INVESTIGATIONS ON ASH AGGLOMERATION IN CFBC BOILER … · 2018. 8. 8. · advantages it has ash-related operational problems, such as slagging, fouling, and corrosion. A 2-Dimensional

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 7, July 2018, pp. 1464–1476, Article ID: IJMET_09_07_156

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=7

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

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INVESTIGATIONS ON ASH AGGLOMERATION

IN CFBC BOILER USING COMPUTATIONAL

FLUID DYNAMICS

Suresh K. Badholiya

Research Scholar, RGPV, Bhopal (India)

S.K. Pradhan

Associate Professor, Mechanical Engineering, NITTTR, Bhopal (India)

Anil Kothari

Professor & Head, RGPV, Bhopal (India)

ABSTRACT

A CFD analysis method that is based on the combustion of coal and the slagging

route has been developed and used for predicting the ash deposition tendencies of coal

combustion in utility boilers supported by the data collected from captive power station.

In circulating fluidized bed combustion boilers ash agglomeration may contribute to

deposit formation in the cyclone, return leg and post cyclone flue gas channel. Rapid

ash agglomeration can lead to heavy agglomerate formation, which may finally prevent

circulation in dense phase areas (such as seal pot, return leg and cyclone). Hence

understanding the ash agglomeration behavior before the coal is used, would be

desirable for avoiding ash deposit related problems. Generally, Indian lignite coal used

in heat and power production and despite their environmental and economical

advantages it has ash-related operational problems, such as slagging, fouling, and

corrosion.

A 2-Dimensional model of Circulating Fluidised Bed Combustion (CFBC) Boiler

with solid separator (cyclone) using ANSYS Fluent Computational Fluid Dynamics

(CFD) module is developed in this study. The simulations are done using different

approaches related to the flow behaviour through the CFB system. This study is more

concerned about finding the location of formation of ash agglomeration and

temperature of these areas. During analysis distribution of static temperature, static

pressure, volume fraction of ash and mass imbalance of coal are studied and prediction

of ash agglomeration sites is performed. It is observed that the unburned coal particle,

ash particle and flue gases from the cyclones goes to the back-pass of the boiler and the

bed particles are re-circulated to the combustion chamber through cyclone return leg.

Moreover, the Cyclone return leg is most prominent area for coal ash agglomeration

due to pressure drop and velocity drop. CFD modelling is used in locating the ash

Investigations on Ash Agglomeration In CFBC Boiler Using Computational Fluid Dynamics


deposition region in the Circulating fluidized bed combustors using Indian lignite coal

which is frequently used in heat and power production.

Key words: CFBC boiler, CFD Modelling, Ash Agglomeration, Coal Combustion,

Contours of Static Temperature and Mass Imbalance.

Cite this Article: Suresh K. Badholiya, S.K. Pradhan and Anil Kothari, Investigations

on Ash Agglomeration In CFBC Boiler Using Computational Fluid Dynamics,

International Journal of Mechanical Engineering and Technology, 9(7), 2018, pp. 1464–

1476.

http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=7

1. INTRODUCTION

Fluidized bed combustion is considered to be a suitable technology for burning a wide range of

fuels. A particular advantage is its ability to maintain combustion temperatures 800–900OC.

below ash fusion point and this also leads to lower nitrogen oxides emission. In addition, the

introduction of limestone sorbent offers opportunity for in-bed capture of sulfur dioxide

generated from the combustion process. Therefore, fluidized bed combustion has been

increasingly used by industries and utilities as an effective and environmentally acceptable

means for producing steam for process heat and electricity generation. In a fluidized bed

combustor, the suspension of bed particles can consist of particles of combusting coal, coal ash,

fluidizing sand, limestone, calcium oxide and calcium sulfate. The proper fluidization of these

particles needs to be maintained in order to stabilize the furnace operation. Under certain

conditions, however, agglomeration of bed particles can occur. In the most serious case, rapid

sintering can lead to severe agglomerate formation, causing defluidization and subsequent shut

down of the combustor [1–3]. Brown et al. [4] suggested that agglomeration occurred as a result

of the interaction between the sticky ash with other bed particles.

In CFBC boilers ash sintering may contribute to deposit formation in the cyclone, return leg

and post cyclone flue gas channel. In some cases, rapid sintering can lead to heavy agglomerate

formation, which may finally inhibit circulation in dense phase areas (such as seal pot). Hence

understanding the agglomeration behavior before the fuel is used, would be desirable for

minimizing problems.

CFBC boiler consistently deteriorated from a major operational inconvenience caused by

the ash agglomeration, which is very much detrimental and often undergo to the close down of

the plant. Several researchers observed ash agglomeration related issue in their studies with

using different feed coal [5]. Granting all this the main reason that caused the severe ash

sintering characteristics have been investigated widely, the broad perceptive and prediction of

the particle collision and sticking activity, and the importance of the respective ash

agglomeration mechanism on the ash deposition formation or growth is still inadequate [6]. Ash

agglomeration is interrelated with fusion and phase transition of the ash particles during

combustion, come up when particle temperatures are high enough to invoke partial melting [7].

The application of CFD modeling to study combustion and ash agglomeration phenomenon

is in development stage and it is immature field to study. In this paper an overview of CFD

modeling to study combustion and ash agglomeration possibilities in circulating fluidized bed

systems is presented. The common mathematical equations used for studying combustion are

explained and qualitative/quantitative information is presented.

Suresh K. Badholiya, S.K. Pradhan and Anil Kothari


2. EXISTING RESEARCH EFFORTS

Various related literature such as transactions, proceeding of various national and international

conferences and journals which are available on Google scholar, Elsevier, IEEE, Science direct

etc. are reviewed and the contribution of researchers in the area of CFBC boilers, Ash

agglomeration and related issues are categorised in following three groups:

• Literature related to experimental investigations of ash agglomeration.

• Literature related to modelling of ash agglomeration

• Literature related to removal techniques for ash agglomeration

Skrifvars et al. 1997 [8] tested deposit sample collected from the cyclone inlet and from two

different places in the convective path and found generated sulphur is the main constituents of

deposit formation. Yan et al. 2003 [9] investigated the bed agglomeration in fluidized bed by

instrumental approaches (i.e. XRF, SEM, XRD, and ICP-AES). Bartels et al. 2008 [10] found

Chemical transformations play a vital role during the agglomeration in high-temperature solid

fuel conversion processes. Lin C.L. et al. 2011 [11] inspected the effects of four particle size

distribution PSDs (narrow, Gaussian, binary and flat) on agglomeration and defluidization.

Matjie et al 2011 [12] developed QEMSCAN technique to identify, map and perform a range

of image analysis operations on the minerals and other phases in coals, coal ashes and other

mineral products. Palaniswamy et al. 2013 [13] experimentally explained the effect of limestone

and its grain size in clogging/blocking of cyclone and tough deposits in second pass of CFB

boiler during combustion of high sulfur lignite with high ash content (20 to 30%) in CFB units

in Giral, Rajasthan a state of India. Ma et al. 2014 [14] analyzed agglomerates samples on the

basis of SEM/EDX and XRD to find out ash agglomeration propensity. To minimize ash

agglomeration effects of four additives (CaCO3, Al2O3, Fe2O3 and Kaolin) analyzed. Schimpke

et.al. 2017 [15] Performed Cold compression strength (CCS) tests were done on ashes of three

different coals to determine their initial sintering temperature (IST).

According to the available literature, the hydrodynamic structure of circulating fluidized

beds is generally analyzed using mathematical modelling and experimentation but these

analyses in today’s conditions are very difficult or impossible because of excessive turbulence,

unstable and two-phase flow characteristics of the bed and critical issues like ash

agglomeration. Therefore, the most effective way to do this is the use simulation approach.

Another critical issue is to predict the ash agglomeration location and identifying causing

parameters. The Ash agglomeration offers a number of characteristics such as reduce rate of

reaction (De fluidization), blockage of primary flow, lower portion of cyclone, stand pipe of

cyclone, loop seal and return leg. Only a limited number of studies have investigated the ash

deposition growth through a dynamic CFD simulation Xin Yang et al. [6]. Kaer et al. [16]

developed a dynamic CFD model to predict the ash agglomeration formation and heat transfer

rates and the research focused on straw combustion and studied the ash deposition rate caused

by different sintering mechanisms.

Hence, the objective of the present work is to demonstrate that the Computational fluid

dynamic based simulation approach as a useful tool for studying the ash agglomeration location

and identifying causing parameters for CFBC applications.



3. MATHEMATICAL DESCRIPTION OF CFD MODELS IN CFB UNITS

3.1. Primary Equations

The heading equations of fluid flow have formulated many hypotheses to simplify the result of

the equation [17-19]:-

• The mass of fluid is conserved.

• (Newton’s second law) the rate of change of momentum equals the sum of the forces on a fluid

particle.

• (First law of thermodynamics) the rate of change of energy is equal to the sum of the rate of

heat addition and the rate of work done on a fluid particle.

The basic equation can be summarized as

3.2. Mass Conservation

The mass balance for a fluid element or the continuity equation states that the rate of increase

of mass in a fluid element equals the net rate of flow of mass into the fluid element.

The continuity equation can be express as follows:

��

��+

�(��)

�+

�(�)

��+

�(��)

� = 0 3.1

Where ρ is density, t is time, u, v, w are velocity components in x, y and z respectively

3.3. Momentum Equation

Newton’s second law states that “the rate of change of momentum of a fluid particle equals the

sum of the forces acting on the particle.” [20]

Applying this to a fluid passing through an infinitesimal, fixed control volume yields the

following equations:-

• The x-component of the momentum equation:

��

��=

�(�� )

�+

��

��+

��

� + �� 3.2.a

• The y-component of the momentum equation :

��

��=

��

�+

�(�� )

��+

��

� + �� 3.2.b

• The z-component of the momentum equation :

��

��=

��

�+

��

��+

�(�� )

� + �� 3.2.c

Where, ρ is static pressure, τ is viscous stress, τij is viscous stress component acts in the j-

direction on the surface normal to i-direction.

3.4. Energy Equation

The energy equation is derived from the first law of thermodynamics, which states that “the

rate of change of energy of a fluid particle is equal to the rate of heat addition to the fluid particle

plus the rate of work done on the particle.”[19,20]

�(��)

��+ �� (��) = 0 = −��(!�) + ∅ + �� (# $%&� ') + �� 3.3

Φ Dissipation function represent the long stress term.



3.5. General Transport Equation

It is clear that there are significant commonalities between the various equations. If a general

variable Φ is introduced, the conservative form of all fluid flow equations can usefully be

written in the following form: [20]

�(�∅)

��+ �� (�∅�) = �� (Γ $%&� ∅) + �∅ 3.4

The equation 3.9 is the so-called transport equation of property ϕ.[19]

4. DIMENSIONAL SPECIFICATION AND MODELLING

Data related to Geometry and running parameters is collected from industries who are using

CFBC boiler for power generation like HEG Mandideep with capacity of 32 MW (16 x 2 = 32

MW), BLA Thermal Power Plant Gadarwara having capacity of 135 MW (45 x 3 = 135 MW),

Maihar cement power plant Maihar with capacity of 31.2 MW (15.7 x 2 = 31.2 MW), Vikram

Cement power plant Neemuch having capacity of 46 MW (23 x 2 = 46 MW).

It has captive power plant of 46 MW having Circulating Fluidized Bed boiler using lignite

coal and pet coke as fuel. Data pertaining to dimensions and relative positions of Boiler Bed,

Combustor, cyclone, standpipe and return leg is collected from the plant.

The main purposes of the present model is to create a simulated environment in which how

combustion takes place in CFBC boiler can be analyze. The coal and ash circulation behavior

in boiler can also be seen using this simulated environment. ANSYS workbench fluent is used

to create the geometry based on the collected data and meshing is performed using ICEM CFD.

Figure 1 Schematic diagram & meshing of CFBC boiler

In Figure 1 showing geometrical model of CFBC boiler with cyclone is prepared using

ANSYS workbench. The model is developed using actual dimensions collected from M/s.

UltraTech Cement Limited boiler.

The grids are selected for all the meshes for doing CFD analysis. As the CFBC boiler for

which analysis is carried out, quadrate type mesh is selected. This specifies that the mesh is

composed primarily of quadrate mesh elements. The quality of the created mesh is checked and

after convergence 1822 nodes and 1653 element mesh is finalized.

5. NUMERICAL STUDY

The boundary conditions are as equally important as the selection of the proper mathematical

model. At the inlet, velocities of all phases are specified. At the outlet, the pressure was assumed

to be Atmospheric pressure.



Standard k- ε models for CFD analysis is used for the simulation. In present case, Discrete

Phase Model interaction with 10 number of continuous Phase iterations per DPM iteration is

used. Both mean and RMS values are used for contour plots for DPM variables. Maximum

number of steps 500 and step length factor 5 are used for tracking parameters.

Three types of coal are used as fuel in the modelled CFBC boiler for simulation and

observation. viz. Lignite Volatile coal, Barsingsar India-61 Lignite (BIL) and Suruka India

Lignite (SIL). The properties of Lignite Volatile coal are taken from ANSYS database while

for BIL and SIL coal the properties are estimated using coal calculator.

Four parameters viz. coal mass flow rate, coal particle size, Primary air velocity and

Secondary air velocity are taken as input parameters while Bed temperature as performance

parameter. Based on the literature and information gathered from boiler operation industrial

practices three level values have been selected for each input parameter. Therefore 34 = 81

experiments has to be done and observed. Taguchi approach has been used for designing the

experiments with L9 orthogonal array composed of 3 columns and 9 rows, which mean that 9

experiments have been carried out using simulated environment for each type of selected coal.

Using simulated environment, the degree of accuracy of prediction using 9 experiments in

comparison to 81 for Lignite volatile coal has also been verified and it is found that the

percentage error of prediction with two sets is 5.3%. Further, out of these nine representative

cases, temperature and pressure values at different location of one simulated case (out of nine)

for Lignite volatile coal is validated with the corresponding operational temperature and

pressure data collected from CFBC Boiler of M/s. UltraTech Cement Limited.

5.1. Case of Lignite Volatile Coal

Combustion behavior of Lignite volatile coal is analyzed using nine different sets as given in

table 1. The properties of Lignite volatile coal are taken from fluent database which are also

matching with the actual values of coal used in CFBC Boiler of M/s. UltraTech Cement

Limited. Contours of distribution of static temperature and mass balance are obtain using these

nine simulated cases and sample results for ‘Best Bed temperature’, ‘Minimum Bed

temperature’ and ‘Maximum Bed temperature’ are represented through figure 6, 7 and 8

respectively.

Table 1 Input parameters for simulation of Lignite volatile coal combustion behaviour

S.No.

Input Parameters

Coal Mass

flow (kg/s)

Coal particle

size(m)

PA velocity

(m/s)

SA velocity

(m/s)

1 10 0.004 3 10

2 10 0.006 4.5 15

3 10 0.008 6 20

4 14 0.004 4.5 20

5 14 0.006 6 10

6 14 0.008 3 15

7 18 0.004 6 15

8 18 0.006 3 20

9 18 0.008 4.5 10

5.1.1. Contours of best bed average temperature: The input parameters are Coal mass flow

rate-10kg/s, 0.004 m particle size of lignite coal, primary Air -3 m/s, secondary Air -10 m/s.



(a) (b)

Figure 2: (a) Contours of static temperature (b) contours of Mass Imbalance

From Fig. 2 (a) Visualize Bed average temperature is 1184.46 K this is normal temperature

of boiler bed possibility of ash agglomeration is low. Fig. 2 (b) shows maximum mass

imbalance at stand pipe and wall of boiler so possibility of ash agglomeration in these areas.

5.1.2. Contours of minimum average temperature: The input parameters are Coal mass flow

rate-18kg/s, 0.008 m particle size of lignite coal, primary Air -4.5 m/s, secondary Air -10 m/s

(a) (b)

Figure 3: (a) Contours of static temperature (b) Contours of Mass Imbalance

From Fig. 3 (a) Visualize Bed average temperature is 1139.15 K this is normal temperature

of boiler bed possibility of ash agglomeration is low. Fig.3 (b) shows mass imbalance normal

in all area of bed and cyclone.



Table 2 Input and output parameters using Lignite volatile coal

S.No.

Input Parameters Output Parameters

Coal

Mass

flow

(kg/s)

Coal

particle

size(m)

PA velocity

(m/s)

SA

velocity

(m/s)

Bed

Average

temp.(K)

Maximum

temp.(K)

1 10 0.004 3 10 1184.46 2163.46

2 10 0.006 4.5 15 1294.66 2254.03

3 10 0.008 6 20 1378.65 2231.04

4 14 0.004 4.5 20 1369.87 2281.73

5 14 0.006 6 10 1172.44 2169.35

6 14 0.008 3 15 1316.13 2247.24

7 18 0.004 6 15 1237.42 2248.22

8 18 0.006 3 20 1354.92 2266.53

9 18 0.008 4.5 10 1139.15 2141.13

The results of all the nine simulated experiments are tabulated in Table 3 and it clearly predicts

that

• Experiment number 1, 5, 7 and 9 show favorable condition for boiler if the identified boiler

operates at these input parameters and hence there is no possibility of ash agglomeration.

• While Experiment number 2, 3, 4, 6 and 8 give alarming bed temperature values and shows that

these operating conditions are not suitable for the operation of identified boiler. There is high

possibility of ash agglomeration.

5.2. Case of Barsingsar India-61 Lignite (BIL) Coal

Similarly, combustion behavior of Barsingsar India-61 Lignite (BIL) coal is also analyzed using

nine different sets as given in table 2. The properties of this type of coal, coal Proximate and

Ultimate Analyses values for this type of coal are calculated using available online coal

calculator and shown through table 4.

Again, contours of distribution of static temperature and mass balance are obtain using

selected nine simulated cases through ANSYS Fluent and sample results for ‘Best Bed

temperature’, ‘Minimum Bed temperature’ and ‘Maximum Bed temperature’ are represented

through figure 9 and 10 respectively.

5.2.1 Contours maximum and targeted bed average temperature: The input parameters are

coal mass flow rate-10 kg/s, 0.008 m Particle size of lignite coal, Primary Air -6 m/s, Secondary

Air -30 m/s

(a) (b)




From Fig. 4 (a), the visualize Bed average temperature is 1215.62 K which is normal

temperature of boiler bed and hence possibility of ash agglomeration is low while Fig. 4 (b)

shows normal mass imbalance at combustor and cyclone.

5.2.2 Contours of minimum bed average temperature: The input parameters are Coal mass

flow rate-18kg/s, 0.008 m Particle size of lignite coal, Primary Air -4.5 m/s, Secondary Air -20

m/s.

(a) (b)

Figure 5 (a) Contours of static temperature (b) Contours of Mass Imbalance

From Fig. 5 (a) Visualize Bed average temperature is 973.77 K this is alarming lower

temperature of boiler bed possibility of ash agglomeration is low. Fig. 5 (b) shows maximum

mass imbalance at left side wall of third secondary air nozzle. This operating parameter is not

suitable for boiler.

Table 3 Input and output parameters using Lignite volatile coal

S.No.


Coal

Mass

flow

(kg/s)

Coal

particle

size(m)

PA

velocity

(m/s)

SA

velocity

(m/s)

Bed

Average

temp.(K)

Maximum

temp.(K)

1 10 0.004 3 20 1120.42 1950.57

2 10 0.006 4.5 25 1168.39 1939.01

3 10 0.008 6 30 1215.62 2092.55

4 14 0.004 4.5 30 1138.31 1960.28

5 14 0.006 6 20 1022.41 1867.02

6 14 0.008 3 25 1105.54 1957.86

7 18 0.004 6 25 1008.59 1949.16

8 18 0.006 3 30 1108.71 2020.27

9 18 0.008 4.5 20 973.77 1968.43

The results of all the nine simulated experiments with Barsingsar India-61 Lignite (BIL)

coal are tabulated in Table 5 and it clearly predicts that

1. Experiment number 5, 7, 8 and 9 show operating conditions that are not suitable for

boiler operation hence there is high possibility of ash agglomeration, while,

2. Experiment number 3 represents Best operating condition and experiment number 1, 2,

4 and 6 depict normal operating conditions for the identified CFBC boiler.



5.3. Case of Suruka India Lignite (SIL) Coal

Similarly, combustion behavior of Suruka India Lignite (SIL) Coal is also analyzed using nine

different sets as given in table 1. The properties of this type of coal, coal Proximate and Ultimate

Analyses values for this type of coal are calculated using available online coal calculator.

Again, contours of distribution of static temperature and mass balance are obtain using

selected nine simulated cases through ANSYS Fluent and sample results for ‘Best Bed

temperature’, ‘Minimum Bed temperature’ and ‘Maximum Bed temperature’ are represented

through figure 11, 12 and 13 respectively.

5.3.1. Contours of Maximum Bed Average Temperature: The input parameters are Coal

mass flow rate-10kg/s, 0.008 m Particle size of lignite coal, Primary Air -6 m/s, Secondary Air

-20 m/s

(a) (b)


From Fig. 6 (a) represents Bed average temperature as 1387.21 K which is normal

temperature of boiler bed hence possibility of ash agglomeration is low while Fig.6 (b) shows

maximum mass imbalance at return leg and lower portion of cyclone so possibility of ash

agglomeration in these areas will be high.

5.3.2 Contours of Minimum Bed Average Temperature: The input parameters are Coal mass

flow rate-18kg/s, 0.008 m Particle size of lignite coal, Primary Air -4.5 m/s, Secondary Air -10

m/s.

(a) (b)

Figure 7 (a) Contours of static temperature (b) Contours of Mass Imbalance



From Fig. 7 (a) predicts Bed average temperature as 1049.05 K which is below than the

normal temperature of boiler bed hence possibility of ash agglomeration is low and Fig. 7 (b)

shows maximum mass imbalance at lower portion of cyclone so possibility of ash

agglomeration is high in these areas.

Table 4 Input and output parameters using Suruka India Lignite (SIL) coal

S.No.


Coal

Mass

flow

(kg/s)

Coal

particle

size(m)

PA

velocity

(m/s)

SA

velocity

(m/s)

Bed

Average

temp.(K)

Maximum

temp.(K)

1 10 0.004 3 10 1128.67 2585.00

2 10 0.006 4.5 15 1270.97 2686.00

3 10 0.008 6 20 1387.21 2768.00

4 14 0.004 4.5 20 1297.02 2783.00

5 14 0.006 6 10 1058.73 2689.04

6 14 0.008 3 15 1217.02 2618.51

7 18 0.004 6 15 1103.08 2481.03

8 18 0.006 3 20 1278.26 2707.86

9 18 0.008 4.5 10 1049.05 2736.03

The results of all the nine simulated experiments with Suruka India Lignite (SIL) coal are

tabulated in Table 7 and it clearly predicts that

• Experiment number 2, 3, 4 & 8 is not favorable parameter for the selected boiler operation hence

there is high possibility of ash agglomeration, while,

• Experiment no. 6 is most favorable condition for boiler operation and Experiment number 1 is

good normal operating condition.

• Experiment number 5, 7 & 9 is not favorable parameter for boiler operation.

• Best parameter is Coal mass flow rate 14 kg/s, Coal particle size 0.008 m, Primary Air Velocity

3 m/s, Secondary Air Velocity 15 m/s. which give most favorable bed temperature.

6. CONCLUDING REMARKS

Nine experiments have been done for each type of lignite coal quality by changing three levels

of Coal mass flow rate, Coal particle size, Primary air velocity and Secondary air velocity. As

per operation manual of the selected plant, the normal operation bed temperature varies between

1123 K-1223 K, Alarming bed low temperature is 1033 K and Alarming bed higher temperature

is 1228 K. Accordingly boiler should run with these temperature for smooth running of the

plant. In view of above static temperature plots, few places like secondary air inlet, cyclone

lower part, stand pipe and return leg are showing more temperature than normal bed operating

temperature which is indication of possible ash agglomeration or ash deposition. To avoid these

problems the temperature of boiler and cyclone should be in between acceptable limits. The

boiler temperature can be control by coal mass flow rate, coal particle size, Primary air Velocity

and Secondary Air Velocity. Observation tables show various bed average temperature for

different quality coal at different input parameters values.

For Lignite volatile coal, input parameters under experiment numbers 1, 5 and 9 result in

smooth working of boiler while other experiment show alarming bed average temperature. In



case of BIL coal, input parameters under experiment numbers 1, 2, 4 and 6 result in smooth

working of boiler while experiment numbers 5, 7, 8 and 9 result in operating condition which

are not suitable for BIL coal. Input parameters used in experiment number 3 will result in best

operating conditions. Similarly, for SIL Coal, parameters of experiment number 6 are most

favorable operating condition, parameters of experiment no. 1, 5, 7 and 9 give normal operating

conditions while parameters of experiment no. 2, 3, 4 and 8 are not suitable due to alarming bed

average temperature.

In this work, analysis of coal combustion in circulating fluidized bed has been performed at

four input parameters with three different levels using ANSYS fluent software. Following

conclusions are drawn from the computational analysis in this present work. It is observed that

the three quality coal (lignite volatile, BIL and SIL) require different operating conditions for

maintaining boiler bed average temperature. It is also observed that coal mass flow rate and

coal particle size have less effect on the boiler bed average temperature while Primary air

velocity and Secondary air velocity show significant effect on boiler bed average temperature.

To avoid ash agglomeration in case of lignite volatile coal the primary air flow should be 4.5

m/s and 6 m/s, and the secondary air flow should be maintained between 10 m/s and 15 m/s.

For BIL coal the Primary air flow should be 3 to 6 m/s with Secondary air flow should be in

the range of 20-30 m/s to avoid ash agglomeration. In case of SIL coal the preferable values to

avoid ash agglomeration are primary air flow should be 3 to 6 m/s and Secondary air flow

should be 10 to 15 m/s. The mass imbalance locations shows ash agglomeration or ash

deposition prone zone and hence the Secondary air nozzle, cyclone lower part, stand pipe, J

valve and return leg area are mainly ash agglomeration prone zone.

This CFD model of CFBC boiler helps to predict best input parameters values for a

particular coal quality. It is very easy for the operator and practicing engineer to maintain

favorable boiler bed average temperature for any coal quality using this model. This model also

help to predict the best operating conditions for any type of coal quality by which ash

agglomeration can be minimized.

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INVESTIGATIONS ON ASH AGGLOMERATION IN CFBC BOILER … · 2018. 8. 8. · advantages it has ash-related operational problems, such as slagging, fouling, and corrosion. A 2-Dimensional