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Detection of biogas composition by mid-wavelengthinfrared spectral analysis
João Afonso Ribeiro e Costa Toipa
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisor: Prof. Edgar Caetano Fernandes
Examination CommitteeChairperson: Prof. Paulo Rui Alves FernandesSupervisor: Prof. Edgar Caetano Fernandes
Member of the Committee: Dr. Luísa Maria Leal da Silva Marques
November 2017
Acknowledgments
First and foremost, I’d like to deeply thank Professor Edgar Fernandes not only for the scientific
guidance, but also for his availability and proximity on a daily basis, never hesitating to take
time to help with any question.
To Professor Teodoro Trindade and Sandra Dias, lab manager of the Laboratory of Ther-
mofluids, Combustion and Energy Systems I’d also like to thank for their experimental guid-
ance.
I’d also like to thank Professor Mario Nuno Berberan e Santos and the LQF laboratory
managers Jorge Teixeira and Marta Coelho for their guidance and availability while doing the
FT-IR spectrometry tests and extend my gratefulness to engineer Claudia Araujo Moreira and
Engineer Ricardo Sousa e Silva of the Sarspec company for their help while completing this
work and supporting the development of this system.
On a personal level, I’d like to acknowledge all my lab mates, who made this one of the
most enjoyable projects I’ve ever been involved with.
To all my house mates, colleagues and friends that have accompanied me on this journey
since the day I entered this university I’d also like to deeply thank you all, with a special mention
to Joao, Marcia and Mariana.
To my girlfriend, Barbara, for all her love and patience, for always having time to hear me
complain, improving my mood and giving me all the support I needed.
Last but not least, to my family, whom I dedicate this thesis to, for their never ending love
and support, without which this journey wouldn’t have reached its end.
iii
Resumo
Neste trabalho e apresentada a avaliacao experimental de um sistema de deteccao de metano e
dioxido de carbono, aplicado num contexto de producao de biogas. Baseia-se nos princıpios
de absorcao de energia e foca-se na regiao infravermelha do espectro eletromagnetico, a gama
em que esses dois gases, os dominantes em misturas de biogas, absorvem a luz incidente. Com
duas configuracoes, este sistema usa uma fonte de luz e sensor, ambos funcionais na regiao do
infravermelho de 1000 nm ate 5000 nm, para medir o sinal de diferentes concentracoes de am-
bos os gases e procura-se encontrar uma correlacao entre o integral do sinal e a concentracao. A
Configuracao A inclui a fonte de luz, uma celula de gas e o sensor, enquanto a configuracao B
adiciona um monocromador a esta configuracao de forma a focar em bandas especıficas. Tendo
em conta as funcoes de transferencia da fonte de luz, sensor infravermelho, monocromador e
gases, e criada uma referencia teorica para a qual os resultados experimentais sao compara-
dos com grande proximidade. Uma correlacao linear clara entre a intensidade do sinal e a
concentracao, bem como a capacidade de analisar bandas estreitas especıficas na configuracao
B, sustentam a viabilidade deste sistema, nomeadamente na sua segunda configuracao.
Palavras-chave: biogas, espectroscopia, absorcao, infravermelho, metano, dioxido
de carbono
v
Abstract
In this work is presented the design, assembly and experimental assessment of a methane and
carbon dioxide evaluation system applied in a biogas production environment. It’s based on the
principles of energy absorption and focused in the infrared region of the electromagnetic spec-
trum, the range where these two gases, the dominant ones in biogas mixtures, absorb the most
incident light. With two configurations, this system uses an infrared light source and sensor,
operating in the wavelength range of 1000 nm to 5000 nm, to measure the signal for different
concentrations of both gases and trying to find a correlation between the signal’s integral and
the concentration. Configuration A includes the light source, a gas cell and the sensor, while
configuration B adds a monochromator to this setup in a way of focusing on specific bands.
Taking into account the transfer functions of the light source, infrared sensor, monochromator
and gases, a theoretical benchmark is created to which the experimental results are compared
with great proximity. A clear linear correlation between signal integral and concentration, as
well as the ability to look into specific narrow bands in configuration B, sustain the viability of
this system, namely in its second configuration.
Keywords: biogas, spectroscopy, infrared, absorption, methane, carbon dioxide
vii
Contents
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Resumo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Biogas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Factors Influencing Biogas Production and Measuring Techniques . . . 4
1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Experimental Setup 11
2.1 Light Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Infrared Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Monochromator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Gas Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.5 Configuration A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6 Configuration B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.7 Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
ix
3 Results 29
3.1 Configuration A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Configuration B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Conclusions 37
4.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Bibliography 39
A FT-IR Spectroscopy Results 43
B Technical Datasheets 57
x
List of Tables
1.1 Biogas composition and qualities for two types of substrate [3]. . . . . . . . . . 3
2.1 Gas containers technical details. . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 All samples tested. CH4 mixtures are in the left part of the table and CO2 are
in the right side. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Integral range, corresponding gas and color code associated with each band. . . 22
2.4 Integral band and corresponding monochromator position . . . . . . . . . . . . 26
2.5 Flow controller associated uncertainties. . . . . . . . . . . . . . . . . . . . . . 27
xi
List of Figures
1.1 Modern biogas plant [1]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Sketch of the experimental setup for absorption-QEPAS [24]. . . . . . . . . . . 6
1.3 Absorption of different molecules across the infrared spectrum. [28] . . . . . . 7
1.4 Individual spectrum of each element of a biogas mixture [29]. . . . . . . . . . 8
1.5 Spectrum of the dominant components of a biogas mixture [29]. . . . . . . . . 9
2.1 Scheme representing both configurations of the system being tested . . . . . . 11
2.2 Infrared light source and spectral behaviour curve. (a) Sarspec LS-MIR. (b)
Spectral radiance vs. λ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Infrared sensor and spectral behaviour curve. (a) Luxell FPA 256 mounted in
the system structure. (b) Specific detectivity vs. λ. . . . . . . . . . . . . . . . . 13
2.4 Background noise after hardware dark current subtraction is performed. This
error signal is extracted from an average of 200 frames for a typical case of
system operation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5 Optical path scheme and photographs of the inside of the monochromator and
diffraction grating. (a) Inside view of the monochromator with optical path
drawn. (b) Oriel monochromator optical design [35]. (c) 77301 diffraction
grating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 Monochromator and spectral efficiency curve. (a) Oriel 1/8m hand operated
monochromator. (b) Diffraction grating efficiency vs. λ. . . . . . . . . . . . . 17
2.7 Stainless steel gas cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.8 FT-IR experimental setup schematics and photographs. (a) Experimental setup
scheme. (b) Gas filling setup. (c) Bruker ALPHA spectrometer with gas cell
performing a test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.9 Absorbance spectrum for a biogas mixture given by the FT-IR spectrometer.
Case in point, 40% CO2 and 60% CH4. . . . . . . . . . . . . . . . . . . . . . 20
xiii
2.10 Peak absorbance at λ=3315 nm for CH4 as a function of its percentage in a
CH4/N2 mixture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.11 Integral bands of Table 2.3 with the respective color code. . . . . . . . . . . . . 22
2.12 Plot of the integral values for CO2 and CH4 in the bands defined in Table 2.3 as
a function of concentration. Each curve corresponds in color to the respective
band following what’s shown in that same table. . . . . . . . . . . . . . . . . . 23
2.13 System configuration A. 1 represents the light source, 2 the gas cell and 3 the
sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.14 Signal detected by the sensor after absorption by the biogas mixture according
to equation 2.1. Case in point: 60% CH4 and 40% CO2. . . . . . . . . . . . . 24
2.15 System configuration with monochromator. 1 represents the light source, 2 the
gas cell, 3 the monochromator and 4 the sensor. . . . . . . . . . . . . . . . . . 25
2.16 Signal integral in the defined intervals of Table 2.4. . . . . . . . . . . . . . . . 26
3.1 Typical signal measured by the software and signal integral area. . . . . . . . . 30
3.2 Comparison of theoretical and experimental values of the CH4 and CO2 inte-
grals as a function of concentration. (a) CH4 analysis. Linear relation for the
experimental values given by Equation 3.3. (b) CO2 analysis. Linear relation
for the experimental values given by Equation 3.5. . . . . . . . . . . . . . . . . 31
3.3 Evolution of the signal integral for configuration A as the gas cell goes from 0%
in volume ofCH4 to 45% to then 75%. The left axis displays the signal integral,
while the right one shows theCH4 concentration calculated in accordance to the
linear correlation calibrated for this case. . . . . . . . . . . . . . . . . . . . . . 33
3.4 Comparison of theoretical and experimental values of the CH4 and CO2 inte-
grals as a function of concentration. (a) CH4 band: 3080 - 3560 nm. Linear
relation for the experimental values given by Equation 3.7. (b) CO2 band: 2520
- 3000 nm. Linear relation for the experimental values given by Equation 3.8.
(c) CO2 band: 4104 - 4584 nm. . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5 Evolution of the signal integral for configuration B as the gas cell goes from 0%
in volume of CO2 to 75% and then 45%. The left axis displays the signal inte-
gral, while the right one shows the CO2 concentration calculated in accordance
to the linear correlation calibrated for this case. . . . . . . . . . . . . . . . . . 36
xiv
A.1 5% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
A.2 10% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
A.3 15% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
A.4 20% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
A.5 25% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
A.6 30% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
A.7 35% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
A.8 40% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
A.9 45% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
A.10 50% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
A.11 55% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
A.12 60% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
A.13 65% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
A.14 75% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
A.15 80% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
A.16 85% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
A.17 90% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
A.18 95% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
A.19 100% CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
A.20 5% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
A.21 10% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
A.22 15% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
A.23 20% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
A.24 25% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
A.25 30% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
A.26 35% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
A.27 40% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
A.28 45% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
A.29 50% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
A.30 55% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
A.31 60% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
A.32 65% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
xv
A.33 70% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
A.34 75% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
A.35 80% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
A.36 85% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
A.37 90% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
A.38 95% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
A.39 100% CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
xvi
Nomenclature
α Molar absorptivity.
λ Wavelength.
ν Wavenumber.
A Absorbance
Ad Detector area.
c Concentration.
Ci Integrator capacity.
D∗ Specific detectivity.
eQ Flow controller associated error.
f Frequency bandwidth.
I Radiation intensity after absorption.
I0 Radiation intensity.
Is Signal measured current.
Irms Root mean square current.
l Path length.
P0 Incident radiant power.
Qm Measured flow.
Qimax Maximum flow capacity.
xvii
RI Responsivity related to measured current.
RV Responsivity related to measured voltage.
ti Integration time.
Vs Signal measured voltage.
Vrms Root mean square voltage.
xviii
Glossary
FPA Focal plane array.
FT-IR Fourier transform infrared spectroscopy.
GHG Greenhouse gases.
LPM Liters-per-minute.
LS Light source.
MWIR or MIR Mid-wavelength infrared.
NEP Noise equivalent power.
NIST National Institute of Standards and Technology.
RMS Root mean square.
SLPM Standard liters-per-minute.
TF Transfer function.
xix
Chapter 1
Introduction
1.1 Motivation
As society evolves and the world’s population grows, the amount of organic waste produced
by human activities is also increasing dramatically. Whether its origin is agriculture, animal
production, municipal waste or sewage, the need for more efficient ways of disposing or trans-
forming this potential source of pollution into something useful grows everyday.
That way, biogas production comes into play as one of the most sensible applications of
organic waste by converting it into usable energy, serving as a more ecological alternative to the
use of fossil fuels, avoiding additional emission of greenhouse gases (GHG) during its inception.
Today’s ideal of decentralized energy sources has brought the potential of local biogas plants
in big agricultural or animal production farms to the forefront of self-sustainable systems. Many
farms nowadays are able to become self-sufficient with the energy produced from small di-
gesters, where all the organic waste they produced can be used as the main source for the
energy that will guarantee its functioning, avoiding the need to buy electricity from the grid. A
model of a modern biogas plant is displayed in Figure 1.1.
Constant evolution in biology and engineering, along with the deployment of numerous
policies favouring the support of renewable energy sources in the European Union has trans-
formed biogas production into an industry producing 10.5 Mtoe/y in Europe alone. Globally,
this value is around 17.2 Mtoe/y, meaning that production in the EU accounts for 60% of the
world’s production [2].
To demonstrate the potential of this energy source, new plants must be built and further im-
provements should be made to the ones currently operating. Only with cutting-edge technology
1
Figure 1.1: Modern biogas plant [1].
can renewable energy sources provide economic value to justify its use vs. the widespread use
of cheap but polluting fossil fuels.
With that in mind, the work here presented strives to contribute to this wave of innovation
and development of new ideas in the energetic field. A parternership between the two faculties
of the Universidade de Lisboa - Instituto Superior Tecnico and Instituto Superior de Agronomia
- brings the focus into the pilot plant located in ISA’s campus, and seeks to upgrade the facil-
ities and technologies in a conjoined effort to improve the quality of biogas produced and its
energetic value.
1.2 Literature Review
1.2.1 Biogas
Biogas is a mixture of methane (CH4) and carbon dioxide (CO2), also containing very small
percentages of a number of different gases such as hydrogen sulphide(H2S), nitrogen (N2),
oxygen (O2), hydrogen (H2), ammonia (NH3) and, evidently, water vapour (H2O). Table 1.1
indicates the presence of each component on a dry basis. Based on the chemical process of
anaerobic digestion, its production can be easily described as the oxygen-less biochemical di-
gestion of organic waste into the mixture of those two gases by action of anaerobic microorgan-
isms.
2
Table 1.1: Biogas composition and qualities for two types of substrate [3].
Gas composites/features Formula UnitsBiogasSewage gas Agricultural gas
Methane CH4 % by vol. 65-75 45-75Carbon dioxide CO2 % by vol. 20-35 25-55Carbon monoxide CO % by vol. <0.2 <0.2Nitrogen N2 % by vol. 3.4 0.01-5.00Oxygen O2 % by vol. 0.5 0.01-2.00Hydrogen H2 % by vol. trace 0.5Hydrogen sulfide H2S mg/Nm3 <8000 10-30Ammonium NH3 mg/Nm3 trace 0.01-2.5Siloxanes mg/Nm3 <0.1-5.0 traceBenzene, Toluene, Xylene mg/Nm3 <0.1-5.0 0.0CFC mg/Nm3 0 20-1000Net Calorific Value kWh/Nm3 6.0-7.5 5.0-7.5Normal Density kg/m3 1.16 1.16Wobbe Index kWh/Nm3 7.3Methane number - 134 124-150
The process of making the organic material suitable for fermentation begins with multiple
treatment operations, such as filtration, thickening, dewatering, drying, hygienization and desin-
tegration, after which it becomes designated as substrate [4]. Inside the digester, a large domed
container, the four key parts of anaerobic digestion naturally occur: hydrolisis, acidogenesis,
acetogenesis and methanogenesis [5].
To allow it to then be used as an energy source, the biogas trapped in the dome of the digester
will be transferred to a gas container and posteriorly it must go through a gas upgrading station
in the plant where CO2 and all the other trace gases are removed from the mixture, leaving
only a concentration of methane that is up to natural gas standards. This process involves two
phases, the first one is cleaning, which is the removal of H2S, due to its corrosive nature,
while the second is the enrichment of CH4, or in other words the removal of CO2 from the
mixture [6, 7].
Besides this, looking from a perspective of self-sustenance for agricultural-based biogas
plants, the waste that’s left after the transformation process is concluded, called digestate, can
also be used as fertilizer, guaranteeing maximum efficiency of the whole process.
Scientific works from the academic and industrial communities, as well as economic reports
from bureaucratic entities, are looking into the possibility of expanding the amount of self-
sustainable farms by harnessing the energetic power of biogas to then independently suppress
3
the farms’ needs [8, 9].
A cattle slaughtering facility in Ireland that incorporates the anaerobic digestion process as
a means of recovering energy (efficiency of over 100% of its energy demands) [10] is one great
example of self-sustainability in agricultural operations. The potential of small-scale biogas
plants as an answer to the energetic demands in bottom-of-the-pyramid and rural populations is
explored in India and presented in the work of Surie, G. (2017) [11].
1.2.2 Factors Influencing Biogas Production and Measuring Techniques
It bears mentioning that the concentration of methane and carbon dioxide can vary based on a
large number of factors, the most important being the type of substrate used and the conditions
inside the digester during production [12, 13, 14]. The concentration of methane can range
from 45% to 75% [3, 4], and it is directly related to the energetic value of biogas, whether it is
combusted or oxidized, therefore it is of great importance maximizing its production. Thus, the
precise detection and determination of each component’s concentration in this mixture is a very
important factor as that’s the only way of knowing what is being produced.
To tackle this need, multiple methods have been tested and implemented in the past years,
not only by way of a direct analysis of the gas itself but also by studying the anaerobic digestion
process by looking at different types of substrates and employing several different techniques
to predict the methane and energetic potential of the mixture before it’s produced [15].
The standard method is the biochemical methane potential (BMP) test which has the purpose
of measuring the anaerobic biodegradability of a certain sample. It is a long duration test that
can last up to 30 days and its aim is to establish the correlation between the specific organic
waste being tested and the amount of methane produced [16].
Alternatively, other methods such as chemical composition analysis take a deeper look at the
substrate whether it’s by looking at its elemental composition or at the components that define
it, such as dry matter, voltatile solids and fatty acids [17].
Spectroscopy and other processes have risen as very valid alternatives to the BMP test as
well, namely in the near infrared (NIR) region, where several studies have shown that the sample
can be identified and categorized by its spectrum, allowing for the theoretical methane output
to be calculated based on empirical mathematical correlations [18, 19, 20].
4
On the other hand, the large amount of variables to be considered in this case such as en-
vironmental conditions and different protocols, as well as distinct substrates and equipments
make the real-time monitoring of CO2 and CH4 percentages the most efficient way of under-
standing the behaviour of anaerobic degradation process [21]. Moreover, the solubilization of
carbon dioxide in the substrate usually means that the theoretical values of methane are lower
than the real ones measured [22].
For direct analysis of the biogas mixtures being produced, gas chromatography is the most
common method, as its precision and reliability, as well as its suitability for the measurement of
gas in contact with its liquid phase, make this an ideal solution [21]. Another common method
is the use of an FT-IR spectrometer to identify the concentration of each component through the
biogas sample spectrum.
However, in a biogas plant it is known that the gas being produced may vary in its compo-
sition through a period of time, therefore real-time monitoring of each species concentration is
of the utmost importance. As the two methods listed above are based on extracting a sample
and studying it in a lab separate from the production line, instant knowledge of what is being
produced is not attainable.
Therefore, while the technology needed to do gas chromatography is costlier and harder
to integrate in the biogas production system, the widespread existence of infrared sensors and
emitters make spectrometry easier to transform into a cost-efficient, small-sized and direct so-
lution of instant, real-time biogas measurement.
Spectrometry has its foundations in the concept of energy absorbance in the infrared region.
All chemical species in nature absorb electromagnetic radiation, meaning the molecules of a
certain sample make a transition from a lower to a higher energy state when bombarded with
light in their respective band of absorption. Each species has a unique fingerprint - a spectrum -
that displays the absorption of radiation by its atoms or molecules as a function of wavelength.
In the case of biogas, this phenomenon is most relevant in the MIR region for its components,
thus the logical interest of investigating the correlation between absorbance in this large band
and the concentration in the mixture.
5
Learning about this correlation, given by the Beer-Lambert Law, allows us to look at the
spectrum of a biogas mixture, finding the total energy absorbed in this band and identifying the
gases present and their percentage.
The Beer-Lambert Law relates radiant power in a beam of electromagnetic radiation to the
length of the path of the beam in an absorbing medium and to the concentration of the absorbing
species, respectively.
Equation 1.1 shows this relation in mathematical terms, where I0 is the incident power and
I the power after absorption; as usual for any figure of power, the unit is the Watt. For the
right part of this equation, l is the path length in cm, c the concentration in mol.dm−3 and α
is the molar absorptivity, given by dm3.mol−1.cm−1. This parameter α is an intrinsic property
of every species and is a function of the wavelength λ [23]. This means absorbance is unitless,
even though it can sometimes be represented in Absorbance Units (AU).
A = −log10(I/I0) = αlc (1.1)
Scientific texts with solutions based in infrared absorption for application in real-time biogas
constitution monitoring are few and far between but works like that of Kohring et al.(2015),
present an interesting application of these concepts [24]. It is an LED based system in the mid
infrared spectral region and the combination of quartz-enhanced photoacoustic spectroscopy
(QEPAS) with an absorption path leads to a sensor setup suitable for application in a biogas
plant. The sensor system contains an electronics unit and two gas sensors, one calibrated for
the detection of CH4 and the other for CO2. It is displayed in Figure 1.2.
Figure 1.2: Sketch of the experimental setup for absorption-QEPAS [24].
6
Using this same principle of absorption-QEPAS, the work of Wu et al. (2015) strives to
detect trace gases in the compositon of biogas instead of the main components [25]. Focusing on
identifying the concentration of NH3 and H2S - two gases that are toxic, reactive and corrosive
-, two well-defined absorption lines with no interference from H2O are identified (6322.45
cm−1 for NH3 and 6328.88 cm−1 for H2S) and the utilized sensor is optimized for detection
in those very same wavenumbers.
Other works like Eichmann et al. (2014) introduce a Raman scattering-based system to anal-
yse all the components of biogas including water vapour [26]. Raman spectroscopy is a tech-
nique with the purpose of measuring vibrational, rotational, and other low-frequency modes
in a system [27]. In this work it is defended that virtually all molecular species can be si-
multaneously studied with a single light source, allowing relative concentration measurements
independent of the laser power [26].
To better understand why MIR spectral analysis is well-suited for evaluation of biogas mix-
tures, Figure 1.3 shows the infrared region and what types of molecules usually absorb in which
bands, immediately showing how almost every biogas component absorbs electromagnetic ra-
diation in the mid-wavelength infrared range.
Figure 1.3: Absorption of different molecules across the infrared spectrum. [28]
Taking the information from Figure 1.3 of each individual gas that composes a biogas mix-
ture and displaying the infrared spectrum of each one it’s easier to see the overlapping of bands
where energy is absorbed between molecules. This is displayed in Figure 1.4 for a limited
wavenumber range.
7
Figure 1.4: Individual spectrum of each element of a biogas mixture [29].
The data here presented is extracted from the NIST Chemistry WebBook [29]. While the
concentration for each gas in Figure 1.4 is unknown, all gases are diluted in N2 in known
pressures:
• 150mmHg of CH4 diluted to 600mmHg;
• 200mmHg of CO2 diluted to 600mmHg;
• 600mmHg of H2S diluted to 600mmHg;
• 50mmHg of NH3 diluted to 600mmHg;
• 400mmHg of CO diluted to 600mmHg
Information on the state of water vapour is not given by NIST.
The first conclusion to be drawn from Figures 1.3 and 1.4 is that O2, N2 and H2 do not
absorb infrared light, therefore making it invisible in the spectrum of any biogas mixture.
Also, it can be seen that water vapour can be a problem for many different reasons, the main
one being that it absorbs all across the spectrum, most noticeably in the range where there is
CO2, NH3, CH4 and H2S presence, meaning it can easily disrupt any measurements. This
explains why dry-basis analysis is preferred when studying and evaluating biogas mixtures.
8
However, taking into account the information in Table 1.1, we can conclude that most of
these gases will not have a relevant presence in spectroscopic analysis of a typical biogas sam-
ple. Therefore, due to the overwhelming concentration of methane and carbon dioxide in biogas
mixtures, those will be the main components to be trialled in this system. In Figure 1.5 both
species’ spectrum is displayed for a wavenumber range between 2000 cm−1 and 4000 cm−1.
Figure 1.5: Spectrum of the dominant components of a biogas mixture [29].
As it can be seen, there is no juxtaposition between both spectrums, therefore the viability
of this solution appears even greater.
Both for CO2 and CH4, the dominant species in biogas, the absorption of energy in the in-
frared region cause the molecules to chaotically oscillate around its nucleus; this is called a vi-
brational molecular state, where small amplitude movements between atoms make the molecule
behave similarly to an harmonic oscillator [30].
In the case of CH4, since it’s an alkane, its behaviour is caused by the existence of stretch-
ing vibrations in the C-H bond in the range of 3000 cm−1 – 2850 cm−1 with the well-defined
energetic transitions due to the rigid rotation of the molecule defining the constant interval be-
tween peaks in this range [31, 32]. For CO2, the peak around 2300 cm−1 is due to assymetrical
stretching in the C=O bonds [33].
9
1.3 Objectives
This work presents the design, assembly, tests and results of a proposed biogas sensor. Its
ultimate goal is the distinction and correct measurement of the two main components in a biogas
mixture, CH4 and CO2, in the absence of H2O vapour.
The proposed way of achieving this is measuring the absorption of infrared light by the gas
samples and establishing a correlation between the signal and the concentration of each species.
To correctly determine the validity and sensitivity of this system, the theoretical value of
incident radiation detected by the signal can be calculated based on the transfer functions of all
the components, so as to have a benchmark to which experimental values can be compared.
1.4 Thesis Outline
This thesis is divided into four chapters. Chapter 1 begins with the motivation for this work
and introduces the central topic of this work, biogas. This chapter also presents the literature
review for the current state of biogas production and the most relevant measurement techniques.
Finally, the objectives for this work are outlined at the end of this chapter.
The experimental setup and instruments description is detailed in Chapter 2, outlining at
the same time the two configurations of the system tested in this work. The absorption spectra
of different CH4 and CO2 concentrations obtained through FT-IR spectrometry tests are also
shown in this chapter.
Results of this system’s behaviour are presented and discussed in Chapter 3 for both config-
urations.
Finally, Chapter 4 is where the conclusions are written alongside some suggestions on the
future expansion of this work.
10
Chapter 2
Experimental Setup
Two distinct configurations were here implemented and tested, as shown in the schematics of
Figure 2.1.
Figure 2.1: Scheme representing both configurations of the system being tested
In configuration A, the sensor integrated in this proposed system detects a signal dependent
on the incident radiant power. This signal is a product of the transfer functions of the light
source, the absorbing gas and the sensor. This is given by Equation 2.1.
TFLS × TFGas × TFSensor = Signal (2.1)
Configuration B of the system sees the introduction of a monochromator as a way to dis-
perse the light so as for the sensor to only receive radiation in a continuous and narrow window,
11
displaying only light in relevant bands where it has been absorved the most. The monochro-
mator also has a curve representing its spectral behaviour, this way the previous equation is
rewritten as Equation 2.2.
TFLS × TFGas × TFMonochromator × TFSensor = Signal (2.2)
So, before testing this system and evaluating its performance as a mean of evaluating bio-
gas mixtures, theoretical values must be assessed so the experimental values obtained can be
compared to them, this way it’s necessary to define the experimental setup and each transfer
function.
2.1 Light Source
The light source is a Sarspec LS-MIR, a Si3N4 (silicon nitride) emitter with an emitting power
of 18W and working wavelength range from 500 nm to 5000 nm. Its spectral behaviour is
shown in Figure 2.2 alongside a picture of the device.
(a) (b)
Figure 2.2: Infrared light source and spectral behaviour curve. (a) Sarspec LS-MIR. (b) Spectralradiance vs. λ.
This equipment is air-cooled and the infrared beam can hit temperatures as high as 1150◦C.
It also includes a shutter to avoid overexposing the sample without turning off the lamp and
an intensity controller to allow for manual regulation of light intensity (although this parameter
was not altered for the whole duration of this work, working at maximum intensity all along).
12
2.2 Infrared Sensor
The sensor implemented in the system is a focal plane array, the NIT Luxell FPA 256, attached
to an electronic device designed to read and process the signal current, the Luxell CORE-S.
The FPA is a vapour phase deposition PbSe (lead selenide) focal plane array with 256 pixels
and response in the 1000 nm to 5000 nm range. After radiant flux is received by the detector, a
signal current is generated. The relation between incident light and signal current needs to take
into account the spectral sensitivity curve. The curve made available by the manufacturer, as
seen in Figure 2.3, displays the specific detectivity (D∗) as a function of wavelength. This is a
figure of merit which has the purpose of characterizing the detector’s performance.
(a) (b)
Figure 2.3: Infrared sensor and spectral behaviour curve. (a) Luxell FPA 256 mounted in thesystem structure. (b) Specific detectivity vs. λ.
Specific detectivity relates the concept of noise equivalent power with the area of the detec-
tor and its frequency bandwidth through Equation 2.3. Its unit is Jones; 1 Jones = 1 [cm.Hz1/2/W] [34].
D∗ =(Adf)1/2
NEP(2.3)
While Ad is the detector area, NEP is the noise equivalent power and the frequency band-
width f is directly computed from the integration time ti defined in the software as shown in
Equation 2.4.
f =1
2ti(2.4)
13
However, specific detectivity appears as an obstacle in establishing the relationship between
radiant intensity emitted by the light source, energy absorbed by the gas sample and signal
detected by the sensor as this figure of merit is not easily correlated with radiant intensity.
Therefore, to better comprehend it, it’s needed to not only introduce the concept of responsivity
but also define noise equivalent power.
NEP is a figure of merit used to characterize the sensitivity of an IR detector and it’s defined
as the minimum radiant incident power that produces a signal equal to the root mean square
noise and is given by Equation 2.5.
NEP =VrmsRV
(2.5a)
NEP =IrmsRI
(2.5b)
Responsivity, R, is a figure of merit that establishes the relation between the output sig-
nal in response to the optical input represented by Equation 2.6. Its units are either A/W or
V/W depending if a current or a voltage is measured, respectively, per Watt of incident radiant
power. It is also a function of the wavelength, meaning it has a different behaviour along the
electromagnetic spectrum.
RV =VsP0
(2.6a)
RI =IsP0
(2.6b)
This is therefore the figure of merit needed to perfectly establish a relation between incident,
absorbed and detected radiation along our system. Further developing the mathematics here
involved show responsivity as directly proportional to the specific detectivity as exhibited by
Equation 2.7.
RV =Vrms√
Ad
2ti
D∗ (2.7a)
RI =Irms√
Ad
2ti
D∗ (2.7b)
14
It is known Ad and f are constant, and even withouth knowing the rms noise voltage or
current, we can infer that there is a direct proportionality between the responsivity and the
specific detectivity. Therefore, the given curve can be used to qualitatively relate all the transfer
functions of this system and evaluate the validity of the experimental results obtained.
In what regards what’s seen in the software, it’s the result of the processing by the Luxell
CORE-S of the signal current converted from the radiant incident power. This hardware has
a dark current subtraction function integrated into it and an integrator amplifier with the gain
formula of Equation 2.8.
V0 = I0tiCi
(2.8)
In this case, I0 is the signal current alongside the residual dark current being directed to the
amplifier, ti the user defined integration time and ci the 10pF integrator capacity. Afterwards,
the corresponding voltage, V0, is digitized by a bipolar 14bit ±10V span analog-to-digital con-
verter. This is what is finally observed in the software connected to the sensor as a real-time
movie, displaying approximately 180 frames per second.
It should be noted that even though the system has an hardware-integrated dark current
subtraction function, a residual background noise is always present. This can be accounted for,
a 200 frame averaged value for this error is taken from a typical system operating case and is
displayed in Figure 2.4.
Figure 2.4: Background noise after hardware dark current subtraction is performed. This errorsignal is extracted from an average of 200 frames for a typical case of system operation.
15
2.3 Monochromator
A monochromator is a manually operated instrument that receives incident light through the
entrance slit with a wide range of wavelengths. Light is then directed by a set of mirrors to a
diffraction grating where it’s diffracted and spatially separated in the same way as a prism sep-
arates visible light into colors. Posteriorly, this light is directed to the exit slit through a similar
set of mirrors. This is an entirely mechanic instrument and the hand operated crank precisely
controls the grating position and, therefore, the narrow wavelength band being outputted by it.
This design is shown in Figure 2.5.
(a)
(b)
(c)
Figure 2.5: Optical path scheme and photographs of the inside of the monochromator anddiffraction grating. (a) Inside view of the monochromator with optical path drawn. (b) Orielmonochromator optical design [35]. (c) 77301 diffraction grating.
16
The instrument here implemented is an Oriel 1/8m hand operated monochromator with in-
terchangeable diffraction gratings; the one used here allows for diffraction in the range of 2500
nm to 8000 nm. The number read on the counter on top of the monochromator is calibrated to
allow identification of the wavelength coming out of it depending on the grating’s factor scale;
the grating here used - Oriel 77301 monochromator grating - has a scaling factor of 8, meaning
the wavelength leaving the center of the slit is 8 times higher than the number displayed. In the
case of this unit, with exit slit fully open it is known that a band of 480 nm is outputted.
This way, it allows for the signal to be detected by the sensor as a spectrum, with each
pixel receiving radiant light in a different wavelength and the full 256 pixels reading a 480 nm
fraction of what is seen in Figure 2.14.
The efficiency of the diffraction grating however is not constant and also a function of
λ. Therefore, its transfer function is the spectral curve presented in Figure 2.6 alongside the
monochromator.
(a) (b)
Figure 2.6: Monochromator and spectral efficiency curve. (a) Oriel 1/8m hand operatedmonochromator. (b) Diffraction grating efficiency vs. λ.
2.4 Gas Cell
The gas cell used to house the samples to be studied is made of stainless steel with a 7cm path
length, 25 mm diameter and 2 mm thickness ZnSe (zinc selenide) windows and two valves for
entrance and exit of the gases and displayed in Figure 2.7.
Monomixtures of both CH4 and CO2 are diluted in N2, a molecule with no relevance in the
17
Figure 2.7: Stainless steel gas cell.
infrared region, and posteriorly evaluated by the system. Gas containers of CH4, CO2 and N2
are each connected to an Alicat standard mass flow controller with 5 liter-per-minute maximum
capacity, which in turn connect to a mixing chamber 4 times as large as the gas cell so as to
guarantee the samples filling the cell are well mixed.
Gas containers from Air Liquide, were used in this work, with the properties described in
Table 2.1.
Table 2.1: Gas containers technical details.
Gas Purity
CH4 99.999%CO2 99.99%N2 99.99%
This gas cell also presents another obstacle to the analysis of gaseousH2O, as the possibility
of water condensing in the walls and, most importantly, the lenses of this unit can prove to be a
real problem to the correct operation of this system.
It is necessary to properly study what is inside the cell, how much energy it is absorbing
and how it relates to the other transfer functions. This is where the previously mentioned Beer-
Lambert Law makes it possible to relate the incident light with the absorbance to obtain the
outgoing intensity. Equation 2.9 displays this correlation.
I = I010−αlc (2.9)
18
To obtain the absorbance term, αlc, precise spectroscopic analysis with access to an FT-IR
spectrometer was performed to acquire the needed data.
FT-IR spectrometry is a technique based on the principle of Fourier Transform Spectroscopy,
a technique that uses interference of light rather than dispersion to measure the spectrum of a
substance. The basis of this technique is the Fourier-pair relationship between the interferogram
(interference function) of a substance and its spectrum [36]. What we obtain here, as referred,
is a spectrum of absorbance as a function of wavelength.
The scheme in Figure 2.8 show the new experimental setup implemented to perform this
technique.
(a)
(b) (c)
Figure 2.8: FT-IR experimental setup schematics and photographs. (a) Experimental setupscheme. (b) Gas filling setup. (c) Bruker ALPHA spectrometer with gas cell performing a test.
19
The device used for this was the Bruker ALPHA, with spectral range from 1250 nm to
25000 nm, maximum resolution of 0.95 nm and a wavelength precision of 0.01 nm.
As described by the Beer-Lambert Law, absorbance should be directly proportional to the
concentration, assuming a constant path length, as is the case with this system. However, to
guarantee the absence of absorption saturation outside the peaks, tests were done for both CH4
and CO2, diluted with N2. Figure 2.9 displays an example of a spectrum for a mixture of 60%
CH4 and 40% CO2 obtained with the Bruker ALPHA spectrometer.
Figure 2.9: Absorbance spectrum for a biogas mixture given by the FT-IR spectrometer. Casein point, 40% CO2 and 60% CH4.
The sample concentrations range from 5% to 100% in 5% intervals. Three tests were done
for each concentration and to further guarantee the reliability of the spectrum obtained, each
sample was scanned 16 times by the device and an average spectrum of those scans is pre-
sented. Even though higher numbers of scans were tested with certain samples, it could easily
be confirmed that the results were not affected. The total mixtures tested are displayed in Ta-
ble 2.2. All the corresponding spectra resulting from these tests are displayed in Appendix
A.
However, this equipment has limitations for absorbance values greater than 2.5AU according
to manufacturer recommendations. In the case of both CH4 and CO2, their peaks easily surpass
these values even for low concentrations, therefore saturating in these specific wavelengths,
λCH4 ≈ 3315 nm and λCO2 ≈ 4330 nm. After all spectra are collected it’s important to compare
20
Table 2.2: All samples tested. CH4 mixtures are in the left part of the table and CO2 are in theright side.
CH4 (%) N2 (%) CO2 (%) N2 (%)100 0 100 095 5 95 590 10 90 1085 15 85 1580 20 80 2075 25 75 2570 30 70 3065 35 65 3560 40 60 4055 45 55 4550 50 50 5045 55 45 5540 60 40 6035 65 35 6530 70 30 7025 75 25 7520 80 20 8015 85 15 8510 90 10 905 95 5 95
them and verify the Beer-Lambert Law. In the first place, at peak wavelengths for each mixture
it’s easily verifiable that for concentrations as low as 15%, where the absorbance is greater than
the maximum recommended 2.5AU, exists a saturation in this exact wavelength. This is shown
in Figure 2.10.
Figure 2.10: Peak absorbance at λ=3315 nm for CH4 as a function of its percentage in aCH4/N2 mixture.
21
As the same is known to happen to CO2 in the previously referred peak wavelength, this
shows that what should be looked at are the energetic bands rather than the peak to draw better
conclusions about the concentration. These wavelength intervals are defined in Table 2.3 and
Figure 2.11.
Table 2.3: Integral range, corresponding gas and color code associated with each band.
Gas Color Wavelength band
CH4Red 2105 - 2500 nm
Black 3100 - 3950 nm
CO2Green 2650 - 2860 nmBlue 4100 - 4550 nm
Figure 2.11: Integral bands of Table 2.3 with the respective color code.
After an averaging of the three samples for each concentration, by integrating the spectrum
in these defined bands it is possible to find out that the linear relation established by Beer-
Lambert is followed almost perfectly except for the CO2 peak represented in Figure 2.11 in the
color blue, where the referred absorbance saturation takes a major role in stopping that linearity.
The plot of each integral as a function of concentration can be observed in Figure 2.12 and it
follows the previously established color code.
22
Figure 2.12: Plot of the integral values for CO2 and CH4 in the bands defined in Table 2.3 asa function of concentration. Each curve corresponds in color to the respective band followingwhat’s shown in that same table.
With this information, and comparing it to the data from the NIST Chemical WebBook,
it can be concluded that only one band doesn’t respect the linearity that is expected from the
Beer-Lambert Law, more specifically, the larger CO2 band. It is easy, however, to justify the be-
haviour of the integral curve for this band with the fact that this spectrometer cannot process the
absorbance for values greater than 2.5AU, meaning saturation greatly influences the measured
values here observed.
On the other hand, both CH4 bands and the small CO2 band, the expected linearity is in fact
observed, as higher concentrations of both species are accompanied by a directly proportional
raise in absorbance values.
2.5 Configuration A
The most direct system is composed by the components previously described with the exception
of the monochromator and it is shown in figures 2.13 and 2.1.
In this configuration, light goes through the gas cell, filled with the gas being studied in the
same way as the spectrometry test.
In conclusion, the composed transfer function given by equation 2.1 after the incident light
is detected by the sensor should follow the behaviour shown in Figure 2.14.
23
Figure 2.13: System configuration A. 1 represents the light source, 2 the gas cell and 3 thesensor.
Figure 2.14: Signal detected by the sensor after absorption by the biogas mixture according toequation 2.1. Case in point: 60% CH4 and 40% CO2.
On first sight, it can be inferred that with higher concentrations, the integral of the signal
represented in Figure 2.14 will decrease, following the Beer-Lambert theory, linearly.
As in the case of the FT-IR spectrometry tests, separate samples of CH4 and CO2 are
studied. Tests were done in 5% intervals of gas concentration from 100% to 5% mixed with N2.
24
2.6 Configuration B
This configuration of the system, schematized in Figure 2.1 and shown in Figure 2.15, sees the
introduction of a monochromator to allow it to work as a spectrometer.
Figure 2.15: System configuration with monochromator. 1 represents the light source, 2 the gascell, 3 the monochromator and 4 the sensor.
The intervals to be studied with this configuration of the system aim to show all the relevant
wavelength bands for both gases and are therefore an approximation of the ones shown in Ta-
ble 2.3. Knowing that the window of the monochromator outputs 480 nm of light when the slit
is fully open, the real intervals are defined by the wavelength chosen in the readable counter.
Shorter intervals were avoided as the closure of the exit slit meant the signal read would drop
significantly in intensity. In Table 2.4 these intervals are well defined. As previously referred,
the diffraction grating can’t display the CH4 band in the 2105 - 2500 nm wavelength range.
The product of the four transfer functions given by Equation 2.2 in this configuration of
the system results in the signal of Figure 2.14 with the transfer function of the monochromator
accounted for. Looking at the intervals determined in the above table, the areas of the signal
integrated look like Figure 2.16.
25
Table 2.4: Integral band and corresponding monochromator position
Gas Counter number WavelengthCH4 415 3080 - 3560 nm
CO2345 2520 - 3000 nm543 4104 - 4584 nm
Figure 2.16: Signal integral in the defined intervals of Table 2.4.
The main disadvantage of this configuration is the grating not diffracting light for wave-
lengths lower than 2500 nm, removing the possibility of detecting the band of CH4 in the 2100
nm - 2500 nm range. The much lower signal intensity detected by the sensor with this iteration
of this system is the second biggest disadvantage, in the first place because the light source
is farther away from it meaning there is more dispersion and in the second place because the
diffraction grating has a varying efficiency, resulting in a lower signal for some wavelengths.
2.7 Uncertainty Analysis
Every experimental test has an associated uncertainty. In the case of this system, the flow
controllers and sensor are the two main sources of uncertainty.
The Alicat flow controllers used to control the filling of the gas cell have an associated
uncertainty of 0.8% of the flow measured over 0.2% of its full scale. Therefore, the error eQ is
26
given by Equation 2.10.
eQ = ±(0.008Qm + 0.002Qimax) × 100% (2.10)
It’s evident this uncertainty depends not only on the measured flow rate, Qm, but also on
the maximum operating value of the flow controller i, Qimax, meaning lower capacity flow
controllers have lower associated errors.
The highest measured flow rate was taken into account for all flow controllers in this calcu-
lation and results are presented in Table 2.5.
Table 2.5: Flow controller associated uncertainties.
Gas Qm (SLPM) Qimax (SLPM) eQ (%)
N2 0.2 5 1.16CO2 0.2 1 0.36CH4 0.2 1 0.36
To determine the error of the sensor signal, 200 frames were integrated and the mean value
calculated as a reference value. Afterwards, the root mean square (RMS) deviation was com-
puted and the error values obtained. A typical value of the sensor associated error is 0.9733%,
with a mean value of 241178.57 and an RMS deviation of 2347.44.
27
Chapter 3
Results
Experimental results and the respective comparison with theoretical values are presented in this
section. The integrals of both theoretical and experimental signals were integrated with the
trapezoidal numerical method for every case. They are presented here qualitatively as a fraction
of a standard integral value, in both cases when the gas cell is filled with N2, an expressionless
molecule in the infrared region, meaning the signal is absorbed with maximum possible inten-
sity for each configuration. This way, a qualitative analysis can be done to draw conclusions.
3.1 Configuration A
What the software exports is a real-time movie of the signal detected, displaying approximately
180 frames per second. After covering the sensor, 200 frames of the signal are collected to
define the background noise and allow the system to remove it when the test is being processed.
The measured signal, after background noise is removed usually has the format displayed in
Figure 3.1.
With this configuration, the sensor is bombarded with incident light across the whole de-
tected range, from 1000 nm - 5000 nm. However, unlike what happens in configuration B, this
light is not spatially separated and only an intensity is measured.
Theoretical values in this case correspond to the integral of the signal given by Equation 2.1.
All three transfer functions are defined by datasets of 11690 values and trapezoidal numerical
integration is applied accross the defined wavelength range. This is represented by Equation 3.1.
∫ 5000
2000
Signal(λ)dλ ≈11690∑k=1
f(λk−1) + f(λk)
2∆λk (3.1)
29
Figure 3.1: Typical signal measured by the software and signal integral area.
On the other hand, experimental values are calculated from a matrix of 200 frames × 256
pixels, where an average of those 200 frames is integrated just as described in Equation 3.2 for
an interval of 256 pixels.
∫ 256
1
Signal(x)dx ≈256∑k=1
f(xk−1) + f(xk)
2∆xk (3.2)
In both Figures 3.2a and 3.2b are presented the theoretical vs. the experimental values.
Looking firstly at the plot of Figure 3.2a, a strong linear correlation between the signal inte-
gral and the concentration can be seen for the experimental values, as described by Equation 3.3,
with a corresponding coefficient of determination of R2 = 0.991865. The theoretical curve also
displays that same linearity - R2 = 0.996541 - however with a different slope than the exper-
imental one. What establishes this dissociation between the two sets of values is the fact that
while the experimental signal is the product of the whole light emitting range, the theoretical
signal is measured in a range of 2000 nm - 5000 nm due to the fact that the FT-IR spectrometer
only outputted noise-free values starting with that wavelength.
%CH4 =
IntegralCH4
IntegralN2− 0.9825
−8.4795 × 10−4(3.3)
RV =VsP0
(3.4a)
30
(a)
(b)
Figure 3.2: Comparison of theoretical and experimental values of the CH4 and CO2 integralsas a function of concentration. (a) CH4 analysis. Linear relation for the experimental valuesgiven by Equation 3.3. (b) CO2 analysis. Linear relation for the experimental values given byEquation 3.5.
RI =IsP0
(3.4b)
This means that for the plot of Figure 3.2b, while showing a great similarity with theoretical
values, it is still being radiated by the same emitter as in the previous case, therefore exper-
imental values should naturally be higher as the integral value needs to account for the extra
1000 nm of energy received by the detector. For concentrations lower than 60% the relation of
integral fraction and concentration of CO2 shows the expected linearity. The linear correlation
between signal integral and CO2 concentration is also represented in Equation 3.5. The coeffi-
31
cient of determination for the theoretical curve in Figure 3.2b is R2 = 0.998666, while for the
experimental curve that value is R2 = 0.862249.
%CO2 =
IntegralCO2
IntegralN2− 0.9747
−3.4015 × 10−4(3.5)
The results from Figure 3.2b however show that the measured CO2 signal follows closely
the one from the theoretical value even though an extra 1000 nm of radiation are being detected
by the sensor as compared to the theory.
This is not a completely valid result however and it may be due to the lower spectral response
of the system in the bands where this species absorbs energy, meaning the detected signal must
be more susceptible to external variables such as temperature and humidity or internal variables
like the sensor noise. This also helps justify the high variance of results for the CO2 measure-
ment as it is an important factor when raising questions about the validity of this system, as the
concentration of a CO2 sample cannot effectively be calculated without stable conditions.
Furthermore, when observing a biogas mixture, this sensor allows no distinction between
CO2 and CH4 as the light incides on it in its full electromanetic spectrum, therefore displaying
the signal after both have absorbed energy with no way of evaluating each species’ weight.
This means that, while this configuration of the system proves to be effective as a way of
identifying the concentration of either CO2 or CH4 when in the presence of other elements
invisible in the infrared region - like N2 -, this is not a valid way of correctly assessing the
quality of the biogas being produced.
To showcase a typical case of system operation in configuration A, tests were done in a pre-
commercial scope. The signal integrals as a function of time resulting of these tests are shown
in Figure 3.3.
It must be noted, however, that these tests were conducted after the system was reassem-
bled, meaning it was therefore subject to a set of calibration tests in order to adjust to the new
conditions of the system and obtain a new linear correlation similar to the ones of Equations 3.3
and 3.5. The calibration process involves as well the definition of the reference integral value,
for when the gas cell is filled with only N2. Only after that can one take an integral value and
immediately find out the gas concentration.
For the case of Figure 3.3, it is evident that the system responds well to the variations in
concentration, showing that the linear relation derived from the calibrations for this case shows
close proximity to the injected values. For example, in the demarked regions of this figure, when
32
Figure 3.3: Evolution of the signal integral for configuration A as the gas cell goes from 0% involume of CH4 to 45% to then 75%. The left axis displays the signal integral, while the rightone shows the CH4 concentration calculated in accordance to the linear correlation calibratedfor this case.
the gas is injected through the flowmeters and the supposed concentration is 45% and 75% and
supposed to be stable, the average CH4 concentration according to that correlation is 44.4517%
and 73.6876%, respectively. This proves the great sensibility of the system for concentrations
of methane in the range of typical values in biogas mixtures, as seen in Table 1.1 [3].
3.2 Configuration B
The theoretical values for configuration B as well as the experimental ones are calculated as
described by Equations 3.1 and 3.2, respectively. The theoretical values, however, are integrated
in different intervals than in configuration A, depending on each case, corresponding to the ones
of Table 2.3.
In this configuration, as light is diffracted at the exit of the monochromator, the 256 pixels
of the sensor detect the continuous spectrum of light in narrow 480 nm windows as seen in Fig-
ure 2.16, depending on the monochromator position. Therefore, Equation 3.2 can be rewritten
as Equation 3.6.
33
∫ 480
0
Signal(λ)dλ ≈256∑k=1
f(xk−1) + f(xk)
2∆xk (3.6)
Figure 3.4 displays the correlation between theoretical and experimental values based on
this integration method.
Looking at all the plots of Figure 3.4, it becomes apparent that the signal measured by the
sensor in this configuration never follows the theoretical value. However, Figures 3.4a and
3.4b denote the existence of a linear relation between the value of the integrated signal and the
concentration as expected.
Looking at Figure 3.4c on the other hand it’s possible to observe not only a great variance
in the observed values, showing an excess of sensitivity for this band, but also the lack of a
tendency in the experimental values where the diffence to the theoretical values is significant,
making it impossible for the concentration to be calculated. This may be due to the fact that
the sensor as well as the light source have a reduced sensitivity and radiance in this band, as
the curves corresponding to each of those two devices decrease dramatically for wavelengths
greater than 4000 nm.
The linear correlation seen in the other two studied bands should be enough to create a
valid correlation between the measured signal and the concentration of that respective element.
However, the lower intensity of light that reaches the sensor in this configuration means that
the detected signal will be lower and therefore it will be more susceptibile to noise and other
environmental perturbations that can greatly affect the signal being detected.
As it was the case for configuration A, the signal integral relates to the concentration in a
linear way. From the most relevant results, shown in Figures 3.4a and 3.4b, that linear rela-
tion can be written as in Equations 3.7 and 3.8, respectively. For the case of Figure 3.4a, the
coefficient of determination for the theoretical curve is R2 = 0.99638 and R2 = 0.917426 for
the experimental curve, while for Figure 3.4b those same coefficients are R2 = 0.991004 and
R2 = 0.924941 for the theoretical and experimental curves, respectively.
%CH4 =
IntegralCH4
IntegralN2− 0.9168
−1.7925 × 10−3(3.7)
%CO2 =
IntegralCO2
IntegralN2− 0.9468
−8.3645 × 10−4(3.8)
34
(a)
(b)
(c)
Figure 3.4: Comparison of theoretical and experimental values of the CH4 and CO2 integralsas a function of concentration. (a) CH4 band: 3080 - 3560 nm. Linear relation for the exper-imental values given by Equation 3.7. (b) CO2 band: 2520 - 3000 nm. Linear relation for theexperimental values given by Equation 3.8. (c) CO2 band: 4104 - 4584 nm.
35
In a similar manner as in configuration A, in a pre-commercial approach, a typical case
of system operation for fluctuating concentrations of gas is shown in Figure 3.5. Likewise,
calibration tests were done to find a new linear correlation in the same vein as Equations 3.7
and 3.8 and to establish a new integral reference value for 0% gas concentration. In the case
here depicted, the monochromator is in position 415, focused on the CH4 detection band, 3080
- 3560 nm.
Figure 3.5: Evolution of the signal integral for configuration B as the gas cell goes from 0% involume of CO2 to 75% and then 45%. The left axis displays the signal integral, while the rightone shows the CO2 concentration calculated in accordance to the linear correlation calibratedfor this case.
As it can be seen, the system once again responds accordingly to the variation in CH4 con-
centration, showing close proximity to the injected values. The average concentration calculated
through the calibrated correlation in the demarked region of 75% injected CH4 is 72.5654%,
while in the 45% region that value is 48.0539%.
This, once again, proves the quality of configuration B as a means of evaluating the CH4
concentration in biogas mixtures as it behaves well between the typical values of methane con-
centration.
36
Chapter 4
Conclusions
The purpose of this work is the experimental assessment of the validity and applicability of the
system here proposed as a biogas sensor that is able to discretize and evaluate the percentage of
CO2 and CH4 in this mixture after H2O is removed.
Looking at all tests and results, it’s evident that only the second configuration of this system
can ever be considered for use in a biogas plant, as the first one allows no distinction between
each gas that composes the produced biogas. It can also be noted that accross all tests results
follow the linear behaviour that is to be expected.
The use of the monochromator allows for detection of radiant light in the bands where both
gases absorb and presents the only reliable option for identifying specific species in biogas
mixtures. However, the discrete bands observable in this system impose another limitation of
this system because it means that identification of only one gas at a time is possible. To evaluate
the concentration of both gases, two filters designed for operation in two different bands are
needed, more specifically the ones that presented best results: between 2520 nm - 3000 nm for
CO2 and 3080 nm - 3560 nm for CH4.
The linearity observed in the signals for these two bands make possible the calculation of
the percentage of each gas in the mixture, however calibration of the system is of paramount im-
portance to consider the results reliable because of the significant noise values. If this approach
is selected, as the observed signal shows variance, stable conditions are absolutely necessary
in the surroundings of the system, as temperature and humidity play a big part in affecting this
sensor and the measured signal due to the lower intensity of light.
It must also be noted that with this system it is absolutely needed to remove water vapour
from this mixture, namely for CO2 measurement, as the band with best results (2520 nm -
37
3000 nm) for the evaluation of that gas is also where H2O absorbs most energy, as seen in
Figure 1.4. H2S can also be a nuisance as it also absorbs in that range, however, because of its
very corrosive properties it should also be removed before analysis so operation of this system
is not affected.
Finally, the first configuration demonstrates potential use as a sensor for CO2 and CH4 as
long as its diluted in a medium with no species absorbing energy in the 1000 nm to 5000 nm
range. One such application for a sensor like this inside a biogas plant is the evaluation of
biomethane to attest its high quality, as after biogas is processed inside an upgrading station it
becomes free of CO2, H2O vapour and other trace gases. Taking into the excellent measure-
ments for CH4 by this system in its simple configuration it becomes a very cost-efficient sensor
for this purpose.
It should be noted, however, that this system while proving great responsivity to concen-
tration variations needs to go through a set of calibration tests before it becomes available for
commercial use. That way, when installing a system like this in a biogas plant, the system
should be calibrated to the operating conditions first and then sealed and kept untouched to
make sure optimum and stable performance can be achieved, as meddling with it can scramble
the extracted values, making a correct evaluation of the concentration impossible.
4.1 Future Work
This work can be expanded in a number of ways, the most evident being the calibration and
optimization of this system for operation in real biogas plants for methane and carbon dioxide
detection.
The behaviour of this system in regards to the detection of trace gases in biogas mixtures
is also a viable path of investigation, as with access to different instrumentation that allows
the system to focus on the relevant absorbing bands for those species this can become a real
scenario.
Finally, work should be done in designing this system with modifications to the gas cell in
a way that allows for water vapour to be analysed with no fear of it damaging the experimental
setup.
38
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42
Built for IR applicationsWith one 18 W Silicon Nitride emitter, the Light Source LS-MIR
was specially designed for Infrared applications providing a
wavelength range from 500 to 5000+ nm. This is an high flexibility
product that includes shutter, slide filter holder, intensity control
and remote operation.
Slide filter holder, intensity control and shutter for maximum flexibilityThe flexibility of this light source is granted not only for its wide
wavelength range but also for the included slide filter holder
allowing the selection of a specific range. The intensity control
offers the user the possibility to select the correct intensity
for each application. The LS-MIR includes a shutter to avoid
overexpose the sample without turning off the lamp.
Remote operationAll features from LS-MIR can be controlled remotely via the
D-SUB connector and TTL compatible signals, namely the shutter,
intensity control and emitter operation.
Extended emitter lifetimeThe emitter included in this instrument has an estimated lifetime
of 5000 h reducing usage cost.
Large collection opticsThere’s no point in having an high power emitter when a high percentage of light is not coupled into the fiber. For that reason
Sarspec uses 12.7 mm diameter lens offering high efficiency light collection.
LIGHT SOURCES
WWW.SARSPEC.COM WWW.SARSPEC.COM
Typical spectral
radiance for the
LS-MIR
21
LS-MIR Specifications Table
Parameter Value
Wavelength range 500–5000 nm
Emitter Silicon nitride
Power Requirements 15 Vdc / 2 Adc max.
Power Cons. (Max.) 23 W
Warm-Up Time Approx. 5 min
Shutter / Filter holder Yes / Yes
Local Intensity Control Yes
Emitter Power 18 W
Emitter lifetime 5000 h
Emitter Temperature 1150º C
Remote OperationShutter, Emitter operation (TTL compatible signals)
Intensity control (analog and PWM)
Fiber Connector SMA 905
Cooling Air cooling
Dimensions 200 (L) x 92 (D) x 72 (H) mm
Weight 1.1 kg
Ordering Information
Light Source LS-MIR LS-MIR
Replacement emitter for LS-MIR B-MIR
LIGHT SOURCES
WWW.SARSPEC.COM 22
LUXELL FPA Uncooled VPD PbSe, linear array
FPA formats available: Biasing voltage (typ):Pixel size: LUXELL 64, 128 FPA: LUXELL 256 FPA:Pixel pitch: LUXELL 64, 128 FPA: LUXELL 256 FPA:Pixel resistance (typ): LUXELL 64, 128 FPA: LUXELL 256 FPA:Spectral response (typ):Spectral response (LVF version):Peak wavelength of detection:D*(peak) (typ):Responsivity (@Vb=0.5V)(A/W): LUXELL 64, 128 FPA: LUXELL 256 FPA:Quantum effi ciency:Packaging:Dimensions (mm):Operating temperature:Response time:Readout electronics:Readout method:
64, 128 and 256 pixels5 V
1000 μm 600 μm
100 μm 60 μm
300 - 800 k250 - 700 k
1-5 microns3.0 - 4.6 microns
3.7 microns2·109 Jones
0.0250.090
2%SMD, LCC68 footprint
24 x 24 x 2.25C - 70 C
2 microsecondsnot included
multiplexed (blocks of 16 px)
Typical detector spectral response [D*(peak)(500 K, 1000 Hz, 1 Hz)]
Dimensions (mm): 109 x 100 x 73Weight: < 500 gramsFPGA: XILINX VIRTEX 4 (PowerPC + Linux OS embedded)Integration time: > 4.10 μs, selectableFrame rate in lines per second (max): LUXELL 64 FPA: 20,000 LUXELL 128 FPA: 10,000 LUXELL 256 FPA: 5,000Intelligent dark current substractionStart-up time: < 20 secondsOptimum temperature detection range: > 70 CSystem communication and control: Ethernet 10/100Data transmission: TCP/IP, raw format, 16 bitsControl: UDPPower: 12 VDC, 1 ASoftware: LUXELL SOFTWARE SUITE (Acqusition+Visualization SW)
LUXELL CORE-HSCompatible with all LUXELL FPA versions
LUXELL CORE-SCompatible with all LUXELL FPA versions
The LUXELL FPA footprint is LCC68 compatible and is packaged as an SMD component to allow a perfect integration into your circuitry. It has a sapphire window that is replaced with an LVF in the spectroscopy version
Left - FPA dimensions in mm
LUXELL SERIESTypical applications
LUXELL ACQUISITION SOFTWARE
As a part of the LUXELL SOFTWARE SUITE, it allows control of the CORE system and data acquisition to your PC, maximizing the performance of the LUXELL FPA series.
Integration time: 2 - 100 μs, selectableFrame rate (max): LUXELL 64 FPA version: 1,200 lines per second LUXELL 128 FPA version: 600 lines per second LUXELL 256 FPA version: 300 lines per secondIntelligent dark current substractionStart-up time: < 5 secondsDimensions (mm): 60 x 60 x 40Microcontroller: ARM CORTEX M3System communication and control: USB 2.0 full speed, raw format, 14 bitsUSB powered (5 VDC, 0.5 A)Software: LUXELL SW SUITE (Acq+Vis)
The LUXELL SERIES is a product from New Infrared Technologies, S.L.For more information, visit us at www.niteurope.com, or contact us at [email protected](C) New Infrared Technologies, S.L. - 07/2012 - All rights reserved - The information shown in this document is property of New Infrared Technologies and subject to changes without prior communication.The images shown in this brochure may not reflect the current product appearance.
Quality inspection in production lines: glass container industry
Welding inspection and defect detection: steel industry
High-speed gas fl ow study and characterization
Railway industry: hot-boxes and hot-wheel inspection
Defective welding
1/8 m MONOCHROMATOR GRATINGS
GRATING ASSEMBLY: 77301 1/8 m Monochromator Grating
SPECIFICATIONS
>-o z LU
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100
90
80
70
60
50
40
30
20
10
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1
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ORIEL INSTRUMENTS
SHADED AREA IS BEYOND MECHANICAL LIMIT OF MONOCHROMATOR
1 1 1
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(P-PLANE)
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
WAVELENGTH (nm)
Line Density:
Blaze Wavelength:
Type:
Reciprocal Dispersion at Blaze Wavelength:
Wavelength Counter Multiplier:
Primary Wavelength Region':
150 I/mm
4nm
Ruled
52 nm/mm
8
2.5-8 nm+
Usable Wavelength Region^: 2.5-8 nm*
77700 Series Monochromator/Spectrograph equivalent grating (page): 77750 (24)
77200 1/4 m Monochromator equivalent grating (page): 77236 (50)
77400 1/8 m Spectrograph equivalent grating (page): N/A
Unmounted equivalent grating (page): 77947/77948 (86)
'Wavelength region where the grating efficiency is >20%. Wavelength region where the grating efficiency is >10%. *The wavelength region is limited at the long wavelength end by the mechanical constraints of the monochromator.
36 250 LONG BEACH BLVD.. P.O. BOX 872, STRATFORD, CT. U.S.A. 06497 - TEL: (203) 377-8282 - FAX: (203) 378-2457 « ELECTRONIC MAIL: [email protected]