Article
Experimental Studies on Wood Pellets Combustion in a Fixed
Bed Combustor Using Taguchi Method
Carlos Castro *, Lelis Fraga
, Eduardo Ferreira, Jorge Martins * , Pedro Ribeiro and José C. Teixeira *
MEtRICs, Department of Mechanical Engineering, Engineering School, University of Minho, Azurém,
4800-058 Guimarães, Portugal; lelisfraga@hotmail.com (L.F.); ef@dem.uminho.pt (E.F.);
pedro_ribeiro@dem.uminho.pt (P.R.)
* Correspondence: id7607@alunos.uminho.pt (C.C.); jmartins@dem.uminho.pt (J.M.); jt@dem.uminho.pt (J.C.T.)
Abstract: The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to
program the experimental design. Several parameters were measured, including gas emissions (CO),
fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results
show that the lower CO emission and the higher efficiency were obtained at medium thermal loads
and the highest temperature on the fuel bed was obtained at about 1/4 of its height (15 mm). The
results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the
most important factors contributing to the CO reduction and also increase the fuel bed temperature.
Keywords: wood pellet; excess air; thermal load; gas emissions; thermal efficiency; Taguchi method
Citation: Castro, C.; Fraga, L.;
Ferreira, E.; Martins, J.; Ribeiro, P.;
Teixeira, J.C. Experimental Studies on
1. Introduction
Wood Pellets Combustion in a Fixed
Biomass is one of the renewable resources that contributes to the energy mix, reducing
the negative impact on the environment, creating more jobs, among other factors [1,2].
With the goal of decreasing the share of fossil fuels and its contribution to climate change,
developing technologies to efficiently convert energy from the renewable sources is a
sustainable manner to mitigate the problem [3–5]. World consumption of energy has
increased gradually due to population growth and to technological development [6,7].
The highest efficiency registered on the combustion of biomass generally corresponds
to the lowest CO emissions, a behaviour that can be explained by the complete oxidation
of the hydrocarbons contained on the wood [8,9]. The complete combustion is determined
by several factors that can be summarized as good mixing and adequate residence time at
high temperatures [8].
Various studies have been conducted in order to establish good methods in how to
achieve a high combustion efficiency using biomass resources. Such high efficiency of
biomass/waste combustion was obtained from advanced grate-firing [10]. Other studies
also report that the boiler efficiency is also determined by the load factor and number of
ignitions [11], suggesting that high operational loads and reduced number of ignitions
correspond to the optimal operating conditions. Four appliances with different designs
and several fuel types were investigated by Kinsey et al. [12]. Among these appliances the
thermal, and combustion efficiencies, along with CO, CO2 and other gas emissions were
observed. The results showed that the four units corresponding to the tested pellet-burning
appliances had the highest overall operating efficiency and lowest emissions.
On the other hand, accumulated ash on fixed grate biomass pellet boilers can sinter,
imposing deficiencies on air supply and, thus, cause a poor combustion, with high CO
emission. Although this phenomenon is linked to the mineral composition of the ash, it
can be mitigated by controlling fuel bed temperature and primary/secondary air split
ratio [13].
Bed Combustor Using Taguchi
Method. Fuels 2021, 2, 376–392.
https://doi.org/10.3390/fuels2040022
Academic Editor: Elna
Heimdal Nilsson
Received: 20 August 2021
Accepted: 15 September 2021
Published: 23 September 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
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distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Fuels 2021, 2, 376–392. https://doi.org/10.3390/fuels2040022
https://www.mdpi.com/journal/fuels
Fuels 2021, 2
377
Applying different operating parameters in a wood pellets boiler, one can determine
the combustion behaviour and overall performance of the boiler. Nonetheless, the boiler
needs to be operated in an optimum way to improve its efficiency and lower the pollutant
gas emission. The aim of this study was to investigate the influence of the thermal load,
primary/secondary air split ratio, grate area and excess air on the gases of wood pellet
combustion in a domestic boiler with a nominal power of 20 kW. The Taguchi method
was applied to set the research plan and a statistical analysis (ANOVA) was performed to
evaluate the results.
The Taguchi method is an approach for industrial product design built on statistically
designed experiments. The key of this method is the parameter design, allowing the study
of the influence of several factors in one or more parameters [14].
Several authors have used the Taguchi method to study the combustion or pretreatment of biomass. As examples, Huang et al. [15] used the Taguchi method to optimize
the torrefaction conditions for biochar cofiring. They concluded that the fuel characteristics
were significantly influenced by the temperature, and to a less extent by the residence time
and N2 flow rate. Li et al. [16] applied the method to optimize the pre-treatment (torrefaction) of biomass to improve energy yield and heating value. Like Huang, Li concluded that
the temperature was the major influencer on the torrefaction, followed by the residence
time and the N2 flow rate. On another work, Li et al. [17] used the method to optimize
the combustion efficiency of a fluidized bed fed with torrefied biomass. They concluded
that the degree of torrefaction of the biomaterials had the strongest influence on the total
combustion efficiency, followed by the bed temperature and oxygen concentration with the
same importance, and with less influence on the oxygen inlet.
According to the literature, the Taguchi method appears to be a suitable method to
identify the influence of several factors on the performance of a process, and it has been
used for optimizing the operational conditions. This method it is also known for the
reduced number of tests needed to obtain conclusions and quantify the influence of the
factors on the parameters, reducing time and resources. In this work, the Taguchi method
was used to discover the parameters that have a major influence on gas emissions in order
to optimize the combustion efficiency of a 20 kW boiler.
2. Materials and Methods
The experimental tests were realized using ENplus A1 Class standardized 6 mm
diameter pine wood pellets [18]. Its relevant properties were measured and can be seen in
Table 1.
Table 1. Properties of pine wood pellets.
Analysis
Value (wt.%)
Proximate (as received)
Moisture
Volatile matter
Ash
Fixed carbon
Lower Heating Value (MJ/kg)
6.9
77.8
0.6
14.7
17.1
Ultimate (dry ash free)
Carbon (C)
Hydrogen (H)
Nitrogen (N)
Sulphur (S)
Oxygen (O)
50.8
5.4
1.6
0.037
42.2
The properties of the wood pellets were measured according to the norms:
•
•
Moisture: EN ISO 18134:2015
Volatile Matter: EN ISO 18123:2015
Fuels 2021, 2, FOR PEER REVIEW
Fuels 2021, 2
3
Table 1. Properties of pine wood pellets.
378
Analysis
Value (wt. %)
Proximate (as received)
Moisture
6.9
•
Ash:
EN ISO 18122:2015
Volatile
matter
77.8
•
Low
Heating
Value: EN 14918:2009
•
C,
H, N: EN ISO 16948:2015
Ash
0.6
•
Sulphur:
EN
ISO
16994:2016
Fixed carbon
14.7
Lower
Heating
Value
17.1the previous
The
Fixed
Carbon
and(MJ/kg)
the Oxygen were obtained by calculation, using
values.
The
fixed
Ultimate
(dry
ashcarbon
free) was calculated following the Equation (1):
Carbon (C)
50.8
wt.% Fixed Carbon = 100% − wt.% Moisture − wt.% Ash − wt.% Volatile Matter (1)
Hydrogen (H)
5.4
Nitrogen
(N)was calculated following the Equation (2):
1.6
The
Oxygen
Sulphur (S)
0.037
Oxygen (O)
wt.% O = 100% − wt.% C − wt.% H − wt.% N − wt.%42.2
S
(2)
2.1.
2.1.Experimental
ExperimentalSetup
Setup
The
experiment
The experimentwas
wasconducted
conductedatatthe
theHeat
Heatand
andFluids
FluidsLaboratory
Laboratoryofofthe
theDepartment
Department
of
Mechanical
Engineering
of
the
University
of
Minho.
A
prototype,
computer-controlled
of Mechanical Engineering of the University of Minho. A prototype, computer-controlled
20
pellet
boiler
designed
byby
a research
team,
waswas
used.
A representation
20kW
kWdomestic
domesticwood
wood
pellet
boiler
designed
a research
team,
used.
A representaof
the
experimental
setup
used
in
this
study
is
presented
in
Figure
1.
The
tion of the experimental setup used in this study is presented in Figure 1. Thepellets
pelletswere
were
transported
from
the
pellets
storage
tank
by
means
of
a
feeding
auger
and
discharged
transported from the pellets storage tank by means of a feeding auger and discharged
through
throughthe
thetop
topof
ofthe
thecombustion
combustionchamber
chamberby
bygravity.
gravity.AAscale
scalewas
wasplaced
placedunder
underthe
thepellet
pellet
tank,
allowing
the
measurement
of
the
fuel
consumption.
tank, allowing the measurement of the fuel consumption.
Figure1.1.Test
Testfacility
facilitysetup:
setup:1.1.Pellets
Pelletshopper;
hopper;
Feeding
gauger;
Feeding
gauger
driver;
4. Scale;
Figure
2. 2.
Feeding
gauger;
3. 3.
Feeding
gauger
driver;
4. Scale;
5.
5.
Burner
grate;
6.
Primary
air
duct;
7.
Secondary
air
duct;
8.
Circulating
water
pump;
9.
Flow
meter;
Burner grate; 6. Primary air duct; 7. Secondary air duct; 8. Circulating water pump; 9. Flow meter; 10.
10. Expansion
vessel;
11. Water
cooling
12.Extractor;
Air Extractor;
13. Stack;
14. gas
Flueprobe
gas probe
to anaExpansion
vessel;
11. Water
cooling
unit; unit;
12. Air
13. Stack;
14. Flue
to analyser.
lyser.
The boiler external dimensions were 127 cm height, 43 cm width and 35 cm depth. The
The boiler
external
dimensions
were
cm
height, 43 cm
width
andof35
depth.
combustion
chamber
was 53
cm in height
and127
had
a rectangular
cross
section
30cm
× 25
cm.
The
combustion
chamber
was
53
cm
in
height
and
had
a
rectangular
cross
section
of
30 ×
The combustion chamber was lined with 20 mm thick slabs of fire clay in order to avoid the
25
cm.
The
combustion
chamber
was
lined
with
20
mm
thick
slabs
of
fire
clay
in
order
formation of cold walls that would lower the combustion rates and may lead to incompleteto
avoid the formation
of cold
walls
would lower
the by
combustion
rates and
may
combustion.
Then, useful
heat
wasthat
transferred
to water
means of double
pass
firelead
tubeto
incomplete
combustion.
Then,
useful
heatthe
was
transferred
to water
by 2).
means of double
heat
exchanger
made up of
20 pipes
inside
±80
L water tank
(Figure
pass fire tube heat exchanger made up of 20 pipes inside the ±80 L water tank (Figure 2).
Fuels 2021, 2
the
grate and
in Figure
2. injectors for primary and secondary air supply. The bottom of the grate was
covered
by
metal box
creatingby
theanash
pan and
theworking
primary at
airfrequencies
plenum. This
assembly
An air aextractor,
powered
electric
motor
between
0 to
was
put
together
with
bolts
and
allowed
us
to
easily
change
the
grate.
A
sealing
material
60 Hz, was located at the exhaust draft. This extractor forced the air to flow into the
burner
(rock
was and
applied
to the connection
between
theflow
partsrate
to prevent
air
by thewool)
primary
secondary
air ducts. The
total air
was set uncontrolled
by adjusting the
leaks
during and
operation.
The primary airair
intake
was installed
on anthe
ashprimary
pan wall,
air extractor
the primary/secondary
split pipe
is adjusted
by throttling
air
379
along
with
ignition
upperby
section
of the
burner
hadtoaminimize
perforatedthe
surrounding
supply.
Thethe
whole
unitcoil.
wasThe
covered
a jacket
of rock
wool
heat losses
collar,
constituting the secondary air plenum.
to the outside.
The grate used in this study was of rectangular shape and three different cross sectional areas were tested (Figure 3).
The temperature on the fuel bed was measured using four type K thermocouples,
installed at four different points/heights. A simple scheme of the fuel bed set is presented
in Figure 2.
An air extractor, powered by an electric motor working at frequencies between 0 to
60 Hz, was located at the exhaust draft. This extractor forced the air to flow into the burner
by the primary and secondary air ducts. The total air flow rate was set by adjusting the
air extractor and the primary/secondary air split is adjusted by throttling the primary air
supply. The whole unit was covered by a jacket of rock wool to minimize the heat losses
to the outside.
Figure2.2.Boiler
Boilerunit
unitand
andFuel
FuelBed
Bed representation, adapted
Figure
adaptedfrom
from[19]:
[19]:1.1.Primary
Primaryair
airduct;
duct;2.2.Secondary
Secondary
duct;
3. Fuel
Inlet;
4. Ignition
Coil;
5. Fire
Clay
Slabs;
6. Grate;
7. Stack;
8. Heat
Exchanger;
9.
airair
duct;
3. Fuel
Inlet;
4. Ignition
Coil;
5. Fire
Clay
Slabs;
6. Grate;
7. Stack;
8. Heat
Exchanger;
9. Fuel
Fuel
10–13.
Type
K Thermocouples
5,25
15,and
25 and
60 mm
height,
respectively.
Bed;Bed;
10–13.
Type
K Thermocouples
at 5,at
15,
60 mm
height,
respectively.
The burner was cantered at the bottom of the combustion chamber and comprised
the grate and injectors for primary and secondary air supply. The bottom of the grate was
covered by a metal box creating the ash pan and the primary air plenum. This assembly
was put together with bolts and allowed us to easily change the grate. A sealing material
(rock wool) was applied to the connection between the parts to prevent uncontrolled air
leaks during operation. The primary air intake pipe was installed on an ash pan wall, along
with the ignition coil. The upper section of the burner had a perforated surrounding collar,
constituting
airrepresentation,
plenum.
Figure
2. Boiler the
unitsecondary
and Fuel Bed
adapted from [19]: 1. Primary air duct; 2. SecondThe
grate
used
in
this
study
was 5.
ofFire
rectangular
shape
and three
different
sectional
ary air duct; 3. Fuel Inlet; 4. Ignition Coil;
Clay Slabs;
6. Grate;
7. Stack;
8. Heatcross
Exchanger;
9.
areas
were
tested
3).
Fuel
Bed;
10–13.
Type(Figure
K Thermocouples
at 5, 15, 25 and 60 mm height, respectively.
Figure 3. Grate Unit: 1–2. Primary air orifices; 3. Ignition hole; 4. Secondary air orifice; 5. Secondary
air inlet 6. Bottom Grate.
Figure3.3.Grate
GrateUnit:
Unit:1–2.
1–2.
Primary
orifices;3.3.Ignition
Ignitionhole;
hole;4.4.Secondary
Secondaryairairorifice;
orifice;5.5.Secondary
Secondary
Figure
Primary
airair
orifices;
inlet6.6.Bottom
BottomGrate.
Grate.
airairinlet
The temperature on the fuel bed was measured using four type K thermocouples,
installed at four different points/heights. A simple scheme of the fuel bed set is presented
in Figure 2.
An air extractor, powered by an electric motor working at frequencies between 0 to
60 Hz, was located at the exhaust draft. This extractor forced the air to flow into the burner
by the primary and secondary air ducts. The total air flow rate was set by adjusting the
air extractor and the primary/secondary air split is adjusted by throttling the primary air
Fuels 2021, 2
380
supply. The whole unit was covered by a jacket of rock wool to minimize the heat losses to
the outside.
A heat exchanger (Figure 1) was placed at the top of the boiler, enabling the produced
heat to be removed from the boiler. Water was circulated using a pump (maximum flow
rate of 500 L/h) that moved the water to an air-cooled heat exchanger. The flow rate was
controlled by a valve and measured by a calibrated rotameter.
Finally, a vacuum pump was used to extract a sample of the flue gas into the gas
analyser for measurement purposes. Before entering the vacuum pump, the sample was
cooled and filtered in order to remove any moisture and particles.
A computer unit, composed of a National Instruments data acquisition system, was
used as a data acquisition and control device. This system is divided in several parts,
namely: a chassis (PXI-1052); controller (PCI-8105); acquisition board (PXI-6259); thermocouples module (SCXI-1102); analog inputs module (SCXI-1100); digital inputs module
(SCXI-1326); and output digital module (SCXI-163) [20].
The controller works as a computer running Windows XP as the operative system.
LabView was used to develop the interface and the boiler control program.
2.2. Test Procedure
Several factors were studied in this experiment including power (10, 13 and 16 kW),
grate area (90 × 75, 115 × 75, and 115 × 96 mm2 ), excess air ratio (1.5, 1.7, and 2.1) and
primary/secondary air split ratio (20/80, 30/70, and 37/67). The temperature at the centre
of the fuel bed was measured at four different heights, namely 5, 15, 25 and 60 mm. The
thermocouples were introduced from the bottom of the grate allowing the contact with the
pellets.
With respect to the primary air, most of the air was introduced at the bottom of
the grate through orifices of a rectangular shape (3 × 26 mm2 ), and some air was also
introduced at the bottom side of the grate through orifices of 4 mm diameter (Figure 3).
The number of orifices depended on the cross-sectional area of the grate. Regarding the
secondary air, 12 orifices with a diameter of 4 mm were located at the top of the burner, in
each side, 92 mm above the fuel bed.
To continuously measure the CO and O2 composition of the flue gas, a Signal Instruments 9000MGA gas analyser was used, and the CO concentration was corrected to 13% of
O2 . The equipment uses a parametric oxygen sensor with a repeatability of ±0.01% for the
O2 measurement and an infrared sensor with a repeatability better than ±1% or ±0.5 ppm
for the CO and CO2 measurement [21]. The sample gas cooling and conditioning system is
presented in Figure 4. For each experiment the boiler was running for 4 h after a warmup
period. The boiler was shut down if instabilities and irreversible build-up of pellets on the
grate were observed. The temperature on both hot and cold water pipes was recorded in
order to assess the removed thermal power and consequently allowing for boiler efficiency
calculations.
The parameter that better describes the combustion quality is the CO emission. Meanwhile, during the long run tests, the CO concentration could fluctuate and increase significantly as the fuel bed (FB) rises. If that occurred, the CO emission considered in the study
was the one registered before the occurrence of those instabilities in the FB.
Fuels2021,
2021,2,2 FOR PEER REVIEW
Fuels
381
6
Figure4.4.Flue
Fluegas
gasanalyser
analyserscheme:
scheme:1.1.Gas
Gasanalyser;
analyser;2.2.Data
Dataacquisition
acquisitionsystem;
system;3.3.Cooling
Coolingloop;
loop;4.4.
Figure
Filter;5.5.Vacuum
Vacuumpump;
pump;6.6.Water
Waterinlet.
inlet.
Filter;
2.3. Design of Experiments Using Taguchi Method
2.3. Design of Experiments Using Taguchi Method
The application of the Taguchi method in the planning of experimental projects can
The application of the Taguchi method in the planning of experimental projects can
significantly reduce the amount of tests required to evaluate the influence of several factors
significantly reduce the amount of tests required to evaluate the influence of several facconsidered in a particular process [22]. In addition, this method also allows one to confirm
tors considered in a particular process [22]. In addition, this method also allows one to
the importance and relative weight of each factor in a particular response or outcome
confirm the importance and relative weight of each factor in a particular response or outexpected. The key of the Taguchi method is the parameter design [14].
come expected. The key of the Taguchi method is the parameter design [14].
In the Taguchi method, multiple parameters (factors) and several values of these
In the Taguchi method, multiple parameters (factors) and several values of these paparameters (factor levels) are arranged according to standard orthogonal arrays, enabling
rameters (factor levels) are arranged according to standard orthogonal arrays, enabling a
a dramatic decrease in full-factorial trial experiments [23]. As described by Ferreira [22],
dramatic decrease in full-factorial trial experiments [23]. As described by Ferreira [22],
there are three phases in applying the method:
there are three phases in applying the method:
(1) Selection of factors and eventual interaction;
(1)
and eventual
(2) Selection
Planningofoffactors
the experiment;
andinteraction;
(2)
theinterpretation
experiment; and
(3) Planning
Analysisofand
of the results.
(3) Analysis and interpretation of the results.
In addition, for the experimental plan, at least 3 levels on each parameter should
In addition,
for the the
experimental
plan,
least 3 levels
each of
parameter
shouldand
be
be applied
to evaluate
influence of
eachatparameter.
Theonvalue
the parameters
applied
to
evaluate
the
influence
of
each
parameter.
The
value
of
the
parameters
and
the
the three different levels for each selected parameter for this experiment are presented in
three
forexperimental
each selectedplan,
parameter
this
experiment
are presented
in
Tabledifferent
2. Basedlevels
on this
the testfor
plan
matrix
to be used
is the orthogonal
Table
2.
Based
on
this
experimental
plan,
the
test
plan
matrix
to
be
used
is
the
orthogonal
matrix L27, which consists of 27 experiments and 13 columns (Table 3).
matrix L27, which consists of 27 experiments and 13 columns (Table 3).
The
notation
a Taguchi
orthogonalplan,
arrayusing
can the
be written
as L27 (313). In this notaTable
2. Factor
andof
Level
of the experimental
Taguchi method.
tion, 27 is the number of the experimental runs, 3 is the number of levels and 13 is the
number of experimental factors. Furthermore, the L27 matrix Level
with 13 columns can be used
Factor
for the study of seven factors and three interactions,
where
each
two
1
2 interaction requires
3
columns (see
A Table 3) [22].
Power (kW)
10
13
16
B
C
D
Excess air (%)
Grate area
(mm2 )
Split ratio (P/S)
50
70
110
90 × 75
115 × 75
115 × 96
20/80
30/70
37/63
Grate height 61 (mm)
Fuels 2021, 2
382
Table 3. Matrix L27 , with indication parameter (1, 2, 5, 10), interaction (3, 4, 6, 7, 8, 11), and
independent (9, 12, 13) [22].
Test
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Power
(kW)
EA
(%)
A
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
B
2
1
1
1
2
2
2
3
3
3
1
1
1
2
2
2
3
3
3
1
1
1
2
2
2
3
3
3
GA
(mm2 )
A×B
3
4
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
2
3
2
3
2
3
3
1
3
1
3
1
1
2
1
2
1
2
3
2
3
2
3
2
1
3
1
3
1
3
2
1
2
1
2
1
C
5
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
SR
A×C
6
7
1
1
2
2
3
3
1
1
2
2
3
3
1
1
2
2
3
3
2
3
3
1
1
2
2
3
3
1
1
2
2
3
3
1
1
2
3
2
1
3
2
1
3
2
1
3
2
1
3
2
1
3
2
1
B×C
8
1
2
3
2
3
1
3
1
2
1
2
3
2
3
1
3
1
2
1
2
3
2
3
1
3
1
2
e
9
1
2
3
2
3
1
3
1
2
2
3
1
3
1
2
1
2
3
3
1
2
1
2
3
2
3
1
D
10
1
2
3
2
3
1
3
1
2
3
1
2
1
2
3
2
3
1
2
3
1
3
1
2
1
2
3
B×C
11
1
2
3
3
1
2
2
3
1
1
2
3
3
1
2
2
3
1
1
2
3
3
1
2
2
3
1
e
12
1
2
3
3
1
2
2
3
1
2
3
1
1
2
3
3
1
2
3
1
2
2
1
3
1
2
3
e
13
1
2
3
3
1
2
2
3
1
3
1
2
2
3
1
1
2
3
2
3
1
1
2
3
3
1
2
The notation of a Taguchi orthogonal array can be written as L27 (313). In this notation,
27 is the number of the experimental runs, 3 is the number of levels and 13 is the number
of experimental factors. Furthermore, the L27 matrix with 13 columns can be used for the
study of seven factors and three interactions, where each interaction requires two columns
(see Table 3) [22].
According to Ferreira [22], for the optimization process, the deviation to the optimal
value depends on the dispersion of results. Thus, the analysis of variance (ANOVA) is
based on the mean and variance of each test. ANOVA provides the dispersion present
in a specific set of data, identifying their origins and evaluating the contribution of each
data point to the total dispersion. This method allows for the testing of the significance of
the effects relative to the random error, also known as noise. The data obtained from the
experiment was transformed into a signal-to-noise ratio (S/N) to measure the deviation of
quality of the parameters from the desired values. The S/N ratio is always <0. A higher
value ratio corresponds to a better quality characteristic of the observed parameter. Thus,
values close to zero indicate the best conditions [24,25]. In the Taguchi method, three types
of characteristic performance are selected in the analysis, including the-larger-the-better,
the-smaller-the-better, and the-nominal-the-better (on-target-better) [23,24].
For the analysis of variance for CO, the-smaller-the-better was selected for the calculation of S/N Equation (3) [22]:
2
S/N = −10 log X + σx 2
(3)
Fuels 2021, 2
383
And for temperature, the-nominal-the-better was selected, represented by the Equation (4):
S/N = −10 log σx 2
(4)
2
where X is the average value of the data and σx 2 is the variance.
The boiler efficiency was calculated according to the NF EN 303-5 standard following
Equation (5) [26]:
Pn
(5)
ηb = 100
Pi
where Pn is the nominal useful power of the boiler (kW) that was calculated by Equation (6):
.
Pn = mw C pw ∆T w
(6)
.
where mw is the water mass flow rate (kg·s−1 ), C pw is the heat capacity of water (kJ·kg−1 ·K−1 )
and ∆T w is the temperature difference between the boiler and the heat exchanger.
Pi is the power input and can be calculated as (Equation (7)):
.
Pi = m f LHV f
(7)
.
where m f is the mass flow rate of the fuel (kg·s−1 ) and the LHV f is the Low Heating Value
of the fuel (kJ·kg−1 ).
3. Results and Discussion
Considering four factors and three levels, in a normal matrix it would be necessary
for 81 tests (without interactions) to perform all the combinations. The implementation of
the Taguchi method allowed the reduction to 27 tests and added interactions and external
factors to the matrix, substantially reducing the cost and time needed to understand the
interaction between factors.
The statistical analysis (ANOVA) from the Taguchi method gives a better understanding on the influence of each parameter, which is important to understand and optimize the
process.
For all the 27 tests, the overall efficiency of the boiler varied between 64% and 92%
with an average of 83%.
Figure 5 depicts the relationship between GA and thermal efficiency at different power,
SR, and EA. Figure 5a shows that at 10 kW with 50% of EA (EA1), the efficiency was higher
for the combination of middle GA (GA2) with middle SR (SR2). For 70% of EA (middle
EA: EA2), the best combination was found using the middle GA (GA2), but with higher SR
(SR3). As for an EA of 110% (EA3), it was noted that the thermal efficiency was higher for
both the combination of smaller GA (GA1) with SR3 and larger GA (GA3) with middle SR
(SR2).
In brief, Figure 5a shows that at low power (10 kW), the best combination of the
parameters is either EA3-GA1-SR3 or EA3-GA3-SR2 in order to obtain the higher combustion efficiency. This means that lower power conditions require a high EA. The reduced
efficiency at lower thermal loads may be due to the size of the combustion chamber, which
will require a higher EA to provide adequate mixing with the fuel. The opposite may be
observed for higher power levels (see Figure 5c).
Figure 5b shows the results for the 13 kW load. It can be observed that at 50% of EA the
thermal efficiency was higher for both the combination of smaller GA (GA1) with SR3 and
larger GA (GA3) with middle SR (SR2). For 70% of EA (EA2), the combination of GA1 with
lower SR (SR1) and GA3 with higher SR (SR3) presented the highest thermal efficiencies.
For 110% of EA, the thermal efficiency was slightly higher for GA1 and GA2, with the
combination with SR2 and SR3, respectively. These results show that, at medium power
(13 kW), the best combination of the parameters is either EA2-GA1-SR1 or EA2-GA3-SR3
in order to produce the highest thermal efficiency.
Fuels 2021, 2
Fuels 2021, 2, FOR PEER REVIEW
384
9
90
85
Efficiency (%)
80
75
70
SR3, SR1, SR2; EA3
65
SR2, SR3, SR1; EA2
SR1, SR2, SR3; EA1
60
6000
8000
10000
12000
GA (mm²)
(a)
95
Efficiency (%)
90
85
80
75
SR2, SR3, SR1; EA3
SR1, SR2, SR3;EA2
SR3, SR1, SR2; EA1
70
6000
8000
10000
12000
GA (mm²)
(b)
92
90
Efficiency (%)
88
86
84
SR1, SR2, SR3; EA3
82
SR3, SR1, SR2; EA2
SR2, SR3, SR1; EA1
80
6000
8000
10000
12000
GA (mm²)
(c)
Figure 5. Relationship between
and GAbetween
with different
EA and
(a)with
at 10different
kW, (b) atEA
13 and
kW, SR:
and (a)
(c) at
Figure efficiency
5. Relationship
efficiency
andSR:
GA
at 16
10 kW.
kW, (b) at
13 kW, and (c) at 16 kW.
Fuels 2021, 2
385
Figure 5c depicts the results for the 16 kW load. In this condition, for 50% of EA,
the thermal efficiency was higher at middle GA (GA2) combined with SR3. For 110%
of EA (EA3), the maximum efficiency occurred at the combination of GA2 with middle
SR (SR2). For 70% of EA, the thermal efficiency was higher for the combination of GA3
with SR2. Globally, this analysis shows that the best combination of the parameters was
EA1-GA2-SR3. This means that at a higher power level, a lower EA and middle GA with
higher SR most likely result in high thermal efficiency.
Overall, the data in Figure 5 indicate that an efficient combustion can be obtained with
any grate, once the EA and SR are properly adjusted for any specific power level. This is
in line with the results obtained from Verma et al. [27]. High efficiencies usually cannot
be obtained at high EA due to thermal stack losses, as stated by Serrano et al. [28]. On
the other hand, high CO emission can also induce low efficiencies because this condition
shows, basically, that some fuel was left unburned. At low power levels, increasing excess
air promotes a better air/fuel mixing and higher carbon conversion which decreases CO
emissions. So, the efficiency of the boiler is a compromise between thermal and chemical
losses on the stack [29]. This can explain the higher level of excess air required at low
power level to guarantee maximum efficiency.
3.1. ANOVA Analysis of CO Emissions
The mean values of the S/N indices for each of the three levels of each parameter or
interaction on CO are presented in Table 4. The last line of each table shows the maximum
value of the difference between the averages of the indices for each of the parameters. These
results evaluate the relative weight of the influence of each parameter on the response
value. Based on its analysis, it can be verified that the split ratio (SR) index (parameter D,
dif. = 5.6) has the highest contribution to CO reduction, followed by parameter A (power,
dif. = 5.2), the interaction of parameters power and EA (dif. = 3.1), parameter C (GA,
dif. = 2.9) and parameter B (EA, dif. = 2.6). There is a parameter that was not identified,
whose influence is higher than that of the interaction of parameters power and EA, GA,
and EA. This indication is supported by the value of the left-hand column of the SR value
(column 10 is identified as “e”) and presents a dif. = 3.3. This result may show the influence
of parameters that were not considered but make some significant contribution to the
process, for example the combustion chamber temperature.
Table 5 presents the analysis based only on the mean values of the CO concentration
for each parameter. These data confirm the trend observed by the analysis of the S/N
indices, on how to minimize the response value. It shows that the most important is SR,
with a difference of about 207 ppm, followed by power at 194 ppm, and other unidentified
parameters (column 9 is identified as “e”) at 144 ppm.
Table 4. Mean values of S/N index and maximum differences between levels on CO.
P
EA
S/N
A
B
1
2
3
dif.
−49.9
−44.7
−47.8
5.2
−46.8
−46.5
−49.1
2.6
GA
A×B
−48.7
−46.1
−47.5
−49.2
−46.7
−46.4
3.1
C
−46.4
−46.5
−49.6
2.9
SR
A×C
−47.0
−46.3
−49.0
−48.6
−47.4
−46.3
2.7
B×C
e
D
B×C
e
e
−47.4
−46.5
−48.4
2.2
−47.2
−45.9
−49.2
3.3
−45.3
−46.1
−50.9
5.6
−48.2
−46.2
−47.9
-
−48.0
−47.1
−47.2
0.8
−48.6
−46.1
−47.6
2.5
Table 5. Analysis of the relative contribution of the parameters based on the mean concentration of CO.
P
EA
GA
SR
Average
A
B
A×B
C
A×C
B×C
e
D
B×C
e
e
1
2
3
dif.
376.8
182.7
256.9
194.1
285.8
231.8
298.9
67.2
333.5
349.0
230.1
228.5
252.9
239.0
120.5
240.1
229.1
347.2
118.2
246.7
290.4
232.8
247.8
337.0
278.2
104.2
245.4
236.0
335.1
114.5
249.3
211.6
355.5
143.9
198.6
212.6
405.3
206.7
284.5
220.6
311.4
272.4
245.5
298.6
53.1
288.5
233.5
294.4
60.9
230.1 228.5
252.9 239.0
120.5
229.1
347.2
118.2
232.8 247.8
337.0 278.2
104.2
236.0
335.1
114.5
211.6
355.5
143.9
212.6
405.3
206.7
220.6
311.4
245.5
298.6
53.1
233.5
294.4
60.9
386
Figure 6 shows the dependence of the response value with the different parameters,
for the various levels of analysis. These data show the evolution through the three levels
for each of the parameters with respect to the CO concentration. Figure 6a shows that the
Figure 6CO
shows
the dependence
the response
value
different parameters,
lowest observed
emission
occurs at of
medium
power
(13 with
kW).the
Meanwhile,
Figure 6b–d
for the various
levels
of analysis.
Theseat
data
thelevel
evolution
the three
levels
demonstrates
that CO
emission
is lower
theshow
lower
of thethrough
parameter
applied
and
for
each
of
the
parameters
with
respect
to
the
CO
concentration.
Figure
6a
shows
that
the
increases with the increasing of the EA, GA, and SR, respectively. This means that an isoobserved
at medium
power
(13unburned
kW). Meanwhile,
Figuretranslat6b–d
lated lowest
increase
of any CO
oneemission
of thoseoccurs
parameters
creates
more
substances,
demonstrates that CO emission is lower at the lower level of the parameter applied and ining into a rise in CO emissions.
creases with the increasing of the EA, GA, and SR, respectively. This means that an isolated
increase of any one of those parameters creates more unburned substances, translating into
a rise in CO emissions.
–35
–40
–40
S/N (dB)
–35
–45
–50
–55
A1
A2
–45
–50
–55
A3
B1
B2
P (level)
EA (level)
(a)
(b)
–35
–35
–40
–40
S/N (dB)
Fuels 2021, 2
231.8
298.9
67.2
S/N (dB)
182.7
256.9
194.1
S/N (dB)
2
3
dif.
–45
–50
–55
B3
–45
–50
–55
C1
C2
GA (level)
(c)
C3
D1
D2
D3
SR (level)
(d)
Figure 6. Individual influence of factors on the response (CO) S/N obtained for the three leves of (a)
Power (b) Excess Air (c) Grate Area (d) Split Ratio.
Figure 7 presents the interaction between all parameters on CO emission. From its
analysis one can identify that there is an important interaction between middle power and
lower EA (Figure 7a), and between middle power and lower GA (Figure 7b). Based on
Figure 7 and the value of maximum differences between levels on CO (Figure 6), we can
conclude that the best combination to reduce the variance (more stable and best result) is
A2-B1-C1-D1, corresponding to middle power, at low excess air, small grate area and low
split ratio.
analysis one can identify that there is an important interaction between middle power and
lower EA (Figure 7a), and between middle power and lower GA (Figure 7b). Based on
Figure 7 and the value of maximum differences between levels on CO (Figure 6), we can
conclude that the best combination to reduce the variance (more stable and best result) is
A2-B1-C1-D1, corresponding to middle power, at low excess air, small grate area and low 387
split ratio.
–35
Power (Level 1)
Power (Level 2)
Power (Level 3)
S/N (dB)
–40
–45
–50
–35
Power (Level 1)
Power (Level 2)
Power (Level 3)
–40
S/N (dB)
Fuels 2021, 2
–45
–50
–55
B1
B2
–55
B3
C1
EA (level)
(a)
C2
GA (level)
C3
(b)
Figure
7. Indices
of interaction
between
factors
on CO
S/NS/N
values
for: for:
(a) each
EA level
at different
Figure
7. Indices
of interaction
between
factors
on CO
values
(a) each
EA level
at different
power
levels;
(b) each
GAGA
level
at different
power
levels.
power
levels;
(b) each
level
at different
power
levels.
analysis
of variance
(ANOVA),
F-test
performed.
an F-test,
ForFor
the the
analysis
of variance
(ANOVA),
an an
F-test
waswas
performed.
OnOn
an F-test,
thethe
F F
value
corresponds
a ratio
between
variance
a parameter
variance
of the
value
corresponds
to atoratio
between
thethe
variance
of aofparameter
andand
thethe
variance
of the
error.
Table
6 shows
results
of the
F-test
from
analysis
of variance.
determine
error.
Table
6 shows
thethe
results
of the
F-test
from
thethe
analysis
of variance.
To To
determine
whether
F value
two
variances
statisticallyhigh,
high,one
oneshould
shouldconsider:
consider:(i)
(i)the
thelevel
level of
whether
an an
F value
of of
two
variances
is is
statistically
confidence
required;
(ii)
the
degrees
of
freedom
associated
with
the
variance
of
the
of confidence required; (ii) the degrees of freedom associated with the variance ofsample
the
in theinnumerator;
and (iii)
of freedom
(df ) associated
with the
sample
variance
sample
the numerator;
andthe
(iii)degrees
the degrees
of freedom
(df) associated
with
the sample
in thein
denominator.
The critical
valuevalue
of F is
compared
withwith
the Fthe
value
of a of
ratio
variance
the denominator.
The critical
ofthen
F is then
compared
F value
of sample
variances.
TheThe
analysis
of of
variance
is is
a more
objective
and
a ratio
of sample
variances.
analysis
variance
a more
objective
andquantifiable
quantifiabletest,
test,allowing
allowingconclusions
conclusionswhich
whichare
arenot
notpossible
possiblewith
withthe
thesimple
simpleanalysis
analysisof
ofthe
themeans
meansor
orthe
S/N
indices.
Table
7
presents
the
statistical
calculation
of
the
F
critical
according
to
the S/N indices. Table 7 presents the statistical calculation of the F critical according to thethe
level
of risk
α (1%,
and
10%),
final
configuration
of Table
6. This
method
level
of risk
α (1%,
5%,5%,
and
10%),
forfor
thethe
final
configuration
of Table
6. This
method
of of
analysis
was
previously
applied
by
Ferreira
[22].
analysis was previously applied by Ferreira [22].
P
ANOVA
1
A
df
sq
var
pool
F
sq’
%
2
121.14
60.57
n
3.96
90.59
14.59
In Table 6, only the parameters that contribute significantly to the reduction of CO
Table 6. ANOVA analysis.
concentration are identified. After applying the analysis of variance, from Table 6, it is
(SR) is higher than the critical
value
EA observed that the
GA F value associated to parameter D
SR
Error
for the confidence index (risk α = 0.05) but less than α = 0.01, calculated forCalculation
the same
2
3&4
5
6&7
8 & 11
9
10
12
13
number of degrees of freedom (df = 2). From these results, one can conclude that, with a
Error
contributes
by
B confidence
A × B index
C higher
A ×than
C 95%,
B × Cit can be
e statedDthat the eSR intensity
e
Total
Exp.
about 21% to the reduction of CO concentration. The same scenario occurs for the F value
2 associated
4
4
4
2 the value
2 for α 2= 0.05. This
2
22 that the
26
with 2power, which
is higher
than
means
35.84
73.10
49.06
60.09
36.08
50.35
163.76
3.74
27.84
336.09
620.99
confidence index is the same as the previous parameter referred to the SR. Based on that,
17.92
18.28
24.53
15.02
9.02
25.18
81.88
1.87
13.92
15.28
23.88
it can be
said that,
with a sconfidence
index of
over 95%,
the influence
ofspower contributes
s
s
s
s
s
n
s
n
15%
to the- reduction
- approximately
- of CO concentration.
5.36
Looking
at -Table 6, one
that
from
SR followed
- can conclude
- the main
133.20effect comes
- the397.20
620.99
- by power
- (A). However,
-the value- associated
- with21.45
100.00
the experimental
error
is 63.96
63.96%. This
value is indeed significant and deserves careful consideration, as it has a dimension to
mask the influence of the most significant parameters. In practice, this error can be the
Table 7. ANOVA F critic calculation for CO.
result of several factors: an important parameter not considered in the study, unsuitability
F Critic
Degree of Freedom (df )
Nominator
Denominator
α
0.10
0.05
0.01
2
22
4
22
2.56
3.44
5.72
2.22
2.82
4.31
Fuels 2021, 2
388
In Table 6, only the parameters that contribute significantly to the reduction of CO
concentration are identified. After applying the analysis of variance, from Table 6, it is
observed that the F value associated to parameter D (SR) is higher than the critical value for
the confidence index (risk α = 0.05) but less than α = 0.01, calculated for the same number
of degrees of freedom (df = 2). From these results, one can conclude that, with a confidence
index higher than 95%, it can be stated that the SR intensity contributes by about 21% to the
reduction of CO concentration. The same scenario occurs for the F value associated with
power, which is higher than the value for α = 0.05. This means that the confidence index is
the same as the previous parameter referred to the SR. Based on that, it can be said that,
with a confidence index of over 95%, the influence of power contributes approximately
15% to the reduction of CO concentration.
Looking at Table 6, one can conclude that the main effect comes from the SR followed
by power (A). However, the value associated with the experimental error is 63.96%. This
value is indeed significant and deserves careful consideration, as it has a dimension to
mask the influence of the most significant parameters. In practice, this error can be the
result of several factors: an important parameter not considered in the study, unsuitability
of the selected levels, misadjustment with the level of factors, and any deficiency in the
control of the chosen parameters or instabilities in the operation, as stated by Ferreira [22].
3.2. ANOVA Analysis on Temperature
The mean values of the S/N indices for each of the three levels of all parameters
or interaction with fuel bed temperature are presented in Table 8. The data show the
maximum value of the difference between the averages of the indices for each of the
parameters. These results evaluate the relative weight of influence of each parameter on
the response value.
Table 8. Mean values of the S/N index and maximum differences between levels on temperature.
P
EA
GA
SR
S/N
A
B
A×B
C
A×C
B×C
e
D
e
e
dif. (5 mm)
dif. (15 mm)
dif. (25 mm)
dif. (60 mm)
4.22
2.42
2.64
2.97
4.55
2.27
2.01
0.90
2.78
3.13
6.60
4.43
2.76
1.67
5.66
4.42
1.61
2.51
4.08
1.80
2.16
4.25
2.67
3.36
0.75
4.08
3.95
1.31
3.70
4.21
7.91
4.95
2.14
2.10
3.98
3.96
0.59
3.77
2.65
1.21
Table 8 presents the mean value of the S/N index and the maximum difference between levels on the temperature at different heights in the fuel bed. For temperatures
at 5 mm, the excess air (EA) has the highest contribution to increasing the temperature,
followed by power, SR, the interaction of parameters power and EA, and the parameter
GA. For temperatures at 15 mm, the highest contribution is the interaction of parameters EA and GA, SR, and one unidentified parameter (column 10) followed by another
unidentified parameter (column 14), the interaction of power and EA, and power and GA.
For temperatures at 25 mm, the highest contribution is from the parameter SR, followed
by interaction of parameters power and EA, GA, the interaction of power and GA, and
two unidentified parameters (column 13 and 10). For temperatures at 60 mm, the highest
contribution is from parameter SR, followed by interaction of parameters power and EA,
GA, one unidentified parameter (column 13), and the interaction of EA and GA.
The temperature behaviour on the fuel bed at 5, 15, 25, and 60 mm was also studied.
Table 9 presents the analysis based only on the mean values of temperature at 5, 15, 25, and
60 mm observed for each parameter. The data confirms the trend observed by the analysis
of the S/N indices.
dif. (60 mm) 2.97
4.43
4.42
1.80
3.36
1.31
4.95
3.96
1.21
The temperature behaviour on the fuel bed at 5, 15, 25, and 60 mm was also studied.
Table 9 presents the analysis based only on the mean values of temperature at 5, 15, 25,
and 60 mm observed for each parameter. The data confirms the trend observed by the
analysis of the S/N indices.
389
9. Analysis
the relative
contribution
the parameters
the mean
temperature.
Table Table
9. Analysis
of the of
relative
contribution
of the of
parameters
on theon
mean
temperature.
P
Average
Average A
dif. (5 dif.
mm)
(5 mm)1.7
(15 mm)
dif. (15dif.
mm)
108.4
(25 mm)
dif. (25dif.
mm)
102.4
dif. (60 mm)
dif. (60 mm) 110.3
PEA
AB
47.1
1.7
108.4
33.5
102.4
10.8
110.3
44.4
EA
GA
A
B × B A ×CB
18.1 18.1
52.6
47.1
33.5
54.1 54.1
72.3
10.8
70.6 70.6
102.2
44.4
34.2
34.2 142.9
GA
AC× C
43.3
52.6
72.3
49.5
102.2
69.9
142.9
61.2
AB××CC
34.9
43.3
49.5
42.4
69.9
67.2
61.2
104.5
SR
SR
eD
Be× C D e
43.5
55.1
5.5
34.9
43.5 55.1
42.4 38.4
73.8 49.1
38.4
73.8
67.2 51.3
51.1 63.2
51.3
51.1
104.5
41.6 87.2
41.6 87.2 31.0
ee
23.0
5.5
49.1
31.4
63.2
39.4
31.0
20.6
e
23.0
31.4
39.4
20.6
-20
-20
-25
-25
S/N (dB)
S/N (dB)
8 shows
the evolution
S/N
for temperature
at 5height
mm height
with
the different
FigureFigure
8 shows
the evolution
of S/Noffor
temperature
at 5 mm
with the
different
parameters,
forvarious
the various
levels
analysis.
Thetemperature
temperaturedecreases
decreases as power
parameters,
for the
levels
of of
analysis.
The
powerincreases,
ina behaviour
that
may
result
from
thethe
accumulation
of of
wood
pellets
onon
thethe
burner
at high
creases,
a behaviour
that
may
result
from
accumulation
wood
pellets
burner
power
levels,
The parameters
parametersEA
EA and
at high
power
levels,moving
movingaway
awaythe
thereaction
reactionzone
zonefrom
from the
the grate.
grate. The
GApresent
present
almost
same
behaviour,
showing
lower
power
higher
and GA
almost
thethe
same
behaviour,
showing
thatthat
lower
power
levelslevels
yieldyield
higher
temperatures.
This
phenomenon
can
be
explained
by
the
fact
that
higher
EA
and
temperatures. This phenomenon can be explained by the fact that higher EA and GA pro- GA
provide
morethe
airgrate
intothat
the may
gratereduce
that may
reduce the temperature.
For SR,
the higher
vide more
air into
the temperature.
For SR, the higher
temperobserved
at the
middle
set point,
a situation
that
represent the
ature temperature
was observedwas
at the
middle set
point,
a situation
that may
represent
themay
competition
competition
between
two effects:
a temperature
increase
a result
of the by
heat
generated
between
two effects:
a temperature
increase
as a result
of the as
heat
generated
higher
by
higher
devolatilization
that
occurs
with
increasing
primary
air
and
the
cooling
effect of
devolatilization that occurs with increasing primary air and the cooling effect of a greater
a
greater
air
mass
crossing
the
fuel
bed.
air mass crossing the fuel bed.
-30
-35
-40
-30
-35
-40
-45
A1
-45
A2
A3
P (level)
B1
-20
-25
-25
S/N (dB)
-20
-30
-35
B2
B3
EA (level)
(b)
(a)
S/N (dB)
Fuels 2021, 2
0.90
-30
-35
-40
-40
-45
-45
C1
C2
C3
GA (level
(c)
D1
D2
D3
SR (level
(d)
FigureFigure
8. Individual
influence
of factors
on the temperature
at 5 mm,atS/N
obtained
for the three
8. Individual
influence
of factors
on the temperature
5 mm,
S/N obtained
for the three
leves of
(a)
Power
(b)
Excess
Air
(c)
Grate
Area
(d)
Split
Ratio.
leves of (a) Power (b) Excess Air (c) Grate Area (d) Split Ratio.
Figure 9 presents the interaction between all parameters for the temperature at 5 mm.
From these graphs it can identified that there is an important interaction between lower
power and lower EA (Figure 9a), and between lower power and lower GA (Figure 9b).
Based on Figure 9 and the value of maximum differences between levels on temperature at
5 mm (Table 8), the best combination to reduce the variance (more stable and best result)
–20
–20
–25
–25
S/N (dB)
S/N (dB)
Fuels 2021, 2
Figure 9 presents the interaction between all parameters for the temperature at 5 mm.
From these graphs it can identified that there is an important interaction between lower
power and lower EA (Figure 9a), and between lower power and lower GA (Figure 9b). 390
Based on Figure 9 and the value of maximum differences between levels on temperature
at 5 mm (Table 8), the best combination to reduce the variance (more stable and best result)
is A1-B1-C1-D2, corresponding to low power level, low excess air, low grate area and inis A1-B1-C1-D2, corresponding to low power level, low excess air, low grate area and
termediate split ratio.
intermediate split ratio.
–30
–35
Power (Level 1)
Power (Level 2)
Power (Level 3)
–40
–45
B1
B2
EA (level)
B3
–30
–35
Power (Level 1)
Power (Level 2)
Power (Level 3)
–40
–45
C1
C2
GA (level)
(a)
C3
(b)
Figure
9. Indices
of interaction
between
factorsfactors
(T at 5(T
mm)
values
(a) each
EAeach
levelEA
at level at
Figure
9. Indices
of interaction
between
at 5S/N
mm)
S/N for:
values
for: (a)
different
power
levels;
(b)
each
GA
level
at
different
power
levels.
different power levels; (b) each GA level at different power levels.
Table
10 presents
overall
influences
of the
parameters
applied
in this
study
Table
10 presents
the the
overall
influences
of the
parameters
applied
in this
study
on on
temperature
at different
heights.
show
most
influencing
the the
fuelfuel
bedbed
temperature
at different
heights.
The The
datadata
show
thatthat
the the
most
influencing
parameter
on temperature
the temperature
at different
heights
is the
which
is contributing
about
parameter
on the
at different
heights
is the
SR, SR,
which
is contributing
about
5, 25,
60 mm
respectively.
second
parameter
most
12%,12%,
21%,21%,
andand
19%19%
at 5,at25,
andand
60 mm
respectively.
TheThe
second
parameter
thatthat
most
influences
the temperature
is power,
which
contributes
approximately
5%5at 5
influences
the temperature
is power,
which
contributes
approximately
14%14%
andand
5% at
60 mm.
andand
60 mm.
Table
10. The
overall
influences
of parameters
the parameters
on fuel
temperature.
Table
10. The
overall
influences
of the
on fuel
bed bed
temperature.
P
EA
P
EA
GA GA
Height
AB
BA × BA × B C C A × A
A
C×C
B × CB ×eC
Height (mm)
(mm)
1
2
3
&
4
5
6
&
7
8
1
2
3&4
5
6 & 7 & 118 & 911
5
14.4%
17.6%
- 5
14.4% 17.6%-15
- 15 -- -25 -- -25
- 60
5.0%
17.2%
17.2%
9.3%
60
5.0%
17.2% 17.2%
9.3%
-
SR
e
D
10
9
11.8%
-21.0%
19.2%
SR
e e e e
12
10
12 13 13
- 11.8%- - - - 21.0%- - 19.2% 12.8% 12.8% D
4. Conclusions
4. Conclusions
main
purpose
of this
work
to study
the influence
of thermal
the thermal
The The
main
purpose
of this
work
waswas
to study
the influence
of the
load,load,
pri- primary/secondary
air
split
ratio,
grate
area
and
excess
air
on
the
gases
of
wood
pellet
mary/secondary air split ratio, grate area and excess air on the gases of wood pellet comcombustion
in
a
purpose-built
wood
pellet
boiler
by
using
the
Taguchi
method.
The
boiler
bustion in a purpose-built wood pellet boiler by using the Taguchi method. The boiler
was
designed
by
a
research
team
and
was
computer-controlled,
capable
of
changing
was designed by a research team and was computer-controlled, capable of changing the the
air flow,
primary-secondary
air split
ratio
dimensions
of the
grate.
fuelfuel
rate,rate,
air flow,
primary-secondary
air split
ratio
andand
the the
dimensions
of the
fuelfuel
grate.
The
Taguchi
method
was
applied
in
order
to
optimize
the
process.
It
has
been
used
The Taguchi method was applied in order to optimize the process. It has been used by
other
authors and
most
efficient
in the
field.
The The
Taguchi
method
by other authors
and isisconsidered
consideredone
oneofofthe
the
most
efficient
in the
field.
Taguchi
allows
one to
comprehend
the interaction
and the
of factors
in order
to optimize
method
allows
one
to comprehend
the interaction
andinfluence
the influence
of factors
in order
to
the
process.
At
the
same
time,
due
to
its
simplicity,
it
allows
the
reduction
ofofthe
optimize the process. At the same time, due to its simplicity, it allows the reduction
thetotal
number of tests, reducing time and cost.
total number of tests, reducing time and cost.
The highest thermal efficiency obtained was 92%. This efficiency corresponded to the
lowest CO emissions, and it was obtained at middle power, middle excess air (EA), higher
split ratio (SR) and higher grate area (GR).
From the Taguchi method and the statistical analysis (ANOVA), it was concluded
that the split ratio was the parameter contributing the most for the CO reduction on a
percentage of 21.5%, followed by power with 14.6%. The data show that the medium
power level (13 kW) has the highest efficiency and lowest CO emissions compared to both
lower (10 kW) and higher power (16 kW). The excess air (EA), grate area (GA), and SR
Fuels 2021, 2
391
show the same trend for the efficiency and CO emissions, where lower and middle values
for those parameters correspond to optimal performances.
Regarding the average temperature in the fuel bed, the results indicated that the
highest temperature was observed at 15 mm followed by 25, 5 and 60 mm in height. The
SR and power levels are the most important parameters contributing to increase the fuel
bed temperature.
Author Contributions: Conceptualization, L.F.; Data curation, L.F., J.C.T. and E.F.; Formal analysis:
C.C. and P.R.; Investigation: L.F., C.C. and P.R.; Methodology: L.F., C.C. and P.R.; Project Administration: J.C.T.; Resources: J.C.T. and J.M.; Supervision: J.C.T. and E.F. All authors have read and agreed
to the published version of the manuscript.
Funding: This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the
R&D Units, MEtRICs Project Scope: UIDB/04077/2020; Lelis Fraga was supported through a PhD
Grant by Fundo de Desenvolvimento Capital Humano of the Government of Timor Leste.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: This work is supported by FCT—Fundação para a Ciência e Tecnologia within
the R&D Units Project Scope: UIDB/04077/2020 (METRICS Centre). Lelis Fraga Acknowledges the
absence of leave granted by the University of Timor Leste in Dili.
Conflicts of Interest: The authors declare no conflict of interest.
Nomenclature
α
C pw
∆Tw
df
EA
F
FB
GA
LHV
.
mf
.
mw
ηb
P/S
Pi
Pn
sq
SR
S/N
var
X
σx 2
Level of risk
Heat capacity of water (kJ.kg−1 .K−1 )
Temperature difference (K)
Degrees of freedom
Excess of Air
Ratio between the variance of a parameter and the variance of the error
Fuel Bed
Grate Area (m2 )
Low Heating Value
Fuel mass flow rate (kg.s−1 )
Water mass flow rate (kg.s−1 )
Boiler efficiency (%)
Primary and Secondary
Power input (kW)
Nominal boiler power (kW)
Sum of square
Split Ratio
Signal to Noise
Variance
Average value
Variance
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