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Co-combustion of Municipal Solid Waste and Coal in a Circulating Fluidized Bed.

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Dev. Chem. Eng. Mineral Process., 10(5/6), pp. 639-646, 2002.
Co-combustion of Municipal Solid Waste
and Coal in a Circulating Fluidized Bed
Changqing Dong*, Baosheng Jin and Jixiang Lan
Education Ministry Key Laboratory on Clean Coal Power Generation
and Combustion Technology, Thermoenergy Engineering Research
Institute, Southeast University,Nanjing 21 0096, l?R. China
Experiments on the co-combustion of municipal solid waste (MSW) and coal were
peflormed in a 0.2 Mbvth circulatingjluidized bed and focused on the emissions of
NO, N20, HCI and SO,.The results show that the addition of MSW lea& to lower NO,
N20, and SO2 emissions. Howevel; hydrogen chloride concentrations increase with
the amount of waste added. A feedforward neural network model was proposed to
predict the variation of N O emission with the mixture composition.
Introduction
The utilization of a variety of fuels becomes necessary to secure long-term energy
supplies. Industrial or municipal solid waste (MSW) and biomass are a useful
replacement energy resource for their reasonable calorific heating values. Several
studies on the co-combustion of fuel mixtures have shown that the blends of low-rank
coal and biomass can be successfully combusted in a circulating fluidized bed (CFB,)
and that increasing the proportion of biomass improves the combustion efficiency and
lowers the environmental impact [1-4]. Desroches-Ducarne et al. [ 5 ] have shown
that the co-combustion of MSW/coal mixtures in a CFB is feasible. This study
presents the results of co-combustion tests of MSW and coal in a 0.2 MWth CFB.
Experimental Details
(4
Fuels and their composition
Municipal solid waste used in this experiment was collected fiom an urban area of
Nanjing, China. Its main components were classified in 19 categories and ground to
*Authorfor correspondence (cqdong@sina.com.cn).
639
Changqing Dong, Baosheng Jin and Jixiang Lan
obtain a mean diameter of 5 mm. All the categories were finally mixed to achieve a
fuel with constant composition corresponding to typical China MSW [6]. The coal
used in this experiment was Xuzhou bituminous coal. The properties of the fuels are
given in Table 1 in the form of average values from several samples.
Tablel. Coal and MSWproperties.
Xuzhou bituminous coal
Municipal solid waste
Moisture (wt.%)
8.42
43.7
Ash (wt.% dry matter)
16.75
24.3
Volatiles (wt.% daf)
3 1.42
92.2
Calorific heating value (Wkg-l)
23362
4200
C wt.% daf
66.39
54.7
H wt.% daf
0 wt.% daf
N wt.% daf
4.7
6.89
7.84
34.7
1.17
1.38
S wt.% daf
0.97
0.27
C1 wt.% daf
0.12
1
Ca wt.% daf
0.1
1.02
0.097
1.7
Proximate analysis
Ultimate analysis
Cd(S + 0.5 C1) intrinsic
(ii) The circulalingfluidized bed
The tests were carried out in a 0.2 MWth circulating fluidized bed shown in Figure 1.
The experimental system is composed of a riser 23 cm i.d. and 7 m height, fuel (MSW,
coal) feeding systems, a line for fly ash and bed materials circulation, and fumes
cooling and filtration system. The CFB is preheated to the coal ignition temperature
by a start-up burner. Coal is fed into the bed by a screw feeder, and MSW is fed into
the bed by a rotary feeder. The total combustion air can be divided into two streams:
primary air preheated to about 400°C and distributed at the bottom of the bed;
secondary air injected through the airtight end of the MSW feeding pathway and
640
Co-combustion of Municipal Solid Waste and Coal in a Circulating Fluidized Bed
preventing MSW blocking the pathway. At the top of the riser, a cyclone aIlows the
recovery of entrained particles.
(iii) Analysis
The CFB allows for the continuous measurement of temperature and pressure drops at
different heights in the reactor. The major gas components, obtained by extractive
samplings of the exhaust gas, are analyzed by a multi-function flue gas analyzer. HCl
in the flue gas is sampled and measured by the silver nitrate volume method. The fly
ash emitted fiom the exit of the cyclone separator is sampled by a constant velocity
sampling method. The bottom ash is collected at the slag exit. Bottom ash and fly ash
sampling are carried out to research the combustion efficiency. All the tests were
performed under the same condition: at 970°C (dense region in CFB) with excess air
of 30 vol.%, and 100 vol.% of the primary air. In this paper, the impact of the
MSWkoal mixing ratio on NO, SOz, HCI and N20 emission and combustion
efficiency are presented. The following equations were used:
Combustion eficiency (%)
=
100-
Sulfur retention (%) = 100Chlorine retention (%)
Unbumedcarbonflow(kg/s)x33.91(MJ/kg)x l o o
Fuel flow(kg/s) x HHV(MJ/kg)
Measured SO @pm)
XI00
Calculated uncontrolled SO (ppm)
= 100-
Measured HCl(ppm)
loo
Calculated uncontrolled HCl(ppm)
CFB
Figure 1. The circulatingfluidized bed (CFB) combustor
641
Changqing Dong, Baosheng Jin and Jixiang Lan
Results and Discussion
(a) Combustion efficiency
Figure 2 shows the combustion efficiency vs. MSWhoal mixing ratio. Combustion
efficiency improves at higher MSW mixing ratios. It is caused by high volatile matter
contents in the MSW. The volatile matter dominates the MSW combustion, and the
carbon consumption is faster during MSW incineration. Therefore, the concentration
of char in the bed is lower.
(b) Acid gas emission
SO2 emission is clearly lower at higher mixing ratios as shown in Figure 2. The
reason is the high Ca and low sulfur content in MSW. HCl emission increases with
MSW added. This is caused by the increase of chloride content in the fuel mixture.
Results of previous studies concerning coal or waste combustion have shown that a
significant retention of sulphur and chloride can occur when sorbents are introduced
into the CFB [7, 81. Other studies have noted that calcium, potassium or sodium
which are present in relatively large amounts in biomass fuel ashes, may also act as
sorbents and be active for acid gas emissions reduction [9, 101. The relationships
between sulfur and chloride retention efficiencies vs. Ca/(S + 0.5 C1) molar ratio are
presented in Figure 3. The acid gas retention efficiencies increase as the proportion of
coal added decreases. It is shown that the sulfur retention has a strong correlation with
Ca/(S + 0.5 C1) molar ratio, but for the chlorine retention, correlation is not so strong.
(c) Nitric and nitrous oxides
As shown in Figure 2, NO emission is also clearly lower at higher MSW mixing
ratios. At the same time, N 2 0 production decreases gradually as the fraction of the
MSW increases. According to the results of the coal firing test, it is expected that NO
emission should be higher due to the effect of higher temperature in the dilute region,
but NO emission decreases in this co-combustion experiment as the MSW increases.
However:
The volatile product from the MSW particles suppressed the diffision of oxygen
to the carbon surface. The reduction reactions that caused the decrease of nitric
oxide and nitrous oxide were promoted.
Radicals such as H and OH arising fiom the combustion of biomass volatile
matter reduce N20through the following reactions:
642
Co-combustion of Municipal Solid Waste and Coal in a Circulating Fluidized Bed
0
0
N2O+H -+ N 2 + O H
N2O+OH -+ N2 +HO,
MSW ash contains some calcium, potassium and sodium, which have a catalytic
effect on N20decomposition.
(4 Prediction of NO emissions
NO emissions has been given by
.,. , . ,
99.5
A simplified prediction model of
~
NO is specifically reduced by CO
BI).s
97.0
I
I . I .
I ,
., . , . ,,
-
.......................
50
. .,.l.,,,.I.,.l.,.,.l.
on the char surface. Because the
composition of MSW is very
complex, many elements may 3 DO ____._._.__
influence the formation and
50 :
_ _ _ _ _ _ _1
______.__
~ _.
._ __ _ _ _ _ -_m_ _ _ _
DO I..._ _ _.
destruction of gaseous pollutants,
such as operating parameters and
40
the composition of the fuel mixture.
30
The impact of mixing ratio is
20
considered in this paper. Based on
10
the advantages of neural networks,
0
such as being able to use some a
priori unknown information hidden
in data and having a universal
approximate, three-layer feed
forward neural networks are
constructed to predict the NO
emission (seen in Figure 5 ) . The
MSW/coal
first layer is named input layer, the
third layer the output-layer, and the
Figure 2. Combustion eflciency and emissions
layer between them is the hidden
vs. MSW mixing ratio.
layer. The mixing ratio R is used as
the input signals and the concentrations of NO are used as output signals. Each neuron
in a particular layer is connected with all neurons in the next layer. The connection
between ith and jth neuron is characterized by the weight coefficient wij. The output
_.t__..__.__..____...-.
-.-\*
9
..-/.-
.---*--:
1 . 1 . 1 . 1 . 1 ’ 1 ‘ 1 . 1 ’ 1 . 1 . 1 ’ 1 .
643
Changqing Dong, Baosheng Jin and Jixiang Lan
value of xi is determined by:
x, = f ( 5 , )
ei
where is the potential of the ith neuron, and the function f
h c t i o n . In addition:
(el ) is called the transfer
4-2
-A-3
-+- 4
0.0 0.2 0.4 0.6 0.8 1.0
Relative height
ca/(s+o.scl)
Figure 3. Sulfirr and chlorine retention vs.
Ca/(S+O.SCI).
The weight coefficients wi, are revised
to minimize the s u m of the squared
differences between the computed and
required output values. This is
accomplished by minimization of the
object function E by the relation:
1
E = -(xo
2
-XJ
relative height.
hidden layer
(4)
where x,, and xh are the computed and
required activities of the output neuron.
644
Figure 4. Temperature drafl vs.
Figure 5. Feedfonvard neural networks.
Co-combustion of Municipal Solid Waste and Coal in a Circulating Fluidized Bed
A back-propagation training algorithm is used to vary weight coefficients. Therefore:
where h is the rate of learning (A). The training mode begins with random numbers of
the weights and proceeds iteratively. Figure 6 illustrates the results of calculations
together with the measured NO emissions. It appears that when the hidden layer
consisted of 5 neurons and the computed model agreed well with experimental data.
- calculated wlue
o expenmsnt wlue
x training wlue
9.-
= 100-
0
0
0
0
50
--
8
0
0
t
01
0
1
2
3
R
4
5
6
Figure 6. Comparison of calculated and experimental data.
Conclusions
Combustion tests performed on different mixing ratios of MSW to coal show that the
additions of municipal refuse leads to:
0
Higher combustion efficiencies due to the increased volatile content of the
0
mixing fuel.
An increase in acid gas retention. Both sulfur retention and chlorine retention
have a correlation with Ca/(S + 0.5 Cl).
0
The NO and N20 emissions decreased as the amount of MSW increased.
645
Changqing Dong, Baosheng Jin and Jixiang Lan
Acknowledgements
This work has been supported by the Foundation of Key Laboratory of Education
Ministry of China and the Key Science and Technology Project of the Education
Ministry of China. The support of these organizations is gratehlly acknowledged.
References
1.
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NO and CO emissions as functions of temperature and air staging. Fuel, 78(9), 1065-1072.
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Roesler, J.F., Yetter, R.A., and Dryer, EL. 1995. Kinetic interaction of CO, N0,and HCI emissions in
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Andries, J., Vedoop, M., and Hein, K. 1997. Co-combustion of coal and biomass in a pressurized
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4.
Armesto, L., Cabanillas, A,, and Bahillo, A. 1997. Coal and biomass co-combustion on fluidized bed:
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Desroches-Ducarne, E.J., Dolignier, C.J., and Marty, E. 1998. Modelling of gaseous pollutants
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The Statistical Department of China 1997. Chinese Statistics Yearbook 1997[M]. China Statistical
Publishing House, Beijing, P.R. China
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Ichiro, N., and Heejoon, K. 1998. Study on characteristics of self-desulfurization and
self-denitrification in biobriquettc combustion. International Symposium on Combustion, Combustion
Institute. pp.2973-2979.
8.
Matsuka, M., Takeda, K., and Miyatani, T. 1996. Simultaneous chlorination and sulphation of
calcined limestone. Chem. Eng. Sci., 51(l I), 2529-2534.
9.
Nordin, A. 1995. Optimization of sulfur retention in ash when co-combusting high sulfur fuels and
biomass fuels in a small pilot scale fluidized bed. Fuel, 74(4), 615-622.
10. Dayton, D.C., Deirdre, B.O., and Nordin A. 1999. Effect of coal minerals on chlorine and alkali
metals released during biornass/coal co-firing. Energy and Fuels, 13(6), 1203-1211.
11. Svozil, D., Kvasnicka, V., and Pospkhal, J. 1997. Introduction to multi-layer feed-forward neural
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Received: 6 June 2001; Accepted aster revision: 14 March 2002.
646
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