3. Blending of high moisture coal for Indian power
boiler -issues
• Deterioration of mill performance .
• Heat loss in the Boiler due to high moisture
• Possibility of slag formation due to blending.
4. Outline of the study
• Mill Temperature modeling
• Flame temperature modeling
• Furnace temperature modeling
• Ash slagging modeling
• Determining blending ratio
5. Mill temperature model
• The mill temperature is modelled from mass and
heat balance.
• Model described by Jian-Lin Wei et al [16] modified
for incorporating moisture component of coal used
as
Where K1to K17 are determined through Genetic
algorithm
6. Flame temperature model
• The flame temperature is modelled from mixed
adiabatic temperature of the constituent burning
gases.
using the equation become
, T= (Tadb-Tref)
Xri =Reactant , Ypj= Product, Cp= Sp. Heat.
Tadb/Tref =Adiabatic flame temperature/ ref. temp
Pj
j
PjRi
i
Ri hYhX TCh avgp ,
i j
T
T
PPipPj
T
T
RRipRi
ad
ref
R
ref
dTcYdTcX ,,
7. Furnace temperature model
• The furnace zone flue gas temperature is modelled
as.
• = Mass flow rate, H/Δh= Enthalpy/ change in enthalpy
• σ = Stiffen Boltzman const. ε = Flame emissivity
• Aww = Water wall area. Tfl = Flue gas temp.
flfl
wwadwwashashairairfuelfuelfuelfuel
reffl
CM
TTAhMhMHMhM
TT
)()(
)(
44
M
8. Simulating the model for a 500 MW boiler
• Two coal sample data (one of domestic coal and one
from high moisture imported coal) used for flame
temperature model simulation.
• Numbers of blended scenario created with change
in blending ratio.
• Mass fraction of fuel component determined separately
for each blend type and used as input to the model.
9. • Model is simulated on excel spread sheet**. (Emissivity of the
flame calculated from mean beam length and furnace geometry. The value obtained is
0.67. The fly ash specific heat capacity is assumed as 0.227 Kcal/deg K.)
• Result shows that with increment in blending ratio, flame
and furnace temperature slightly reduces
• However, the coal mill outlet temperature gets
significantly reduce.
• This because of cumulative effect of reduction in 2ndary
air temperature as well as introduction of high moisture
in coal inlet
**The component –II (combustion) spread sheet of ‘Engineering
software’ (www.engineering-4e.com ) used for determining the
enthalpy of individual reactants and products.
13. Furnace Slagging possibility
• The ash fusion temperature (AFT) of blends increased
with increasing amounts of Al2O3, CaO, K2O, Na2O and
TiO2.
• as per Carpenter (1995) the base to acid ratio is
commonly used to predict the slagging propensity of a
coal. It is defined as:
• A value of the base to acid ratio between 0.4 and 0.7
indicates a high slagging propensity. Values outside
this range indicate a lesser likelihood to slag.
• The blended coal acid to base ratio is tabled in next
slide.
14. Ash quality in blended coal
Oxide
(%)
SiO2 TiO2 Al2O3 CaO MgO Fe2O3 MnO
K2O&Na2
0
Ratio(A)
Fe:Ca
Ratio (B)
Basic:
Acidic
(1) (2) (3) (4) (5) (6) (7) (8)
(6)/ (4) [(4)+(5)+
(6)+(8)]/
[(1)+(2)+
(3)]
Coal-I 54.80 1.88 24.50 3.57 1.87 9.08 0.10 1.50 2.54 0.19
Coal-II 52.00 0.00 31.80 2.70 4.70 4.90 0.00 2.70 1.81 0.18
Blend
(90/10) 54.73 1.84 24.69 3.54 1.94 8.98 0.10 1.53 2.53 0.20
Blend
(85/15/) 54.73 1.84 24.68 3.55 1.93 8.98 0.10 1.53 2.53 0.20
Blend
(80/20) 54.70 1.82 24.76 3.54 1.97 8.93 0.10 1.54 2.53 0.20
Blend(
75/25) 54.67 1.80 24.84 3.53 2.00 8.89 0.10 1.56 2.52 0.20
Ratio (A) range of slagging is from 0.3 to 3.0 with maximum slagging possibility near 1.0
Ratio (B) range of slagging is from 0.4 to 0.7
15. 0.00
0.50
1.00
1.50
2.00
2.50
3.00
0 5 10 15 20 25 30
basic:Acidicindex
Coal blending %
Ash slagging study
Iron:Calcium
base to acid
Fig 3
Iron: Calcium ratio beyond the range from 0.3 to 1.0 indicate lesser likely hood of
slagging
Acid to base ratio beyond 0.4 to 0.7 indicated lesser likely hood of slagging.
16. Conclusion
• A study conducted on blending of high moisture imported
coal with Indian coal.
• Moisture in blended coal reduces flame temperature and mill
outlet temperature.
• Ash generation is less.
• The blend apparently not adversely effecting the slagging of
the ash.
17. Reference:
[1] Naveen Chandralal, D. Mahapatra, D. Shome and P. Dasgupta. Behaviour of low
rank high moisture coal in large stockpile under ambient condition. American
International Journal of Research in Formal, Applied and Natural Science, 14-
210, 2014.
[2] Shimogori, Yoshizako, Mark Richardson, Characteristics of Coal Ash Emissivity
in high temperature atmosphere- ISME International Journal, series B, Vol
4.9, No. 2, 2006
[3] Chanǵan Wang, Yinhe Liv, Xiaoming Zhang, Defuche A study on Coal properties
and combustion characteristics of blended coals in Northwestern China–
Energy & Fuels, American Chemical Society publication, 2011
[4] Yonghua Li. A new Distinguish Method of Blending of Coals slagging
characteristics-, Energy & Power Engineering, 2011
[5] Prabir Basu, Cen Kefa, Louis Jeslin. Boilers and Burners, Springler, 1999
[6] Terry Wall, Liza Elliot, Dick Sanders, Ashley Conroy A review of the state of the
art in Coal blending for power generation –, University of
Newcastel, Australia, 2011
[7] Lecture presentation by Dr. P.M.V. Subbarao. A cause- Effect analysis of
Furnace heat transfer – IIT, Delhi
[8] Energy Conversion with plot 1.1 – Engineering software Demo version,
www.engineering- 4e.com
[9] Report of the group for studying range of blending of imported coal with
domestic coal. Central Electricity Authority, India, 2013
18. [10] The coal resource, A comprehensive overview of coal. World coal
Institute, UK.
[11] Mikael Höök, Werner Zittel, Jörg Schindler, Kjell Aleklett. Global coal
production outlooks based on a logistic model. Fuel, volume 89, issue
11, Nov 2010, Pg 3546-3558.
[12] Protosh Saxena, The optimum mix- impact of coal quality variation on powe
r plant. Power Line July 2013.
[13] A Chandra and H Chandra. Impact of Indian and imported coal on Indian
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[14] NTPC's Study on Blending and Right Mix of Imported Coal, 2007
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coal in NTPC Simhadri- A case study. International O&M conference,
NTPC, 2013
[16] Jian-Lin Wei, Jihong Wang and Q.H. Wu. Development of a multisegment
coal mill model using an evolutionary computation technique. IEEE
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