Thesis R Den Blanken WVT2008 10

8/10/2019 Thesis R Den Blanken WVT2008 10

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Equivalent width method for quantitative FTIR analysis

of biomass pyrolysis using HITRAN database

Ing. R. den Blanken I

AbstractAlternative energy sources are becoming more and more interesting, both due to environmental

issues as well as due to depletion of conventional fuels. One of the front-runners to substitute

fossil fuels is biomass.

A major drawback of biomass combustion is the formation of nitrogen oxides (NOx).

To reduce the emission of NOx during combustion, it is important to understand the chemicalmechanisms. Especially ammonia (NH3) and hydrogen cyanide (HCN) play an important role as

precursor for NOx.

Kinetic models to predict the formation of NH3 and HCN are developed at the TU/e research

group Combustion Technology. To validate these models, it important to perform accurate andreproducible experiments. In order to provide these measurements, an experimental setup has

been developed based on infrared absorption principle and an electrically heated grid reactor. In

the grid reactor, biomass can be pyrolysed.

Though absorption measurements are relatively easy to perform, interpreting these measurements

and converting them to absolute values might be complicated by the nonlinear dependence of

measured absorbance on the pressure, temperature and concentration of the gas species. The

conventional approach - to perform quantitative measurements - is to compare the absorption

peaks with previously conducted calibration plots. The applicability of this approach can be

limited because the concentrations, pressure and temperature might vary in the reactor. Therefore,

the linear absorption approach works best for measurements where temperature and pressure are

constant and the concentrations are not too high. More importantly, it is restricted in some cases

as it is not possible to produce the required calibration curves.

In this thesis, a novel quantitative approach is developed for Fourier Transform Infrared

spectroscopy. Measured data is compared with integration of parameters tabulated in databases

containing spectroscopic data, like the HITRAN database.

This new method, equivalent width method, is advantageous when the database containing the

spectroscopic data is accurate enough. Experiments preformed in this thesis demonstrate that the

HITRAN database is accurate for ammonia, carbon monoxide, nitric oxide, acetylene and

methane.

The advantages of this method can be encapsulated as follows:

- Independent of apodization function and other machine parameters- Spectral simulation possible for interfering lines. Hence estimation of quantitative values is

possible for interfering lines.

- Less sensitive to noise due to peak integration (in comparison with the peak height method).- Less cumbersome and time consuming, as calibration gases and calibration scans are not

necessary.

Quantitative pyrolysis experiments have been performed for the species: methane, carbonmonoxide, ethylene, hydrogen cyanide and acetylene.


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Ing. R. den Blanken II

Contents

1 Introduction............................................................................................................... 11.1 Background......................................................................................................... 11.2 Research objectives............................................................................................. 2

1.3 Approach............................................................................................................. 2

2 Pyrolysis..................................................................................................................... 3

3 Fourier transform Infrared spectroscopy ................................................................ 63.1 Infrared absorption.............................................................................................. 6

3.1.1 Beer-Lambert law ................................................................................. 10

3.1.2 Limitations of the Beer-Lambert law.................................................... 113.1.3 Conventional quantitative analyze........................................................ 11

3.1.4 Molar absorptivity................................................................................. 123.1.5 Temperature dependence ...................................................................... 16

3.2 Michelson interferometer.................................................................................. 17

3.2.1 Fourier transform.................................................................................. 193.2.2 Advantages of FTIR.............................................................................. 20

4 Experimental Set-up ............................................................................................... 214.1 Preparation of calibration gas ........................................................................... 21

4.2 FTIR setup with sample cell and heated grid.................................................... 22

5 Calibration curves ................................................................................................... 265.1 Ammonia (NH3)................................................................................................ 27

5.2 Carbon monoxide (CO)..................................................................................... 29

5.3 Methane (CH4) .................................................................................................. 31

6 Equivalent width for quantitative measurements .................................................. 326.1 Equivalent width method.................................................................................. 32

6.2 Simulation of FTIR spectrum ........................................................................... 346.3 Theoretical temperature estimation................................................................... 35

6.4 Theoretical testing of the equivalent width method.......................................... 36

7 Experimental results ............................................................................................... 417.1 Experiments and results with glass cell ............................................................ 41

7.1.1 Conclusion ............................................................................................ 547.2 Experiments with heated grid reactor ............................................................... 55

8 Discussion on the pyrolysis experiment ................................................................. 58

9 Conclusion and recommendations ......................................................................... 59

10 Acknowledgements.................................................................................................. 62

11 References ............................................................................................................... 63


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Ing. R. den Blanken III

Appendix A Integration range of spectral features............................................... 65

Appendix B Apodization.......................................................................................... 70Appendix C Baseline correction.............................................................................. 72

Appendix D Ammonia decrease in time ................................................................. 75

Appendix E FTIR settings ....................................................................................... 77


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Ing. R. den Blanken 1

1 Introduction

1.1 Background

Biomass originates from living and recently dead biological materials that can be used as

a fuel. For thousands of years, biomass was the most important energy source known to

mankind. The fossil fuels have taken over that role during the last centuries. At the end ofthe 19th century, the use of coal started to increase. After World War II, inexpensive

gasoline became available leading to an increasing oil usage. However, the resources of

fossil fuels are limited. Also the combustion of fossil fuels has been proven to be harmfulfor the environment due to harmful emissions.

For example, the concentration of carbon dioxide (CO2) increases in the atmosphere

because of the large-scale utilization of fossil fuels. The increase of greenhouse gases(like CO2 ) are believed to be responsible for the global warming. [1]

The world community started to look for substitute for fossil fuels, one of the front-

runners being biomass.

Biomass is a CO2 neutral raw material. CO2is consumed from the air by growth of plants.By photosynthesis the CO2 is converted into complex biochemical compounds like

cellulose and lignin. By combustion of the biological material, the same amount of CO2is

exhausted. In this way, there is no increase of CO2and therefore it is often referred to as a

closed cycle in which the total amount of CO2is conserved [1]. When the time period of

this cycle is short, it is called the carbon-cycle. Because of the short time period of the

carbon-cycle it can be seen as CO2neutralA drawback of biomass combustion is the formation of nitrogen oxides like NO, NO2and

N2O (collectively known as NOx). Emissions of nitrogen oxides contribute to formationof photochemical smog and ozone in the air, and also cause acid rain.

When biomass combustion takes place, the important NOx formation route is viaoxidation of ammonia (NH3) and hydrogen cyanide (HCN) which originate from the

nitrogen that is chemically bound in the fuel (the so called fuel-NO x mechanism). The

pyrolysis of biomass, which is the first step in the combustion process, leads to the

formation of these NOx precursors. To reduce NOx emissions it is important tounderstand the mechanism behind the formation of NH3and HCN.

At the TU/e research group Combustion Technology one of the central themes is to

research the thermal decomposition of biomass. The group has been developing a modelto predict the thermal decomposition of biomass. Accurate experimental data are neededto validate the model. A heated grid reactor with a heating rate of 1000K/s is available to

study this process. The biomass decomposition products are investigated with different

diagnostics techniques. One of them is FTIR spectrometry.


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1.2 Research objectives

To validate the model that has been developed at the TU/e research group CombustionTechnology, accurate experimental data is needed at different pyrolysis conditions. The

goal of this research is to develop and test the methodology for quantitative

measurements of major biomass pyrolysis products, such as CO, CH4, NH3, HCN, C2H2,C2H4, etc.

1.3 Approach

This research continues the work of [2]. In that work the NH3 (ammonia) and HCN

(hydrogen cyanide) formation from fast pyrolysis of MDF (Medium-Density Fiberboard)

was studied. Quantitative measurements of HCN were not preformed.To provide the measurements of biomass pyrolysis products, experimental methodologyshould be developed.

In previous works [2, 3], the concentrations of thermal decomposition products were

determined by comparing the FTIR (Fourier transform infrared spectroscopy) data with

the calibration spectra measured at the same temperature, pressure and apodizationfunction. Changing one of these settings requires a whole new set of calibration data. In

this thesis, a new approach is tested. The suggested method is based on evaluation of the

concentration from the measured spectra using the molecular spectroscopic databases,

like the HITRAN database. This approach is novel for FTIR spectroscopy with anintermediate resolution and will be later on in the report, referred to as the equivalent

width method. The method will be applied for measurements of various thermaldecomposition products like methane, carbon monoxide, ethylene, hydrogen cyanide,nitric oxide and acetylene. The measurements will be carried out on the Fourier

Transform Infrared (FTIR) spectrometer, available in the Biomass laboratory at the

Eindhoven Technical University.

Report structure

A lot of research has been reported on biomass pyrolysis, however, most of these studies

have been performed for slow pyrolysis processes. Chapter 2 gives a basic description ofpyrolysis and provides the expected species and concentrations from fast pyrolysis of

woody biomass. Chapter 3 gives background information about Fourier Transform

Infrared spectroscopy. Chapter 4 describes the experimental setup. The conventional

approach to FTIR quantitative measurements is described in chapter 5. Realmeasurements are used to provide calibration curves for the molecules carbon monoxide,methane and ammonia. These curves are shown in chapter 5. Chapter 6 discusses the

equivalent width method. The theory is tested with measurements from calibration gases

in well-defined conditions like temperature, pressure. The results are given in chapter 7.

The second part of chapter 7 describes the performed pyrolysis experiments and the

results. The conclusions and recommendations can be found in chapter 8.It is recommended to print or copy this report in color. In the figures, some colors are

used for explanation purposes, or to distinguish the spectra of different molecules.


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2 Pyrolysis

The thermal decomposition of chemical compounds in absence of an oxidizer is called

pyrolysis. This is the initial step in biomass gasification and combustion into gaseous,

liquid and solid products. A biomass material like wood is placed in an inert atmosphere.It is then heated up to a final temperature with a specific heating rate and pressure.

Because of heating, reaction of the compounds in the wood takes place. The heating rate,

time of reaction (residence time), pressure and temperature are parameters that influencethe final products of the pyrolysis The final compounds consist mainly of hydro carbons

(CzHy), carbon oxides (CzOx), precursors of nitrogen oxides (NOx) like NH3and HCN,

tars (CzHyOx), charcoal and lower molecular weight gases like hydrogen.

Pyrolysis can be divided into three main groups:

Slow pyrolysis (torrefication)

The biomass is heated by a heating rate below 120K/min with a temperature below

~600K. The treatment yields a solid product with lower moisture and higher energy

content.

Fast pyrolysis (liquefaction)

This short process (0.5 5 seconds) takes place under high pressure (100-200 bar), theheating rates are higher than 10,000K/s and the final temperature is below ~900K. The

goal of this process is the optimal production of liquid fuel.

Fast pyrolysis (gaseous)

To get an optimal gas fraction, the end temperatures, in this process, are higher than the

previous two groups and will rise to 1000K. The heating rate can be up to10,000K/s. Theresidence time is typically around 2-5 seconds, to minimize secondary reactions. The

pressure is much lower than for liquefaction.

[1, 4, 5]

There are some discussions as to what kind of material can be considered as biomass.However, it is clear that the final products of the pyrolysis are very different from each

other depending on the type of used biomass. In this research, biomass will be seen as

woody compounds. Mostly woody biomass is a combination of three species: cellulose

(C6H10O5), hemi cellulose (C5H8O4) and lignin (C40H44O6). The combination of thesethree compounds depends on the type of wood. Table 2.1 gives the composition of

different wood species.


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Table 2.1 composition of woody biomass in weight percentage [1, 6]Tree species cellulose hemicelluloses lignin

beech 45.2 32.7 22.1

white birch 44.5 36.6 18.9

red maple 44.8 31.2 24

eastern white cedar 48.9 20.4 30.7

eastern hemlock 45.2 22.3 32.5

jack pine 45 26.4 28.6

white spruce 48.5 21.4 27.1

MDF 32.5 59.1 8.4

As can be seen from the above table, the composition per species of wood varies. Hencethe combination of gases from pyrolysis can be different depending on the type of wood.

The decomposition at slow pyrolysis is described by [1], the thermal decomposition ofwood in an inert atmosphere is measured with Thermo-Gravimetric Analysis (TGA).

When heating of the material starts, first the moisture content decreases, this process

starts at a temperature below 308K. Decomposition of hemicellulose and cellulosehappens above 473K. First the hemicellulose will decompose and later the cellulose.

Lignin is decomposed at temperatures above 673K. At the end of the process, char and

ash remain.

Expected gas composition after fast pyrolysis

In this research, the grid reactor (described in chapter 4.2) is used to conduct the pyrolysisof woody biomass. Biomass thermal decomposition products are studied in detail. In this

research the following reports are used to estimate the concentration and species in the

thermal decomposition products:

- fast pyrolysis of sweet gum hardwood [7]

- fast pyrolysis of milled wood lignin [8]- fast pyrolysis of cellulose [9]

- pyrolysis of wood pellets [29]

In table 2.2 the maximum expectancy of the biomass source is used to indicate the

expected concentration of pyrolysis products


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Table 2.2 Overview of pyrolysis decomposition products by use of [7, 8, 9, 29],

the expected concentration for the heated grid reactor is given in ppm and iscalculated for a biomass sample weight of 5 mg [10].

Molecule

Cellu* LigninSweet

Gum

Wood

pelletsmin max

min

[ppm]

max

[ppm]

CO 22.6% 19.0% 17.0% 5.10% 1.82 8.06 2210 9782

CO2 3.4% 4.1% 6.1% 4.88% 0.76 1.39 927 1682

CH4 2.6% 3.2% 2.3% 1.15% 0.72 2.00 872 2427

C2H4 2.2% 0.9% 1.3% 0.09% 0.03 0.78 39 945

C2H6 0.3% 0.3% 0.2% NA 0.07 0.10 81 121

H2O 9.2% 3.8% 5.1% 13.60% 2.11 7.56 2562 9169

HCHO NA 1.4% 2.0% 3.15% 0.47 1.05 566 1274

C3H6 0.8% 0.3% 0.4% NA 0.07 0.19 87 231

CH3OH 1.0% 1.7% 1.5% 0.64% 0.20 0.53 243 645

CH3CHO 1.7% 0.9% 1.4% 3.97% 0.20 0.90 248 1095

NH3 NA NA NA 0.03%

HCN NA NA NA 0.05%

Cellu*=Cellulose selected with a final temperature of 1000C

wt%

Expected

concentration

(5 mg biomass)

210.02

0.02

mmol species

per g of

biomass

22

The amount of NH3 and HCN from the wood pellets is very low but previous research

indicated that concentration of NH3can be of the order of few hundred ppm during MDFpyrolysis [2].


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3 Fourier transform Infrared spectroscopy

One of the main objectives of this research is to develop accurate methodology for

quantitative measurements of thermal decomposition products. In principle, there are

many methods for measuring the product concentrations in the thermal decompositiongases.

In the last decades, absorption techniques have proven to be capable of providing precise

and detailed information of combustion and pyrolysis experiments.In particular, Fourier transform infrared spectroscopy (FTIR) has thus far proved to be a

reliable method for quantitative measurements of thermal decomposition species from

biomass pyrolysis. [11]

3.1 Infrared absorption

The wavelength of light can be given in different units. The wavelength, , is expressed

in. Besides the wavelength, spectroscopy also uses the unit wavenumber, or frequency . The wavenumber is preferable because of its linear relation with energy. Figure 3.1shows the spectrum of electromagnetic radiation (light) in their different units.

With formulae (3.1) and (3.2), c the speed of light, it is possible to convert between the

different units.

c

= (3.1)

1

= (3.2)


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Figure 3.1: The regions of the electromagnetic spectrum [12]

Most molecules absorb energy in the infrared region. The infrared spectrum ranges from13,000 to 100 cm-1 [13].The infrared spectrum can be divided into the near, middle and

far infrared spectrum. The mid infrared region is between 4000-400 cm-1. The research

preformed in this work will mainly be in this range.Depending on the type and amount of atoms in the molecules, the absorption regions aregiven. For the X-H stretching, the region is from 4000-2500 cm-1, the triple bond regionfrom 2500-2000 cm-1 the double bond region 2000-1500 cm-1and the fingerprint region

1500-600 cm-1. [13]

A molecule absorbs infrared light when the incoming radiation will create a change in

electric dipole moment during the vibration. This is the selection rule for infraredspectroscopy.

Diatomic molecules with the same atoms, in principle, can not be excited to vibrate,

because the do not have a dipole moment. Nitrogen (N2), Oxygen (O2) and Hydrogen

(H2) are examples of these molecules. Because these molecules do not interfere with theinfrared spectrum (do not absorb infrared light) these molecules are ideal as buffer gas.

The relation of energy and electromagnetic radiation (light) is given by

E h c = (3.3)

Where

The Plancks constant is h (6.62610-27) and the speed of light c.


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A molecule can absorb vibrational and rotational energy, whereby a transition occurs

from energy state E to the energy state E [14]. This is given by

E E h c = (3.4)And

E E

h c

=

(3.5)

Rotational transition

The rotational energy Eris defined as

( )2

21

8r

hE J J

I= + 0, 1, 2...J= (3.6)

And the rotational term F(J)

( )2( ) 18rE hF J J J

h c c I = = +

(3.7)

Where I is the moment of inertia, and J the rotational quantum number.

The term

28

hB

c I=

(3.8)

B is known as rotational constant. [14]

Vibrational transition

The vibrational energy Evis defined as

( )v 1 2vE h= + v 0, 1, 2...= (3.9)

And the vibrational term, which indicates the frequency steps between the vibrationalquantum numbers.

1(v) v

2

vE

Gh c

= = +

(3.10)

The bonds between the atoms will break when enough energy is supplied to the molecule.

Hence the potential energy will not increase to infinity when the vibrational quantum

number is increased above a certain limit. [14]

Simultaneously rotation and vibrational transition

Of course, rotation and vibration of molecules happens simultaneously. The interatomic

distance, depends not only on the centrifugal forces of rotation but also on the vibrationalstate. These vibrational and rotational interactions increase the moment of inertia. The

actual rotational constant Bvis smaller then the rotational constant B from equation (3.8)


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Figure 3.2 Term scheme of rotational and vibrational

transition. [12]

1

v 2vB B

= + (3.11)Where is a constant smaller then B.

Finally the wavenumber of the absorbed radiation results from:

( ) ( ) ( ) ( )v vv v 1 1G G B J J B J J = + + + (3.12)

Where ( ) ( )v vG G is the vibrational transition and ( ) ( )v v1 1B J J B J J + + is the

rotational transition. [14]

Figure 3.2 give the term schematic ofrotational and vibrational transitions.

For J= +1 the R-branch is given

and for J= -1 the P-branch is given.When J=0 there is pure vibrational

transition. This branch is called the

Q-branch. For the Q-branch all thespectral lines are grouped, which

results in an intense absorbance line.

Normal modes of vibration

Diatomic molecules can only have

one vibrational degree of freedom.

Multi-atomic molecules can have

more possible number of vibrationalmodes. The molecules will have 3N

degrees of freedom in total. In three

of these the atoms move in the same

direction, thereby the center of themass will change in the same

position. These motions are called

the transition motions. For linear

molecules there are two rotationalmotions and non-linear molecules

have three rotational motions.

The number of vibrational modes is given by3 6non linearZ N = (3.13)

For non-linear molecules with N, the number of atoms.

And for linear molecules:

3 5linearZ N= (3.14)

For example H2O is a non-linear molecule which has three vibrational degrees offreedom.


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3.1.1 Beer-Lambert lawWhen light (radiant beam) passes through a gas or other substance, some part of the light,with wavelength corresponding to the energy difference between levels, is absorbed by

molecule.

Figure 3.3 shows a typical setup for absorption technique. A radiation beam passing

through a sample, the incoming intensity of the beam is I0and the outgoing intensity is I.

The beam travels over lwhere absorption by the analyte (molecule of interest) occurs.If the radiant beam is assumed to be monochromatic [15], The Beer-Lambert law can be

written as

( ) ( ) ( )0nlk

I I e

=

(3.15)

Where n is the concentration of absorbing species [molecules/cm3], l the pathlength [cm]

and ( )k the absorption coefficient or the molar absorbtivity [cm2/molecules] [16] of the

absorber.

Figure 3.3 typical experimental direct absorption setup.

The transmittance intensity, also called transmittance is defined by

0

I

I= (3.16)

The amount of absorbed radiation is defined as absorption factor

1 = (3.17)And the absorbance is defined by

( )lnA = (3.18)

Absorbance ranges from 0 to , whereas the absorption factor ranges from 0 to 1.[15]

According to equation (3.15) and equation (3.18) the absorbance is described by

( )A nlk = (3.19)

It is clear that there is a linear relationship between absorbance and concentration


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3.1.2 Limitations of the Beer-Lambert lawThe Beer-Lambert law states that there is a linear relationship between absorbance andconcentration. There are some chemical and instrumental factors that can influence this

relationship.

According to [17], the reasons for deviation from the linear relationship are:

scattering of light due to particulates in the sample

fluorescence or phosphorescence of the sample

deviations in absorptivity coefficients at high concentrations (>0.01M) due to

electrostatic interactions between molecules in close proximity changes in refractive index at high analyte concentration

shifts in chemical equilibria as a function of concentration non-monochromatic radiation, deviations can be minimized by using a relatively

flat part of the absorption spectrum such as the maximum of an absorption band

stray light

Also instrumental problems can cause nonlinearity of Beer-Lambert law, special FTIR

spectrometry. More information about this can be found in chapter 5.[18]

3.1.3 Conventional quantitative analyze

According to the traditional approach, calibration curves should be made first to

determine the concentration of an analyte in the gas mixture. A calibration curve givesthe relation between peak absorbance (or area of a spectral feature) and concentration.

Equation (3.19) shows that the absorbance is proportional to the concentration of theanalyte in the gas mixture. Of course, the calibration curve must be made at the same

conditions as the measurements. Calibration gases with the precisely known

concentrations of the analyte are used. The calibration gas concentrations have to be both

lower and higher than the expected gas concentration in the experiment. The calibrationcurves can be made from peak absorbance or the area of a spectral feature. When the area

of the feature is used, the noise in the spectrum is summed and thus reduced which results

in higher accuracy of experimental results. [18]

Chapter 5 will give more details about this approach to the quantification for different

species.


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3.1.4 Molar absorptivityThe molar absorbtivity can be described by

( )*k S f = (3.20)

Where S is the spectral line intensity and ( )f the normalized line profile.

The spectral line intensities for many molecules are listed in the HITRAN (High-

resolution TRANsmission) database. The spectral line intensity depends on molecular

spectroscopic parameters, which, in turn, are functions of temperature.The temperature dependence of the spectral line intensity, Spq, is given by

1 expexp( )

( ) ( )* * *( )

exp 1 exp

pqi

ref

pq ref

pqi

ref ref

hcE hcQ T kTkT

S T S T Q T hcE hc

kT kT

=

(3.21)

where S(Tref) is the spectral line intensity at 296K, h is Plancks constant [ergs s], pq is

the spectral line intensity transition frequency [cm-1], c is the speed of light [cm/s], k is

the Boltzmann contant [J/K], Eiis the lower state energy [cm-1], T is the temperature [K]

and Q(T) is the total internal partition sum given by

( )( ) 2exp 1.5ln( ) 2.5ln( ) 3.6518FQ T M T R

= + (3.22)

With

( ) 2 1 2 31 2 3 4 5 6 7lnF g g x g x g x g x g x g x = + + + + + + (3.23)

And4*10x T = (3.24)

M is the molar mass [g/mol], R is the gas constant 8.3144 [J/mol K].

The line halfwidth at half maximum can be corrected for temperature and pressure. The

pressure broadened line halfwidth ( ),p T for a gas at pressure p [atm], temperature [K]and partial pressure ps[atm] is calculated with

( ) ( ) ( ) ( )( ), * , * , *n

refair ref ref s self ref ref s

Tp T p T p p p T p

T

= +

(3.25)

In this equationair [cm

-1/atm] is the air-broadened halfwidth at half maximum at Tref=

296K and pref=1 atm, self [cm-1/atm] is the self-broadened halfwidth at half maximum

and n is the coefficient of temperature dependence of the air-broadened halfwidth.


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Difference in pressure may shift the transition wavenumber (pq ), the relationship of this

shift is given by

( )*pq pq refp p = + (3.26)

In which ( )refp is the air-broadened pressure shift [cm-1/atm] at Tref and Pref and is

transition dependent.

In the atmosphere, a spectral line is broadened about the transition wavenumberpq , the

spread being represented by the normalized line shape function ( ), , ,pqf T p [1/cm-1].

In the lower atmosphere, pressure broadening of spectral lines dominates and if a Lorentzprofile is assumed,

( ) ( )

( )22 *

,1, , , *

,pq

pq

p Tf T p

p T

=

+

, (3.27)

the monochromatic absorption coefficient [1/(molecule cm-2

)] at wavenumber due tothis transition is then given by

( ) ( ), , ( )* , , ,pq pq pqk T p S T f T p = . (3.28)

Using equation (3.19) the absorbance can be obtained by

( )A=n*l* , ,pqk T p (3.29)

HITRAN database

The high-resolution transmission molecular absorption database (HITRAN) is acompilation of spectroscopic constants. [19] At this moment the HITRAN database

contains spectroscopic data for thirty-nine molecules. The first edition of the database

was made available in late 1986 and consisted of twenty-eight gases. The database isestablished by the Air Force at the Air Force Geophysics Laboratory (AFGL) in the late

1960s.

The spectroscopic constants in the HITRAN 2004 database are from different sources andall have their own uncertainty. The uncertainty is given in the database. Table 3.1 gives

the codes (numbers from 0 up to 8) which are used. For example spectral line 2099.2cm -1

for a CO spectrum has the following uncertainly codes: 467 660The first code, 4, stand for the line position with an uncertainty of < 0.0001 and 2% and


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Table 3.1 Uncertainty codes adopted of HITRAN [16]

Table 3.2 Uncertaintys of the HITRAN database per type of molecule

' S air self n

Methane 0.0001 and


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Figure 3.4 Simulated absorbance spectra (Molspec/ HITRAN 92) for species that can interfere or get

liberated from pyrolysis.

The HITRAN database is constantly updated. To use the updated version of HITRAN acomputer program is written to simulate the spectra. The software program can simulate

one of the thirty-nine molecules in the 2004 database. The program converts the

spectroscopic data to absorbance,

( )A=n*l* , ,pqk T p (3.29)

Transmittance( )-n*l* , ,

=e

pqk T p

(3.30)or to the absorption factor( )-n*l* , ,

1 =1-e pqk T p

= (3.31)


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3.1.5 Temperature dependence

The Boltzmann fraction (fb) describes the influence of temperature on spectral linestrength. In the temperature range of 273-600K, this is the most dominating factor. The

Boltzmann distribution is wavenumber dependent, which means that some peaks have a

larger influence than other peaks. The Boltzmann distribution is given by:

exp

( )

ii

p

b

Eg

n kTf

N Q T

= = (3.32)

Where np/N is the fraction number of particles, gi(lower Statistical weight) is the number

of states which have energy Ei , T is the temperature [K], Ei is the lower state energy[cm-1], and k is the Boltzmann contant [J/K]. Q(T) is given by equation 3.22

Figure 3.5 Boltzmann fractions as a function of temperature for different spectral lines.

Figure 3.5 shows Boltzmann fraction as a function of temperature for different spectral

lines. The absorption temperature dependence is not ideal for quantitative analysis. It has

been observed that some spectral lines are more sensitive to temperature fluctuations thanothers. For example, spectral lines 1123, 1142 and 1104 cm-1 are less influenced by

temperature while the spectral lines 1014 and 1067 cm-1 are strongly influenced. Spectral

lines which are less influenced by temperature are preferred for quantitative analysis,

because of the smaller variations in cases when the temperature is not known exactly.


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Figure 3.7 Interferogram from a polychromatic

source

Monochromatic light sources

In this section, an explanation on how the Michelson interferometer work with amonochromatic source is given, followed by an explanation on polychromatic sources.

The source emits radiation [I()] with a wavelength [cm] and its wavenumber [cm-

1]. The reflectance and transmittance of the beam splitter is 50%. The movable mirror is

kept constant at different positions. The difference in pathlength of the split beams is

2*(M-F) (figure 3.6). This optical path difference is called the retardation . When themovable mirror is placed in a position such that the retardation is zero, the two beams are

in phase. In this case, the two beams recombine at the beamsplitter constructively, and the

intensity of the beam passing to the detector is sum of the two light beams. Therefore all

the light from the course reaches the detector.If the movable mirror is placed at a distance , the retardation is now . The beams

interfere destructively. All the light returns to the source and no radiation is measured atthe detector. A displacement of gives a retardation of and the beams recombine

again constructively. For monochromatic light it is not possible to find the zeroretardation. When the mirror is moved at a constant velocity, the detector will measure a

sinusoidal signal. The maximum signal is detected when the retardation is an integral

multiple of . The intensity of the beam at the detector is given by I(). The intensity ofthe beam at the detector is given by

( ) ( )' ' 'I I = (3.33)

( )where is an integer

and for is not integer

n n

n

=

( ) ( ) ( )' 0.5 ' ' 1 cos 2I I = + (3.34)The modulated (ac) component is important for spectrometric measurements, and it is this

component that is referred to in the interferogram I(). The interferogram from a

monochromatic source measured with an ideal interferometer is given by

( ) ( ) ( )0.5 ' 1 cos 2 'I I = + (3.35)

Polychromatic sources

When radiation of more wavenumbers is

emitted, the interferogram is the resultantcorresponding to each wavenumber Figure

3.7 shows an interferogram from a

polychromatic source. Here, the zeroretardation can be found by the maximum

signal.

For the measurements, it is important to

convert the interferogram as an function ofretardation I() to a function of

wavenumber I(). Mathematically this is

done by Fourier transform. The next

chapter will explain the Fourier transform.


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Figure 3.8 air spectrum computed from an interferogram

like figure 3.7

3.2.1 Fourier transform

In the previous chapter it is seen that the interferogram has to be converted to a spectrum.This is done by Fourier transform. When the source is a continuum, the interferogram is

described by the integral

( ) ( ) ( )' cos 2 ' 'I B d

= (3.36)

Where ( ) ( ) ( )' 0.5 ' 'B H I = and ( )'H is a correction factor for non-ideal beam

splitter efficiency, detector response and amplifier characteristics. [21]

I() is an even function, so equation (3.36) can be rewritten as

( ) ( ) ( )0

' 2 cos 2 'B I d

= (3.37)The above equation shows that the mirror of the interferometer has to be moved from 0

till . Understandably, this is not a practical range.

To solve the above problem, a truncation function D() is used. Which is unity between

= -and + and zero to all other points.

D() =1 if +D() =0 if >||

This function is called Boxcar

truncation function. By analogy to

equation (3.36) the spectrum is given by

( ) ( ) ( )' cos2 'B I D d

= (3.38)

[21]With the equation above, it is possible

to convert the interferogram (figure 3.7)

to a spectrum like figure 3.8.

More truncation functions like theBoxcar apodization are explained in

appendix B


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3.2.2 Advantages of FTIRIn the previous chapters, a brief description of infrared spectroscopy has been given, aswell as how FTIR works. Here the main advantages of FTIR are elaborated upon:

Multiplex advantage, also named as the Fellgett advantage. All the wavenumbers are

measured simultaneously, resulting in a faster scan than dispersive spectroscopy.

Therefore the noise is reduced by a factor N , where N is the number of scanning. For

measuring a spectrum, the peak level has to be at least three times higher than the noise

[18]. Hence FTIR can detect lower concentrations (smaller peaks) in comparison to a

normal spectrometer. [14]

Throughput advantage, also named Jacquinot advantage. There are no slits in a FTIRspectrometer that reduce the energy throughput. Hence FTIR can reach the same

resolution with a higher energy throughput than a normal spectrometer. The higherenergy will reduce the noise in the measurement.

Connes advantage. The wavenumber stability obtained from a FTIR is clearly higher thanfor dispersive spectrometers. This is possible because of the accurate HE-Ne laser which

provides an internal reference for every interferogram.

To measure thermal decomposition products from biomass pyrolysis, the FTIR

spectrometry is a good method to obtain the most of the molecules.


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4 Experimental Set-up

4.1 Preparation of calibration gas

Calibration gases used in experiments were directly supplied from cylinders under initial

pressure of ~150-200 bar. The purity of the pure gas (N2and CH4), better than 99.99%,

was guaranteed by the manufacturer. Cylinders with a calibration gas concentration inbuffer gas were ordered directly from Linde gas with an accuracy of 2%.

Figure 4.1 shows the mixing setup used in the experiments. The sample cell was filled

directly from the calibration cylinders or in the case when lower concentrations of studiedmolecules were needed, calibration gas was preliminarily mixed with buffer gases.

Mass flow controllers from the manufacturer Bronkhorst High-Tech BV were used. The

accuracy of the mass flow controllers is 0.2% for the full scale and 0.8% on the requiredflow [22]

Figure 4.1 Mixing setup to provide accurate calibration gases.


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4.2 FTIR setup with sample cell and heated grid

To measure spectra, FTIR setup at the biomass laboratory has already been used. [2]

performed investigations of NH3release of MDF pyrolysis and updated the Perkin ElmerFTIR Spectrum GX dual source FTIR spectrometer to a wavenumber range of 10,000 to

400 cm-1by replacing the beam splitter.

Depending on the type of measurements, the settings of the Perkin Elmer FTIR

spectrometer can be varied to achieve optimal performance.The software program Spectrum controls the spectrometer. The settings during

measurements are given in table 4.1. The motivation of these setting can be found inappendix E.

Table 4.1 FTIR settings during experiments

Setting value unit

Number of scans 16 -

Scan range 4,000-400 cm-1

Mirror velocity (OPD) 0.2 cm/s

Apodization function weak -

Resolution 0.5 cm-1

J-stop wavenumber 4,000 cm-1

Gain 8 -

Figure 4.2 Schematic overview of the FTIR setup with the glass sample cell in the beam ( the glass

cell can be replaced by the reactor). The mixing set-up from chapter 4.1 is connected to the valves of

the cell.

Before pyrolysis experiments, the method should be validated. To do so, themeasurements are preformed in a glass sample cell at reproducible conditions with

calibration gases. The pathlength of the cell is 10 cm and the windows are made of ZnSe.

Figure 4.2 shows the schematic of the FTIR setup.


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Heated grid reactor

The reactor is made of stainless steel, the ZnSe windows allow the infrared beam to passthrough the reactor. The pathlength of the reactor is 163 mm and the volume is 100.8 cm 3

[2]. Inside the reactor, there are some copper and brass components. Figure 4.3 shows the

heated grid reactor used for this research.

Figure 4.3 Heated grid reactor used in this research.

The grid inside the reactor is made of metal, usually stainless steel or platinum. The gridis heated electrically by passing current through it. The temperature of the grid is

monitored and controlled by a software program written in Labview (figure 4.4 gives a

schematic overview of the controller and monitor of the reactor). To measure the

temperature of the grid, a thermocouple is welded to the grid. More information how tomake the grid and weld the thermocouple onto the grid can be found in [2] appendix H.

Figure 4.4 Schematic overview of the monitor and controller to heat the reactor and grid inside the

reactor.


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Concentration detection limits.

For the FTIR setup and heated grid reactor, the minimal detection limit concentrations areestimated in table 4.3. In order to detect a spectral line, the absorbance has to be 3 times

higher than the noise in the baseline. [24]

The absorbance of the spectral lines is dependent on the temperature, pressure, pathlengthand machine parameters like apodization function.

From experiments in the reactor, the peak absorbance from a just recognizable spectral

feature is taken and compared with the peak absorbance of simulated spectra (described

in chapter 6). When the peak absorbance reaches the value of the experiment, theminimal detection limit concentration is known.

Table 4.3 Detection limit for the expected molecules.

MoleculeMinimal

detection

at 293K

Wave

number

min

[ppm]

max

[ppm] [ppm] [cm

-1]

CO 2210 9782 55 2169

CO2 927 1682 10 2363

CH4 872 2427 50 3086

C2H4 39 945 50 950

C2H6 81 121 50 2987

H2O 2562 9169 125 3838NH3 21 180 35 1146

HCN 20 71222

Expectedconcentration

(5 mg biomass)


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5 Calibration curves

Figure 5.1 shows a log-log plot of calculated FTIR absorbance as a function of True

absorbance (absorbance spectrum without ILS function, equation 3.29) for different

resolutions (for more details on the calculated spectra, see chapter 6). From the figure, itis obvious that the FTIR absorbance depends nonlinearly on the true absorbance. Hence it

can be inferred that the FTIR absorbance depends nonlinearly on the concentration of an

analyte. At a higher resolution, the peak value is lower; the linear behavior ends atdifferent concentrations depending on the resolution.

It may be seen that, when R2> 0.998, the linearity of the plot of FTIR absorbance as a

function of to true absorbance is good [24] Figure 5.2 shows that changes in resolution

influence the linearity of the curve. By using the weak apodization function andresolution of R= 0.5, the calibration plots are made from peaks with a true absorbance

lower than 1.05 and for FTIR absorbance lower then 0.4 (and when log 10 is used, FTIR

absorbance of 0.17 is chosen) When experimental spectra are used, the absorbance cannot

be too low in order to decrease noise and baseline problems, (more information aboutbaseline corrections can be found in appendix C).

Figure 5.1 plot of log FTIR absorbance as a function to log true absorbance with the weak function

and resolution of R=1, R=0.5 and R=0.3.


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Table 5.3 Data of calibration curves for NH3peaks

Peak [cm-1

] P1 P2 R2

847 .5 4 .38068E-05 -0.003433 0.992069852 .5 6.3405E-05 -0.003506 0.994899

868 .0 9 .62684E-05 -0.007461 0.993747

872 .0 3 .16385E-05 -0.003419 0.987787

888 .0 7 .81284E-05 -0.008994 0.988869

892 .0 6 .66234E-05 -0.005455 0.991734

908 .0 0 .000101018 -0.006152 0.996142

928 .0 0 .000108217 0 .004268 0.998477

948 .0 3 .73194E-05 -0.003885 0.987376

952 .0 4 .24767E-05 -0.003729 0.994514

964 .0 0 .000150415 -0.000754 0.998286

993 .0 0 .000106442 -0 .00522 0.998163

1007.5 8 .83647E-05 -0.004097 0.997432

1012.0 5 .21216E-05 -0.003986 0.9908061027.0 6 .62423E-05 -0.004792 0.996427

1032.5 9 .69818E-05 -0.004055 0.998547

1046 .5 0 .000145592 0 .008207 0.998187

1065 .5 0 .000112994 0.00173 0.999784

10 84 .5 0.0 001 31 81 0 .0 11 56 5 0.9 94 875

1103 .0 0.00010353 -0.000214 0.999364

1122.0 0 .000110674 -0.003706 0.997526

1141.0 6 .31493E-05 -0.004877 0.993143

overall, the calibration curves aremade for twenty-two absorption

features. The calibration curve is

given by

A=P1 ppm +P2 (5.1)

Where A is peak absorbance, ppm

the concentration in parts per million

and P1, P2 coefficients that can befound in table 5.3.

Special care has to be taken with

ammonia because the absorbance of

the spectrum decreases with time.Previous research in this field has

suggested that ammonia condenses

on the wall or is absorbed by somemetal components [2]. Appendix Dgives more information of the

ammonia decrease in time.

Figure 5.3a Calibration curve for NH3peak 1122 cm-1

Figure 5.3b Calibration curve for NH3peak 1046 cm-1


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5.2 Carbon monoxide (CO)

One of the most common constituents in pyrolysis gases from biomass is carbonmonoxide. The estimated concentration CO from cellulose is 10,000ppm. (chapter 2). CO

has strong absorbance peaks in the region of 2250-2020 cm-1. For high concentrations,

peak at 2228 cm-1

is usable because this peak has a small absorbance. Hence thecalibration curve is linear at higher concentrations. For small concentrations < 3,000 ppm

peak at 2099cm-1 is recommended. The advantage of this peak is the weak temperature

dependence for a temperature range of 273-600K. Figure 5.4a, b shows the calibrationcurves for these two peaks.

To make the calibration curves, eight concentrations are prepared with a calibration gas

of 3% CO in N22%. Table 5.4 shows the used calibration gases with their accuracy. In

total, calibration curves for twenty-seven absorption features are made. The calibrationcurve is given by equation (5.1) and [table 5.6a] for calibration curves with a

concentration


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Table 5.5 a) carbon monoxide predictions for four calibration concentrations with calibration curvefor peak 2099 cm

-1.and predictions for eight calibration concentrations with calibration curve for

peak 2228 cm-1

.

calibration gass [ % ] [ppm] [ % ] [ppm]

200 81 162 21 42

650 20 130 5.7 37

1508 16 241 5 75

2477 7 173 2.1 52

5008 3.5 175 - -

8001 6 480 - -

18085 0.5 90 - -23073 0.9 208 - -

error errorCO ppm

2228 cm-1

2099 cm-1

Table 5.6a Data of calibration curves for CO peaks 5000ppm

Peak [cm-1

] P1 P2 R2

2055.3 5.15999E-06 0 .0023614 0 .998344

2061.5 9.72397E-07 0 .0005043 0 .9989667

2212 .5 9.73268E-06 0 .006554 0 .9944065

2215.8 7.58682E-06 0 .0040806 0 .99620722227.5 1.97069E-06 0 .0002691 0 .9995412


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5.3 Methane (CH4)

The absorbance peaks of CH4can be influenced by the absorbance peaks of C2H2, C2H4

and H2O. The two main IR spectra regions for methane are 1400-1200 cm-1

and 3200-2800 cm-1. For both ranges, a peak is chosen where the influence from other molecules,

released during a typical biomass pyrolysis, is minimal. The maximum absorbance at a

concentration of 6,000 ppm is below 0.17. Hence the curves are linear. Table 5.7 reports

the used calibration mixtures and their expected accuracy. In total, 50 measurements aredone to produce the calibration curves which are showed in figure 5.5 a and b. [10]

The accuracy of the curves is 250 ppm for min/max value and the standard deviation is150 ppm. [10]

Table 5.7 Data on the calibration gases used to make the methane calibration curve.[10]

composition CH4in N2[ppm] accuracy in CH4content

1032 2% (20 ppm)1345 1.9% (25 ppm)1480 1.9% (27 ppm)2017 1.8% (36 ppm)2509 1.8% (45 ppm)3174 2.2% (68 ppm)4077 1.8% (75 ppm)4947 1.6% (81 ppm)6043 1.5% (90 ppm)6969 1.4% (99 ppm)

Figure 5.5a Calibration curve for CH4peak 1248 cm-1

[10] Figure 5.5b Calibration curve for CH4peak 3131 cm-1

[10]


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6 Equivalent width for quantitative measurements

Although FTIR measurements are relatively easy to perform, interpreting these

measurements and converting them to absolute values might be complicated due to the

nonlinear dependence of measured absorbance on the pressure, temperature andconcentration of the gas species. The conventional approach - to perform quantitative

measurements - is the one described in the previous chapter. The usefulness of the

conventional FTIR approach can be limited because of the fact that the concentrations,pressure and temperature might vary in the reactor. This approach works best for

measurements where temperature and pressure are constant and the concentrations are not

too large. More importantly, in some cases, it is not possible to produce calibration

curves. For example, when the temperature in the reactor is too high it might be difficultto verify the actual concentration of the calibration gas in reactor. Or the pressure in

which the experiment takes place is so high that the calibration gases react or decompose.

Another approach is to fit a simulated spectrum with the measured spectrum. The

simulated spectrum is computed from tabulated spectroscopic constants. When the data isaccurate, this approach produces reliable results [25].

In this thesis, the equivalent width approach is used, which will be explained in this

chapter.

6.1 Equivalent width method

For FTIR measurements, the Beer-Lambert law is given by [15]

( ) ( ) ( )-n*l*

0 0

0

ek

oI I ILS d

= (6.1)

Where ( )k is the absorption coefficient at wavelength , n the concentration, l is thepathlength and ILS is the normalized instrument line function of a spectrometer.

Integration over the normalized instrument line shape function of a spectrometer

gives[15]:

( )0

1ILS d

= (6.2)The transmittance measured by a spectrometer is:

( )

( )

( ) ( ) ( )

( ) ( )

( ) ( )

* *

0 0 0 * *0

0

0 0 00 0 0

* *

*

n l k

n l km

m

I e ILS dIe ILS d

I I ILS d

= = =

(6.3)

With Im the measured intensity, assuming that I0is frequency independent.

Integration the equation (6.3) over 0gives


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( )

( )( )

( ) ( ) ( ) ( )* * * * * *0

0 0 0 0 00 00 0 0 0 0 0

*

n l k n l k n l k m

m

I

d e ILS d d ILS d e d e d I

= = = (6.4)

or limiting over the integration range and change transmittance in absorption factor:

( ) ( )

( )( )( )

2 2* *0

01 1

1n l km m

m

I Id e d

I

= (6.5)

Equation 6.5 gives the relation between integration of the absorption factor which is

obtained from measurements (left hand side) and, integration of parameters which aretabulated in spectroscopic databases (right side).

Figure 6.1 shows the integration of calculated theoretical spectrum (see next subchapter)as a function of CO concentration for peak near 2099cm-1. When the integrated

absorption factor is obtained from measurements, the concentration can be found using

the shown curve.

Figure 6. 1 calibration curve for the integrated absorption factor in a function to concentration.


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6.2 Simulation of FTIR spectrum

Transmittance is given by (see equation 6.3)

( ) ( ) ( )00

ILSm d

= (6.6)

For the absorbance spectrum Am() is used

( ) ( )( )logm mA = (6.7)

And for the absorption factor

( ) ( ) ( )00

1 ILSm

d

= (6.8)

The ILS function depends on the resolution R and the apodization function of the

spectrometer [26].

Table 6.1 shows some ILS function for commonly used apodization functions.

Table 6.1 ILS functions for commonly used apodization functions [27]

Apodization ILS

Boxcar ( )( )02sinc R

Weak ( )( ) ( )( )( )( )( )

0 0

0

2 2

22

3 sinc cosR R

R

Medium

( )( )( )( )

( )( )( )( )

( )( )

0 0

0 0

0

2 2

2 22 2

22

3 3-15 1- sinc cosR R

R R

R

+

Strong

( )( )( )( ) ( )( )

( )( )

( )( )

0 0

0 0

0

2 22 2

2 2

42

15 5105 1- sinc 2 cosR R

R R

R

+

Triangular ( )( )( )( )

0

0

2

2

2sinR

RR


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Figure 6. 2a) Comparison of simulated and measured CO spectrum, b) zoom of figure 6.2a

The software program has been written in order to simulate a FTIR spectrum [Dr. A.V.

Sepman].As an example, the CO spectrum simulated and measured are compared in figure 6.2,

with the following parameters: concentration - 30,000 ppm ; step - 0.1; resolution - 0.5,

apodization weak; room temperature and atmospheric pressure. The figure shows agood agreement between the simulated and measured spectrum. Because of self

apodization of FTIR spectrometer [26] and uncertainties in the spectroscopic database,

[16] the fit is not perfect.

6.3 Theoretical temperature estimation

In chapter 3.1.5, it has been demonstrated that the molar absorptivity depends upon the

Boltzman relation. The peak absorbances of different spectral features behave differently

with the variation of temperature. Therefore, the temperature can be estimated bycomparing the behaviour of various different peaks. As an example, a peak that has weak

temperature dependence and one that has strong temperature dependence between 273-

600K have been chosen.Equation (3.21) shows the absorbance is at a specific wavenumber, depending on the

molar absorptivity, the pathlength and concentration. The last two are the same for both

wavenumbers.

Dividing the molar absorptivity at a specific wavenumber [ ( )1,k T ] by the molar

absorptivity at another wavenumber [ ( )2 ,k T ] gives


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( )

( )

1

2

,

( ),

k T

f Tk T

= (6.9)

Where ( )f T is a relation between the molar absorptivities.

Figure 6.3 shows ( )f T as a function to temperature. For this figure one spectral line at1

and2 is used.

Figure 6.3 Theoretical temperature factor between two NH3peaks at 1122 and 1013 cm-1

A heated cell is ordered to test this method. Measurements have to be performed for

different temperatures. The temperature dependence of the spectral features can thus befound. With this temperature relation from the measurements, it is possible to estimate

the temperature of the analyte.

6.4 Theoretical testing of the equivalent width method

Firstly, the method is tested with the simulated spectrum described in chapter 6.2.

The integration area is taken over the true absorption factor spectrum where a peak

reaches the baseline.(figure 6.4) The integration area should not be large in order toprevent the influence of other spectral features from molecules that originate from

biomass pyrolysis.

Figure 6.4 shows a NH3peak at a wavenumber of 1122 cm-1

.In practice, the integrationarea cannot be selected at the range where the side lobes of the peak reaches zero. A part


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of the area from the FTIR spectrum is lost in the side lobes of the spectrum. Care is taken

that the integration area of the FTIR spectrum is more than 95% of the True spectrum.The integration range used in this thesis can be found in appendix A.

Figure 6. 4 example of an integration area for a peak in the NH3spectrum that stands alone. The

area is chosen where the baseline is almost zero and there is no significant contribution from other

spectral features.

Starting with ammonia, there are four spectra that are simulated with the concentrations:202, 703, 1388 and 2900 ppm NH3. The input conditions and parameters are for normal

room conditions and the commonly used FTIR settings (see appendix E). These

concentrations have been chosen because calibration cylinders with the same

concentrations are available in the lab.Figure 6.5 shows the weighted concentration (the weighted concentration is the

quantitative value achieved with the equivalent width method divided by the actualconcentration) for the simulated spectra of NH3. It is clear that the calculated

concentrations are 5% of the real value.


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Figure 6. 5 the weighted concentration of simulated NH3 spectrum for the concentrations: 202, 703,

1388 and 2900 ppm. The standard deviation is given in the graph. The background of the graph

shows the NH3 true absorbance peaks.

Figure 6.6 shows the weighted concentration for six simulated carbon monoxide spectra

with different concentrations (200, 650, 1500, 2500, 5000, 8000 ppm CO in N 2). It can beseen that the calculated concentrations are 10% of the real value. The concentrations

from the peaks in the range 2200-2100 cm-1have an error of 2%.


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Figure 6.6 the weighted concentration of simulated CO spectra for the concentrations: 200, 650, 1500,

2500, 5000 and 8000 ppm. The standard deviation is given in the graph. The background of the graph

shows the CO true absorbance peaks.

In equation (6.5), the ILS is cancelled out when quantitative concentration estimation is

used by the equivalent width method. In practice, the integration area can not be from 0to . Some area of the peak can be lost in the side lobes, depending on the type of

apodization. This behaviour is stronger for apodization functions which have big side

lobes. Some CO spectra have been simulated with the apodization functions: Boxcar,

Medium, Weak, Strong and Triangle, all are performed for a concentration of 1500 ppmand standard room conditions with commonly used FTIR settings. (More information

about the apodization functions can be found in chaper 6.2 and Appendix B) Figure 6.7

shows that all the concentrations calculated from the simulated spectra, with different

apodization functions, are accurate upto 10% of the real concentration. The weak

function performs quite satisfactorily; source [21] has been proven that the weak functionis a good function for quantitative measurements.


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Figure 6. 7 the weighted concentration of simulated CO spectrum for a concentration of 1500 ppm

with the apodization functions: Boxcar, Medium, Weak, Strong and Triangle. The background of the

graph shows the CO true absorbance peaks.


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7 Experimental results

7.1 Experiments and results with glass cell

In this chapter, the results for the equivalent width method, described in chapter 6.4, are

presented. In figures, the weighted concentration on the Y axis, the quantitative valueachieved by use of the equivalent width method divided by the actual concentration is

depicted.

Ammonia (NH3)

Figure 7.1 shows the measured and simulated spectrum, for room temperature,

atmospheric pressure and 1996 ppm NH3, there is a good agreement. The concentrationmeasurements in figure 7.2 are performed with the calibration gases in table 7.1. In all,

six measurements have been performed. In Figure 7.2, the mean value of these six

measurements and the standard deviation multiplied with the student factor [10] are

given. The mean concentrations determined with the peaks in the range 1150-1050 cm -1are consistently within the range of 10% of the actual value. The peaks in the lower

range are less accurate, but still the calculated concentrations are in 15% of the actual

value.

Table 7.1 Data on the calibration gases used to test the

equivalent width method.

composition accuracy in NH3content

498 ppm NH3, rest N2 2% (10 ppm)

1996 ppm NH3, rest N2 2% (40 ppm)


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Figure 7.1 Comparison of measured and simulated NH3spectrum with a concentration of 1996 ppm

at room conditions.

Figure 7.2 Normalized measured NH3concentration.


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Carbon monoxide (CO)Figure 7.3 shows the measured and simulated spectrum, for room temperature,

atmospheric pressure and 30,000 ppm CO, there is a good agreement. In total, twenty-

three CO measurements have been performed with the calibration gases, as given in table7.2. Figure 7.4 shows the averaged weighted concentrations for CO. For all the selected

peaks, the mean measured concentrations are within 10% deviation from the actual value.



composition accuracy in CO content

650 ppm CO, rest N2 2.6% (17 ppm)

1508 ppm CO, rest N2 2.6% (39 ppm)2477 ppm CO, rest N2 2.7% (77 ppm)

5008 ppm CO, rest N2 2.3% (115 ppm)

8001 ppm CO, rest N2 2.3% (184 ppm)

18085 ppm CO, rest N2 2.4% (434 ppm)

23073 ppm CO, rest N2 2.4% (554 ppm)

30000 ppm CO, rest N2 2% (600 ppm)

Figure 7.3 Comparison of measured and simulated CO spectrum with an concentration of 30,000

ppm at room conditions.


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Figure 7.4 Normalized measured CO concentration.

Methane (CH4)

Fifty measurements have been performed for methane. Figure 7.5 shows the weightedconcentration for the methane spectrum at a range 1400-1200 cm-1

The mean concentrations are within 8% of the actual value. Figure 7.6 is for the range3200-2800 cm-1. This range is more accurate than the range 400-1200 cm-1. The average

errors from all peaks indicate that the measured concentration is about 3% higher than the

actual value. [10]


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Figure 7.5 Normalized measured CH4concentration range one. [10]

Figure 7.6 Normalized measured CH4concentration range two. [10]


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Ethylene (C2H4)Figure 7.7 shows the measured and simulated spectrum, for room temperature,

atmospheric pressure and 2509 ppm C2H4, there is a not-so-good agreement.

In total, five measurements have been performed with the C2H4 concentration incalibration gas as given in table 7.3. The figures 7.8 and 7.9 show the weighted

concentration of the measured spectra. The calculated values at the Q branch have a small

standard deviation. The mean concentration is within 20% of the actual value.

Figure 7.10 shows the visual ethylene spectrum. Two small ranges appear that are not yet

added in the HITRAN database. It is important to know that these rages can influence thespectral features of other molecules during a pyrolysis experiment.

Table 7.3 Data on the calibration gases used to test theequivalent width method.

composition accuracy in C2H4 content

2509 ppm C2H4, rest N2 2% (50 ppm)

Figure 7.7 Comparison of measured and simulated C2H4spectrum with an concentration of 2509

ppm at room conditions.


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Figure 7.8 Normalized measured C2H4concentration range one.

Figure 7.9 Normalized measured C2H4concentration range two.


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Figure 7.10 Measured absorbance spectrum of C2H4. Two C2H4absorbance ranges are visible that

are not added in HITRAN04. Because of air movements in the spectrometer, some CO2and H2O

peaks are visible.

Hydrogen cyanide (HCN)

Quantitative analysis with HCN has not been performed by previous researchers in the

biomass laboratory at the Technical University Eindhoven. The difficulty withconducting experiments with HCN is that it is highly toxic. 80l liquid HCN is added in

the purged sample cell with argon (The HCN is added by a syringe, by this procedure;

some air enters the cell during the injection procedure). The IR ranges around 600 cm -1,

1400 cm-1

and 3300 cm-1

are added in the HITRAN database. Quantitative calculationwith the equivalent width method shows a concentration of 48% HCN. The compared

spectrums in figure 7.11 have a good agreement. Figure 7.12 shows the HCN spectrumfrom 4000-400 cm-1. Figure 7.13 shows the weighted concentration for the HCN peaks.

The mean concentrations are within 10% of the estimated value. The peaks at range1500-1420 cm-1 are influenced by water and will not be used to determine the

concentration.


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Figure 7.11 Comparison of measured and simulated HCN spectrum. The pressure is set to 1.34 atm

and the concentration to 48% of the simulated spectrum. At the 1550 cm-1

range water lines interfere

with the HCN spectrum. This contamination is due to the addition of HCN in the cell by opening the

valve and injected liquid HCN.


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Figure 7.12 Measured absorbance spectrum of HCN. Because of air entering in the cell between

sampling with HCN, some CO2and H2O peaks are visible.

Figure 7.13 Normalized measured HCN concentration, actual concentration is set to 48%.


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Nitric oxide (NO)

The NO spectral lines are in the range of 1960 to 1760 cm-1

. In this range, there are alsospectral lines of water. The presence of water in the atmosphere outside the sample cell

interferes with the NO spectrum. For this reason, only a few NO peaks that are almost not

interfered by the water will be used to check the equivalent width method. Theconcentration of the calibration gas is given in table 7.4. In total, three measurements

have been performed for this molecule.



composition accuracy in NO content

400 ppm NO, rest N2 2% (8 ppm)

Figure 7.14 shows the measured and simulated spectrum, for room temperature,

atmospheric pressure and 400 ppm NO, there is a good agreement. At wavenumber

1888.8 cm-1, a water peak from the air is present. And figure 7.15 shows the weighted

concentration. The standard deviation multiplied by the student factor is rather high in

comparison with the results from other molecules. This is because of water interference

in the NO spectrum.

The noise and imperfect baseline has a higher influence when the spectral lines for thelow NO concentrations are small.

Figure 7.14 Comparison of measured and simulated NO spectrum with 400 ppm, at 1888.8 cm-1

a

small water peak is visible.


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Figure 7.15 Normalized measured NO concentration.

Acetylene (C2H2)

Acetylene has three IR ranges 850-650 cm-1

, 1420-1240 cm-1

and 3360-3200 cm-1

.Figure 7.16 shows the measured and simulated spectrum, for room temperature,

atmospheric pressure and 9950 ppm C2H2,there is a good agreement.

In total, three measurements have been performed with the calibration gas in table 7.5.The figure 7.17 shows the weighted concentration of the measured spectra. The

calculated values at the Q branch have a small standard deviation and are in good

agreement with the actual concentration.

The second and third ranges are not used for this quantitative analysis, because thesimulated and measured spectra do not show a good agreement.

Table 7.5 Data on the calibration gases used to test the equivalent width method.

composition accuracy in C2H2content

9950 ppm C2H2, rest N2 1% ( 100ppm)


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Figure 7.16 Comparison of measured and simulated C2H2spectrum with 9950 ppm.

Figure 7.17 Normalized measured C2H2concentration range one.


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Propane (C3H8)

Propane has not yet been added in the HITRAN database. In order to confirm where thepropane spectrum can influence the spectral lines from other species in the thermal

decomposition products from biomass pyrolysis, a calibration gas of 152 ppm 2% is

measured. Figure7.18 shows the propane spectrum.

Figure 7.18 Measured absorbance spectrum of C3H8. Because of air movements in the spectrometer

some CO2and H2O peaks are visible.

7.1.1 Conclusion

The first indication whether the equivalent width method provides good results for the

quantitative analyze is the comparison of the simulated and measured spectra. For themolecules NH3, CO, CH4, C2H2, NO, both the spectra (measured and simulated) overlap

quite well, though for some peak heights, a small difference has been observed. But thearea of the both spectra agrees well. The molecule C2H4is more difficult: the spectra do

not match well. The area of the simulated spectrum is 20% smaller than the area of the

measured spectrum. The molecule C2H4has only been recently added in the HITRAN04database. The spectral line strengths for the ranges 1900 cm-1and 1450 cm-1 are not yet

added in the HITRAN database (figure 7.10). This might indicate that some of the

spectral lines are still missing in the 3000 and 950 cm-1

range.


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7.2 Experiments with heated grid reactor

For the following measurements, MDF has been pyrolysed in the heated grid reactor.

When quantitative measurements are performed for gases which contain spectrallyinterfering molecules, it is preferable to use stand-alone peaks. Figure 7.19 shows the

absorbance spectrum of NH3and C2H4, the spectrum is measured in the sample cell with

a calibration gas.

For example: The C2H4spectrum overlaps with the NH3spectrum. The NH3peaks above1080 cm-1do not have interference of C2H4and can be used for quantitative analysis.

Figure 7.19 Measured absorbance spectrum of 1998ppm NH3and 2509ppm C2H4. The peaks at

wavenumber 988 and 978 cm-1

are free from NH3interference; the peaks from 1200-1080 cm-1

are

free from C2H4interference.

10 mg MDF is placed on the grid of the reactor. The grid is heated to a temperature of

600C, at a rate of 120K/s, for 10 seconds. Before the pyrolysis experiment takes place,the reactor is purged with argon. The flow of the argon is very low and the flush time is

set at 30 minutes. The low flow was used to ensure that the biomass will not be blown

away from the grid. After pyrolysis, a part of the MDF was carbonized and stuck to the

grid. The amounts of carbonized MDF which remains on the grid could not be


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determined. Figure 7.20 shows the absorbance spectrum of the decomposition products

from the experiment.The visual identification of the species (H2O, HCN, CO2, CO, CH4, C2H2and C2H4) is

given in figure 7.20.

Figure 7.20 Absorbance spectrum of 10 mg MDF pyrolysis at 600C. (On the y axis is the absorbance

value is the one with log10

.)

Concentrations

The peaks that do not interfere with other peaks in the spectrum are suitable for the

quantitative analyse. Most of the CO peaks in the spectrum of figure 7.20 are free from

interference. By using the calibration plot in figure 5.4b, the CO concentration isestimated to be 13,013 ppm. The methane peak at 1248 cm-1 is free from interference.

With the calibration curve like the one shown in figure 5.5a, the methane concentration is

estimated to be 2,583 ppm.Ammonia is not detected in this experiment. The concentrations calculated with the

equivalent width method for the free-from-interference peaks are given in table 7.6. The

systematic error in the table has to be subtracted from the measured concentration.

Almost all acetylene peaks are influenced by peaks from the different species in the

spectrum. It has been observed that, at the range near 700 cm -1, CO2and HCN interfere,and at the range near 1300 cm-1 the CH4 spectrum interferes the C2H2spectrum. In the

range 3300 cm-1again the HCN spectrum interferes the C2H2spectrum. The peak at 766


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cm-1 has a very small influence of the HCN peak. Hence this peak was used for

estimation of the C2H2concentration.

Table 7.6 Concentrations calculated with equivalent width method.

Species formula Peak concentration sistematic error

[cm-

] [ppm] [%]

Carbon monoxide CO 2099 12,717 10

Methane CH4 1248 2,548 8

Ethylene C2H4 950 1,018 20

Hydrogen cyanide HCN 3371 95 10

Acetylene C2H2 766 161 10

Spectral simulation for two species is an option when there are non- isolated peaks. TheCH4range around 3000 cm

-1is strongly influenced by C2H4. This can be elaborated using

the following example:

From table 7.6, the C2H4concentration is 1,018 ppm, which has been estimated from thepeak at 950 cm-1, using the equivalent width method. In the range of 3200-2800 cm-1,

besides CH4, the spectrum also contains ethylene. The concentration of ethylene is now

known, from a non-interfering region. Using this, the concentration of the methane is

calculated. In this way, the CH4peaks at 3113 cm-1, 3123 cm-1, 3131cm-1and 3140 cm-1

give a concentration of 2813 ppm, 2672 ppm, 2724 ppm and 2340 ppm respectively. Thismethod is less accurate when compared to the one where free peaks without interference

are taken. However it is attractive option but when a spectrum is influenced by other

species with known concentration.


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8 Discussion on the pyrolysis experiment

As can be seen from figure 7.20, the baseline in the spectrum of the pyrolysis experiment

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