determination of key factors affecting start-up …...also like to give my thanks to tirthankar...
TRANSCRIPT
Determination of Key Factors Affecting Start-Up Phase
Particulate Matter Emissions in a Modern Wood Stove
by
Derek Fong
A thesis submitted in conformity with the requirements
for the degree of Master of Applied Science
Department of Mechanical and Industrial Engineering
University of Toronto
© Copyright by Derek Fong 2018
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Determination of Key Factors Affecting Start-Up Phase
Particulate Matter Emissions in a Modern Wood Stove
Derek Fong
Master of Applied Science
Department of Mechanical and Industrial Engineering
University of Toronto
2018
Abstract
This research investigates the effect of inlet air flow-rate and airflow location on PM emissions
during the start-up phase in a modern wood stove. To do this, the airflow directed towards the fuel
bed was increased over the start-up period and compared to the baseline case. The experimental
setup consists of a wood stove equipped with instruments to measure temperature, wood burn-rate,
O2 and CO2 volume fractions, and the PM emission rate over time. An order of magnitude
reduction in start-up phase PM emissions per unit of energy was found when adding additional
fuel-bed air during start-up. Furthermore, it was observed that an excess amount of secondary air
entered the wood stove during the start-up phase, leading to sub-optimal combustion conditions.
In future studies, it is recommended to further optimize the primary and secondary airflow rates
during start-up and to further standardize the test methodology to reduce variance.
iii
Acknowledgments
Working in the Combustion Research Laboratory has been a challenging and rewarding
experience. I would like to thank Professor Murray Thomson for his continued support throughout
the project. His guidance enabled me to persevere through countless challenges and face problems
in a creative way. I would also like to thank my parents for their support throughout this journey.
Raul Morales Delgado played an important role in this project by preparing all test wood, assisting
with assembly of the experiment, and providing his advice and feedback on my questions. I would
also like to give my thanks to Tirthankar Mitra, who educated me on topics such as the use of
laboratory equipment, Anton Sediako, for his support installing and calibrating the laser extinction
setup, and Sam Shi, for his work producing detailed CFD models of the wood stove. I would also
like to extend my thanks to the entire Combustion Research Laboratory for their assistance with
any support I required.
Many thanks to Ryan de Rose, Paul Hodges, and Blaine Whitley from Wolf Steel Ltd., as they
have been instrumental in this project by providing technical advice, designing and building
adapters and the dilution tower, and supplying the test wood.
Finally, I would like to thank Ontario Centres of Excellence and Wolf Steel Ltd. for their financial
support.
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Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Chapter 1 ..........................................................................................................................................1
Introduction .................................................................................................................................1
1.1 Motivation ............................................................................................................................1
1.2 Objectives ............................................................................................................................2
Chapter 2 ..........................................................................................................................................4
Literature Review ........................................................................................................................4
2.1 Wood Properties...................................................................................................................4
2.1.1 Wood Structure ........................................................................................................4
2.1.2 Elemental Composition ............................................................................................5
2.1.3 Energy Density.........................................................................................................6
2.2 Wood Combustion ...............................................................................................................6
2.2.1 Combustion Phases ..................................................................................................6
2.2.2 Emissions from Wood Stoves ................................................................................10
2.3 Emission Reduction Methods ............................................................................................12
2.3.1 Air and Fuel Mixing...............................................................................................13
2.3.2 Oxidation Catalysts ................................................................................................14
2.3.3 CFD Modeling .......................................................................................................16
Chapter 3 ........................................................................................................................................18
Experimental Setup ...................................................................................................................18
3.1 Experimental Setup Overview ...........................................................................................18
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3.2 Wood Stove and Dilution Tunnel ......................................................................................18
3.3 Instrumentation ..................................................................................................................21
3.3.1 Instrumentation Overview .....................................................................................21
3.3.2 Mass Measurement ................................................................................................22
3.3.3 Temperature Measurement ....................................................................................22
3.3.4 O2 and CO2 Volume-Fraction Measurement .........................................................23
3.3.5 PM Measurement ...................................................................................................25
3.4 Data Acquisition, Visualization, and Analysis ..................................................................26
Chapter 4 ........................................................................................................................................27
Experimental Methodology .......................................................................................................27
4.1 Experimental Methodology: Preface .................................................................................27
4.2 Wood Characteristics and Preparation ...............................................................................28
4.3 Test-Wood Moisture Readings ..........................................................................................29
4.4 Burn Procedure ..................................................................................................................30
4.5 PM Sample Preparation and Weighing ..............................................................................30
4.6 Data Analysis .....................................................................................................................31
4.6.1 MATLAB Script ....................................................................................................31
4.6.2 PM Mass to Emission Factor Conversions ............................................................33
Chapter 5 ........................................................................................................................................35
Results and Discussion ..............................................................................................................35
5.1 Repeatability ......................................................................................................................35
5.2 Results ................................................................................................................................36
5.2.1 Visual results ..........................................................................................................36
5.2.2 Temperature Results ..............................................................................................37
5.2.3 Excess Oxygen Results ..........................................................................................39
5.2.4 Burn-Rate Results ..................................................................................................41
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5.2.5 PM Emission Results .............................................................................................42
5.2.6 Synthesis of Results ...............................................................................................45
5.3 Start-Up Phase PM Reduction Methods ............................................................................46
Chapter 6 ........................................................................................................................................48
Conclusions and Recommendations .........................................................................................48
6.1 Conclusions ........................................................................................................................48
6.2 Future Work .......................................................................................................................48
Bibliography ..............................................................................................................................50
Appendix A ....................................................................................................................................55
Appendix B ....................................................................................................................................59
Appendix C ....................................................................................................................................61
vii
List of Tables
Table 2.1: The elemental composition by mass of common North American tree species [19] .... 6
Table 3.1: Dilution tunnel specifications ...................................................................................... 19
Table 4.1: Wood specifications..................................................................................................... 28
Table 4.2: Summary of baseline and modified airflow conditions ............................................... 30
Table 5.1: Summary of PM samples from baseline tests and modified airflow tests ................... 44
Table 5.2: Summary of PM emission factors................................................................................ 45
viii
List of Figures
Figure 2.1: The molecular structure of cellulose, showing the 1-4 glyosidic bonds [15]............... 4
Figure 2.2: Cross-linked lignin structure [17]................................................................................. 5
Figure 2.3: The mass loss of beech wood volatiles over time using the TGA method [20] ........... 7
Figure 2.4: The pyrolysis pathways for cellulose [24] ................................................................... 7
Figure 2.5: CO concentration, guaiacol signal intensity, and naphthalene signal intensity plotted
over the duration of a test-burn [26] ............................................................................................... 9
Figure 2.6: The combustion mechanism of wood proposed by Fitzpatrick et al. [12] ................. 11
Figure 2.7: PM concentration plotted as a function of time, using a wood stove [14] ................. 12
Figure 2.8: OM concentration (Green) and inorganic matter (IM) concentration as a function of
time, using a wood stove [26] ....................................................................................................... 12
Figure 2.9: Organic Carbon (OGC or OM) concentration and CO concentration as a function of
time, using a wood stove [5] ......................................................................................................... 12
Figure 2.10: Schematic of air staging in a wood stove [35] (left) and a manifold-style secondary-
air inlet (right) ............................................................................................................................... 14
Figure 2.11: Ceramic monolith (left) and metal mesh sheet [5] (right) catalyst designs .............. 15
Figure 2.12: The Tammann temperatures of platinum group elements [39] ................................ 16
Figure 2.13: CFD-Simulation results of a wood stove cross-section without (left) and with (right)
multiple air inlets. The left set of images compares temperature profiles in C and the right set of
images compares CO monoxide profiles in ppm [14] .................................................................. 17
Figure 3.1: Dilution tunnel CAD model (left) and photo (right) .................................................. 19
Figure 3.2: Labeled Cutaway CAD model of the Napoleon NZ3000H wood stove .................... 20
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Figure 3.3: CAD model of the secondary air manifold in the Napoleon NZ3000H wood stove . 20
Figure 3.4: Experimental setup ..................................................................................................... 21
Figure 3.5: Temperature sampling location of the primary air (yellow), secondary air (blue),
stack (pink), and catalyst (red) ...................................................................................................... 22
Figure 3.6: Thermocouples installed in the primary (left) and secondary (right) air inlets .......... 23
Figure 3.7: Cut-away schematic of a UEGO sensor [42] ............................................................. 24
Figure 3.8: Filter probe components [11] ..................................................................................... 25
Figure 3.9: PM sampling train components .................................................................................. 26
Figure 4.1: Kindling load (left) and pre-burn load (right), shortly after ignition ......................... 29
Figure 4.2: Douglas fir test load ................................................................................................... 29
Figure 5.1: Stack temperature as a function of time for the baseline and modified tests over 120
minutes (right) and 30 minutes (left) ............................................................................................ 37
Figure 5.2: Stack temperature as a function of inlet air flow-rate ................................................ 38
Figure 5.3: Excess oxygen as a function of time for the baseline and modified tests over 120
minutes (right) and 30 minutes (left) ............................................................................................ 39
Figure 5.4: Excess oxygen as a function of inlet air flow-rate ..................................................... 40
Figure 5.5: Burn rate as a function of time for the baseline and modified tests over 120 minutes
(right) and 30 minutes (left) .......................................................................................................... 41
Figure 5.6: Burn rate as a function of inlet air flow-rate .............................................................. 42
Figure 5.7: Start-up phase PM concentration as a function of wood consumption ...................... 43
Figure 5.8: A PM filter sample from the baseline test for the first 15 minute (left), the second 15
minutes (centre) and the entire test (right) .................................................................................... 44
Chapter 1
Introduction
1.1 Motivation
With oil, natural gas, and coal accounting for nearly 70% of total global energy use in 2015 [1],
there is no doubt that fossil fuels play a crucial role in the livelihood of modern society. Despite
their high energy-density and ubiquity, fossils fuels are non-renewable and contribute to climate
change. To create a healthier ecosystem and a sustainable future, there has been an international
effort to advance renewable forms of energy such as biomass, solar, wind and geothermal energy.
Bioenergy is any form of energy derived from biological sources such as wood, crops, organic
waste, and algae. For industries that can make use of waste biomass, such as in the forestry industry
or pulp and paper industry, biomass is a promising alternative fuel that reduces net CO2 emissions
to the atmosphere and can be more cost effective than fossil fuels. In rural areas with an abundance
of wood, or in remote communities that consume large amounts of imported diesel fuel [2],
residential wood stoves can serve as an efficient and low-cost source of heat. Modern stoves can
heat multiple rooms in a home as well as cook food, allowing the home to be off-the-grid. With an
energy density of between 18 and 21 MJ/kg [3], the energy density of wood is much lower than
that of fossil fuels (54 MJ/kg [4]), but significantly more than competing forms of energy storage
such as batteries.
Despite the promising aspects of residential wood combustion, wood stoves are notorious for their
high particulate matter (PM) emissions due to the insufficient mixing between fuel and air inside
of the firebox [5]. To put this issue into perspective, Wu et al. [6] found that residential wood
burning contributed to 24—31% of all PM2.5 in the city of Seattle. Numerous epidemiological
studies have been performed over decades which have studied the health endpoints of PM exposure
[7]. Particles with an aerodynamic diameter less than 10µm penetrate the respiratory tract, with
smaller particles reaching farther down the tract [8], [9]. These particles then enter the blood stream
and have been identified to cause a wide range of health conditions such as asthma, chronic
2
bronchitis, cancer, and cardiovascular diseases [7]. Typically, particles exceeding 10µm in size do
not enter the respiratory tract due to the processes of sneezing and coughing [10].
Because of the harmful impact of wood stoves, North American and European governments have
created wood stove certification tests to limit the amount of PM1 (henceforth referred to as PM)
emissions that can be collected over the course of a test. In the United States and Canada, the
newest EPA 5G standard [11] will be enforced in the year 2020 and will decrease the maximum
PM emission rate from the previous standard of 4.0 g/hr in 2015 to 2.0 g/hr. It has therefore become
imperative for stove manufacturers to develop cleaner burning wood stoves.
1.2 Objectives
Many studies have been conducted on wood stoves to better understand the PM formation
mechanism, the burning stages of log-wood, and the emission control strategies to diagnose poor
combustion performance. Despite this, few have validated methods to optimize both the airflow
location (in contact with the fuel bed or above the fuel bed) and air-amount during the start-up
phase, where most PM emissions occur. For example, Fitzpatrick et al. [12] modified air flow-
rates in a large box with burning wood and observed a decrease in total PM emissions with an
increase in the mass flow-rate of air. The study did not however consider the transient air-demand
during each burn phase, nor did it consider the effect of air location; the air was directed at the fuel
bed for the duration of the experiment. Reichert et al. [13] performed a similar test but instead
varied the draft flow-rate of the chimney stack. Again, this study again did not consider the location
of the inlet air or the airflow conditions over time. In a review paper by Brunner et al. [14], air
staging using primary and secondary air was cited as the most effective method of PM emission
reduction. Overall PM emission results were given with and without air staging, but optimization
of the air staging—with a focus on start-up emissions, was not explored.
To address this literature gap, the objective of this study is to identify the effect that inlet air flow-
rate and airflow location have on the formation of PM emissions during the first 30 minutes of the
wood combustion process, hereby referred to as the “start-up” period. This information will then
be used to establish heuristics that will to help develop low PM emitting wood stoves which will
meet stringent government regulations.
3
The specific objectives of this study are as follows:
• Build an experimental setup using a modern wood stove that allows for the collection of
the following operating parameters: temperature, wood mass change, exhaust CO2 volume
fraction, exhaust O2 volume fraction, total PM mass, and inlet air flow-rate
• Develop an experimental methodology, based off the EPA 5G standard
• Perform unmodified tests and use the acquired data and visual observations to understand
the key combustion characteristics during the 30-minute start-up phase
• Modify the baseline test by increasing the air entering the bottom of the firebox during the
start- up period and compare the key combustion parameters to the unmodified test
• Understand the effect that the airflow location and the airflow amount have on emissions
Chapter 2
Literature Review
2.1 Wood Properties
2.1.1 Wood Structure
With water removed, wood can be defined as a three-dimensional biopolymer composite
composed of carbohydrates (65-75%), lignin (18-35%), and extractives (0.5%-10%) [15], [16],
[17]. In general, softwoods have a higher cellulose and lignin content than hardwoods, while
hardwoods have a higher pentosan content. It is however difficult to identify individual tree species
due to the high degree of variance between samples of the same species and a similar composition
between different species [17].
Carbohydrates are a combination of cellulose (40%-50%), hemicellulose (25%-30%), and trace
amounts of sugar polymers. The combination of cellulose and hemicellulose is referred to as
holocellulose [15]. Cellulose determines the structure of the cell wall, as the molecules gather into
long bundles called microfibrils [18]. Cellulose is made up of linear chains of anhydroglucose
which are bonded together through glyosidic bonds, notated as 1-4β. The molecular structure of
cellulose is presented in Figure 2.1. Hemicellulose is closely tied to cellulose, but are highly
branched, non-linear combinations of compounds such as glucose, mannose, galactose, xylose,
arabinose, 4-O methylglucuronic acid, and galacturonic acid residues. Hemicellulose forms a
network with microfibrils and lignin [15].
Figure 2.1: The molecular structure of cellulose, showing the 1-4 glyosidic bonds [15]
5
Lignin consists of many cross-linked phenolic polymers (chemical compounds which contain a
hydroxyl group bonded to an aromatic hydrocarbon), which provide rigidity to the secondary cell
walls of wood and bark to make them more resilient and less permeable to water. Figure 2.2
illustrates the chemical structure of lignin.
Figure 2.2: Cross-linked lignin structure [17]
The remaining components that are not lignin, cellulose, or hemicellulose are referred to as
extractives and ash. Extractives include, but are not limited to, fatty acids, waxes, proteins,
terpenes, and essential oils. These components contribute to vital tree processes such as providing
defense against microbial attacks and functioning as intermediates in the metabolism process. Ash
is the remaining inorganic residue that does not fall into the extractives category. Woods typically
contain 1% of ash or less [15].
2.1.2 Elemental Composition
Across wood types, the primary elements by mass are carbon (45% - 50%), oxygen (40−50%),
hydrogen (about 6%), nitrogen (less than 1%), and ash (0.5% - 3%) [19]. In a study by Etiégni et
al. [20], the major components of ash were found to be Ca, K, Mg, and Si. These elements take
the form of carbonates (51% - 63% by mass, depending on the temperature) and oxides. Table 2.1
summarizes the fuel properties of common hardwood and softwood species in North America.
6
Table 2.1: The elemental composition by mass of common North American tree species [19]
2.1.3 Energy Density
Wood has a higher heating value (HHV) of approximately 18 MJ/kg (dry), with a variance of
approximately 15% [3]. When compared with the HHV values of natural gas (52.2 MJ/kg),
gasoline (46.4 MJ/kg), and anthracite coal (30.2 MJ/kg) [4], wood has an energy density that is
approximately 1.5—3 times lower than conventional fossil fuels.
2.2 Wood Combustion
2.2.1 Combustion Phases
The following phases divide the wood combustion process: start-up, steady-state, and char burning
[12], [21]. These phases are occurring simultaneously, with one phase predominating. To identify
the start and end of each phase, researchers look at the emission characteristics over time. This
section discusses the chemical phenomena occurring during each phase and the methods of
detecting when each phase occurs.
Start-Up Phase (Drying and Pyrolysis)
During the start-up phase, moisture evaporates from the wood and the pyrolysis process begins.
Figure 2.3 shows the results of a thermogravimetric analysis (TGA) test conducted by Nussbaumer
[21] to investigate the mass loss over time of beech wood in air. The drop in the sample mass
between 100C and 200C corresponds to the moisture evaporation of the wood. In addition, the
small number of extractives volatilize between 200C and 300C [22].
7
Figure 2.3: The mass loss of beech wood volatiles over time using the TGA method [20]
Pyrolysis is the irreversible, thermal decomposition process of biomass between 300C and 600C
and in the absence of oxygen. Two categories of reactions occur during the pyrolysis process:
Dehydration to create char through cross-linking reactions and the depolymerisation of biomass
through the splitting of glycosidic bonds [22], [23]. Pyrolysis always produces solids (char),
liquids (organics, water) and vapors (CO, CO2 etc.), with a ratio that depends on the feedstock and
heating conditions. Figure 2.4 summarizes the pyrolysis reactions for cellulose.
Figure 2.4: The pyrolysis pathways for cellulose [24]
Endothermic pyrolysis reactions occur below 400C—450C while exothermic reactions occur at
higher temperatures. Only when enough energy is supplied from exothermic reactions or from
outside sources can pyrolysis become self-sustaining solely through exothermic reactions.
Equations (2.1) through (2.6) are the major exothermic pyrolysis reactions that occur [24]. The
water gas shift reaction (2.2) is the rate-controlling reaction, as hydrogen is required as a reactant
for the methanation and methanol formation reactions. The formation of char is also a highly
8
exothermic process. Given average char yields of 35% of the original mass, a significant amount
of energy is produced from this reaction.
Char formation
0.17𝐶6𝐻10𝑂5 → 𝐶 + 0.85𝐻2𝑂 (2.1)
Water gas shift
𝐶𝑂 + 𝐻2𝑂 → 𝐶𝑂2 + 𝐻2 (2.2)
Methanation
𝐶𝑂 + 3𝐻2 → 𝐶𝐻4 + 𝐻2𝑂 (2.3)
𝐶𝑂2 + 4𝐻2 → 𝐶𝐻4 + 2𝐻2𝑂 (2.4)
Methanol Formation
𝐶𝑂 + 2𝐻2 → 𝐶𝐻3𝑂𝐻 (2.5)
𝐶𝑂2 + 3𝐻2 → 𝐶𝐻3𝑂𝐻 + 𝐻2𝑂 (2.6)
Both temperature and residence time affect the yield of char, liquids, and gases. At low
temperatures (<400C) and long residence times, char and liquid formation predominates. Under
these conditions, lignin yields approximately 50% of its weight as char and cellulose yields 8%-
15% [24]. In the temperature range of 450C—600C, char, tar, volatile liquids, and gases are
produced. With high temperatures (>400C), high heating rates, and long residence times, the
formation of volatile gases predominate [22].
One method of determining the start-up phase duration is through monitoring the CO concentration
of the exhaust gas, as it is a good indicator of the combustion state [25], [26]. CO is indicative of
sub-optimal combustion conditions and a dominant pyrolysis process. During the start-up phase,
CO increases sharply and slowly decreases. When the CO reaches a stable value, the steady
operation phase has been reached. In a unique approach by Elasser et al. [26], the start-up phase
was found using the signal intensity of guaiacol and naphthalene using a mass spectrometer. In
this study, the start-up phase is broken into two phases. Phase one is defined as the ignition period,
where pyrolysis increases rapidly. Phase two is the transition from start-up into stable combustion.
The signal intensity spike of guaiacol, a pyrolysis product of lignin, best describes start and end of
phase one. The decaying signal intensity of naphthalene, a polyaromatic hydrocarbon (PAH), was
9
used to characterize the decaying pyrolysis processes and the onset of the steady combustion.
Figure 2.5 shows the CO, guaiacol, and naphthalene signals, with Phase 1 and Phase 2 representing
the start-up period.
Figure 2.5: CO concentration, guaiacol signal intensity, and naphthalene signal intensity
plotted over the duration of a test-burn [26]
Steady Operation Phase (Oxidation)
During the steady operation phase, pyrolysis of the wood surface continues at a slower rate and
oxidation reactions occur between volatiles, char, and air. Oxidation requires temperatures of
800C—850C [14], [21] and a lean air/fuel ratio. H2O and CO2 are produced, as are trace amounts
of equilibrium products such as CO. Equation (2.7) is the combustion equation for wood by
Nussbaumer et al. [21], using the averaged chemical formula of a variety of tree species. The
equivalence ratio is denoted by the symbol ∅.
𝐶𝐻1.44𝑂0.66 +1.03
∅ (𝑂2 + 3.76𝑁2) → 𝐶𝑂2 + 0.72𝐻2𝑂 + (
1
∅− 1) 𝑂2 +
3.87
∅𝑁2 (2.7)
During this time, char is continuously formed [26], primarily from lignin [24]. Because the surface
char layer has a low heat conductivity and few remaining volatiles, the burn rate decreases with
increasing char layer depth. That said, extensive cracking of the charred surface allows air to
penetrate deeper and internal volatiles to escape [27].
The steady operation phase is observed as the period when the naphthalene signal intensity and
CO emissions are sufficiently low and CO2 emissions are high. The end of the steady operation
10
phase and the beginning of the decay phase is when the CO2 concentration has decreased
sufficiently and CO emissions once again increase [28], [26].
Decay Phase (Char burning)
Following the steady operation phase, all that remains of the wood is char. Burning of char through
surface oxidation or glowing combustion involves the direct oxidation of carbon to form carbon
monoxide, shown in equation (2.8). As mentioned previously, an increasing CO concentration at
the end of the combustion process indicates the start of the decay phase.
𝐶 +1
2𝑂2 → 𝐶𝑂 (2.8)
2.2.2 Emissions from Wood Stoves
Wood stoves are notorious for their high PM and gaseous emissions, particularly during start-up
and under improper firing conditions. The ideal wood stove would ensure perfect mixing between
air and fuel, resulting only in complete combustion emissions and fly ash that does not leave the
stove. In reality, such a stove does not exist. By understanding the composition of gaseous and
particulate emissions and the relative emission rate of each burn phase, emission control strategies
can be created. In addition, wood tracers can be detected in the atmosphere to determine the
contribution of wood to total atmospheric PM.
In a proposed mechanism by Fitzpatrick et al. [12] (shown in Figure 2.6), cellulose and
hemicellulose form monomers and oligomers such as hydroxyacetaldehyde, acetic acid, hydroxy-
propanone and levoglucosan. Amongst these, there is a consensus that levoglucosan is the most
prevalent source of fine particle emissions, with reported mass fraction values 5% and 24% by
Fine et al. [29] and 4.1% to 15.1% by Schmidl et al. [30]. Mannosan, a product of hemicellulose,
was commonly found to be the second most abundant source of fine particle emissions. With
sufficient temperatures and oxygen, these products leave as volatile organic compounds (VOC’s),
oxygenated cyclic or PAH compounds, or oxygenated soot species.
Lignin has different decomposition products from carbohydrates, attributed to its cross-linked
phenoic structure. Pyrolysis products with a similar structure to lignin such as syringyl, guaicyl,
and phenol can leave as smoke species in low temperature or fuel rich combustion regions [31].
11
At temperature of at least 800C [14], carbohydrates are oxidized into CO, H2, and complete
combustion products. Due to their more stable structure, lignin decomposes into lower number
hydrocarbons (C2Hx, C3Hx), PAH’s, and various oxygenated PAH’s. Through the HACA growth
mechanism, both soot and oxygenated soot species are formed.
Figure 2.6: The combustion mechanism of wood proposed by Fitzpatrick et al. [12]
Fly ash particles are formed from the release of volatile inorganic elements which then form alkali
metal compounds like sulfates, chlorides, and carbonates [32]. Bottom ash is ash that is too large
to leave with the volatiles, so it remains in the stove [33]. Pettersson et al. [28] found that coarse
fly ash particles do not significantly affect PM emissions. Furthermore, the PM filter samples were
all black, showing that most PM emissions are carbonaceous in nature.
Numerous studies have looked at particulate and gaseous emissions over time using a wide range
wood stove designs. Despite the use of different test conditions and wood stoves across studies, a
consensus was reached about what type of emissions occur during each phase. During the start-up
phase, the highest amount of PM emissions was observed, as is shown in Figure 2.7. Organic
matter (OM) was also found in very high concentrations during start-up and in low concentrations
for the remaining burn duration, as is shown by the green plot in Figure 2.8 and the black plot in
12
Figure 2.9. During the start-up phase, CO emissions are high due to suboptimal operating
conditions and subsequent incomplete combustion. CO again increases during the decay phase.
This is due to the char oxidation reaction, shown by the red plot in Figure 2.9.
Figure 2.7: PM concentration plotted as a function of time, using a wood stove [14]
Figure 2.8: OM concentration (Green) and inorganic matter (IM) concentration as a
function of time, using a wood stove [26]
Figure 2.9: Organic Carbon (OGC or OM) concentration and CO concentration as a
function of time, using a wood stove [5]
2.3 Emission Reduction Methods
All wood stoves act in a non-ideal manner, resulting in the PM and gaseous emissions discussed
in Section 2.2.2. Studies using modern wood stoves have found that burning wood at very high or
very low heat output conditions leads to high PM emissions, revealing an inability for many stoves
13
to burn cleanly during start-up or when used with too much or too little wood. High output
conditions (when the wood has a high surface to volume ratio, a large mass, or a low moisture
content) produce significantly more emissions than “regular” conditions at the same settings due
to a lack of oxygen supply and insufficient mixing. Likewise, low heat output conditions (low
oxygen settings, low surface/volume ratio wood pieces, high moisture content) also lead to high
emissions due to excessively fuel lean conditions and subsequently lower temperatures [28]. In a
study by Tissari et al. [34], a fivefold increase in PM emission concentration was found when
burning small logs that had a higher surface area because of increased gasification and insufficient
air supply. Most wood stoves sold today operate best within a narrow band of heat outputs and
wood conditions. To design a stove that produces less PM and gaseous emissions for a wider range
of heat outputs, four conditions must be met [32]:
1. Enough turbulence to mix the fuel with air
2. A sufficient supply of oxygen
3. Sufficiently high temperatures of at least 800C [14]
4. A sufficient residence time of at least 0.5 seconds [21]
Optimizing the air and fuel mixing (condition one) and ensuring the correct local air/fuel ratio
(condition two) is widely regarded as the most effective way to curb undesirable particulate and
gaseous emissions. If these two conditions are met, both the required temperature and residence
time will be satisfied [14]. The following sections introduce design heuristics, technology, and
modelling as ways to achieve complete combustion from wood stoves.
2.3.1 Air and Fuel Mixing
Proper mixing between air and fuel ensures that complete combustion can occur. Doing this whilst
maintaining low amount of excess air ensures that combustion temperatures are maximized [14].
Air staging can be used to achieve this by utilizing multiple air inlets to improve the mixing
between air and fuel. The primary combustion air is directed towards the fuel bed and is used to
control the fuel burn rate by volatilizing gases. It is recommended that the primary airflow is
reduced as much as possible to reduce ash melting and slag formation, and to maximize the
secondary airflow rate, and hence mixing. The primary air is not enough to fully oxidize the gases
due to fuel rich conditions at the fuel bed, so secondary air is supplied above the fuel bed to oxidize
14
the combustible gases formed from devolatilization and gasification [14], [21]. The secondary air
inlet can take the form of an inlet port (see Figure 2.10, left), or a manifold (see Figure 2.10, right).
Figure 2.10: Schematic of air staging in a wood stove [35] (left) and a manifold-style
secondary-air inlet (right)
The wood combustion process is highly transient over time and varies dramatically based on the
wood characteristics and quantity. Automatic adjustment of the primary and secondary air flow-
rates is therefore important to ensure that the optimal air to fuel ratios are maintained regardless of
burn phase or user operation. To achieve this, self-adjusting control system such as CO/
controllers [36], CO/temperature controllers [37], and induced draft controllers [38] are used.
These systems serve to increase or decrease the primary and secondary air amounts depending on
factors such as exhaust gas temperature, draft, CO concentration, and O2 concentration.
2.3.2 Oxidation Catalysts
Catalytic combustors are widely employed in wood stoves as an economical solution to gaseous
and particulate emissions. Using a catalyst material at the exhaust of a wood stove increases the
rate of oxidation by adding an intermediate reaction step which facilitates the overall reaction. In
a recent study by Hukkanen et al. [5], the addition of a platinum/palladium catalyst to a wood stove
reduced total PM emissions by 30% and CO emissions by 21% over burn duration; a significant
result.
15
In modern wood stoves, oxidation catalysts take the form of platinum and palladium nanoparticles
bonded to a highly porous support media such as a monolith, mesh sheets, or packed bed [39] (See
Figure 2.11). These designs maximize the surface area in contact with the exhaust gas and
minimize the cost of platinum and palladium without adversely affecting performance.
Figure 2.11: Ceramic monolith (left) and metal mesh sheet [5] (right) catalyst designs
There are drawbacks associated with catalysts which have been the subject of multiple studies.
These drawbacks are the poor performance at low temperatures, catalyst deactivation due to
fouling and poisoning, and thermal deactivation [40]. Moisture also affects the performance of
catalysts by increasing the amount of energy required to oxidize particles and therefore increasing
char production rather than volatiles [41]. In addition, catalysts must also be chosen to minimize
cost without losing effectiveness [39]. Catalyst materials are chosen based on their ability to
minimize the drawbacks and maximize oxidation. In a study comparing the effectiveness of metal
oxide (Cu-Mn) and platinum catalysts, two of the most common types, the platinum catalysts
proved to be more effective [40]. Compared to the metal oxide catalyst, Platinum displayed an
increase in naphthalene and CO oxidation in the presence of moisture, achieved higher oxidation
of unsaturated hydrocarbons, and was less susceptible to deactivation by poisoning and coke
buildup.
Platinum also makes an excellent catalyst due to its high melting temperature of 1768C. The
catalyst melting temperature should be significantly higher than the temperatures it will be exposed
to prevent coalescence of the thin nanoparticle platinum layer applied to the substrate. When
heating nanoparticles, the Tammann temperature goes into effect. This is the temperature at which
the bulk mobility of nanoparticles becomes significant, causing particle coalescence. The
Tammann temperature is typically half of melting temperature [39]. Figure 2.12 shows the
16
Tammann temperature of platinum group members in addition to metals that are nearby on the
periodic table.
Figure 2.12: The Tammann temperatures of platinum group elements [39]
With North American and European standards becoming increasingly strict, it has become
imperative for wood stove manufacturers to produce products that not only meet regulations, but
do not increase the cost for consumers. As a result, many oxidation catalysts contain a blend of
platinum and palladium, as palladium has a lower cost than platinum but has similar properties.
That said, platinum has a lower tendency to become poisoned than palladium [39], so an optimal
price/performance ratio between metals is imperative.
2.3.3 CFD Modeling
CFD modelling is a powerful tool that can be used to achieve the goal of designing a wood stove
that has good air/fuel mixing and hence low PM and gaseous emissions. CFD models of the firebox
allow for the visualization of flow velocity, gas concentration, and temperature. This allows for
rapid design modification and validation that would not be possible by creating physical
prototypes.
To show the capabilities of using CFD models to improve wood stove design, Brunner et al. [14]
compared the performance of two stove designs using CFD modelling. The CO and temperature
results of the study are shown in Figure 2.13, where the original design (left of each set of images)
has one air inlet at the front and the modified design (right of each set of images) has a secondary
air inlet at the rear of the combustion chamber. From this study, it is evident that the modified
design has higher combustion temperatures and lower concentrations of exiting CO emissions.
This serves as evidence that the modified design performs more favorably.
17
Figure 2.13: CFD-Simulation results of a wood stove cross-section without (left) and with
(right) multiple air inlets. The left set of images compares temperature profiles in C and
the right set of images compares CO monoxide profiles in ppm [14]
18
Chapter 3
Experimental Setup
3.1 Experimental Setup Overview
An experimental setup was built to collect real-time operating conditions and emissions data.
Furthermore, the experimental setup provides a foundation for future experimental work. The
experimental setup comprises of a wood stove, dilution tunnel, and sensors to monitor the
temperature, mass change, CO2 and O2 concentrations and PM mass over time. Sections 3.2
through 3.4 provide information about each component.
3.2 Wood Stove and Dilution Tunnel
A dilution tunnel was installed to dilute the exhaust gas leaving the wood stove stack and to vent
the diluted gas into the atmosphere. The dilution tunnel serves two purposes:
1. The tower replicates the typical height of a residential chimney and the wide diameter hood
ensures mixing between exhaust gas and dilution air. This arrangement closely represents
real-world emissions by simulating the conditions when wood stove exhaust leaves a
house.
2. The dilution tunnel is designed to meet the EPA 5-G regulations, allowing for PM
measurements in this study to be compared directly to the EPA regulations.
The dilution tunnel comprises of a tower, hood, piping, blower, and chimney. It was specifically
designed to be freestanding and fit within the limited laboratory space. The dilution tunnel
specifications are shown in Table 3.1. The tower (Wolf Steel, custom built) has three pillars and a
triangular base, designed so the base fits within the space below the floor scale. The hood (Wolf
Steel, custom built) is connected to double-walled piping that contains one baffled section to create
better mixing of the exhaust gases as well as a vertical section that is angled downwards. A blower
(Dayton, model 1TDT2) is connected to the dilution tunnel and vents exhaust gas through the
laboratory’s chimney. Figure 3.1 shows a CAD model of the dilution tunnel with the wood stove
and scale installed in addition to a photo of the tower.
19
Table 3.1: Dilution tunnel specifications
Dimension Value
Tower height (to the base of the hood) 14'
Hood diameter, base 4'
Hood diameter, top 6"
Piping diameter 6"
Blower flowrate 549 SCFM
The wood stove used in this experiment is a residential wood stove (Napoleon, model NZ3000H).
This stove was chosen because although the unit has emission control technologies in the form of
multiple air inlets and an oxidation catalyst, there is still room for improvement. This type of stove
best represents products sold in the North American Market, as most new stoves contain emission
control technology but struggle to meet upcoming PM emission regulations.
Figure 3.1: Dilution tunnel CAD model (left) and photo (right)
The interior of the wood stove has an unlined interior volume of 0.08m3 and is lined with a
refractory ceramic material to retain heat. The wood stove interior will hereafter be referred to as
the firebox. The unit has three air inlets: primary, secondary, and pilot. The primary air enters
through a long, narrow channel above the glass and is designed to keep the glass clean and provide
air to the fuel-bed. The secondary air enters through a manifold at the top of the firebox with the
20
purpose of oxidizing volatiles leaving the wood. The pilot air inlet is a small, circular opening
which always directs air to the middle of the fuel-bed to ensure that the fuel bed receives enough
oxygen when the primary air inlet is closed. All inlets operate on an induced draft and the only
control is a lever which adjusts the primary air inlet opening. To further reduce PM emissions, a
platinum/palladium oxidation catalyst is located at the exit of the firebox which serves to fully
oxidize incomplete combustion products. For this study, the oxidation catalyst was removed to
minimize unaccounted-for variables. Figure 3.2 and Figure 3.3 show the design and location of
the NZ3000H wood stove, inlets, outlet, and catalyst.
Figure 3.2: Labeled Cutaway CAD model of the Napoleon NZ3000H wood stove
Figure 3.3: CAD model of the secondary air manifold in the Napoleon NZ3000H wood
stove
Secondary air manifold
Oxidation catalyst (removed) Primary air inlet
Pilot air inlet
Exhaust stack
21
3.3 Instrumentation
3.3.1 Instrumentation Overview
In this study, mass, temperature, and emissions data were collected in real-time over the course of
an entire test. A schematic of the experimental setup showing all data collection locations and
types is shown in Figure 3.4. In the following sections, the purpose and design of each instrument
is detailed.
Figure 3.4: Experimental setup
22
3.3.2 Mass Measurement
The wood stove is mounted on a floor scale (Pennsylvania, model 7500, analog output option
installed) to measure the mass change over time, and hence the wood burn-rate. The load limit of
the scale is 1000 lbs. and the resolution is ± 0.1lbs. An analog output upgrade was made to the
control-box of the scale to allow an analog voltage signal to be connected to the data acquisition
module (DAQ).
3.3.3 Temperature Measurement
K-type thermocouples (Omega, various models) with an operating range of -200—1250 °C ±
0.75% were installed at the primary air inlet, secondary air inlet, catalyst, exhaust stack, and
dilution tunnel. Figure 3.5 shows the locations of the thermocouples within the wood stove and
Figure 3.6 shows the exterior view of the primary and secondary air inlet thermocouples. All
thermocouples were connected to the DAQ using K-type extension wires. The thermocouple
measuring the primary air inlet temperature was installed by drilling a hole in the pipe leading to
the narrow channel opening. Likewise, the secondary air inlet thermocouple was installed in the
rectangular pipe leading to the secondary air manifold. The thermocouple was installed as close to
the inlet air manifold as possible to accurately access the degree by which the secondary air is
preheated by the inner walls of the firebox.
Figure 3.5: Temperature sampling location of the primary air (yellow), secondary air
(blue), stack (pink), and catalyst (red)
23
Figure 3.6: Thermocouples installed in the primary (left) and secondary (right) air inlets
3.3.4 O2 and CO2 Volume-Fraction Measurement
The volume fraction of O2 (% dry) and CO2 (% dry) is measured in real-time from the undiluted
exhaust gas from a sampling probe connected to an O2 sensor and CO2 sensor in series, as is shown
in Figure 3.4. O2 and CO2 measurements are collected to calculate the percent excess oxygen over
time, combustion efficiency, and exhaust dilution. When combined with the mass readings from
the floor scale, a full mass balance can be found.
Connected to the exhaust stack through a hole is a 6’ x 1/4” stainless steel (SS) probe, bent to
minimize obstruction. The long probe was chosen to allow the sample gas to cool sufficiently
before entering the O2 and CO2 analyzers. To further cool the gases and fully condense moisture,
a 6’ coiled copper tube was attached downstream to the SS probe. Copper was chosen for this
purpose as it has a low heat capacity, hence maximizing conductive heat transfer. To remove
moisture and particulates below a size of 0.1µm, a borosilicate glass microfiber filter (Headline
Filters, model 12-32-70C) installed in a bowl filter holder (Headline Filters, model 315a) was
attached downstream of the copper coil. Upon exit of the filter, sample gas is dry, cool, and
particulate free for analysis. One issue encountered during operation was the clogging of the filter
with condensed water and particulates. To address this issue, a new filter was installed every two
burns and the gas measurement assembly was not turned on during the preheating stages of the
experiment to minimize particulate and moisture buildup.
Oxygen is measured using a lambda sensor (Engine Control and Monitoring, model OXY6200)
with an operating range of 0-25.0 ± 0.2 % O2. A lambda sensor was chosen for this application
24
due to its robust design, fast response time, and high accuracy. The sensor type is a universal
exhaust-gas oxygen (UEGO) sensor, which employs a zirconia element to create a voltage based
on the oxygen concentration difference between the ambient air and the flue gas [42]. Figure 3.7
is a labeled cut-away schematic of a UEGO sensor.
Figure 3.7: Cut-away schematic of a UEGO sensor [42]
The dry CO2 volume fraction is measured using a nondispersive infrared sensor (NDIR) (Nova
Analytical Systems, model 4281SRM) with an operating range of 0—50.0 ± 0.5 % CO2. The NDIR
measures CO2 levels by pulsing infra-red (IR) beams through a sample cell. CO2 absorbs light with
a wavelength of 4.3m, so a 4.3m bandpass filter is installed directly in front of the detector [43].
An NDIR was chosen for its high degree of accuracy, low maintenance requirements, and built-in
pump. Because the NDIR is the last element of the gas sampling assembly, the built-in pump draws
the sampling gas through the entire assembly. Despite the advantages of using an NDIR, the model
used had a slow response time of around 20—30 seconds. Thus, CO2 data had to be shifted
backwards when conducting data analysis to compensate for the delay.
A second NDIR (Nova Analytical Systems, model 7800P-2A) was used to measure the dry CO2
volume fraction in the dilution tunnel. This unit has an operating range of 0—10.0 ± 0.1 % CO2
and minimal response delay. Because of the lower temperatures and dilute exhaust gas mixture,
only an 8” SS probe, heat resistant tubing, and in-line filter were used upstream of the NDIR.
25
3.3.5 PM Measurement
Knowing the PM emission rate of the wood stove being tested is arguably the clearest way to
access the effect of manipulated variables on PM emission rate. In this study, two identical PM
sampling trains with three interchangeable probes were installed in the dilution tunnel to measure
total particulates during the first 15 minutes, between 15 and 30 minutes, and the entire test
duration. It should be noted that the flow through the sampling location is turbulent, as the
Reynolds number was found to 23 000. This was based on a calculation using 100°C exhaust gas,
a 6” pipe diameter and average velocity of 3.5m/s. The PM sampling trains were assembled in
accordance with the EPA 5G method [11] so that results were in line with North American EPA
standards. Each sampling train contains the following components in series: Probe assembly,
drying unit, backflow preventer, rotameter, and vacuum pump.
Each probe is built from a 12” x 3/8” SS tube connected an aluminum filter holder (Pall, model
1235), K-type thermocouple (Omega), polycarbonate filter holder (Pall, model 1119), and a quick-
release connector to connect the probe to a flexible tube. In each of the two filter holders, a 47mm
glass fiber filter with a 1µm pore size (Pall, model 66215) was used. Two filters are used in series
to collect the maximum amount of PM. Figure 3.8 was taken from the EPA 5G document [11] and
depicts probe’s components.
Figure 3.8: Filter probe components [11]
The particulate-free gas then passes through a desiccant packed drying unit (Drierite, model
26800), as even a small amount of moisture can damage the vacuum pump. A backflow preventer
is installed downstream of the drying unit to prevent any disturbances to the filter samples. Finally,
a vacuum pump (GAST, model P704-DOA) is set at a flow-rate of 0.5 ± 0.1 SCFM using a flow-
26
meter with a control dial (King Instrument Company, model 7530). As the gas passing through the
pump has a high CO concentration, the outlets of the pumps are fed back into the dilution tunnel
with tubing. Figure 3.9 shows the PM sampling train components.
Figure 3.9: PM sampling train components
3.4 Data Acquisition, Visualization, and Analysis
All instruments are connected to a Data Acquisition Module (DAQ) (National Instruments, model
USB 6339-BNC) which converts the analog voltage and current signals into digital signals. A
LabVIEW software program was developed to collect, process, display, and save the signals from
the DAQ. LabVIEW is used extensively for a range of applications that require data acquisition,
process control, and industrial automation. LabVIEW is a visual programming language, whereby
the user connects various operation blocks with wires that represent data types. To collect the data,
a virtual instrument (VI) collects all channels of data from the DAQ and performs the necessary
calculations to convert the voltage signal into the correct units. The channels are then displayed on
the front panel as both numbers and a graph so that the operator can see both the current value and
the trend over time. Additionally, the front panel interface includes a timer and data-logging
start/stop buttons. See Appendix B for the complete LabVIEW script used in this study. When the
data logging button is pressed, all data is saved to a tab-separated text file, with each data channel
in a separate column.
Filter probe
Vacuum
pump
Rotameter/
needle valve
Check
valve
Dryer
Sample
gas out
27
Chapter 4
Experimental Methodology
4.1 Experimental Methodology: Preface
When conducting wood stove experiments, it is crucial to minimize variance to ensure
reproducibility. To accomplish this, the experimental methodology was based off the EPA 5G burn
procedure [11], titled “Determination of Particulate Matter Emissions from Wood Heaters”. The
procedure was designed to minimize variance between PM emission tests and includes the
following information:
• The procedure for preheating the wood stove and creating a hot bed of coals
• The type, layout, and dimensions of the test wood
• The PM sampling and weighing procedure using filters
It should be noted that various modifications and omissions were made to the EPA 5G
methodology to tailor it to the needs of the research. Care was taken to ensure that the
modifications made did not adversely affect test variance. The differences between the EPA 5G
methodology and the methodology used in this study are summarized below:
• The wood layout of the test-burn was changed for the wood to ignite easier. It was found
that the EPA layout did not always ignite, causing the test to fail.
• Some measurements such as the dilution tunnel velocity profile, mass of particulate on the
sample probe, and specific calculations such as the average burn-rate were omitted from
the study despite being on the EPA methodology. These measurements were deemed
inconsequential to the study.
• The EPA methodology measures the temperature of each of the firebox walls, whereas the
study did not.
28
4.2 Wood Characteristics and Preparation
Douglas fir was the wood species selected for this study so that results could be directly compared
with Canadian and American certification standards. The test wood arrived as long 2”x4” and
4”x4” crib-wood beams on a pallet. The wood originally had a moisture content of 20%, but this
was reduced to between 13% and 17% as it was left outside for several months. Ideally, the wood
would be at 20% to best simulate certification test conditions, but the due to the long shipping time
and high cost, the wood was still used. Table 4.1 summarizes the properties of the wood used in
this study.
Table 4.1: Wood specifications
Wood type Douglas fir (Pseudotsuga menziesii)
Wood region United States, West Coast
Higher heating value, no bark [41] 19.6-21 MJ/kg, dry
Moisture content 13%-17%
Mass of kindling load 4.0 ± 0.4 kg
Mass of pre-burn load 6.0 ± 0.6 kg
Mass of test load 6.5 ± 0.5 kg
Three batches of wood are prepared for each experiment: kindling, pre-burn, and test load.
Kindling is used first as it easily ignites and quickly forms a bed of hot coals. Once a coal-bed is
established, the pre-burn is added to slowly heat the firebox to a stable point before adding the test
wood. Preheating the wood stove and creating a hot coal bed is necessary because it is difficult to
ignite the large pieces of wood in the test wood without having a point of ignition that is hot enough
and contains enough energy. Furthermore, the wood stove needs time to reach a stable operating
temperature to ensure that heat absorption by the walls of the wood stove do not increase variance.
The test load is a highly standardized load of wood pieces that are held tougher using thin wood
spacers and nails. Data collection begins when the test load is added.
Figure 4.1 shows the arrangement and size of the kindling and pre-burn loads shortly after ignition.
4.0 ± 0.4 kg of finely chopped Douglas fir crib wood is prepared for the kindling and placed in a
low density, stacked pattern. 6.0 ± 0.6 kg of crib wood is prepared for the pre-burn by chopping
Douglas fir crib wood to approximately 1”x1”x15”; larger than the kindling. Figure 4.2 shows the
arrangement of the test load. The test load consists of four 2”x 4’’x10” and four 2”x 2”x10’’ wood
29
sections. Connecting the 2”x4” wood sections to each other and the 4”x4” wood sections to each
other are four 20” long Douglas fir spacers. The total weight of the test wood is 6.5 kg ± 0.5 kg,
dependent on factors like the moisture and knot-content of the wood.
Figure 4.1: Kindling load (left) and pre-burn load (right), shortly after ignition
Figure 4.2: Douglas fir test load
4.3 Test-Wood Moisture Readings
Using a moisture meter (Delmhorst, model J-2000 with hammer electrode package), moisture
samples are taken from every section of test-wood. The average moisture content is then used in
mass balance calculations to improve accuracy. To ensure accurate measurements, it was ensured
that both hammer probes were located along the same wood ring. In addition, three measurements
were taken for each piece of wood; two on one face and one on the opposite face. Refer to
Appendix C for the moisture reading template used to record and average the moisture readings
from each test.
30
4.4 Burn Procedure
By burning the kindling and pre-burn, the wood stove is brought to a constant temperature whereby
the rate of heat loss from the device is approximately equal to the rate of heat added by the wood.
The kindling was lit using a butane torch to reduce the ignition time and to avoid the use of high
ash-content materials such as paper. When the kindling mass was reduced to 1.0 ± 0.1 kg, the pre-
burn was added. After approximately 30 minutes with the bypass gate and primary air inlet open,
both the bypass gate and the primary air inlet were closed to reduce the burn-rate and maximize
heat-transfer to the stove. This warmup period lasted until the mass of the wood was reduced to
0.5 ± 0.1 kg. At this point, a coal bed was created by breaking the coals with a poker, the scale was
zeroed, and the data acquisition system was turned on. The test load was then added, filter pumps
were turned on, and the wood stove was set to the operating conditions specified in Table 4.2. The
test concluded when the mass of the test-burn was reduced to 1.0 ± 0.2 kg.
To study the effect of air flow-rate and airflow location on the ignition phase of the burn test, a
modified test procedure was created to increase the amount of air directed towards the fuel bed
during the first 30 minutes. In the modified test, primary airflow rate was increased using a shop-
vac for 30 minutes before closing the primary valve after 30 minutes. Additionally, the pilot hole
was closed for the duration of the modified test burn to reduce the number of uncontrolled
variables. Table 4.2 summarizes the two test conditions.
Table 4.2: Summary of baseline and modified airflow conditions
Test type
Primary airflow setting
during start-up
Pilot
hole
Baseline Open for 5 minutes Open
Modified Increased airflow for 30
minutes Closed
4.5 PM Sample Preparation and Weighing
PM mass samples were collected from the dilution tunnel using particulate sampling trains to
determine the PM emission rate over a given period. This information is valuable because the
results reveal when most PM emissions occur and allows the impact of test modifications on PM
emission rate to be accessed. Refer to Section 3.3.5 for a detailed description of the particulate
sampling equipment used. For each of the three sampling trains, the front and rear gaskets were
31
wiped with a laboratory tissue to remove residue from the previous tests. Next, two blank filters
and the front and rear gaskets were placed in a 10mm glass dish, one dish per probe. Using an
analytical balance with an accuracy of ± 0.001 mg (Scientech, model SM 128D), the weights of
the glass dishes were recorded to the nearest milligram. After weighing the dishes, the filters and
gaskets were installed on each train and connected to the dilution tunnel.
Shortly before adding the test-fuel, two probes were inserted in the dilution tunnel and connected
to the filter trains. When the test load was added, a timer was started and both dilution tunnel
pumps were turned on and adjusted to a flow-rate of 0.5 SCFM ± 6%. After 15 minutes, one probe
was replaced by the third probe. Care was taken to minimize the switching time to prevent particle
loss. After another 15 minutes, the same probe was removed from the dilution tunnel, leaving only
one probe in use for the duration of the experiment.
Following the test, filters and gaskets from each sampling train were removed using a pair of
tweezers and placed in their respective glass dishes. All sample dishes were then placed into a
desiccant cabinet (Boekel, model 1340 desiccant cabinet and Drierite, model 22005 desiccant) for
a minimum of 24 hours to remove moisture. Following the drying period, samples were re-
weighed. The mass of PM captured during the time the probe was installed, 𝑚𝑛 is shown in
Equation (4.1):
𝑚𝑛 = 𝑚𝑓 + 𝑚𝑔 (4.1)
Where 𝒎𝒇 is the mass of particles collected on the filters and 𝒎𝒈 is the mass of particles collected
on the filter probe gaskets.
4.6 Data Analysis
4.6.1 MATLAB Script
A script was created on MATLAB to conduct calculations and prepare figures. The full script can
be found in Appendix A. All data was first filtered using either a moving-average filter or a median
filter to reduce sensor noise. An average of every 2 minutes of data was then taken to determine
operating parameters with respect to the mass burn rate. 2 minutes was chosen as the time interval
to ensure that mass change between intervals exceeded the error on the scale whilst maximizing
32
the resolution of the data. Excess oxygen and the inlet and exhaust gas flow-rates are calculated
using a stoichiometric equation based on the mass change for every 2 minutes of time, the
elemental composition of Douglas fir, the averaged test load moisture content, and exhaust gas
temperature.
The following assumptions were made when calculating excess oxygen:
• Complete combustion
• The composition of Douglas fir used in the average chemical formula was based off the
ASTM E2515-11 standard [19]
• Ash was not factored into the equation, as it constitutes an insignificant mass fraction of
approximately 0.5% [19]
Equation (4.1) is the combustion reaction used with the addition of moisture:
𝐶𝐻1.69𝑂0.67 + 𝑎𝐻2𝑂 +𝑏
∅(𝑂2 + 3.76𝑁2) → 𝑐𝐶𝑂2 + 𝑑𝐻2𝑂 + 𝑒𝑁2 + 𝑓𝑂2 (4.2)
Where ∅ is the equivalence ratio and 𝑎 through 𝑓 are the stoichiometric coefficients of the reactants
and products. To solve for the unknown variables, Equations (4.3) through (4.11) are used.
𝑛𝑐
𝑚𝑓𝑢𝑒𝑙= (1 − 𝑤) ∙
𝑌𝑐
𝑀𝑊𝑐
(4.3)
𝑛𝐻
𝑛𝐶=
(1 − 𝑤) ∙ 𝑌𝐻
𝑀𝑊𝐻 ∙ (𝑛𝑐
𝑚𝑓𝑢𝑒𝑙)
(4.4)
𝑛𝑂
𝑛𝐶=
(1 − 𝑤) ∙ 𝑌𝐻
𝑀𝑊𝑂 ∙ (𝑛𝑐
𝑚𝑓𝑢𝑒𝑙)
(4.5)
𝑎 =𝑤
𝑀𝑊𝑂2 ∙ (𝑛𝑐
𝑚𝑓𝑢𝑒𝑙)
(4.6)
𝑐 = 1 (4.7)
𝑑 = (
𝑛𝐻
𝑛𝐶) + 2 ∙ 𝑎
2(4.8)
33
𝑓 =𝑥𝑂2
∙ 𝑐
𝑥𝐶𝑂2
(4.9)
𝑏 =(2 ∙ 𝑐 + 𝑑 − (
𝑛𝑂
𝑛𝐶) − 𝑎)
2(4.10)
∅ =(2 ∙ 𝑏)
(2 ∙ 𝑐 + 𝑑 + 2 ∙ 𝑓 − (𝑛𝑂
𝑛𝐶) − 𝑎)
(4.11)
Where 𝑤 is the percent moisture content of the wood, 𝒀𝒄 and 𝑌𝐻 are the mass fractions of carbon
and hydrogen in the fuel, and 𝑥𝑂2 and 𝑥𝐶𝑂2
are the volume fractions of O2 and CO2 in the exhaust
gas. Using the equivalence ratio to find the excess oxygen,
% 𝑒𝑥𝑐𝑒𝑠𝑠 𝑜𝑥𝑦𝑔𝑒𝑛 = (1
∅ − 1) ∙ 100% (4.12)
The total air inlet volumetric flow-rate is another important parameter that shows the change in
draft due to buoyancy over time. Because the inlet air is at near standard temperature (20—30°C)
and pressure (1 ATM), the ideal gas law was used to find the inlet air flow-rate.
�̇�𝑖𝑛 =�̇�𝑎𝑖𝑟,𝑖𝑛 ∙ 𝑅 ∙ 𝑇
𝑃(4.13)
Where,
�̇�𝑎𝑖𝑟,𝑖𝑛 =𝑏
∅∙ �̇�𝑓𝑢𝑒𝑙 ∙ 4.76 (4.14)
The molar flow-rate of inlet air, �̇�𝑎𝑖𝑟,𝑖𝑛 is found by dividing the total number of moles of air, 𝑏
∅,
by the number of elemental moles in each mole of air— 4.76.
4.6.2 PM Mass to Emission Factor Conversions
To convert the PM mass samples from mass units of mg to average PM emission rates in units of
g/hr, mg/M3, and mg/MJ, Equations 4.15 through 4.19 were used. The average dry gas flow-rate
in dscf/min of the dilution tunnel is a function of the water vapor fraction of the gas stream (𝐵𝑤𝑠),
34
average gas velocity in the dilution tunnel (𝑣𝑠), cross-sectional area of the dilution tunnel (𝐴),
average temperature and pressure of the gas stream (𝑇𝑠 and 𝑃𝑠, respectively), and standard absolute
temperature and pressure (𝑇𝑠𝑡𝑑 and 𝑃𝑠𝑡𝑑, respectively):
𝑄𝑠𝑡𝑑 = 60 ∙ (1 − 𝐵𝑤𝑠) ∙ 𝑣𝑠 ∙ 𝐴 ∙ [𝑇𝑠𝑡𝑑 ∙ 𝑃𝑠
𝑇𝑠 ∙ 𝑃𝑠𝑡𝑑] (4.15)
The PM concentration in units of g/dscf is found with Equation 4.15:
𝑐𝑠 = 𝐾2 ∙𝑚𝑛
𝑉𝑚(𝑠𝑡𝑑)(4.16)
Where 𝐾2 = 0.001 g/mg, 𝑚𝑛 is the total mass of PM collected in mg and 𝑉𝑚(𝑠𝑡𝑑) is the total volume
of gas sampled by the filter train. To get the value of 𝑚𝑛, filter samples were weighed in
accordance with Section 4.5. 𝑉𝑚(𝑠𝑡𝑑) was found by multiplying the volumetric flowrate of the
pump by the amount of time the pump was used for. Finally, the emission rate of PM in g/hr can
be found by multiplying the average dry gas flow-rate with the PM concentration using Equation
(4.17):
𝐸 = 𝑐𝑠 ∙ 𝑄𝑠𝑡𝑑 ∙ 60 (4.17)
Equation (4.18) was used to find the PM emission rate per unit of volume:
𝐸 =𝑚𝑛
𝑉𝑚(𝑠𝑡𝑑)(4.18)
Where again, 𝑚𝑛 is the total mass of PM collected in mg and 𝑉𝑚(𝑠𝑡𝑑) is the total volume of gas
sampled by the filter train. Equation (4.18) was used to find the PM emission factor per unit of
energy:
𝐸 =𝑚𝑛 ∙ 𝐹𝑑𝑖𝑙
∆𝑚𝑓𝑢𝑒𝑙 ∙ 𝐻𝐻𝑉(4.19)
Where 𝐹𝑑𝑖𝑙 is the multiplication factor used to convert the PM sample flow rate into the dilution
tunnel flow rate, 𝑚𝑛 is the mass of PM sample collected, ∆𝑚𝑓𝑢𝑒𝑙 is the mass of fuel consumed in
the specified time, and 𝐻𝐻𝑉 is the higher heating value of Douglas fir wood given the
concentration of moisture in the wood.
35
Chapter 5
Results and Discussion
5.1 Repeatability
A high degree of variance was observed between repeat tests, with some tests exhibiting higher or
lower temperatures, burn rates, and inlet airflow rates. Despite this, distinct trends were observed
by both the baseline and modified tests, and correlations between operating conditions were largely
unaffected by variance. The following are potential causes for variance between repeat tests:
• Differences in the wood moisture content between tests, a factor which was difficult to
standardize given the limited supply of test-wood available
• Differences in the shape and stacking pattern of the kindling/pre-burn, as the stacking
pattern and exact wood size for the preheating loads were not standardized to the same
degree as the test load
• Differences in the point of ignition, as the wood did not always ignite evenly depending on
the geometry of the wood and local fuel-bed temperature
• Variance in the ambient air temperature
In the third baseline test, B3, an uncharacteristic variance was observed. This test had a
significantly faster ignition time, higher stack temperatures and burn-rates, and lower PM
emissions than all other baseline tests. Whilst the exact cause for this variance is unknown, over-
firing of the preheating stage of the experiment may have been the cause. To address this,
recommendations have been provided in Section 6.2 to prevent this from happening in the future.
Thus, test B3 was still included in the PM emission results and multivariate scatter plots but
omitted from all other plots as it does not represent a typical use case.
36
5.2 Results
5.2.1 Visual results
In two of the three baseline tests (B1 and B2), wood ignition occurred directly in front of the pilot
air inlet, where air enters directly in front of the hot coals. The flame then spread between the gap
in the center of the test-load before igniting the top surface of the wood. In the third test (B3), the
fire started evenly along the entire bottom front edge of the wood before spreading to the top
surface. Compared to tests B1 and B2, test B3 exhibited larger and more turbulent flames which
filled the front half of the firebox within 10 minutes. The discrepancy with test B3 is in strong
contrast to the other two tests, as has been discussed in Section 5.2.
All three modified tests (M1, M2, and M3) displayed a much shorter ignition period than the
baseline tests. As soon as the wood was loaded, the coals along the front edge of the firebox glowed
brighter than during the baseline tests. The likely cause for this was the additional primary air
creating more air and fuel mixing and hence more rapid combustion. Ignition occurred along the
bottom front edge of the wood, sometimes only on the right side before spreading. Due to
buoyancy, the flames moved upwards from the front edge, igniting the front face of test load
followed by the top face. By the end of the start-up period, a flame sheet was visible between the
top wood surface and the secondary combustion manifold.
After the start-up period, the steady-state period for both the baseline and modified tests were
characterized by a peak in temperature, burn-rate, and a period of low excess oxygen. Visually, a
sheet of flame over the wood was observed, which is the result of pyrolysis gases being ignited by
the secondary air. Minimal flames were observed at or near to the fuel bed during this time. When
comparing the baseline to the modified tests, the modified test burn-rate was markedly lower than
with the baseline test. This can be attributed to the fact that fewer volatiles remain after the start-
up period due to the high initial burn-rate. With fewer volatiles trapped in the char structure of the
wood, a lower burn rate follows.
During the decay period, only small surface-level flames and glowing were observed. These results
agree with literature, as the char structure makes it increasingly difficult for the few remaining
volatiles to escape the wood [23].
37
5.2.2 Temperature Results
The stack temperature (also referred to as the exhaust gas temperature) is an important parameter
to study, as it has the potential to be used as a measure of combustion performance. In wood
combustion, higher temperatures indicate lower dilution from excess air, higher heat liberation
from volatiles, or both. Based on averaged data, the modified tests had 18% higher temperatures
than the baseline tests. The results are shown in Figure 5.1 for the first 120 minutes (left) and the
start-up period (right). Taken alone, this marked increase in temperature indicates that either the
fuel bed air contributed directly to the combustion of volatiles, there was a decrease in the excess
air, or both.
Figure 5.1: Stack temperature as a function of time for the baseline and modified tests over
120 minutes (right) and 30 minutes (left)
In a wood stove, the only variable that can be manipulated is the inlet airflow rate, also referred to
as draft. To understand the relationship between the stack temperature and draft, a multivariate
scatter plot was used and is shown in Figure 5.2. Multivariate scatter plots are used throughout the
results section to identify correlations between variables and to identify outliers. The points in the
plot are a collection of all six tests (three baseline and three modified tests), with blue points being
the baseline start-up data, red points being the modified start-up data, and black points being all
remaining data from both test types. In all tests, a distinct trend is evident from the steady
state/decay phase data. Because these periods were found to have low PM emissions, the “optimal”
stove conditions have been based off these trends.
38
Despite the high degree of scatter in the data, the steady-state/decay phase stack temperature
increased in a linear fashion with increasing inlet draft. Total inlet draft does not reveal the entire
story however, as the inlet air location (primary, secondary) also affects combustion performance.
A linear equation with the constants shown in Figure 5.2 yielded the highest degree of correlation
when compared to other correlation types, i.e. higher-order polynomials. Despite this, a high
degree of scatter was found in data, with the R-squared value (R2) of 0.2613. This means that only
26% of the data collected follows the linear correlation. The proposed cause for this high degree
of variance is due to local variation in temperature at the sampling point, arising from poor mixing.
To address this issue, it is recommended to ensure adequate turbulence upstream of the temperature
sampling location.
The start-up period data for both tests does not follow the steady-state/decay trend, with values
falling below the correlated data. This shows that much of the inlet air during start-up is leaving
as unburned excess air. On average, increased primary airflow in the modified test produced higher
temperatures than the baseline test at higher flow-rates. If the trend holds true after addressing the
high degree of scatter, this would indicate that additional fuel-bed air contributes directly to
combustion rather than diluting the exhaust gas.
Figure 5.2: Stack temperature as a function of inlet air flow-rate
y = 1.645*x + 306.3 R2 = 0.2613 RMSE = 32.21
39
5.2.3 Excess Oxygen Results
Excess oxygen is the fraction of extra oxygen in the exhaust gas that is above the stoichiometric
requirement for complete combustion of volatile gases. Excess oxygen must be kept low enough
not to dilute the exhaust gas, but high enough to avoid creating products of incomplete combustion
(PIC’s). Figure 5.3 shows the evolution of excess oxygen over time for the baseline and modified
tests. With both test types, start-up phase excess oxygen was significantly higher than the steady
state/decay period, starting at between 300% and 400% and decreasing to around 100% at 30
minutes. Throughout the start-up period, the excess oxygen was markedly lower in the modified
tests. With lower excess oxygen despite having a higher flow-rate, this reinforces the conclusion
that additional fuel bed air directly contributes to start-up phase combustion.
Figure 5.3: Excess oxygen as a function of time for the baseline and modified tests over 120
minutes (right) and 30 minutes (left)
A multivariate scatter plot was used to determine whether the excess oxygen during start-up fell
outside of the optimal conditions. Shown in Figure 5.4, the steady-state/decay excess oxygen
decreased from 150% at low air flow-rates down to 50% at high air flow-rates. This is in line with
literature [14], which recommends that moderately fuel lean conditions are ideal for complete
combustion.
The steady-state/decay phase data was correlated with a second-degree polynomial using bisquare
robust regression, which yielded an R2 of 0.5403. For the proposed correlation to be valid, two
facts must be considered:
40
1. Robust regression (bisquare) was used to remove outliers before creating the correlation.
For increased simplicity, the start-up period was defined as the first 30 minutes of the
combustion process. In reality, the start-up phase lasts longer or shorter depending on the
experiment. To remove the high excess oxygen data points from the tail-end of the start-up
phase and subsequently get a more accurate correlation, robust regression was used.
2. The correlation holds between inlet air flow-rates of 0 and 40 SCFM.
This degree of correlation is high enough to indicate that at decreasing inlet air flow-rates, more
excess oxygen is required for complete combustion. One possible explanation for this is that
buoyancy rather than pyrolysis and oxidation become dominant at low burn rates.
During start-up, excess oxygen far exceeds the optimal conditions for both test types. However,
the modified test had lower excess oxygen values at a higher draft. Two conclusions are inferred
from this information:
1. Low start-up stack temperatures are caused by high amounts of excess oxygen.
2. Because additional fuel-bed air in the modified test oxidized volatiles and did not leave as
excess air, most of the excess air during start-up enters the firebox through the unregulated
secondary air manifold. To confirm this inference, it is recommended to install a device
that can measure the secondary air flow-rate.
Figure 5.4: Excess oxygen as a function of inlet air flow-rate
y = 0.04765*x2 - 4.146*x + 165.9 R2 = 0.5403 RMSE = 19.84 Note: Bisquare robust regression used
41
5.2.4 Burn-Rate Results
Burn-rate is the rate at which moisture, volatiles, and char burning products are released from the
wood through the process of pyrolysis. Figure 5.5 shows the evolution of burn-rate over time for
both the baseline and modified tests. In the baseline test, the burn-rate increased steadily over the
start-up period from approximately 2 kg/hr to 3.5 kg/hr. With the modified test, burn-rate increased
significantly compared to the baseline test, with values ranging from approximately 3.5 kg/hr to 5
kg/hr. This information, paired with the increase in stack temperature, indicates that additional
fuel-bed air during start-up is in direct correlation with the increase in the volatilization rate of
wood.
Figure 5.5: Burn rate as a function of time for the baseline and modified tests over 120
minutes (right) and 30 minutes (left)
A strong linear correlation (R2 = 0.8926) was found between the draft and burn-rate for the steady-
state/decay phases of the combustion cycle, shown in Figure 5.6. This relationship holds true up
to inlet air flow-rates of about 40 SCFM, although it is unclear if the correlation holds true at higher
burn rates due to the increasing spread in the data. Under the assumption that the correlation holds
true for values of up to 60 SCFM, nearly all start-up data has uncharacteristically low burn-rates
given the inlet airflow rate. When comparing the baseline to the modified tests, the modified tests
had a higher draft than the baseline in addition to a higher burn-rate. In line with the previous
results presented, this indicates that the additional fuel-bed directed air contributed to the
volatilization of the wood, but the start-up for both cases still had poor volatilization given the inlet
flow-rate.
42
Figure 5.6: Burn rate as a function of inlet air flow-rate
5.2.5 PM Emission Results
PM emissions plotted as a function of the burn-rate allows one to observe the PM emission factor
only with respect to the wood that was consumed. Emission factors in terms of other parameters
such as temperature or inlet air flow-rate do not give a true reflection of the rate of emission per
unit mass, as the dilution ratio from the dilution tunnel varies greatly and the mass of wood
consumed is not considered.
Figure 5.7 shows the PM emission rate as a function of start-up phase mass burned for the six tests.
An order of magnitude reduction in PM emissions per unit of energy was found with all tests that
had a high volatilization rate during start-up. All modified tests had a more rapid start-up
volatilization rate and hence produced significantly fewer PM emissions. In addition, baseline test
B3 also exhibited a high mass burn rate and low PM emission type during the first 30 minutes.
Test B3 shows that if the mass burn-rate is high, the exact mechanism by which the burn-rate is
increased does not have a large impact on PM emissions. Whilst variance was the cause of the
high burn rate in this case, it shows that there is more than one way to achieve a high volatilization
rate during start-up.
y = 0.09025*x – 0.1974 R2 = 0.8926 RMSE = 0.3827
43
Figure 5.7: Start-up phase PM concentration as a function of wood consumption
Table 5.1 displays the mass of all PM samples from this test. A significant portion of emissions
occurred during the start-up period, emphasizing the importance of minimizing emissions during
this period. In the baseline tests with the exclusion of test B3, the fraction of PM captured during
the 30-minute start-up period varied between 74% and 100% for the baseline test and 39% to 100%
in the modified test. It should be noted that error in the PM measurement means that the start-up
phase PM emissions did not actually reach or exceed 100%. To reduce the variance between tests,
further test standardization is highly recommended. When comparing the mass of PM captured
between the baseline and modified cases, the modified case had on average a 44% lower emission
rate over the entire test.
Figure 5.8 shows the PM filter samples from the baseline test. The filter appearance gives insight
into the composition of the PM, as carbonaceous matter is black in color and ash is a white color.
The dark brown color of all samples shows that most PM emissions are primarily carbonaceous,
which agrees with the findings of Petterson et al. [27].
44
Table 5.1: Summary of PM samples from baseline tests and modified airflow tests
Parameter Units B1 B2 B3 M1 M2 M3
PM mass collected after 15 mins mg 33 23 20 14 15 16
PM mass collected after 30 mins mg 71 56 27 25 26 24
PM mass collected over entire test mg 96 55 31 65 40 24
PM fraction collected after 15 mins % 34 42 66 22 38 67
PM fraction collected after 30 mins % 74 102 88 39 65 100
Total mass collected, averaged mg 76 43
Figure 5.8: A PM filter sample from the baseline test for the first 15 minute (left), the
second 15 minutes (centre) and the entire test (right)
Emission factors were found to describe the PM emissions per unit of volume, energy, and time.
These results are summarized in Table 5.2. The PM emission rate per unit volume across all six
tests are in-line with the data collected from Fitzpatrick et al. [12], whose emissions ranged
between 12 and 29 mg/m3 when looking at the complete burn cycle. In this study, the smoke
concentration in units of g/hr ranged from 7 g/hr to 26 g/hr—significantly higher than reported
value of 3.2 g/hr by Napoleon, the manufacturer of the wood stove. Potential reasons for this
discrepancy include: The removal of the oxidation catalyst, excess air leakage through the front
doors, differences in test methodology between this study and the EPA 5G standard, and a lower
moisture content in the test wood. For the rest of this study, PM concentration values are described
in units of mg/MJ, as it gives the most direct indication of combustion performance.
PM emissions per unit of energy had an average value of 564 mg/MJ for the baseline tests and the
388.19 mg/MJ for the modified test, higher than the 252 mg/MJ value reported by Hukkanen et al.
[5] when testing a stove with the catalyst removed but still of a comparable value. A dramatic
difference PM mass per unit of energy during the start-up period was found between the baseline
45
and modified tests, at an average of 8712 mg/MJ for the baseline test (test was B3 omitted due to
uncharacteristic levels variance) and 829 mg/MJ for the modified test, respectively. For every unit
mass of fuel, a significant reduction in PM was realized by increasing air towards the fuel bed.
Table 5.2: Summary of PM emission factors
Parameter Units B1 B2 B3 M1 M2 M3
PM emissions per unit
volume, start-up mg/m3 167 132 63 59 61 57
PM emissions per unit
volume, full cycle mg/m3 39 27 15 21 14 10
PM emissions per unit
energy, start-up mg/MJ 8493 8931 1074 797 732 957
PM emissions per unit
energy, full cycle mg/MJ 851 572 271 587 361 217
PM emissions per unit
time, start-up g/hr 114 90 43 40 42 38
PM emissions per unit
time, full cycle g/hr 26 18 10 14 10 7
5.2.6 Synthesis of Results
Start-up phase PM emissions made up a significant portion of total PM emissions, confirming the
need for a better understanding of the causes of early PM formation. When wood is first inserted
into the firebox, it is cold and relies on the heat from the coal bed for combustion to begin. During
this period, pyrolysis gases form and leave as incomplete combustion products because they have
not reached an ignition temperature of at least 800C. By increasing the fuel volatilization rate PM
emissions decreased significantly. Adding additional fuel-bed air was proven to be an effective
method of increasing the volatilization rate.
During the start-up phase, the wood stove being tested had low temperatures, high amounts of
excess oxygen, and a low burn rate. These suboptimal conditions were partially caused by high
amounts of secondary air entering the firebox during the initial stages of the combustion process,
as the inlet has no airflow control system in place. Because the secondary air inlet is located far
above the test load, air does not have the momentum to reach the bottom of the wood where
ignition is occurring. The excess amount of secondary air dilutes the exhaust gas air, lowering
temperatures and reducing the burn-rate. In addition, increasing the amount of air directed towards
46
the fuel-bed during this time results in increased volatilization of the wood by creating better
air/fuel mixing where the hot coals contact the wood. It is therefore concluded that to minimize
PM emission formation during start-up, fuel-bed level air should be increased to maximize
volatilization rates and secondary air should be minimized to reduce excess air.
5.3 Start-Up Phase PM Reduction Methods
A significant reduction in PM emissions can be realized if fuel-bed level (primary) and pyrolysis
level (secondary) airflow can be controlled independently with the aim of rapid ignition whilst
keeping excess oxygen at reasonable levels. From the results of this study, the most effective way
to create rapid ignition is to increase the fuel-bed level air and reduce the secondary air until
enough heat has been liberated from the volatiles that pyrolysis and combustion processes can
become self-sustaining. The following sections outline some ways in which these two goals can
be met:
Optimized Air Inlet Design
In the current design, the primary air of the stove must travel downwards in front of the glass to
reach the fuel bed. With this layout, it is hypothesized that much of the air leaves the firebox due
to buoyancy without reaching the fuel-bed. For more fuel-bed air in fact reaches the fuel bed, it is
recommended to create a pilot air inlet that distributes air over a larger surface area. Furthermore,
the pilot inlet can be redesigned to provide oxygen to a greater area than just the front.
Temperature or Draft Control
It is recommended that both the primary and secondary air inlets have airflow-control mechanisms
installed ie. dampers or valves. These inlets should be controlled separately and automatically to
minimize user interaction. During the ignition period, the primary/ pilot air inlet draft should be
maximized until steady-state conditions are reached. At this point, the fuel bed air should be
minimized and the secondary air flow-rate should be maximized. Because temperature and draft
are correlated with mass burn-rate and hence PM emission rate, a temperature-based feedback
system could be used to control the draft of the primary and secondary air inlets. For example, a
high amount of fuel-bed level air and little secondary air could be permitted until a certain
temperature or draft is reached. Given the high degree of variability in the temperature data
47
collected, this control method is only recommended if the exhaust gases have a are sufficiently
turbulent before being sampled to ensure an even temperature distribution.
48
Chapter 6
Conclusions and Recommendations
6.1 Conclusions
The goal of this work was to investigate the factors that affect start-up phase PM emissions in
modern wood burning stoves. An experimental setup was created which comprises of a wood
stove, dilution tunnel, and sensors to monitor the temperature, mass change, CO2 and O2
concentrations, and PM mass over time. A burn methodology was created to standardize tests.
Based on the results of the baseline test, a modified test was created to investigate the effect of
increased fuel-bed air during start-up. Low temperatures, slow ignition, and high PM emissions
were observed during the start-up period in the baseline test. With the addition of increased primary
air, much faster ignition, hotter temperatures, and fewer PM emissions were observed. It was found
that during start-up, a high mass-burn rate results in significantly fewer PM emissions per unit of
energy than at low burn rates. To maximize the burn rate, more air needs to be directed towards
the combustion zone at the fuel bed level and the secondary air must be reduced to avoid air
dilution. Once steady-state conditions are reached, it is recommended to reduce the primary air
and increase the amount of secondary air to ensure complete combustion of the pyrolysis gases.
6.2 Future Work
The following next-steps were chosen with the aim of improving the accuracy of experiments,
increasing the amount of useful information that is collected, and further developing start-up PM
emission reduction methods.
1. Reduce test variance by ensuring a more consistent stacking pattern of the
kindling and pre-burn, moisture content, and coal bed temperature.
2. Implement airflow sensors on the primary, secondary, and pilot air inlets for more
accurate airflow data collection.
49
3. Perform tests with the oxidation catalyst installed to assess its effectiveness at
reducing PM emissions.
4. Investigate the impact of secondary airflow on start-up phase emissions. From this
study, the impact of secondary air on start-up emissions has been observed but not
validated.
5. Implement design changes to the wood stove air inlets to improve fuel bed air
delivery.
6. Compare the effectiveness of different air-control strategies i.e. draft control,
temperature control, oxygen control.
7. Use CFD simulations of the wood stove interior to prototype design changes and
optimize air and fuel mixing.
8. Perform a set of tests that mimic the EPA standard so that performance
improvements can be compared to wood stove certification requirements.
50
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55
Appendix A
MATLAB Code
56
clear
%Upload the raw data
filename0 = '';
rawdata = xlsread(filename0);
%Enter burn specific data
moisture = ;
time_testburn = ;
time_end = ;
%Unit conversions
time_30 = (time_testburn+1800)/60;
time_testburn = time_testburn/60;
time_30 = time_30/60;
time_end = time_end/60
moisture = moisture/100;
%data filtering
filtersize = 10;
b = ones(1,filtersize)/filtersize;
a = 1;
delay = (length(b)-1)/2; % Filter delay to account for moving average offset
filtereddata = filter(b,a,rawdata(:,2:8)); % Moving average filter applied only
to thermocouple readings
t = rawdata(:,1);
primary = filtereddata(:,1);
secondary = filtereddata(:,2);
stack = filtereddata(:,3);
dilution = filtereddata(:,4);
catalyst = filtereddata(:,5);
ls = filtereddata(:,6);
rs = filtereddata(:,7);
co2_dil = rawdata(:,9);
co2_s = rawdata(:,10);
o2 = rawdata(:,11);
mass = rawdata(:,12);
mass = medfilt1(mass, 9); %1-D median filter applied only to mass readings
% Replace negative stack CO2 values with 0
for n = 1:numel(co2_s)
57
if co2_s(n) < 0
co2_s(n) = 0;
end
end
% Averaged data for every 'npoints' values (Ex. 1-60,61-121,122-182)
S = numel(primary);
npoints = 24; % Number of data points being averaged (data points are currently 2
min apart)
t_avg = t(1:npoints:numel(primary));
t_avg = t_avg(2:end);
t_avg = t_avg/60;
rs1 = reshape(primary(1:S - mod(S, npoints)), npoints, []);
primary_avg = sum(rs1, 1).' / npoints;
rs2 = reshape(stack(1:S - mod(S, npoints)), npoints, []);
stack_avg = sum(rs2, 1).' / npoints;
rs3 = reshape(co2_dil(1:S - mod(S, npoints)), npoints, []);
co2_dil_avg = sum(rs3, 1).' / npoints;
rs4 = reshape(co2_s(1:S - mod(S, npoints)), npoints, []);
co2_s_avg = sum(rs4, 1).' / npoints;
rs5 = reshape(o2(1:S - mod(S, npoints)), npoints, []);
o2_avg = sum(rs5, 1).' / npoints;
rs6 = reshape(secondary(1:S - mod(S, npoints)), npoints, []);
secondary_avg = sum(rs6, 1).'/ npoints;
%mass change over 'npoints' samples.
deltamass = abs(diff(mass(1:npoints:end)));
%The large mass change data points are omitted to account for the addition of
fuel logs
for q = 1:numel(deltamass)
if deltamass(q) > 2
deltamass(q) = 0;
end
end
burnrate = deltamass/(npoints*5/3600);
%Combustion reaction calculation to find excess oxygen
molcgfuel = (1-moisture)*0.4873/12;
nC = deltamass*1000*molcgfuel;
58
nH = (1-moisture)*0.0687/molcgfuel*nC;
nO = (1-moisture)*0.439/16/molcgfuel*nC;
nH20 = moisture/18/molcgfuel*nC;
nCO2_out = nC;
nH20_out = (nH+2*nH20)/2;
nO2_out = 1./(co2_s_avg/100).*(o2_avg/100).*nCO2_out;
a = (2*nCO2_out+nH20_out-nO-nH20)/2;
equiv = (2*a)./(2*nCO2_out+nH20_out+2*nO2_out-nO-nH20);
nN2 = a./(equiv)*3.76;
excess = (1./equiv-1)*100;
excessx = prod(isfinite(excess),2); %Remove infinite data points
excess(excessx == 0,:) = 0;
%Inlet and exhaust airflow rate calculation
nair_in = a./equiv*4.76;
Vair_in_scfm = ((nair_in/(npoints*5/(60*2.5))*8.314*298.15))/101325*35.3;
nex_out = nCO2_out + nH20_out + nN2 + nO2_out;
Veg_out_cfm =
(nex_out.*(stack_avg+273.15)*8.314)/101325*(npoints*5/(60*2.5))*35.3;
Veg_out_scfm = (nex_out.*(298.15)*8.314)/101325*(npoints*5/(60*2.5))*35.3;
59
Appendix B
LabVIEW Code
61
Appendix C
Moisture Content Sampling Template
62