Determination of biomass co-combustion process state based on flame image series analysis
The article presents an approach to use some base frequency parameters of flickering such as the frequency having the largest amplitude (base frequency) and centroid of amplitude spectrum for characterization of different combustion process state. The laboratory test stands enabled scaled down 10:1...
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Kotyra, A. Wójcik, W. Gromaszek, K. 2018-06-04T19:45:48Z 2018-06-04T19:45:48Z 2017 Determination of biomass co-combustion process state based on flame image series analysis / A. Kotyra, W. Wójcik, K. Gromaszek // Штучний інтелект. — 2017. — № 2. — С. 142-149. — Бібліогр.: 9 назв. — англ. 1561-5359 https://nasplib.isofts.kiev.ua/handle/123456789/133672 662.612, 004.932 The article presents an approach to use some base frequency parameters of flickering such as the frequency having the largest amplitude (base frequency) and centroid of amplitude spectrum for characterization of different combustion process state. The laboratory test stands enabled scaled down 10:1 combustion conditions. Analysis results show that the frequency spatial information could be helpful in combustion process diagnostics. У статті запропоновано підхід до використання параметрів базової частоти мерехтіння, таких як базова частота та центроїд амплітудного спектра для характеристики різних станів процесів згоряння. Стенд лабораторного випробування дозволив знизити умови горіння 10:1. Результати аналізу показують, що просторова інформація частоти може бути корисною для діагностики процесу згоряння. en Інститут проблем штучного інтелекту МОН України та НАН України Штучний інтелект Прикладні інтелектуальні технології та системи Determination of biomass co-combustion process state based on flame image series analysis Article published earlier |
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Determination of biomass co-combustion process state based on flame image series analysis |
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Determination of biomass co-combustion process state based on flame image series analysis Kotyra, A. Wójcik, W. Gromaszek, K. Прикладні інтелектуальні технології та системи |
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Determination of biomass co-combustion process state based on flame image series analysis |
| title_full |
Determination of biomass co-combustion process state based on flame image series analysis |
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Determination of biomass co-combustion process state based on flame image series analysis |
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Determination of biomass co-combustion process state based on flame image series analysis |
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determination of biomass co-combustion process state based on flame image series analysis |
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Kotyra, A. Wójcik, W. Gromaszek, K. |
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Kotyra, A. Wójcik, W. Gromaszek, K. |
| topic |
Прикладні інтелектуальні технології та системи |
| topic_facet |
Прикладні інтелектуальні технології та системи |
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2017 |
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English |
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Штучний інтелект |
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Інститут проблем штучного інтелекту МОН України та НАН України |
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Article |
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The article presents an approach to use some base frequency parameters of flickering such as the frequency having the largest amplitude (base frequency) and centroid of amplitude spectrum for characterization of different combustion process state. The laboratory test stands enabled scaled down 10:1 combustion conditions. Analysis results show that the frequency spatial information could be helpful in combustion process diagnostics.
У статті запропоновано підхід до використання параметрів базової частоти мерехтіння, таких як базова частота та центроїд амплітудного спектра для характеристики різних станів процесів згоряння. Стенд лабораторного випробування дозволив знизити умови горіння 10:1. Результати аналізу показують, що просторова інформація частоти може бути корисною для діагностики процесу згоряння.
|
| issn |
1561-5359 |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/133672 |
| citation_txt |
Determination of biomass co-combustion process state based on flame image series analysis / A. Kotyra, W. Wójcik, K. Gromaszek // Штучний інтелект. — 2017. — № 2. — С. 142-149. — Бібліогр.: 9 назв. — англ. |
| work_keys_str_mv |
AT kotyraa determinationofbiomasscocombustionprocessstatebasedonflameimageseriesanalysis AT wojcikw determinationofbiomasscocombustionprocessstatebasedonflameimageseriesanalysis AT gromaszekk determinationofbiomasscocombustionprocessstatebasedonflameimageseriesanalysis |
| first_indexed |
2025-11-25T23:46:42Z |
| last_indexed |
2025-11-25T23:46:42Z |
| _version_ |
1850583888810737664 |
| fulltext |
ISSN 1561-5359. Штучний інтелект, 2017, № 2
142 © A. Kotyra, W. Wójcik, K. Gromaszek
UDC 662.612, 004.932
A. Kotyra, W. Wójcik, K. Gromaszek
Lublin University of Technology, Poland
38a, Nadbystrzycka str., Lublin, 20-618
DETERMINATION OF BIOMASS CO-COMBUSTION PROCESS
STATE BASED ON FLAME IMAGE SERIES ANALYSIS
А. Котира, В. Вуйцик, К. Громашек
Люблінський технологічний університет, Польща
вул. Надбистшицька, 38а, м. Люблін, 20-618
ВИЗНАЧЕННЯ СТАНУ ПРОЦЕСУ СПІЛЬНОГО
СПАЛЮВАННЯ БІОМАСИ НА ОСНОВІ АНАЛІЗУ
ПОСЛІДОВНОСТІ ЗОБРАЖЕНЬ ПОЛУМ'Я
The article presents an approach to use some base frequency parameters of flickering such as the
frequency having the largest amplitude (base frequency) and centroid of amplitude spectrum for
characterization of different combustion process state. The laboratory test stands enabled scaled down 10:1
combustion conditions. Analysis results show that the frequency spatial information could be helpful in
combustion process diagnostics.
Keywords: biomass co-combustion, image processing, flame flicker.
У статті запропоновано підхід до використання параметрів базової частоти мерехтіння, таких як
базова частота та центроїд амплітудного спектра для характеристики різних станів процесів згоряння.
Стенд лабораторного випробування дозволив знизити умови горіння 10:1. Результати аналізу
показують, що просторова інформація частоти може бути корисною для діагностики процесу згоряння.
Ключові слова: спалювання біомаси, обробка зображень, полум'яний флікер.
Introduction
Climate protection becomes more and more severe problem. Renewable fuels are
considered as one of the main ways of reducing greenhouse-gas emissions, primarily CO2
for it is absorbed during plant growth and released during combustion. Hence, it does not
contribute to the greenhouse effect.
The most common and cheapest method of energetic biomass utilization is its direct firing
or co-firing with other solid fuels, particularly with coal in the existing power plants. Another
means of its exploitation, mainly in thermo-chemical conversion technologies such as pyrolysis,
gasification, anaerobic digestion has minor significance in bio-energy production [1].
Biomass is a highly volatile fuel. The rate of pulverised biomass combustion is
considerably higher than that of coals [1] being more similar to oil or gas fuels combustion
[1, 2]. However, biomass-coal co-firing has several significant drawbacks. Biomass
contains less carbon and more oxygen than coal, that results in a lower heating value. High
moisture, as well as ash content, can be a reason of possible combustion stability problem.
On the other side, higher chlorine contents rise corrosion rate. The melting point of the ash
can be low. It causes increased slagging and fouling of combustor surfaces that reduce heat
transfer and result in corrosion and erosion problems [3]. Biomass has lower density and
friability than coal that results in possible stratification of fuel mixture contents during its
conveyance to burners. What is more, both physical and chemical biomass parameters of
biomass are unsteady in time [4]. All the mentioned factors make the co-combustion
process difficult to maintain. Thus, ensuring the proper operating point of the combustion
process requires diagnostic system, that would enable to discriminate combustion process
ISSN 1561-5359. Штучний інтелект, 2017, № 2
© A. Kotyra, W. Wójcik, K. Gromaszek 143
states, especially the ones when the process runs in a wrong way leading to raised
emissions of harmful substances, malfunctions or even threat to human life.
Majority of the systems intended to keep the combustion process within the
permissible boundaries utilize analysis of flue gases. The information retrieved is delayed
and averaged among many burners operating inside a typical combustion chamber in
power plant. Furthermore, it is hard to determine which burner operates improperly for the
information obtained is not spatial.
An approach that is based on flame radiation analysis has no drawbacks mentioned
above. Combustion of pulverized fuels takes place in a turbulent flow. Local fluctuations
occur of both fuel and gaseous reagents concentrations, as well as temperature. It leads to
permanent local changes in the combustion process intensity that results in continuous
changes in flame luminosity which can be observed as flame flicker. Combustion process
affects the turbulent movement of its products and reagents determine the way the flame
flicker parameters such as e.g. mean luminosity. For a given fuel mixture at constant air
and fuel flow, the combustion process remains in statistical equilibrium. Thus, flame
flicker is a pointer of ongoing combustion process that is commonly applied in flameout
protection systems. However, such systems evaluate from having single optical channel to
multi-channel and even image processing based [5, 6, 7, 8].
The article presents an approach to use some base frequency parameters of flickering
such as the frequency having the largest amplitude (base frequency) and centroid of
amplitude spectrum for characterization of different combustion process state.
Laboratory combustion tests
Combustion tests were done in a 0.5 MWth (megawatt of thermal) research facility,
enabling scaled down (10:1) combustion conditions. The main part is a cylindrical
combustion chamber of 0.7 m in diameter and 2.5 m long. A low-NOx swirl burner about
0.1 m in diameter is mounted horizontally at the front wall. The stand is equipped with all
the necessary supply systems: primary and secondary air, coal, and oil. Pulverized coal for
combustion is prepared in advance and dumped into the coal feeder bunker. Biomass in a
form of straw is mixed (10% by mass) with coal after passing through the feeder.
burner
combustion chamber
High-speed camera
borescope storage
Fig. 1. Laboratory setup
ISSN 1561-5359. Штучний інтелект, 2017, № 2
144 © A. Kotyra, W. Wójcik, K. Gromaszek
The combustion chamber has two lateral inspection openings on its both sides, that
provide optical access. A borescope with high-speed camera attached was placed near
burner’s nozzle, as shown in fig. 1. The camera has a color CMOS area scan sensor
capable to catch 500 frames per second (fps). The borescope’s direction of view was 90
degrees to its axis. Flame images were transferred from the interior of the combustion
chamber through a 0.7m borescope and were captured at a speed of 150 fps at full
resolution (1280 1024 pixels), transferred and then stored in a high-performance storage
system. The optical parts were cooled with water jacket. Additionally, purging air was used
to avoid dustiness.
Several combustion tests were performed during which blend of pulverized coal and
straw was burned for different settings of the combustion facility. Fuel and air flow rates were
set independently and kept at the same level. The scale measured amount of fuel that was
stored in the bunker. As the feeder had delivered the fuel blend to the burner, scale readings
had tended to decrease. Fuel flow was calculated as a quotient of difference between two
successive readings of scale and length of time interval between them. The experiment took
about four hours while image sequence recordings lasted for about 30 seconds during which
combustion state was in stationary condition. It was due to vast amount of storage memory that
would be needed during the whole combustion test that usually lasted for 3 up to 5 hours. The
fuel and air flows, as well as the other parameters of the stand were recorded in one second
intervals whereas several images captured is equal to 150.
Nine combustion states were set. During the first three, fuel flow was comparatively
high, reaching 58–68 kg/h whereas fuel flow was adjusted at three levels. The fourth
recording was made at the lowest fuel flows (around 20kg/h) with higher, but not steady air
flow. The next two combustion process states fuel flow rate was around 50kg/h with higher
and lower air flows, respectively. The last three states were conducted at fuel flows rate
slightly lower than 40 kg/h.
Color images (24bit RGB) of flame were captured for mentioned above variants and
stored in an uncompressed form. Example two sequences for two different combustion
states were presented in figure 2. As it can be noticed, flame shape is different for different
settings of air as well as pulverized fuel flow rate.
Fig. 2. An example frame sequences captured for two different
settings of fuel and air flows
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© A. Kotyra, W. Wójcik, K. Gromaszek 145
The methods
A sequence of N images was compacted in a single 3-dimensional data structure. The
two spatial dimensions (x, y) corresponded to coordinates of each pixel of a single frame,
whereas the third dimension – to a time at which the given frame was captured. The image
sequence consisted of time series matched to each frame pixel of known coordinates as
shown in figure 3.
Fig. 3. Data structure containing image sequence
An example time series that was obtained for a given pixel coordinate x, y is
presented in Figure 3. Since the images were processed as 8-bit greyscale, the pixel
amplitude was between 0 and 255. Before performing the frequency analysis of the data
obtained, the mean value was subtracted. Time series for each coordinate (x, y) in image
sequence were analysed in frequency domain in order to determine spatial distribution of
flame flicker. For that case, amplitude spectrum (Ax,y) can be expressed in the form of the
following equation:
1
0
,, 2exp
N
k
kqkyxqyx tfjtafA , (1)
where fq – discrete frequency qth sample (q = 0, 1, 2…N-1).
The number of images that are captured in one second (fps) is 1/t where t denotes
time resolution (distance between two neighbouring time samples). The maximum
frequency of flame flickering that can be determined fmax can be calculated based on the
Shannon-Kotelnikov theorem:
2
fps
2
1
max
t
f . (2)
As the number of samples in time and frequency domains equal N, the frequency
resolution f can be expressed as:
N
f
fps
. (3)
The spectra obtained were characterized by the frequency of highest magnitude and
spectral centroid that is defined as [9]:
1
0
,
1
0
,,
N
k
kyx
N
k
kkyxyx fAtffAcentroid (4)
ISSN 1561-5359. Штучний інтелект, 2017, № 2
146 © A. Kotyra, W. Wójcik, K. Gromaszek
During the data acquisition, the combustion process remained in stationary
conditions. The single image series recording lasted 40 seconds at a rate of 150fps, that
corresponded to N = 6000 points in time domain. The resolution of each captured image
was 800800 pixels yielding a total number of 64000 time series. According to Nyquist
Sampling Theorem, the maximum resolvable frequency is 75Hz and following eq. (4) the
frequency resolution obtained was 0.025Hz. The amplitude spectrum obtained for an
example time series is presented in figure 4.
As it can be observed in the spectrum plot, majority of flame flicker power is
contained within frequencies of about 1Hz. Thus, it is necessary to provide the frequency
resolution at least of the order of 0.1Hz.
The laboratory tests were conducted for several combustion states with different
settings of fuel and secondary air flows and different biomass content 10% and 30%. The
air flow was determined by stoichiometry and amount of fuel delivered in a unit of time for
a constant excess air coefficient (λ) that is defined as a quotient the mass of air to combust
1kg of fuel to mass of stoichiometric air. For that reason, secondary air flows for the same
λ are different for different settings of fuel flow.
Fig. 4. Amplitude spectrum obtained for an example time series
Results
Each combustion state was determined by thermal load and the corresponding excess
air coefficient. The desired value of thermal load was adjusted by a proper fuel flow of
known calorific value. Results of centroid distribution for flame flicker for selected
combustion tests are presented in fig. 5. It can be noticed the distribution is almost uniform
regardless of thermal load of the combustion test stand λ, and biomass content in mixture.
The centroid frequency is approximately 0.5Hz.
ISSN 1561-5359. Штучний інтелект, 2017, № 2
© A. Kotyra, W. Wójcik, K. Gromaszek 147
Fig. 5. Spatial distribution of flame flicker frequency centroid for: 10% of biomass in
mixture, λ = 0.75, Pth = 250kW – a); Pth = 380kW – b); 30% of biomass
in mixture, λ = 0.75, Pth = 250kW – c), Pth = 380kW – d)
Figure 6 presents spatial distributions of amplitude corresponding to each centroid
presented in figure 5. It can be noticted, distribution of frequency with maximal amplitude
is different for each combustion state. Slightly higher frequencies can be spotted in the
very centre, where burner is located, reaching about 1.0 – 2.5 Hz. Lower frequencies
(nearly 0Hz) correspond to the region where the radiation emitted by the flame was
reflected by inner parts of the combustion chamber.
Conclusions
Due to borescope placement constraints, the burner is located in the middle of the
captured images and the near-burner zone, cannot be observed from side view. This is
undesired place of probe mounting, however in industry conditions, it is sometimes hard to
gain optical access in a better place in the combustion chamber. The spotted flame flicker
frequencies of the highest amplitudes, as it was mentioned before, are around 1Hz. As it
can be observed in figure 4, amplitudes of flame flicker frequency drop exponentially,
remaining on the same level above 40–45Hz.
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148 © A. Kotyra, W. Wójcik, K. Gromaszek
The length of time window was appropriate to determine frequencies with the
resolution of 0.025Hz. The analysis presented has revealed that centroid of the flame flicker
amplitude spectrum cannot enable discrimination of different combustion process states.
Fig. 6. Spatial distribution of the frequency having the maximum amplitude for: 10%
of biomass in mixture, λ = 0.75, Pth = 250kW – a); Pth = 380kW – b); 30% of biomass
in mixture, λ = 0.75, Pth = 250kW – c), Pth = 380kW – d)
Different situation takes place for the case of the second parameter being examined,
i.e. spatial distribution of flame flicker frequency having the maximal amplitude. The results
obtained have pointed, it is discriminative parameter. The highest frequencies can be found
in the very centre of the examined frames, where burner outlet is present and velocities of air
fuel mixture have higher values. The frequency distributions are different both regarding fuel
flow (fig. 6a, 6b and fig. 6c, 6d) and biomass content in fuel mixture. Higher biomass
content (30% - fig 6c, 6d) corresponds to almost zero frequency outside the burner.
The frequency spatial information could be helpful in combustion process
diagnostics. Comparing to pure amplitude information as irradiance, it is more immune to
ISSN 1561-5359. Штучний інтелект, 2017, № 2
© A. Kotyra, W. Wójcik, K. Gromaszek 149
presence of dust that affects operation of optical parts inside a combustion chamber. The
frequency profiles presented are specific for a given burner type, fuel and combustion
chamber and for every case should be determined independently.
References
1. Demirbas A. Recent advances in biomass conversion technologies, Energy Edu Sci Technol., No 6,
2000. P. 19-41.
2. Marks J. Wood powder: an upgraded wood fuels: the role of renewables, Forest Products Journal,
No 42, 1992. P. 52-58.
3. Pronobis M. The influence of biomass co-combustion on boiler fouling and efficiency. Fuel 85, 2006. P. 474-480.
4. Sami M., Annamalai K., Wooldridge M. Co-firing of coal and biomass fuel blends, Progress in Energy
and Combustion Science, 27, 2001. P. 171-214.
5. Ballester J., García-Armingol T. Diagnostic techniques for the monitoring and control of practical
flames, Prog Energy Combust. 36, 2010. P. 375-411.
6. Docquier N., Candel S. Combustion control and sensors: a review. Prog Energy Comb Sci 28, 2002. P. 107–50.
7. Demirbas A. Combustion characteristics of different biomass fuels. Progress in Energy and Combustion
Science 30, 2004. P. 219-230.
8. Lu G., Gilbert G., Yan Y. Vision based monitoring and characterization of combustion flames. Journal
of Physics: Conference Series 15, 2009. P. 194-200.
9. Kua J.M.K., Thiruvaran T., Nosratighods M., Ambikairajah E., Epps J. Investigation of spectral
centroid magnitude and frequency for speaker recognition. In Proc. Odyssey, The Speaker and
Language Recognition Workshop, Brno, Czech Republic, 2010. P. 34-39.
РЕЗЮМЕ
А. Котира, В. В. Вуйцик, К. Громашек
Визначення стану процесу спільного спалювання біомаси на основі аналізу
послідовності зображень полум'я
У статті представлений підхід до використання інформації, що міститься у
випромінюванні полум'я для виявлення процесу спалювання біомаси. Підхід полягає в
обробці послідовностей зображень, отриманих з камери згоряння, та проведення
частотного аналізу часових рядів, що відповідають кожному пікселю зображення.
Параметри базової частоти досліджувались як частота, що має найбільшу амплітуду
(базову частоту) і центроїд амплітудного спектра. Дані були зібрані під час декількох
випробувань спільного спалювання біомаси, які виконувалися для різних параметрів
горіння - теплових навантажень (250 кВт та 380 кВт) та кількості біомаси в суміші
(10%, 30%). Лабораторний тестовий стенд (макс. 500кВт) дозволив зменшити (10:1)
умови згоряння. Зображення були отримані за допомогою високошвидкісної CMOS-
камери з приєднаним до водяного охолодження борескопом, об'єднаних з
високопродуктивною системою зйомки зображень. Результати аналізу були
представлені у вигляді просторових розподілів зазначених частотних параметрів.
Обговорення результатів в основному зосереджене на можливостях застосування у
діагностичних цілях при визначенні стану процесу згоряння.
Надійшла до редакції 21.09.2017
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