S , r

Electronics

, are obtained from the

Gb

the rate of production of the generation of turbulence due to the buoyancy

 ).

' S

Electronics

, are obtained from the

) 

 S (

Electronics

C2

 S S

k 2





).

' S

rays, and it applies to a wide range of optical thickness, as well as some disadvantages

and Automation

k

2



 d ).

isadvantages

is gas absorption coefficient, intensity, gas local temperature and Boltzmann constant respectively If a is constant along the ray, then I(

and Automation

'

isadvantages

is gas absorption coefficient, intensity, gas local temperature and

(s) can be

and Automation

(11)

(12)

(13) the rate of production of

(14)

called the discrete model) is that the radiation leaving the surface element in a certain range of solid angles can be

isadvantages

(15)

can be

and Automation

“Predicting the effect of random factors …”

(11)

(12)

(14)

called the discrete model) is that the

an be approximated by a single ray The discrete model has some advantages It is a relatively

isadvantages

(15)

can be

and Automation

”

(11)

(12)

(14)

called the discrete model) is that the

an be approximated by a single ray The discrete model has some advantages It is a relatively

isadvantages

(15)

can be

Trang 6

] as exp[

I ]) s a exp[

1 (

T ) s

4











where I0 is the radiant intensity at the start of the incremental path, which is determined

by the appropriate boundary condition The energy source in the fluid due to radiation is then computed by summing the change in intensity along the path of each ray that is traced through the fluid control volume

The radiation intensity approaching a point on a wall surface is integrated to yield the incident radiation heat flux, qin, as











 0 n S in

where  is the hemispherical solid angle, Iin is the intensity of the incoming ray, s is the ray direction vector, n is the normal pointing out of the domain The net radiation heat flux from the surface, qout, is then computed as a sum of the reflected portion of qin and the emissive power of the surface

4 w w in w

where Tw is the surface temperature of the point P on the surface and w is the wall emissivity



 out 0

q

2.2.6.2 Discrete ordinates radiation model (DO)

The discrete ordinates (DO) radiation model solves the radiative transfer equation (RTE) for a finite number of discrete solid angles, each associated with a vector direction

s fixed in the global Cartesian system (x,y,z) The implementation in FLUENT uses a conservative variant of the discrete ordinates model called the finite-volume scheme[16, 17], and its extension to unstructured meshes [18]

The DO model considers the radiative transfer equation (RTE) in the direction s as a field equation Thus, equation may be written as:

' d ) ' s , s ( ).

' s , r I 4

T n a ) s , r I ).

a ( ) s ) s , r I (

4

0 s 4 2



















(20)

The RTE for the spectral intensity I r,s) can be written as:

' d ) ' s , s ( ).

' s , r I 4 I n a ) s , r I ).

a ( ) s ) s , r I

.(

4

0

s b 2

















Here  is the wavelength, a is the spectral absorption coefficient, and Ib is the black body intensity given by the Planck function The scattering coefficient, the scattering phase function, and the refractive index n are assumed independent of wavelength

The non-gray DO implementation divides the radiation spectrum into N wavelength bands,which need not be contiguous or equal in extent The wavelength intervals are supplied, and correspond to values in vacuum (n=1) The RTE is integrated over each wavelength interval, resulting in transport equations for the quantity I, the radiant

Trang 7

110

energy contained in the wavelength band  The behaviour in each band is assumed gray

The black body emission in the wavelength band per unit solid angle is written as:

(22) where F(0  nT) is the fraction of radiant energy emitted by a black body [19] in the

wavelength interval from 0 to  at temperature T in a medium of refractive index n 1 and

2 are the wavelength boundaries of the band

The total intensity I r , s ) in each direction sat position r is computed using

k

k ).

s , r I ) s , r I

where the summation is over the wavelength bands

Boundary conditions for the non-gray DO model are applied on a band basis The

treatment within a band is the same as that for the gray DO model

2.2.7 Chemical reaction model

The reaction rates that appear as source terms in the species transport equations are

computed from Arrhenius rate expressions, from the eddy dissipation model of Magnussen

and Hjertager [20] The formation of CO, CO2, H2O from CmHn, CO in the fuel inlet are

employed The CmHn can be mainly considered as CH4 The difference in reaction rates

can be taken into account using a two-step model, which is only slightly more complicated

than the single-step model It is capable of separating the relatively slow oxidation of CO

to CO2 from the more rapid oxidation of the CH4 to CO and H2O, as followings:

NOx emission consists of mostly nitric oxide (NO), less significant are nitrogen oxide

(NO2) and nitrous oxide (N2O) The formation of NO in combusted gases via the oxidation

of atmospheric nitrogen can be expressed in terms of the overall reaction, which is highly

endothermic As a result, the reaction of N2 with O2 is too slow to account for significant

NO formation [21] Free radicals, produced in flames via the dissociation of O2, attack

nitrogen molecules and begin a simple chain mechanism, which was first postulated by

Zeldovich et al [22], that is,

N2 + O ↔ NO + N

N + O2 ↔ NO + O

2.3 Solving mathematical equations

The segregated solver is the solution algorithm Using this approach, the governing

equations are solved sequentially Each iteration consists of the steps illustrated as

following:

1 Fluid properties are updated, based on the current solution

2 The ui momentum equations are each solved in turn using current values for

pressure and face mass fluxes, in order to update the velocity field

3 Since the velocities obtained in Step 2 may not satisfy the continuity equation

locally, a Poisson-type equation for the pressure correction is derived from the

continuity equation and the linearised momentum equations This pressure

correction equation is then solved to obtain the necessary corrections to the















4 2 1 2

T n )].

T n 0 ( F ) T n 0 ( F

[

Trang 8

pressure and velocity fields and the face mass fluxes such that continuity is satisfied

4 Where appropriate, equations for scalars such as turbulence, energy, species, and radiation are solved using the previously updated values of the other variables

5 When interphase coupling is to be included, the source terms in the appropriate continuous phase equations may be updated with a discrete phase trajectory calculation

6 A check for convergence of the equation set is made

These steps are continued until the convergence criteria are met

2.4 Methodology in the study

Due to the above analysed reasons, the steps to study in this research are:

 Building a random function to simulate the inlet data

 Employing the solver to work out the problem

 Estimating the obtaining results in cases of different data inlet

To solve this problem, the user-defined functions (UDFs) were created In this study, the predictions have been carried out with the created UDFs to simulate the inlet data, which was similar to the experimental results

3 RESULTS AND DISCUSSION

The modelling results in this study gave the understanding about the effects of random factors of temperature and species concentrations at the inlet to the obtained results at the outlet These obtained results have also contributed to the understanding about the transformation of matter and energy while the combustion was taking place in the incinerator

Figure 2a,b,c,d show the temperature profiles in the computing domain at the time 60s, 300s, 540s and 720s The gases from the primary chamber were flowing up to the secondary chamber via the connected pipe The combustion reaction took place in a region nearby the connected pipe The released energy from the reactions cooked the secondary chamber The heat transfer within the chamber took place by convection and radiation At the beginning of the process, the temperature at the outlet was low and it was rising up after 60s

Trang 9

112

Collected data at the inlet and outlet showed that although the temperature at the inlet

was approximately in all the time, the temperature at the outlet was different The

temperature values at the inlet and outlet were 1156.3  1.4x10-5 K and 1171.8  60.6 K,

respectively The temperature values at the inlet varied in a small range also caused a

hugely changing to the data outlet

The species transport has also been affected by the random factors at the inlet Figure

3a,b,c show the contours of CH4 mass fractions at the time of 60s, 300s, and 540s The

species CH4 reacted with oxygen at the region nearby the connected pipe Due to the

reaction rate is too quick, most of the CH4 species were consumed in this region The

unreacted CH4 species transported within the space of the chamber

Figure 3c CH 4 mass fraction profile at 540s

Trang 10

The mass fraction value of CH4 at the inlet and outlet were 0.01282  1.0x10-8 and

0.00804  3.5x10-4 respectively The values at the outlet after 540s were very small and

similar In spite of the variance at the inlet data was infinitesimal, the mean and variance at

the outlet were varied in a relatively wide range

Figure 4c CO mass fraction profile at 540s.

Figure 4a,b,c present the contour of CO mass fraction in the computing domain The

inlet value of CO fraction was 0.05480  1.0x10-8 and the value at the outlet was 0.01762

 4.5x10-3 Similar to the values of CH4, the CO fraction inlet was very tiny whilst the

variance value outlet was fluctuating within 25% of mean value

The values outlet of CO and CH4 fraction were taken into account as still high,

comparing to the values inlet This is because of the oxygen fraction was not sufficient for

the reactions to take place continuously Therefore, the untransformed CO and CH4

fractions appeared at the outlet as spells This caused the mean and variance values at the

outlet were high

4 CONCLUSION

The combustion of oily solid waste was considered as very complicated During the

combustion process, the concentration of gas constituents and properties of flue gas had

been changed rapidly, it affected to the transformation of species within the incinerator

significantly Therefore, in order to estimate the effects of random factors to the process

efficiency, the mean and variance of temperature and species fraction values at the inlet

and outlet were taken into account In this study, in order to monitor the effect of random

factors easily, the secondary air was not taken into account

Định dạng
Số trang	12
Dung lượng	575,3 KB