raman spectroscopy for natural gas process applications · 2020-01-14 · content of natural gas is...

Raman spectroscopy for natural gas

process applications An instrumental and operational survey of theory and practice

Author: Christiaan Mul Bsc

Supervisors: Dr. Freek Ariese (VU), Dr. Jan-Hein Hooijschuur (ASaP)

Master thesis Chemistry track analytical sciences, Date: 12-Dec-2017

Christiaan Mul

RAMAN SPECTROSCOPY FOR NATURAL GAS PROCESS APPLICATIONS

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1 ABSTRACT

This study focusses on online spectrometric applications of the Raman effect for process control

equipment in natural gas utilizing plants. This subject springs from the upcoming variable calorific

value in the Dutch natural gas distribution grid which makes innovation necessary to maintain safe

and competitive processes. Further insight in the industrial landscape and the innovative drive is

given in the first chapter. Available techniques are discussed and the strengths and weaknesses that

come with the introduction of Raman technology are considered.

To gather insight into the technology a full chapter is focused on Raman theory and natural gas. The

fundamental principles upon which the technology is based are discussed as well as the concept of

spectroscopy. Natural gas is a complex mixture of components and impurities. The varying

concentrations of the components in the natural gas influence key parameters. For a detailed look

on natural gas both literature and computer experiment are deployed to report composition ranges,

and predicted spectra.

One of the main subjects is the experimental setup that can be used to measure Raman scattering.

Following the route of the light, the components are each discussed and their function in the whole

described. A completely new process interface is designed for this application and subjected to

robustness and efficiency simulations. Other simulations and experiments are done to attempt

optimization of the interface between the collection fiber and spectrometer entrance slit.

The physical instrumentation only has a detector signal whereas the composition and key

parameters are the valuable results. The processing of a detector signal to a proper result is step-

wise discussed with examples and flow charts. Calibration of the detector, cosmic ray detection,

and dark current and background correction are shown. Additionally some ideas are shared about

the implementation and restrictions of multivariate modelling.

Finally at the discussion it is discussed what worked well, and where the instrumentation may be

optimized with suggested alterations. Both the instrumentation and the operational effectivity of

the application are discussed whereby the results are taken into account. It was found that only few

goals were met, spectra can be recorded from the main components although they differ little over

their tested concentration range. Future research should be focused on increasing the sensitivity of

the measurement, which is found to be the main weakness, and on developing advanced algorithms

for the determination of key parameters of natural gas.

The study reveals that to maintain optimal cost and process control, fast and accurate analysis

methods need to be developed that can measure the anticipated compositional changes1. The main

question is ” is Raman spectroscopy a viable technology for the compositional analysis of natural gas

mixtures?”

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2 TABLE OF CONTENTS

1 ABSTRACT ............................................................................................................................................ 2

2 TABLE OF CONTENTS ......................................................................................................................... 3

3 RAMAN SPECTROSCOPY FOR NATURAL GAS ASSESSMENT ...................................................... 5

3.1 THE RISE OF LIQUEFIED NATURAL GAS (LNG) AND ITS INTRODUCTION INTO THE DUTCH MARKET ..................... 5

3.2 RAMAN TECHNOLOGY ................................................................................................................................................... 7

3.2.1 Diffraction spectrometer, robustness from a static design .................................................................... 7

3.2.2 Fast analysis time can improve process control ....................................................................................... 8

3.2.3 In situ analysis can reduce error and emissions ..................................................................................... 10

3.2.4 Sensitivity and selectivity ................................................................................................................................ 11

3.3 ALTERNATIVE MEASUREMENTS TECHNIQUES ............................................................................................................ 11

4 IN THEORY ......................................................................................................................................... 13

4.1 THE RAMAN-EFFECT .................................................................................................................................................... 13

4.2 NATURAL GAS IN DETAIL ............................................................................................................................................. 16

4.2.1 Literature spectra ............................................................................................................................................... 17

4.2.2 Theoretical prediction of spectra .................................................................................................................. 18

5 INSTRUMENTATION OF THE EXPERIMENTAL SETUP ................................................................. 22

5.1 LIGHT SOURCE, LASER ................................................................................................................................................. 23

5.2 EXCITATION FIBER ........................................................................................................................................................ 25

5.3 OPTICAL PROBE ........................................................................................................................................................... 26

5.3.1 Collimation lenses ............................................................................................................................................. 28

5.3.2 Filters and mirrors ............................................................................................................................................. 28

5.4 EXTENSION TUBE, IMMERSION PROBE ....................................................................................................................... 30

5.5 LIGHT PATH GEOMETRY ............................................................................................................................................... 32

5.6 LENSES AND WINDOWS .............................................................................................................................................. 37

5.7 COLLECTION FIBER ....................................................................................................................................................... 39

5.8 SPECTROGRAPHS ......................................................................................................................................................... 44

5.8.1 Entrance slit ......................................................................................................................................................... 45

5.8.2 Observation times.............................................................................................................................................. 47

5.8.3 Mirrors and grating ........................................................................................................................................... 47

5.8.4 Detector ................................................................................................................................................................. 48

5.9 MEASUREMENT CELL ................................................................................................................................................... 52

6 SIGNAL TO SPECTRUM .................................................................................................................... 54

6.1 SIGNAL CALIBRATION .................................................................................................................................................. 54

6.1.1 Wavelength .......................................................................................................................................................... 54

6.1.2 Raman shift .......................................................................................................................................................... 56

6.1.3 Intensity ................................................................................................................................................................. 57

6.2 SIGNAL PREPARATION ................................................................................................................................................. 57

6.2.1 Number of data points..................................................................................................................................... 57

6.2.2 Cosmic ray detection ........................................................................................................................................ 60

6.2.3 Dark current correction ................................................................................................................................... 61

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6.2.4 Background correction ..................................................................................................................................... 63

6.2.5 Noise filters .......................................................................................................................................................... 66

6.3 RESULTS AFTER SIGNAL PREPARATION ....................................................................................................................... 68

6.4 SIGNAL ANALYSIS BY MODELLING .............................................................................................................................. 72

6.4.1 Univariate model ............................................................................................................................................... 72

6.4.2 Multivariate models .......................................................................................................................................... 73

7 DISCUSSION AND CONCLUSION ................................................................................................... 75

8 REFERENCES ...................................................................................................................................... 78

9 LISTS OF FIGURES, EQUATIONS, AND TABLES ............................................................................ 83

9.1 FIGURES ........................................................................................................................................................................ 83

9.2 EQUATIONS .................................................................................................................................................................. 86

9.3 TABLES .......................................................................................................................................................................... 86

10 GLOSSARY OF TERMS ...................................................................................................................... 87

11 APPENDIX A ...................................................................................................................................... 88

11.1 THEORETICALLY PREDICTED SPECTRA CALCULATED OF PURE COMPOUNDS IN NATURAL GAS............................. 88

11.2 COMBINED PREDICTED SPECTRA ............................................................................................................................. 102

11.3 COMBINED PREDICTED SPECTRA FROM TYPICAL COMPOSITIONS ........................................................................ 108

12 APPENDIX B ..................................................................................................................................... 110

12.1 COMPOSITION OF GAS STANDARD ‘HIGH CALORIFIC NATURAL GAS’ ................................................................. 110

12.2 KEY PARAMETERS OF GAS STANDARD ‘HIGH CALORIFIC NATURAL GAS’ ............................................................ 111

12.3 COMPOSITION OF GAS STANDARD ‘LOW CALORIFIC NATURAL GAS’ .................................................................. 112

12.4 KEY PARAMETERS OF GAS STANDARD ‘LOW CALORIFIC NATURAL GAS’ ............................................................. 113

13 APPENDIX C ..................................................................................................................................... 114

13.1 FORMULAS AND CONSTANTS USED TO CALCULATE ISOTOPIC INFLUENCE .......................................................... 114

14 APPENDIX D .................................................................................................................................... 115

14.1 SECOND MANUFACTURER COMPARISON OF SPECTROGRAPH ............................................................................. 115

14.2 SPECTROMETER DETECTOR SPECIFICATIONS .......................................................................................................... 116

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3 RAMAN SPECTROSCOPY FOR NATURAL GAS ASSESSMENT

Quality measurements of natural gas are essential for a multitude of processes. The quality is

characterized by certain key parameters following from the composition. This study focusses on the

direct assessment of the natural gas quality with the use of Raman spectroscopy.

The development’s innovation cannot be found merely in the application of Raman spectroscopy on

natural gas, but also in the probe design which facilitates insitu analysis. The setup and use of

available components is studied, in theory and where possible in practice. The thesis concludes

upon weighing the results of the experiments and evaluates their practical use.

3.1 THE RISE OF LIQUEFIED NATURAL GAS (LNG) AND ITS INTRODUCTION

INTO THE DUTCH MARKET “The interest in liquefied natural gas (LNG) has recently intensified due to the development of

significant global gas reserves and more advanced techniques for their recovery. An increased

awareness of the human carbon footprint has led to advanced carbon accounting. When natural gas is

used for electricity generation it can diminish carbon dioxide emission, compared to coal, by 10%2.The

abundance of natural gas combined with the worldwide demand for energy leads to many questions

about the efficient production and transportation of LNG3.

Which method is optimal for the transportation of natural gas from the production site to the customer

site is a complex discussion. A few options to choose from include: pipelines, compressed natural gas,

gas to liquids, and gas to solids. To use available resources effectively, the distance covered per energy

unit should be maximised4. The energy density per volume in LNG is approximately a factor 600 higher

than that of natural gas. This property makes it profitable to transport volumes of LNG in containers

over longer distances than would be feasible with pipelines.

Energy transactions are accounted for using energy content per volume per currency5. The energy

content of natural gas is readily measured with conventional techniques such as gas chromatography

or with the faster micro gas chromatography methods. The developments in analysis are aimed at the

development of faster and more precise machines so that a more accurate calculation of the energy

content can be made.” (Citation from Mul, 2015)6

The primary objective in every company is to maximize profit while saving resources. When focused

on the (petro-)chemical or energy industry this can be done by the use of process analysis. In this

study special attention is given to natural gas compositional analysis due to the anticipated

challenges formed by the shift of supply and quality of the gas7. Figure 1 shows the caloric value

distribution throughout the Netherlands. There are two main problems that arise from the new

Liquefied Natural Gas (LNG) supply. First, the change in heat index, the so-called H-gas contains

less nitrogen and has a higher caloric value compared to the conventional G-gas commonly used in

the Netherlands. Second, there are plans to add hydrogen to the gas in order to reduce carbon

dioxide emissions. This would create gas with less carbon emissions, but simultaneously an even

broader array of possible caloric values available from the natural gas distribution network.

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Figure 1 H-gas map of the Netherlands, note the LNG-import dock labelled 'LNG'. Source: gasunietransportservices.nl accessed 16-11-17

Gas turbines can be a major source for relatively clean electricity, certainly when methane is burned

only carbon dioxide and water is formed. Still the gas does not come free of cost, and the turbine

needs to control the air and fuel flows. Currently these processes are primarily controlled by Gas

Chromatographs (GCs) or Wobbe index analyzers. These classic technologies each have their own

positive and negative properties. For instance, GCs rely on the physical separation of the analyte

components, and are therefore limited in the achievable speed. In addition, Wobbe index analyzers

cannot adjust for the different hydrogen oxidation stoichiometry compared to carbon containing

fuels8. In the future it is therefore expected for these gas turbines to run less efficiently than

theoretically possible, a waste of money and fuel.

In short, the current generation of analysis equipment is not sufficiently capable of dealing with fast

changing gas compositions. Innovation in fast and accurate compositional analysis would increase

the possibility for control of cost of the used natural gas, and the control of the fuel to air mixture

with all its advantages. During the course of this study a survey will be made of both the

instrumentation and operation of Raman technology to ensure the highest efficiency. The extent of

some experiments is mainly theoretical whereas also a complete measurement rig is built to bring

theory into practice for further evaluation. The purpose of the survey is aimed at finding an ideal

setup to measure the composition and quality of natural gas with Raman spectroscopy.

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3.2 RAMAN TECHNOLOGY With the image of mammoth tankers and gas turbines it might be hard to imagine laboratory

Raman spectroscopy setup amidst the hard hatted coveralls. But nothing is further from the truth,

Raman spectroscopy is a developing field in process analytical technology9,10,11,12 with a growing

number of applications. This development is expected to proceed as optical components improve in

quality, ruggedness, and affordability.

3.2.1 Diffraction spectrometer, robustness from a stat ic des ign

An advantage of the Raman spectroscopic method is that it can be fully functional without the use

of any moving parts. All major components consist and can operate based on fixed components.

Both Figure 2 and Figure 3 show a diffraction spectrometer, one schematic and an Avantes ULS

with an opened casing. Not only are fixed parts easier to control, the lack of bearings and motors

also reduces maintenance. The application locations are often vibrating due to pumps, turbulent

flows, or other heavy equipment. Ruggedness of the spectroscopy equipment is therefore

considered an advantage.

Figure 2 Schematic view of diffraction spectrometer including lightpaths

Figure 3 Picture of opened spectrometer, Manufacturer: Avantes13

Both a diffraction spectrometer and a filtered band spectrometer can be used for measurements on

a range or selected bandwidths. Whereas the diffraction spectrometer is static, without moving

parts, the filtered spectrometer needs a driver motor and bearing for the filter wheel, as can be seen

on Figure 4. A common practice is to replace the bearing, driver axis/snare, and motor after a

number of operation hours when they become prone to faults. The lifetime of these moving parts is

significantly less, 1 to 5 years, compared to the static components that can last an analyzer’s

lifetime. Each method of light separation and detection has interesting characteristics, that will be

discussed in chapter 5.8. The increased stability and improved maintenance interval make the

diffraction spectrometer the preferred option in a process environment.

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Figure 4 Schematic view of filtered band spectrometer

3.2.2 Fast analysis t ime can improve process control

Processes can be regulated in multiple ways, the most used concepts are feed-back and feed-

forward control. As an example we will examine the Claus process where H2S is partially combusted

and catalytically reformed to elementary sulfur and water, see Figure 5 for a schematic view of the

process. The possibility to analyze the sample within seconds makes it possible to rethink control

mechanisms used in industrial processes.

In traditional feed-back control, the Claus process is equipped with an analyzer to sample the final

gaseous output, so-called ‘tail gas’, to determine the ratio H2S to SO2. This information shows,

among other things, whether the combustion ratio was correct. If the ratio is out of bounds more or

less air is added to the combustion to keep the ideal ratio for the catalytic conversion. The time

needed for the feed gas to combust and flow through multiple condenser and converter steps is,

depending on the specific installation, around 30 minutes. This means if the composition in the feed

gas changes, the process can only be adjusted 30 minutes later, resulting in excessive SO2 output or

insufficient sulfur recovery.

In feed forward control the feed gas is analyzed before it reaches the combustion furnace. With the

use of computers the needed amount of air is then calculated while the process can still be

controlled. The correct adjustment of the process should be known in time, so adjustments to the

trim air can be made in time, from sample to adjustment this would be approx. 10 seconds. A tail

gas analyzer is still needed to observe the catalyst activity, but it will definitely convert more sulfur

if the ratio is precisely controlled. The planned regulation of the process will prevent unnecessary

loss of efficiency or damage to the environment.

The example above shows the possibility of improving multiple qualities dependent on efficient

process control: environmental damage, loss of raw materials, and loss of heat. The development of

the feed forward control was only possible with the development of a fast spectroscopic analysis

method combined with adequate sample handling. Raman spectroscopy has possible applications

to instantly know exactly what is fed into a furnace, turbine, or reactor so that the process itself can

be improved.

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Figure 5 Schematic view of the Claus process for sulfur recovery, indication of feedforward and feedback loop

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3.2.3 In s i tu analysis can reduce error and emissions

In situ, or in English “in the original place”, comprehends a whole different approach to analysis. In

extractive methods the sample is taken from the pipeline, transported, adjusted for analysis, and

only then analyzed. With in situ methods the sample is analyzed directly inside the process. Interest

in this type of measurements is fueled by the possibility to eliminate a loss of accuracy resulting

from the sampling and sample handling method. Another positive effect resulting from in situ

measurements is the reduction of undesirable emissions. Whereas normally the sample is

extracted, analyzed, and discarded, the sample can now be fully used in the continued process.

A good example is the measurement of residual oxygen in combustion furnaces for the optimization

of the burner efficiency. This type of measurement can be done in situ or extractive with the use of a

Zirconium Oxide-sensor14, also known as a lambda-sensor. The sensor is flow-sensitive, therefore

the in situ probe is designed to rely on a diffusion principle where the direct flow over the sensor is

always stable. When the sensor is used extractive, for instance when also a combustion sensor is

needed or for easier maintenance access, a positive flow over the system is needed. A

maladjustment of the flow would lead to a biased signal on the extractive oxygen measurement

compared to the in situ measurement, solely due to the sampling handling.

𝛿(𝑖𝑛 𝑠𝑖𝑡𝑢 𝑍𝑟𝑂2𝑠𝑒𝑛𝑠𝑜𝑟) 𝑉𝑆. 𝛿(𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑎𝑛𝑑 𝑠𝑎𝑚𝑝𝑙𝑒 ℎ𝑎𝑛𝑑𝑙𝑖𝑛𝑔) + 𝛿(𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑣𝑒 𝑍𝑟𝑂2𝑠𝑒𝑛𝑠𝑜𝑟)

To most effectively reduce the total error from an extractive method the possibility to measure in

situ should be considered. Some of the residual oxygen measurement methods that can be applied

extractive but are hard to use in situ are paramagnetic sensors and IR-absorption spectrometry14. To

equally compare methods not only the sensor accuracy should be compared, but the total system

accuracy.

𝛿(𝑖𝑛 𝑠𝑖𝑡𝑢 𝑍𝑟𝑂2𝑠𝑒𝑛𝑠𝑜𝑟) 𝑉𝑆. 𝛿(𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑎𝑛𝑑 𝑠𝑎𝑚𝑝𝑙𝑒 ℎ𝑎𝑛𝑑𝑙𝑖𝑛𝑔)

+ 𝛿(𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑣𝑒 𝐼𝑅 𝑠𝑝𝑒𝑐𝑡𝑟𝑜𝑚𝑒𝑡𝑟𝑦 ) The same discussion can be applied to natural gas measurement applications. International

standards prescribe the use of gas chromatography to determine the composition of natural gas.

The precision of this equipment can be determined in the laboratory by performing a set of

experiments involving standard gas mixtures. To compare Raman-spectroscopy applications to the

current techniques the following equation including the sample handling should be used.

𝛿(𝑖𝑛 𝑠𝑖𝑡𝑢 𝑅𝑎𝑚𝑎𝑛 𝑠𝑝𝑒𝑐𝑡𝑟𝑜𝑠𝑐𝑜𝑝𝑦) 𝑉𝑆. 𝛿(𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑎𝑛𝑑 𝑠𝑎𝑚𝑝𝑙𝑒 ℎ𝑎𝑛𝑑𝑙𝑖𝑛𝑔)

+ 𝛿(𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑣𝑒 𝐺𝐶 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠) Naturally, in situ analysis also has negative points. Turbulent streams in the process pipeline require

a proper probe design to avoid intensity fluctuations and beam steering15. The latter phenomena is

caused by different densities in the gas that causes the whole spectrum to shift. Next to that,

maintenance on the probe might be harder to execute, because the process is hard to access. Block

and bleed valves need to be installed for safe access. In situ analysis compared with extractive

techniques is a trade-off where a good analytical and low-maintenance design has the possibility to

add value.

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3.2.4 Sensi t iv i ty and selectiv i ty

The main challenges in Raman spectroscopy for natural gas applications are the sensitivity and

selectivity for the analytes. Sensitivity to the sample depends on the total available signal, which is

impaired by 2 main factors described further on. The selectivity of the method to distinguish

between different analytes is essentially the result of the chemical qualities of the analytes

themselves.

The signal is expected to be less intense for applications on gasses compared to applications on

liquids or solids due to the reduced sample density, fewer molecules will be present to interact with

the light and produce a signal. Naturally the density will increase with pressure, so not only is a high

pressure good for the signal intensity, also the method is inevitably pressure dependent. Some

authors even report a change in the spectra at different pressure16, indicating not only the

sensitivity, but also the selectivity might be affected by the process pressure.

The efficiency of the Raman-effect is low when compared to fluorescence spectroscopy. The

reported quantum efficiencies for Raman range from 10-6-10-8 whereas for fluorescence up to 0.8

has been reported17,18, the expected Raman signal is thus much weaker. Due to the physical nature

of these limitations solutions should be found with the instrumentation, the signal will need to be

carefully collected and analyzed, and not with the sample.

Natural gas consists mainly of hydrocarbons which are relatively similar to each other, the main

difference is their chain length and molecular weight. Vibrations in the molecules are similar for all

hydrocarbons, because they consist of equal types of atoms, hydrogen and carbon. Raman analysis

measures these vibrations and the selectivity is expected to be a challenge. The method should

render sufficient resolution to separate the vibrations and generate adequate data.

In case the selectivity proves to be overly challenging the subsequent calculations are aimed at

deconvolution of the peaks, or the method will focus on the key parameters. Raman analysis may

detect the complete composition with a single measurement, therefore also a lack of one

component could indicate an increase in another. Composition is the ultimate goal for this analyzer,

though with the calculation of the Caloric value and Wobbe index from the spectrum the analyzer

should be able to work in a process control loop.

3.3 ALTERNATIVE MEASUREMENTS TECHNIQUES When considering the application, measurement of composition and key properties of natural gas,

it also makes sense to determine the alternatives on the market. These measurements are based on

various techniques, each with their own strengths and weaknesses. In the columns of Table 1

techniques are shown that are currently used or have the potential to be used for metering and

regulation analysis. The rows list a number of properties following the previous paragraphs

completed with key differences. Due to the virtually infinite number of possible varieties of

techniques this is by no means a complete comparison.

Gas Chromatography19 and Wobbe-index measurement20 can be considered wide-spread

techniques. IR-spectroscopy is a recent addition to the market and the ‘Tunable Filter

Spectroscopy’-application can be readily used for process regulation21. An interesting alternative to

the spectroscopic methods is the combined sensor-modelling technique22, multiple signals are

combined to compute key characteristics of the natural gas. Naturally also Raman-spectroscopy is

listed to complete the comparison.

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Measurement speed + + - + +

Sensitivity to O2, N2, H2 (Compositional analysis) + - + - -

Possible with complex mixtures + - + - -

Possible in situ measurement + + - - -

Linearity of signal + + + - +

Signal intensity - + + + +

Cost of ownership + + - + -

Static design possibilities + + - - + Table 1: A comparison of the advantages and disadvantages of competitive techniques.

As was described in the paragraph ‘Fast analysis time’ it can make quite a difference to the process

if the measurement is quick enough to regulate feed-forward. Although certain GCs can have

update times of 60 seconds the average time is 3 to 5 minutes for optimal separation and analysis.

All other methods have update times of less than 10 seconds, significantly faster, resulting in more

data-acquisition and better process regulation.

Compositional analysis can only be done with measurement methods selective to the different

analytes. Only the Raman method and gas chromatography are theoretically capable of complete

compositional analysis of the components in natural gas, respectively by deconvolution of

individual signals and separation of components prior to detection. IR cannot measure homonuclear

diatomic molecules, since there will be no shift in the dipole-moment during the vibration. An

estimation can be made for nitrogen 100 % - measured % = nitrogen %, though this is obviously

biased and easily influenced if hydrogen or oxygen is present. Wobbe index measurement is

generally done by measurement of the residual oxygen after combustion, an indirect measurement

which does not correlate to compositional differences. In a similar matter sensor-computational

methods are based upon indirect measurement and are unable to provide a complete composition.

The direct measurement of valuable components is a prerequisite for a full composition result.

A minor concern is the possibility to measure atomic gasses, such as argon. Over the past years gas

chromatography has become more expensive due to the need of inert carrier gasses. Often the

solution is to exchange helium for argon, hydrogen, or nitrogen, since these gasses are more cheap

even though similar resolution can be obtained. An unfortunate result is that the Thermal

Conductivity Detector cannot measure the difference between the carrier gas and the analyte

which is the carrier gas, for instance: with carrier gas hydrogen, the analyte hydrogen cannot be

measured properly. Whereas Raman technology cannot measure atomic gasses, since there are no

molecular vibrations, the possibility is expected to become gradually more expensive with

chromatography.

Another distinct difference of Raman technology and gas chromatography is closely linked to the

ability to do a complete compositional analysis. The possibility to analyze trace components, such

as dihydrogen sulfide or carbonyl sulfide, is a valuable addition. Opportunities arise when

complementary analyses can be done with a single unit.

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4 IN THEORY

4.1 THE RAMAN-EFFECT As was described in 1928 by C. V. Raman and K. S. Krishnan23 there are 2 types of scattering events,

unmodified and modified, also called elastic and inelastic respectively. Elastic scattering of light,

known as Rayleigh scattering, does not influence photon energy of the incident light whereas

inelastic scattering shows the changes in the molecular vibrational and rotational energy during the

scattering event23. In the Jablonski diagram, see Figure 6, these scattering events are schematically

drawn for clarity. The use of the words excitation and relaxation are common for scattering events

even though the molecule is not truly excited to an electronic or vibrational state. A disputed so

called virtual energy state is sometimes referred to, but could also be described as a short lived

electron cloud polarization. Therefore, some authors prefer to use pro- and demoted.

Figure 6 Jablonski diagram showing energy states of different scattering events.

The depicted energy levels in Figure 6 are different for each molecular compound, whereby the S-

states signify various electron configurations, and the V-states multiple vibrations thereof. Figure 7

shows a –CH2– group where the hydrogen atoms vibrate around the carbon. One should note the

different energy levels correspond to different modes of vibration. The depicted vibrations do not

account for the remainder of the molecular vibrations, recoil of the carbon atom, or the vibrations

on the rest groups for instance. These interactions make that the vibrations are slightly different

depending on the weight of the atoms themselves, and the rest groups. Both the Jablonski diagram

and the Molecular vibration diagram are thus merely clarifying schematic approaches of the

underlying theory.

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The measured parameter is the difference between the energy of the incident light and the energy

of the scattered light. For Stokes Raman scattering this gives a positive (red-)shift, and for the anti-

Stokes Raman scattering an equal but opposite energy (blue-)shift, hence the colored arrows in

Figure 6. Both Raman shifts contain the same molecular information24. Because of the more

populated ground (S0) state25, compared to the vibrational states the intensity of the Stokes Raman

scattering is higher compared to the anti-Stokes Raman scattering. Therefore, often only the

Stokes Raman scattered light is used for spectrum interpretation.

Figure 7 Molecular vibrations in a -CH2- group, LRTB: Symmetrical stretch, Asymmetrical stretch, Scissoring, Rocking, Wagging, and Twisting. Whereas the arrows display the initial direction of the vibrations on the plane of the paper, the + and – show the movement perpendicular to this plane.

For all analytical purposes a high S/N ratio is desirable, where the noise is mainly determined by the

instrumentation. The signal is also fundamentally dependent on the wavelength of the scattered

radiation. It was found by Lord Rayleigh in 1871, that the relative intensity of the signal is

approximately proportional to λ-4. The correlation26 of the incident light frequency to the signal

intensity is also expressed in Equation 1 where �̃�0 is an intensity variable. A photon with more

energy would render a higher signal, though if the incident light is too energetic fluorescence or

other interfering effects might occur.

Contrary to an absorbance spectrum, the shape of Raman spectra are independent of the used

wavelength of excitation. The Raman shift, expressed in reciprocal centimeters (cm-1), would not

change although the emitted wavelengths would27. This property of the technique makes it possible

to use different excitation energies such as to prevent photo degradation, absorbance, or

fluorescence from the sample. Another positive effect is that all Raman spectra taken with different

light sources can be directly compared.

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The intensity of Raman scattering is approximately a fraction of 10-6 to 10-8 compared to Rayleigh

scattering28,29. Thus only a very small fraction of the light shows interaction with the sample and

creates a viable signal. Overall the intensity of the scattering can be summarized in the formula

given in Equation 1, see Table 2 for the applicable units and a description of the symbols.

Equation 1: Intensity of Raman scattering, equation reproduced from30

Symbol Description Unit

IR Intensity of Raman effect J/s

𝜂 The experimental factor No unit

I0 intensity of the incident light J/s

n particle density cm-3

(d σ)/(d Ω) differential Raman cross section cm2/sr

Ω collection optic angle sr

Le effective length of the sample cell cm

�̃�𝑅 Wavenumber Raman scattered light cm-1

�̃�0 Wavenumber incident light cm-1 Table 2 Definition of symbols and units in Equation 1

The intensity of the Raman scattering is a direct function of the energy of the incident and scattered

light, as described above. Other parameters are: 𝜂, the experimental factor for the yield of the

experimental setup, I0, the intensity of the incoming light (laser power), n, the particle density,

(dσ)/(dΩ), differential Raman cross section of the analyte31, Ω, collection optic angle, and Le, the

effective length of the sample cell30. Some of these parameters are physical constants, other can be

influenced by the experimental method and setup.

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4.2 NATURAL GAS IN DETAIL Natural gas is a complex mixture and can be found with several impurities. The majority of the gas

consists of methane, and depending on the source or means of transport the other components can

be hydrocarbons or inert components. A limited overview of the expected components in natural

gas from Groningen and Qatar can be found in Table 3.

Bio-gas usually has a higher sulfur impurity content due to its biological background. These

compounds do not only hinder the comfortable consumer usage, but their accompanied acidity

may result in corrosion of wetted parts32. Most notably bio-gas contains close to 100% methane,

more than any other type of natural gas because it is made in methanogenesis33.

Nitrogen is another component present in natural gas, gas fields in Groningen are reported to

contain on average more than 14 %34. In contrast, Liquefied Natural Gas (LNG) contains due to its

physical nature practically no nitrogen. The gas is liquefied to approx. -163 °C, at which nitrogen

remains gaseous and can be separated. Because nitrogen does not burn the relative quantity has

consequences for the fuel quality.

Common names: Molecular formula: Groningen Qatar

Methane C1 C1H4 81.30% 88.2%

Ethane C2 C2H6 2.85% 6.1%

Propane C3 C3H8 0.37% 2.3%

iso - Butane C4 C4H10

normal - Butane C4 C4H10 0.14% C4 lumped

1.0% C4 lumped

iso - Pentane C5 C5H12

normal - Pentane C5 C5H12 0.04% C5 lumped

0.0% C5 lumped

neo - Pentane C5 C5H12

Hexane C6 C6H14 0.05% C6+ lumped

Nitrogen N2 N2 14.35% 2.5%

Carbon Dioxide CO2 CO2 0.89% 0.0%

Hydrogen H2 H2

Oxygen O2 O2 0.01% Table 3 main components in sales gas, with composition in volume % for NG from Groningen34 (Wobbe index of 43,7 MJ/m3) and LNG from Qatar (mixed to a Wobbe index of 54 MJ/m3 for the Dutch market)

Fuel quality can be expressed in a number of ways depending on the operation. Most commonly the

superior calorific value35, expressed as MJ/m3, is used for transport and cost calculation. Other

expressions are the methane number36, expressed in the methane/hydrogen mixture knocking

equivalents37, and the Wobbe index35,38, expressed in MJ/m3. These units are valuable regulation

parameters in gas fueled engines and furnaces.

For the composition of the mixture to be analyzed it is important that either the components are

separated (as is done in a GC) or the signal can be separated. Several applications are known to

analyze mixtures up to C3 and separating the Raman signals11,12,39,40. Other analysis methods,

discussed in 3.3 Alternative measurements techniques, determine these key factors directly without

use of the composition.

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4.2.1 Literature spectra

Table 4 displays a list consisting of the analytes, their measured Raman shift, and the difference

between consecutive peaks. The list immediately shows the difficulty of the sample, many of the

components are alike. The CH-stretch vibrations, around 2900 cm-1, are similar for all

carbohydrates.

Components unordered Components ordered after Raman shift

Raman shift (cm-1) Raman shift (cm-1)

Difference between peaks (Δcm-1) Methane 2917 Hydrogen 587

Methane 1535 iso - Butane 794 207

Ethane 2914 normal - Butane 827 33

Ethane 993 Propane 870 43

Propane 2908 Ethane 993 123

Propane 870 Carbon Dioxide 1285 292

iso - Butane 2880 Carbon Dioxide 1388 103

iso - Butane 794 Methane 1535 147

normal - Butane 2890 Oxygen 1555 20

normal - Butane 827 Nitrogen 2331 776

Nitrogen 2331 iso - Butane 2880 549

Hydrogen 4156 normal - Butane 2890 10

Hydrogen 587 Propane 2908 18

Oxygen 1555 Ethane 2914 6

Carbon Dioxide 1388 Methane 2917 3

Carbon Dioxide 1285 Hydrogen 4156 1239

Table 4 Raman shift (cm-1) of most common components, data reproduced from Kiefer et al. (2008)11

The literature values in Table 4 are likely to only relate to the main isotopes, 1Hydrogen and 12Carbon. Other isotopes would change the vibrations since the weight would alter the oscillation

properties. In Appendix C some calculations are noted for the approximation of isotopic influence

on the vibrational frequency from a classical mechanical point of view. The results shown in Table 5

are for the C-H stretch vibration from methane and show two important trends.

Firstly the change of 12Carbon to 13Carbon shows a small decrease in vibrational frequency. Hereby it

should be noted that the calculation does not adjust for any other bonds of the carbon atom. For

methane more influence would be expected than for propane, since the carbon in propane has

heavier side groups. From this data the isotope effect is expected to be only a minor effect and,

depending on the instrumental resolution, might be seen as minor broadening of the signal.

Secondly the results show that in case deuterium is present, the vibration would be less energetic.

Such a shift would show the peak on a different place in the spectrum, though it would be hard to

measure these vibrations. 2H has a natural occurrence of 0.015 %, which would mean for the

hydrogen rich molecule hexane 0.2% of the signal would be changed in such manner. Shortly, the

deuterium shift is not expected to be a major signal interference or cause of a lost signal.

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Bond components Wavenumber (cm-1)

Hydrogen Carbon12 2917 from literature

Hydrogen Carbon13 2908 Calculated

Deuterium Carbon 12 2142 Calculated

Deuterium Carbon 13 2130 Calculated Table 5 Results of isotope vibrations approximation.

A difficulty for this application is the gaseous phase of the sample. Raman spectroscopy on gasses is

not new41,42, though it is a developing science. Certain effects are to be expected with increasing

pressure. Firstly, a higher signal is expected. The signal intensity equation, Equation 1, showed that

the particle density of the sample is proportional to the signal30. For the same reason scientists have

been building high pressure sample cells43,44. Secondly, not only the spectrum intensity changes,

also the peaks can shift with pressure45. Molecules are more or less dispersed in the sample, when

the density changes they have more or less interaction with each other, thus influencing the shape

of the spectrum43,44. For methane-ethane mixtures this can be a good thing, for instance a built in

pressure measurement46.

4.2.2 Theoretical prediction of spectra

As a part of the research the theoretically predicted vibrations were calculated with the use of

Amsterdam Density Functional, ADF, software. The program is based on Density Functional Theory

and can be controlled with a Graphical User Interface, GUI, where the user can build the applicable

molecule(s) and select the correct calculation mode. There was no in-depth analysis of the used

algorithms, a description of the main computation is given in Van Gisbergen et al. (1990)47, only the

setup and results were operated to analyze natural gas.

The first step in using the ADF-software is building the molecules, atom by atom, in separate job

files. The molecules built to calculate their individual spectra are shown in Table 3. Consecutively

the geometry of the molecules is optimized with the built in algorithm, this is important due to the

symmetry in the molecules. When the built molecule does not have an optimized geometry

symmetric vibrations are not properly calculated. The last step in the calculation setup is the

calculation mode selection and the input of laser frequency. Calculation preset ‘frequencies’ was

selected and modified to include the full Raman prediction and a photon energy of 2.3305 eV

calculated from a laser wavelength of 532 nm. Eventually all vibrations were calculated, both IR and

Raman active, a typical Raman spectra output is shown in Figure 8.

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Figure 8 ADF-Calculation output for methane

In case the symmetry is entered correctly into the software, apart from the frequencies also the

vibrational symmetry, degeneracy, Raman intensity, and linear depolarization ratio is calculated.

Linear and non-linear polarization47 is not applicable to this project, and is further ignored. For this

study only the Raman active vibrations were collected and used.

Peak data is extracted from the ADF software and loaded into MatLab™ for further calculations.

From the peak frequency and intensity a normal distribution is made in which the mean is the peak

frequency, sigma = 10, and the total area is the intensity. These distributions are added up to plot

the spectra in Appendix A, where the separate component spectra and peak data are collected. For

illustration Figure 9 is shown, all separate peaks are summed and displayed as a theoretically

expected spectrum.

It was attempted to calculate such parameters for a mixture of hydrocarbons. These jobs would

have similar settings, although with multiple methane molecules built with the GUI. Unfortunately

these simulations proved to be too computationally intensive for the used computers and had to be

abandoned.

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Figure 9 Theoretically predicted spectrum of methane, data from ADF plotted in MatLab

A comparison of the literature data with the predicted data shows certain discrepancies. Firstly the

carbon dioxide peaks from the literature on 1285 and 1388 cm-1 were not expected. The main peak

from the calculations is predicted as a symmetrical stretch vibration with wavenumber 1182 cm-1.

The wavenumber shift and doublet formation is likely a result from Fermi resonance48,49.

Secondly the comparison of the hydrogen literature and predicted values show an interesting

difference. Where the theory only predicts a single vibrational mode ( 3N-5 for linear molecules),

two peaks are reported50, at 587 and 4155 cm-1. The first a rotational Stokes shift, and the latter a

vibrational shift.

In Figure 10 the predicted spectra are combined in an overlay view wherein the component spectra

are multiplied by their mole-fraction. The largest contribution to the signal originates from

methane, since it has the highest typical concentration in Groningen gas. More detailed overlay

spectra, not-concentration corrected, or corrected for typical Qatar gas can be found in paragraphs

11.2 and 11.3.

Lastly a comparison can made from the concentration corrected spectra from Groningen and Qatar,

the two main gas sources in the Netherlands from own production and LNG import. Figure 11 shows

the absolute theoretical difference when the one is subtracted from the other. It can be seen that

there is a difference, approximately 10 % change of the signal forms the full change in

concentration, and thus with the correct sensitivity and resolution it can be measured.

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Figure 10 Overlay of Theoretically predicted spectra from the components in Groningen-gas multiplied by their typical concentrations.

Figure 11 Theoretical absolute difference of spectra of natural gas from Groningen and Qatar, one subtracted from the other.

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5 INSTRUMENTATION OF THE EXPERIMENTAL SETUP

When it comes to consideration how to apply Raman spectroscopy we have to ask the question:

What do we consider to be essential feats for Raman spectroscopy for natural gas process

applications? Safety first, the use of high power lasers and flammable gasses is a combination to

consider. Secondly the signal is expected to be low, so all needs to be done to gather as much light

as possible. In this chapter the experimental setup is discussed in the same order the light travels.

Figure 12 Schematic view of the experimental setup with the three main components; laser, optical probe and spectrometer.

Figure 12 shows the used optical setup used for the practical experiments. There are three main

components selected for this application. The laser, a Cobolt 04-01 series51,52 Samba™, can be set to

up to 156mW output on 532nm wavelength. The Optical probe, an InPhotonics Ramanprobe™53

incl. Reaction Ramanprobe™54. Multiple spectrometers from different manufacturers were used, to

compare which one would perform the best. These might be the main components, but their

components will be discussed in detail to attempt optimization.

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5.1 L IGHT SOURCE , LASER Light sources can be characterized upon many properties including; spectral purity, emitted

wavelength, and the power stability. For the Raman application several characteristics are

important but foremost the light source should provide a single well defined wavelength, so called

monochromatic light. Monochromatic light can be made with the use of a broadband light source

and various filters41, as Raman did in his original experiment23. In the last few decades, lasers, Light

Amplification by Stimulated Emission of Radiation, LASER, have become widely available and are

commonly used as a light source in Raman spectrometers due to their inherent properties, and

single wavelength emission.

A narrow emission bandwidth of the laser is directly relatable to the resolution of the spectrum. In a

previous paragraph it was explained that the wavelength of Raman radiation is relative to the initial

excitation wavelength. A broad excitation band would result in a broad banded spectrum, with low

resolution. Generally the emitted peak is characterized as the spectral linewidth in FWHM. The

selected laser, a Cobolt 04-01 series Samba™, has a spectral linewidth (FWHM) of < 1 MHz52. From a

calculation over the coherence length it follows the linewidth (FWHM) is <1 * 10-6 nm, much

exceeding the separation of the expected peaks.

The spectral purity of the laser is defined as > 60dB. This measure defines the intensity of the

emitted wavelength relative to the non-lasing emission, the noise. Although this comprises an

important property, this unit is not always given in the datasheets. Nd:YAG, a type of Diode

Pumped Solid State Laser, can be frequency doubled to change the emitted wavelength from 1064

nm to 532 nm. Generally speaking, these resonant non-linear coupled lasers are known to have high

spectral purity55–57 and can be utilized for spectroscopic applications.

The selected wavelength has to be determined per application. A shorter laser wavelength would

increase the Raman intensity, and some studies try to utilize a deep-UV laser58,59,60. Such UV-range

lasers often lead to high fluorescence or photo decay of the analytes61. Additionally UV lasers have a

safety drawback since the light is harmful to but cannot be seen by the naked eye. Multiple

applications are based upon visible lasers with a wavelength (𝛌) of 532 nm62,11,40. Longer wavelength

lasers would generate Raman signals with even lower intensities and a longer wavelength63,64, but

less fluorescence is expected. The excitation wavelengths are part of a trade-off for intensity, photo

degradation, and fluorescence.

As was discussed in paragraph 4.1 not only the signal intensity, but also the signal wavelength is

dependent on the excitation wavelength. With the literature values of the Raman shift then an

estimate can be made of the expected spectrum from a wavelength point of view, which is

important when considering the necessary resolution of the spectrometer. When a shorter

wavelength is chosen the resulting signals will become more compressed.

The aimed for application, in process measurement of natural gas, does not suffer from photo

degradation. In the process the sample can be continuously refreshed by new sample, thus when it

would occur it is expected to have a small effect on the spectrum. Fluorescence could provide some

issues, sales gas is cleaned from impurities, whereas raw gas from the source may contain aromatic

compounds or metal-complexes. Based upon the results found in literature made with a 532 nm

laser, and the expected resolution, fluorescence, and safety considerations a 532 nm emission

wavelength is selected for the experiments.

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Additionally the emitted wavelength of the laser should be as stable as possible. Many diode lasers

are temperature dependent for their exact emission wavelength, a minor temperature shift will

result in a change of the spectrum, the peaks will shift equally to the laser wavelength. For stable

operation it is therefore necessary the emission wavelength does not drift, or that the drift is known

to establish a proper calibration interval. The selected laser has a wavelength stability of 2 pm over

± 2 °C and 8 hours52, showing the importance of temperature control.

Another characteristic that determines if a laser can be used for an application is the laser power. As

was described in Equation 1, the Raman intensity is directly proportional to the light intensity of the

excitation beam. Not only should the light intensity be powerful enough to render a significant

signal, also it should be stable in time to prevent the intensity of the signal to vary. In literature

applications with pulsed lasers can be found58,63,65, these applications rely interpretation of a single

spectrum. The aimed for application should be stable over longer periods of time, where preferably

all spectra are of equal intensity. For this reason a continuous wave laser was selected for these

experiments.

The use of lasers in an environment with flammable gasses is strictly regulated66. Limits are set for

focusing beams, and power per irradiated area. Therefore the laser power should be well controlled

and as low as possible, while maintaining a proper spectrum, though not less than one-third of its

maximum power for stability. A well-defined irradiated area also adds to the safety of the setup.

The 150 mW laser was selected and used on max power, 156 mW, for the experiments, though if

possible to be changed for a less powerful source in the future.

For this study the factory calibration of the laser internal power meter was controlled with a

Coherent FieldMaxII-TO power meter67. Figure 13 shows the streaming data during the experiment

setup, hereby it was attempted to mount the optical probe in front of the power sensor with the

highest efficiency. Later by adjusting the laser power from the laser software, see Figure 14 for a

typical display, the internal laser power limiter could be compared to the external power

measurement. This measurement showed lower power on the external measurement, set 156 mW,

measured 151.7 mW with RSD of 0.19%. The difference in intensity is likely the result of the laser to

fiber and fiber to power meter interfaces. The verification of the internal power meter showed the

set power and internal power measurement can be considered true.

Figure 13 Display of Coherent FieldMaxII-TO power meter software during setup of Laser clamp. 1 measurement per second.

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Figure 14 Typical display of Cobolt Samba laser control software.

5.2 EXCITATION FIBER Both the laser and the optical probe were selected to be pigtailed, equipped with permanently

mounted optical fibers. Hereby the chance to erroneously connect the fibers or spend too much

time on optimizing the interfaces is decreased. Although a single fiber connection sounds simple

enough, special attention was given to the type of the connector and the size of the fiber core.

Optical fiber, sometimes called glass fiber, can be made in many different types and sizes. it is

important to note these fibers guide the light through the core based on total internal reflection.

This principle, based upon the refractive indices of the inner core and the cladding around it, traps

the light in the core allowing it to advance over great distances. Optical fibers can be optimized for

the type of light propagating though the core, there are fibers for single mode light, polarized light,

and multimode light. Also there are fibers optimized for a specific light wavelength by doping the

glass with rare earth elements or specialized coating.

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The size of the fiber should be such that the collimating of the laser light can be done without

significant loss of intensity. Also the smaller the fiber, the harder it becomes to properly align the

core for collimating the light into a beam. Practically the lowest core diameter for an excitation fiber

is around 50 µm, smaller cores have a too small numerical aperture for effective use. In this study a

100 µm core multimode fiber was used for the excitation fiber, both on the laser and the optical

probe.

For scientific purposes the main fiber optic connectors are SMA, ST, and FC/PC, which are only a

fraction of the commercially available choices as can be seen in Figure 15. The remainder of the

connectors is mainly used for telecommunications, hence also some duplex connectors. The FC/PC,

Ferrule Connector / Physical Contact, connectors are spring loaded and appropriate to be used in

high vibration surroundings because they will maintain a stable pressure on the contact surface. To

prevent light reflecting back from the optical probe into the laser cavity the connector can be

polished at an 8 degree angle, a so called APC, Angled Physical Contact, connector. That is not

possible with an ST connector since they are not key-aligned. The used connector for the coupling

of the laser to the optical probe is a type of, an APC connector which has an 8 degree Angled

Physical Contact.

Figure 15 Various types of connectors, edited from source68.

5.3 OPTICAL PROBE Optical probes are fundamentally different from sample probes. The purpose of a classic probe is to

sample representatively and maintain the integrity of the sample for transport to the analyzer.

Optical probes are not made to transfer the sample, but the signals from and to the analyzer69.

Sample probes are not part of this research, from this point on all referenced probes are optical

probes. Although optical process probes become more commercially available, robust optical probe

for the use in field applications remain relatively scarce.

Optical probes can be roughly categorized in two groups; Fiber Optical Probes (FOPs) and Lens

Collection Array (LCAs). Note these descriptions and abbreviations are self-coined, by lack of

alternatives. Immersion probe70 is a coined concept, an optical probe that can be submerged in a

liquid or gaseous sample. Though this only means the optical components are protected with a

(mostly sapphire) window71,72, it does not give any information about the optics itself. Furthermore

also fiber optical probes can be submerged in compatible samples73. Therefore: FOPs and LCAs for

definition see below.

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Fiber Optical Probes (FOPs) are highly modifiable74–76, as Figure 16 illustrates. The excitation fiber

can be modified in width, as can also the collection fibers. The number of collection fibers and their

distance to the excitation fiber can be altered. Also the tip can be beveled, which allows for a higher

collection angle and a better overlap of the excitation and collection cones, thus a more effective

collection of scattered photons. A typical challenge associated with FOPs is cross-talking, when the

excitation and collection fibers show interference77,73.

Figure 16 Various Fiber Optical Probe Configurations. Left to right: Single-fiber with dichroic mirror, two fiber flat tipped with separated excitation and collection fiber, six around one flat tipped, single fiber with dichroic mirror and lens, two fiber beveled with separated excitation and collection fiber, and six around one beveled tip. After Cooney et al. (1996)78.

Lens Collection Arrays (LCAs) are another way to collect scattered light for further processing41,42.

The LCA can be mounted on a breadboard and because of its adjustability it is often used in

nowadays research setups65,79. Combinations of optical fibers with LCA-probes can also be found80

and provide an excellent structure for integration of a Raman instrument into a process

environment. Due to special interest the extension tube with the lens is discussed separately. For

the experiments a LCA from a commercial party, InPhotonics RamanProbeTM,53 in combination with

the InPhotonics Reaction RamanProbeTM,54 was used based on their selection of associated

components, see Figure 17.

Figure 17 Component overview of the factory standard optical probe, copied from80.

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5.3.1 Coll imation lenses

When the light arrives with the excitation fiber the first step would be to modify the light into a

parallel beam, to collimate. The benefit of this would be that consecutive treatment of the light

becomes a more standard beam setup with efficient light coupling. The last step in the probe would

be to converge the Raman signal from a parallel beam into the collection fiber. For the excitation

fiber a 100 µm core fiber was used, and for the collection fiber a 600 µm fiber. These increased core

diameters also increase the numerical aperture and influence the collimation properties.

The excitation light has to be collimated, measurement of the beam showed a beam diameter of

approx. 2,5 to 3 mm. This was done by pointing the beam toward a measurement paper without a

lens. The beam diameter is influenced by the NA of the fiber, and the distance to the collimating

lens. For the excitation fiber a small NA is preferred to diminish reflections into the fiber. The ideal

beam diameter is based upon the laser power and the LIDT, Laser Induced Damage Threshold, of

the optical components.

The signal collection fiber is used to transport the Raman radiation to the spectrometer. To

optimize the signal strength the convergence is done into a fiber with a large diameter core and

high NA. Different to the excitation light collimator the convergence lens has multiple wavelengths

from each signal to diffract. Diffraction of light is wavelength dependent and the efficiency of the

collimation may differ per wavelength. For some applications a reflective collimator, or GRIN lens

may be used to prevent wavelength dependent deviations. A large core diameter may help to

accommodate the focal length shift from a chromatic collimating lens. Further increment in the

core diameter would lead to issues on the other side of the fiber where it has to be coupled to the

spectrometer.

5.3.2 Fi l ters and mirrors

A few filters are used in the experimental setup; a bandpass filter, a long pass filter, and a dichroic

mirror. The selection of these filters was done by the supplier of the probe, the below description

and knowledge was found in an attempt to self-build an optical probe, see Figure 18. The

development of the alternative probe did not follow through due to the foreshadowed

miniaturization and production issues. The theoretical evaluations of components provides valuable

insight into the operation of commercially available models.

Figure 18 3D rendering of an alternative optical probe with collimating lens, optical filters, long pass dichroic mirror, parabolic collimating mirror, and collection fiber.

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General considerations apply when working with optical filters that may be evident. Filters are

fragile, for laser applications it is important to know the LIDT to prevent the laser burning the top

layer. Filters have an optimal orientation with respect to the light beam, firstly to diminish

reflections, secondly, some types have a recommended light direction as well. Dielectric filters

change their transmission maximum with temperature due to the expansion of the dielectric layers,

a temperature log may be recommended. Filters are evaluated on their transmission, which cannot

be 100%, too many filters even in the transmitted range will smother the light intensity. With their

remaining properties optical filters make a powerful method to clean up both the excitation source

and the signal to gather a proper spectrum.

A bandpass filter can have multiple functions within an experimental setup, in definition the filter

only has transmittance for a specific wavelength range. The main function of the filter in this setup

is to clean the laser excitation light from interfering signals, fiber Raman signals, hence the

alternative name ‘laser clean-up filter’. The interference signals are considered to come from

impurities in the core of the fiber itself, since the spectral purity from the laser should be sufficient.

The quality of a bandpass filter should be defined with at least these parameters: Central

wavelength of transmission, FWHM of the highest peak, the transmission of this peak, and the

blocked wavelengths. By installing this filter the quality of the excitation light can be ensured.

To clean up the Raman signal from interfering light multiple filter types may be used. a notch filter

could be used to filter the laser light out to prevent detector saturation and reduce background

signals63, one would then be able to look at both the Stokes and the anti-Stokes shift. Alternatively,

if the application is known and only a small band of the spectrum is of interest this band could be

passed on while the rest is filtered out by the use of a bandpass filter81. This principle is sometimes

also used in specific setups where the spectrograph is exchanged by a filter wheel and an intensity

measurement. High and low pass filters are easily available and commonly used to clean up the

signal. These filters can eliminate Stokes or anti-Stokes Raman scattering together with Rayleigh

scattering from the laser82,83. In this experiment a long pass filter was used for cleanup of the signal.

Figure 19 Short and long pass dichroic mirror, the colors of the arrows indicate the relative wavelength of the light beams

Figure 17 shows the use of a short pass dichroic mirror in the optical probe to separate the laser light

from the Raman signal. Dichroic mirrors are characterized by their cut off wavelength and their

type, short or long pass, shown in Figure 19. Depending on the quality of the mirror the cut of

wavelength can be sharper, or the transmission or reflection higher or lower. The use of a short pass

dichroic mirror has the advantage that the laser light can only reach the signal pathway by

accidental reflection.

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5.4 EXTENSION TUBE , IMMERSION PROBE The concepts extension tube and immersion probe are similar and generally only differentiated

based on the sample phase. Figure 20 shows a collection of immersion probes with and without

further optical components. The primary function is to separate the optics, such as filters and

mirrors, from the sample and provide a surface for a leak tight fit. The second function of these

tubes is to extend the reach in narrow places, or submerge the probe entirely. During these

experiments various tubes have been tested whereby certain opportunities have become evident,

an alternative is proposed and evaluated.

Figure 20 Various commercially available (optical) probes and immersion tubes. Left top Immersion probe made by Solvias84, Right top AirHead™ Gas-phase Raman Probe made by Kaiser Optical Systems inc85, Left bottom Bioprocess in-line Raman Analyzer (probe only) made by Resolution spectra Systems86, Right bottom Fiber Optic Raman Probes made by Wasatch Photonics87.

To reach the optimal in situ place for process measurement some sort of extension tube will be

necessary. Figure 21 shows a cross section of a process pipeline, A, a typical flanged probe tie in

point is mounted upon a nozzle. From, C, the flow through the pipeline that flows with a laminar

flow profile, it follows that the probe tip has to be closer to the middle than the side. A rule of

thumb is the sample should be from the middle one-third of the process line. B, the ideal probe

length is thus the sum of the tie in point nozzle length and one third of D, the pipe diameter.

Unfortunately, the length of the extension tubes is limited because the signal worsens with

increasing length, a simple extension to the ideal length is therefore not possible.

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Figure 21 Schematic cross section of a process pipeline showing: A Flanged tie in point, B Ideal probe length, C Laminar flow profile, D pipe diameter.

The probe will have to be tough enough to withstand the stress and strain of the process conditions.

Wake frequency calculations are aimed to simulate both the static and the dynamic stress on the

proposed structure to determine if this is within permissible limits88. These calculations have not

been supplied or made as a part of this study for any of the extension probes. It is recommended to

assess the final structures before process implementation.

To effectively address the application’s need, an alternative extension tube is proposed, as can be

seen in Figure 22. The new design minimizes the length of the extension tube and mounts the

optical probe at the tip. First the alternative was modeled in 3D modelling software, Solidworks

2017, and then theoretically further evaluated with Zemax OpticStudio 16.5.

Figure 22 Impression renderings of designed alternative probe.

For information about this part of the document, please contact Analytical Solutions and Products bv.

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5.5 L IGHT PATH GEOMETRY All probes from Figure 20 use a backscatter geometry and are equipped with a lens at the probe tip.

The function of the lens is to focus as much light as possible on a single point making this the most

probable location for the Raman-effect to occur. The lens is also used to collimate the Raman signal

back into the extension tube in direction of the optics. For an achromatic lens both focus points

would exactly overlap, since they are corrected for wavelength dependent diffraction. Regular

lenses would show chromatic aberration, see Figure 23, where the signal is diffracted differently

than the excitation light. Other studies reported besides backscatter77 also 90 degree11, or a free

collection angle63.

Figure 23 Lens at the tip of the extension tube, light path illustrates chromatic aberration over the length of the extension tube.

Optical simulations in Zemax Optical Studio 16.5 were performed to evaluate the backscatter

geometry. Figure 24 shows the simulated experimental setup, certain aspects are altered for the

experiments to test their influence. Main components are detector 1 (1 mm behind lens) and

detector 2 (30 mm behind lens), together with the light guides they simulate the extension length.

Unless otherwise specified, a light source a 532 nm unidirectional point source with one million rays

was simulated, and scattering rays are set off. The location of the point source is in the focal point of

the lens at 532 nm calculated in the sequential mode. To simulate different scenarios, selected

components were changed or altered to investigate its influence.

Chromatic aberration, together with optical alignment, is one of the main reasons the extension

length of the probes is limited. By reducing the extension length in the alternative design as much

as possible an attempt is done to counteract the effects. Another applied solution is the use of

bigger lenses with different curvatures. Most extension tubes use 4 to 7 mm lenses, for the

alternative design a 12.7 mm diameter lens was selected. From Snell’s law we know chromatic

aberration is most severe where the diffraction angle is highest, so in case mainly the middle of the

lens is used, where the angles of refraction are smallest, the effects are minimized.

A simulation was done to estimate the degree of chromatic aberration. To do so first the focal point

of the lens was calculated in the sequential mode when irradiated with a 532 nm light source, such

as when the laser is focused on a point in the sample. In the Non sequential mode a point source

with a wavelength of 630 nm (methane Raman signal) was simulated on this distance from the lens.

point source from the 532 nm. It was shown that with a lens of 12.7 mm diameter the chromatic

aberration was negligible.

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Figure 24 Simulated experimental setup. Left to right; Detector 2, r=1.75mm light guide, Detector 1, r=1.75mm light guide, condenser lens, r=1.75mm light guide, Light source, reflective circular surface.

The refractive indices of the gasses surrounding the lens89,90 and the lens itself91 are influenced by

the pressure and temperature in the process. The process thus directly influences the diffraction of

light around the probe tip. Although the refractive indices are similar: methane 1.000444, air

1.000292, relative to a vacuum for 589.3 nm at a pressure of 101325 Pa and temperature of 0 °C92.

This would mean relative to air the influence on the focal point of the probe tip lens would be closer

to the lens.

The area between the lens and the focal point, as shown schematically in Figure 25, may be

considered as the Raman active cone. The tradeoff between the area and the collection angle will

influence the results depending on the sample. The focal length may be reduced in case the sample

transparency is insufficient for the light to reach the focal point. Note both the excitation light as

well as the Raman signal is reflected, double excitation and signal recovery may be expected as will

be shown below. For the natural gas application the focal length will be selected to optimally use

the primary Raman active cone, as well as the secondary Raman active cone.

Figure 25 Schematic view of Raman active cones.


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At the time of the experiments there was to my knowledge no optical probe which utilizes the

secondary Raman active cone by adding a circular reflective surface. A possible explanation could

be the number of potential applications is limited because the sample should be clear and gaseous.

Nevertheless in the alternative probe design a circular reflective surface is proposed because the

natural gas application qualifies. Added benefits of the reflector in a process are the protective

encapsulation of the lens and a volume with less turbulent flow.

Following the experimental setup from Figure 24, it is calculated that the signal capture efficiency is

roughly doubled. In a similar fashion it can be calculated the excitation light is reflected and

traverses the focal point twice. Figure 26 and Figure 27 show the recovery of the light with and

without reflector, 24593 and 12296 hits from a million respectively. Since this is a theoretical

approach the mirror efficiency is set to 100%, the excitation and recovery is doubled to a total signal

increase of a factor 4. In practice, the surface has to suffer imperfections, fouling, and general

material properties, the signal would not be expected to increase by the calculated factor.

Figure 26 Detector 2 Signal: 24593 hits, with reflector. Figure 27 Detector 1 Signal: 12296 hits, without reflector.

Simulated experimental setup. Left to right; Detector 2, r=1.75mm light guide, Detector 1, r=1.75mm light guide, condenser lens, r=1.75mm light guide, Light source, reflective circular surface.

The robustness of the reflector positioning was evaluated in two ways. Firstly the Z axis distance

was altered, and secondly the lens was tilted. In both cases the number of detector hits, expressed

as percentage of signal recovery, was calculated to estimate the effect. The precise overlap of the

lens’ focal point and the center of the circular reflective surface is essential to the functionality. The

focal point may shift due to the refractive index of the sample material, as was noted before.

Additionally the mechanical manufacturability of a lens seat might be constrained with tolerances,

shifting the lens forward and back. In the robustness test both scenarios are simulated, where the

reflector is too close, Figure 28, and too far, Figure 29.

The resulting graph is depicted in Figure 30, from which it is clear there is an optimum distance of

the reflector. It should be noted that the recovery on all points is higher compared to the situation

without the reflector. Detector 1, which is situated 1 mm behind the lens shows a higher intensity in

case the lens is closer, these rays are not collimated but rather scattered. In case the reflector is too

far the collected rays are collimated, except they are less. Generally it can be said the focal point

deviation should be as small as possible, relatively the most intensity is lost with the first

imprecision.

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Figure 28 Robustness test of Z-axis shift. illustration of rays when lens 5 mm too close.

Figure 29 Robustness test of Z-axis shift. illustration of rays when lens 5 mm too far.

Figure 30 Recorded signal recovery of detector 1 and 2 during Z-axis robustness test, simulated experimental setup as in figure 24, 28-29.



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The robustness of the reflector is also tested in case the lens is tilted, see Figure 31. From perfect

alignment to a tilt of 4 degrees the signal intensity on the detectors is calculated. As was expected

the recorded signal on the detector 2 is reduced, see Figure 32. The necessity to align the optical

components to the light path should be clear. Figure 33 shows the intensity of the light on the

detector as a 2 dimensional plane. Note the light guide is not simulated, though the software has

this ability to do a complete ray trace from the approximate shape of the components in Figure 22,

indirect light rays are contemplated later in the design process and are not a part of this study. A

distortion of the image can be seen where the lens diffracts the light differently compared to other

simulations.

Figure 31 Robustness test for lens tilt. Illustration of rays with 4° lens deviation.

Figure 32 Recorded signal recovery of detector 1 and 2 during lens tilt robustness test, simulated experimental setup as in figure 24, 31.

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Figure 33 Recorded intensity on detector 2 with 4° lens deviation, simulated experimental setup as in figure 24, 31.

Naturally, after the experiments and during the writing of this script, the true reason was found for

the limited application of the reflection gathering technique, a patent93. Currently Axiom Analytical

Inc. holds the patent for a multipass sampling system for Raman spectroscopy. Ironically in their

documents a planar mirror is described and extra attention is given to the light guides to facilitate a

longer probe length. For a commercial development one could be forced to investigate different

geometries or technologies, among others; a total attenuation ball, a multipass of the collimated

beam, FOPs, or different means to enhance the signal such as SERS.

From evaluation of the simulation and theory it was found that with the correct optical alignment a

back scatter geometry has the most potential for use in a process environment. Main advantages

being the necessary size of the probe tip and minimum process contact. For the practical

experiments an unenhanced backscatter geometry was used.

5.6 LENSES AND WINDOWS Material, shape, and placement, these are the three critical points upon which lenses and windows

are selected. One should consider the process environment, and determine if the lens will come into

contact with the sample. The used materials themselves, and their mounting should have

appropriate chemical resistance. Secondly the shape and dimension is important, for both lenses

and windows. For instance, as was stated earlier the chromatic aberration is less in the center of a

larger lens. This paragraph will evaluate a number of lenses based on simulations performed with

Zemax Optical Studio 16.5 and focus on above three key qualities.

Figure 34 shows a schematic view of the programmed setup used in the simulations. For each lens

the focal length is determined in the sequential mode and the light source placed accordingly. The

distance from the source to the circular reflective surface is kept stable, even though it was found

this is of no influence being the middle of the circle also the focal point of the lens.


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A recurring topic throughout optical system optimization is the f-number. The f-number is a

measure for the effective amount of light a piece of equipment can use. This expression is a

measure of the focal length (𝑓) and the effective aperture (𝐷), itself is denoted with 𝑁 as is shown in

Equation 2. For the computer simulation the lens and detectors were shielded except for a 3.5 mm

diameter pinhole, which is the effective aperture. The calculated f-numbers for the objective lenses

are listed from the darkest to brightest image in Table 6. The f-number of the lenses correlates to

the recorded signal intensity as expected, the lower the f number, the higher signal recovery.

𝑁 =𝑓

𝐷

Equation 2 f-number

The so called wetted parts of the analyzer need to be compatible with the process medium and

conditions. Table 6 also lists the part no. for reference and the material the lenses are made from.

Only Thorlabs lenses were selected mainly because of the available diversity and the ease of access

to the documentation and zemax files. Many coatings are available from various suppliers which

influences the lens’ reflections, therefore coatings have been purposefully not simulated. Natural

gas is not expected to be too corrosive, the lenses are made from 2 types of materials each

expected to be compatible. Some applications use a window made from a different material to

separate the lens from for instance a corrosive or high pressure process. An obvious disadvantage to

the use of a window is that the focal length of the lens has to accommodate the window thickness,

which increases the f-number. The extension tubes used for the measurements all have a wetted

lens.

Item no. f-number Recorded signal recovery Lens type (part no.)

Material

1 𝑓/5.7 0.4% N-BK7 (Grade A)

2 𝑓/4.3 0.7% N-BK7 (Grade A)

3 𝑓/3 1.4% B270 Optical Crown Glass

4 𝑓/2.3 2.5% B270 Optical Crown Glass Table 6 Properties of the lenses used for simulations and the simulation results expressed in Recorded signal recovery


For information about this part of the document, please contact Analytical Solutions and Products

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5.7 COLLECTION FIBER Some general facts about the collection fiber have already been discussed in 5.2 Excitation fiber,

and 5.3.1 Collimation lenses. Besides optimizing the convergence of the signal into the collection

fiber it is also important to optimize the fiber to slit interface. This chapter provides some thought

about why this interface can be considered a bottleneck in the experimental setup and possible

alternatives to increase the response.

The used spectrometer is configured with a FC/PC fiber optic connector on the slit. Practically this

means the collection fiber of the probe is in direct contact with the slit which immediately results in

loss of signal. A part of the light is reflected or dumped onto the sides of the slit mount and will not

reach the detector, see Figure 35 for a graphical representation of the problem.

Figure 35 Schematic for illustration of the effective fiber to slit surface.

To understand the situation better, a few calculations about the contact surface are posed. First the

total effective surface was calculated for all slit and fiber diameters as shown in Figure 36. It can be

seen that the larger the slit or fiber the higher the effective throughput area is. However, to make

both the fiber and slit as large as possible is not a solution.

The second calculation, see Figure 37, shows the ratio of the effective surface compared to the total

fiber surface. From the graph it can be concluded that the larger the slit compared to the fiber the

more light will enter the spectrometer through the slit. Also in this case we do not find a favorable

solution, where a wider collection fiber may fit properly on a smaller spectrometer slit, the slit does

not have much functionality when the fiber core has a smaller diameter.

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Figure 36 Calculated effective surface depending on slit size and fiber diameter.

Figure 37 Ratio of the effective interface depending on slit size and fiber diameter.

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Since the slit width is chosen to accommodate the application, resolution modification is not

preferred. The fiber diameter can be chosen relatively freely, the tradeoff exists between send light

into the fiber, large diameter fiber can collect more light, and collect more light in the spectrometer

where a small diameter fiber has higher performance. To bypass this tradeoff, an optical system

with various lenses as proposed in Figure 38 might be used.

Figure 38 Proposed lens system to increase light efficiency of fiber to slit interface.

With the use of the lens system more adjustment of the separate parts is needed because the lenses

need to be at a specific distance from the fiber and the slit to fit the f-number of the spectrograph,

which makes this a less robust setup. In a laboratory system this could possibly optimize the

fraction of light from the fiber that is collected and sent through the slit onto the collimating mirror.

For a field application a more robust solution with less parts is preferable. The vertical component of

light in the described lens system is divergent, where in the spectrograph with direct coupling it

would be stray light. The used Ultra Low Scattering, ULS, spectrometer is equipped with a lens

behind the slit to converge the vertical component of the signal for this exact purpose. Another

tactic may be the use of a detector shape whereby also the vertical component is captured due to

the height of the pixels.

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Another option to attempt fiber to slit interface optimization is the use of round to linear fibers.

These fibers are bundled together on one side, similar to the most right probe in Figure 16, and

flattened out in a line on the other. Figure 39 shows the linear interface fiber with light shining

through. The contact surface of a round to linear fiber to the spectrometer slit is then more

effectively used.

Figure 39 Left top: round fiber 600 µm, left bottom; collection fiber to slit interface on round to linear optical fiber 7 x 100 µm, right; Fiber to slit interface with light shining though.

Round to linear fibers may be characterized by their core percentage, a 7 x 100 µm fiber has 77,8 %

of fiber in the core, that is how much fiber is packed within the cladding. This implicates also that

this fiber, when evenly illuminated, collects 22.2 % less light compared to a 300 µm in diameter

round optical fiber, and compared to a 600 µm even 80 % less. Fortunately, collimation of light is

not evenly distributed over the fiber and most of the signal should be collected in the middle and

less at the edges, therefore such light collection losses are significantly overestimated. The concern

remains, when using round to linear fibers an improved fiber to slit interface is formed at the cost of

a decreased collection efficiency.

Figure 40 shows spectra supplied on demand by the manufacturer of the spectrometers,

unfortunately detailed experimental parameters were unclear. The purpose of the experiment was

to demonstrate the signal intensity with round fibers compared to to Round To Linear (RTL) fibers.

The spectra clearly shows an almost twofold increase in the peak height compared to the round

fibers.

Figure 41 shows the results found when the experiment was attempted to be replicated. Multiple

experiments were performed, to remove other variables from the equation, except the optical

probe and coupled fiber. No increase of signal was found, and a defect may be evident. An internally

broken fiber or an optical misalignment of the mirrors or collimation lens may be the issue, although

without specialized equipment it is impossible to determine the precise cause.

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Figure 40 Spectrum supplied by spectrometer manufacturer for comparison of round fibers to Round To Linear (RTL) fibers.

Figure 41 Comparison of optical probe and matching laser. Observation time 2 sec, average of 60 sec, laser power 60 mW, sample pressure 1 Bar, sample flow 50 mL/min, sample natural gas low calorific, measured on ULS spectrometer, 50 µm slit, dark and background corrected.

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5.8 SPECTROGRAPHS During this study various spectrographs have been evaluated, most notably the Ultra Low Straylight

(ULS) integrated spectrometer13 and the AvaSpec-HERO sensline (HSC) spectrometer94, both

manufactured by Avantes. A third spectrograph95 with interesting features was also evaluated but

quickly dismissed as a mismatch for this specific application, see Appendix D for a spectrum

comparison. Figure 42 shows the schematic outline of a diffraction spectrometer with an entrance

slit, collimating mirror, grating, focusing mirror, and detector. The purpose of this chapter is not to

build our own spectrograph, but to understand what choices are available and how to configure the

equipment for optimal use.

Figure 42 Schematic view of diffraction spectrometer including lightpaths.

In Raman spectroscopy diffraction grating spectrographs are a quality determining factor. The

correct separation of the various wavelengths directly influences the quality of the spectrum. A

common way to quantify the resolution of the spectrometer is with the resolving power, see

Equation 3. From Table 4 we know the most intense methane peak is found around 2917 cm-1 with a

predicted minimal separation to ethane of 3 cm-1, with a 532 nm excitation light source this relates

to 629.7 nm with a difference of 0.12 nm. A completely resolved spectrum of a methane and ethane

mixture would then need a spectrometer with a resolving power of 5.3 * 103 at this wavelength.

𝑅𝑒𝑠𝑜𝑙𝑣𝑖𝑛𝑔 𝑝𝑜𝑤𝑒𝑟 =𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑤𝑎𝑣𝑒𝑙𝑒𝑛𝑔𝑡ℎ

𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑡𝑜 𝑜𝑡ℎ𝑒𝑟 𝑤𝑎𝑣𝑒𝑙𝑒𝑛𝑔𝑡ℎ

Equation 3 Resolving power of a spectrograph as a function of measured wavelength and spectral resolution.

The tested spectrometer detectors have approximate resolving powers in the range of 4000 – 8400

and 3100 – 4600 for ULS and HSC, respectively, depending on the measured wavelength, for 630

nm approximately 5250 and 4200, respectively. These resolving powers are based solely on the

detector, other broadening (such as the slit width) is not included in the calculation. A completely

resolved spectrum is therefore not possible.

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5.8.1 Entrance s l i t

Similarly to lenses also the slit of a spectrometer has an f-number, characterizing the slit acceptance

pyramid. The miniaturization of spectrometers has an potentially profitable effect on their signal by

shortening the effective focal length, see Figure 43. Previous paragraphs have expanded upon the

optimization of the fiber to slit interface, this paragraph elaborates on the selected slit chosen for

the experiments and application. Figure 44 shows a photograph of a 100 um slit that was used for

the experiments.

Figure 43 Illustration of slit acceptance pyramid and effective focal length of a spectrometer. Adapted from 96.

Figure 44 Photograph of a 100 µm exchangeable slit used in Avantes spectrometers.

Figure 45 and Figure 46 show a comparison of various slit widths using various optical probes. The

first diagram shows an increase in signal with each increase of the slit width. Interestingly the height

of the signal does not always increase relatively to the slit width. The peak height with a slit width of

50 um is 2 x higher than with a slit width of 25 um. Contrary the peak height with a slit width of 200

um is not 2 x higher than with a slit width of 100 um, but the total light intensity on the detector has

doubled. The first comparison shows the light is not optimally coupled into the spectrometer with a

25 um slit, whereas the latter comparison shows the intensity does not increase, but the resolution

drops when a 200 um slit is used. The second diagram shows the 50 um to 100 um slit comparison

with a different laser, optical probe, and round to linear fiber interface with the slit. It can be clearly

seen the signal does not increase with the expected factor of 2. From this data it was deduced the

50 um slit was the optimal available slit width to couple the signal into the spectrometer because it

gives the highest signal without unnecessarily diminishing the resolution.

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Figure 45 Overlay of various natural gas spectra with various slit widths. Observation time 1 sec, average of 5 sec, laser power 156 mW, sample pressure 36 Bar, No sample flow, measured on ULS spectrometer, dark and background corrected.

Figure 46 Natural gas spectra with various slit widths. Observation time 2 sec, average of 60 sec, laser power 60 mW, sample pressure 1 Bar, sample flow 50 mL/min, sample natural gas low calorific, measured on ULS spectrometer with probe with round to linear collection fiber, dark and background corrected.

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5.8.2 Observat ion t imes

Another important parameter is the observation time because this influences both the signal

intensity and noise. Generally speaking the total analysis time is the sum of the observation time

and the spectrum calculation time. To make the technology competitive the analysis result should

be given as fast as possible, therefore it makes sense to try and reduce the observation and

calculation time as much as possible while maintaining spectrum quality.

The application for natural gas requires continuous calculations to gather the composition and key

parameters from the spectra. From this it follows the observation time should be longer than the

time needed for the calculation, otherwise not all spectra can be used because of calculation lag.

The calculations made for this study never took longer per spectrum than the used observation

time, with optimized algorithms this should not be a concern.

Each recorded spectrum is accompanied by readout noise, most notably the repeatability of the

analog to digital converter on the detector electronics. A longer observation time with fewer

readouts would dampen this noise. Additionally a longer observation time would proportionally

increase the signal intensity, although the shot noise would increase only with the square root of

the number of observations. In short a longer observation time would result in a more intense, less

noisy spectrum.

Throughout the experiments various observation times have been used, as well as addition or

averaging of seemingly arbitrary numbers of detector readouts. The tradeoff is that with a one

second observation time a spectrum can be obtained, though with more spectra summed it

significantly increases in quality.

5.8.3 Mirrors and grating

Figure 42 shows, beside the components, also the path of light through the system. The concave

mirrors are used to first transform the incoming light to a parallel bundle aimed at the grating. The

grating is often of the flat or reflective94 type, even though also concave, echelle95, and holographic

types are available. The function of the grating is to disperse the light based on the composing

wavelengths. Lastly a focusing mirror reflects the light into a focal plane on the detector. These

optics are the domain of specialized optic engineers who calculate the various effects of the

components in the system similarly as was attempted in paragraph 5.5 Light path geometry.

The mirrors and grating are generally fixed into the housing of the spectrometer and cannot be

exchanged or tuned. The used Avantes spectrometers are built by loosely fitting all component in

place, then while illuminating the entrance slit and reading out the detector the components are

glued into the optimal position and mechanically fastened to prevent movement while the glue

sets. A further study into these components was not made, more information about these parts can

be found in Holler and Skoog’s Principles of Instrumental analysis24.

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5.8.4 Detector

With the development of multifaceted detectors it became possible to replace the exit slit form the

monochromator by a detector directly on the focal plane. With this development it become possible

to observe the complete spectrum without scanning through all the wavelengths, which is non-

mechanical and faster. This paragraph is by no means exhaustive about the possible detectors and

their operation, but does attempt to provide an insight into the selected detector for the

experiment.

Charge-coupled device11,30,72 (CCD) detectors are often used for Raman spectroscopy, and are

considered the best choice in the wavelength range 535 to 700 nm. Both the ULS and the HSC

spectrometer have this technology97,98. The third compared spectrometer was equipped with a

scientific CMOS chip95, which might account for the lower S/N. Most kind of detectors have a lower

quantum collection efficiency for wavelengths longer than 1000 nm 24. Figure 47 shows a diagram

from a detector manufacturer with the quantum efficiency for different detector types per

wavelength. Note the difference in quantum efficiency between the FI and BV detectors around 600

to 700 nm, the selection of the correct detector would double the signal intensity. The type of

detector and its spectral response needs to be matched with the wavelength of the expected signal.

Figure 47 Different detector types with their Quantum Efficiency per Wavelength, copied from99. Note the difference in quantum efficiency between the FI and BV detectors around 600 to 700 nm.

For applications in which the signal intensity is limited the dark noise is an important factor that

determines the S/N. Manufacturers will specify the dark current of the image sensors, which is a

part of the dark noise, as number of electrons per pixel per second, e-/pixel/s, at a specific

temperature. Many detectors are temperature controlled or cooled39, the ULS and HSC are kept at 5

and 0 degrees Celsius to suppress the dark current. Deviations from this temperature may result in

so called thermal noise.

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The second factor that is directly measured with the dark noise is the readout noise from the

intensity counting electronics. CCD detectors use the same readout electronics for all pixels,

whereas CMOS chips have circuits per pixel which need to be precisely calibrated100 for the readout

noise to be minimized. The dark current and readout noise were measured at the same time, thus

the total dark noise of both the ULS and HSC spectrometers were compared.

Figure 48 shows a comparison of the dark spectra collected from the ULS and HSC spectrometer

from which it is evident the dark noise on the HSC detector is much lower. Firstly the detector is

kept at a lower temperature which should inhibit the dark current. Secondly, the HSC supposedly

has an updated electronics board to improve the readout noise. These two changes would be

possible on the ULS too and should result in a more stable dark noise. Also the detector type itself is

different, see Appendix D for the full detector specifications.

Both detectors are of the so called ‘back thinned’ type, but differ in their pixel size and number. Also

the HSC spectrometer is equipped with a specific detector with improved etaloning characteristics.

Etaloning is a photon reflection and interference effect in the detector which causes specific pixels

to have different sensitivity. The larger pixels, the reduced number, and the improved etaloning

characteristics improve the dark noise background of the detector.

Figure 48 Dark noise reading of two independent spectrometers with observation time 1 second averaged over 150 measurements.

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During the comparison of both spectrometers it was found both CCD’s did not have the same

measured wavelength range. Figure 49 shows spectra of cyclohexane from both spectrometers for

which the same probe and laser was used. On the ULS spectrometer one can clearly see a peak at 0

cm-1, the laser line. The used optical probe uses both a dichroic mirror and long pass filter to prevent

as much as possible laser light to travel the signal light path, the measurement demonstrates the

excitation light is still present up to the detector. Because for natural gas the laser is of much higher

intensity compared to the signal, it is considered best practice to ‘dump’ the remainder laser light

next to the detector, as depicted in Figure 50. When the adjusted range is so that the laser light falls

next to the detector it cannot influence the detector or signal readout.

Figure 49 Spectrum of cyclohexane measured with both ULS and HSC spectrometer. observation time 1 s, average of n measurements, slit 50 µm, laser power 156mW. note the calibrations on the spectrometers were not checked.

Although the signal for the ULS spectrometer under similar conditions was higher than for the HSC

spectrometer, as can be seen in Figure 51, though the HSC would be the preferred choice. Apart

from the signal intensity also the noise should be considered by calculating the Signal to Noise

ratio. Evaluation of various spectra from both spectrometers it was found the HSC had an S/N of 83

on the peak around 1162 cm-1 whereas the ULS only had a S/N of 16 for the same peak. The

calibration for the HSC appears to be offset by approx. 8 cm-1, this was not considered a problem at

the time since these measurements were made with a temporary demonstration spectrometer.

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Figure 50 Best practice CCD allignment used to function as high (1) and low (2) wavelenght pass filters.

Figure 51 Overlay of 1s cyclohexane spectra made with ULS (higher peak) and HSC (less noise) spectrometer, slit 50 µm, laser power 156mW. note the calibrations on the spectrometers were not checked.

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5.9 MEASUREMENT CELL To test the above equipment two distinct methods were used to recreate the sample process

conditions. Essentially both test setups are volumes which were filled with the appropriate sample

from a pre-defined calibration gas bottles. More detailed information on the used sample gasses

can be found in Appendix B. Before making any measurements the volumes were always flushed

and homogenized with the sample or background gas. Pressure and temperature transmitters were

installed to monitor these parameters. Both of the measurement cells had the purpose to reproduce

sample conditions as can be expected in the field.

First a high pressure sight glass was used, as can be seen in Figure 52. On the left flange the sample

in connection is clearly visible. On the right flange a similar connection made it possible to connect

an extension tube through a high pressure gland and make a pressure tight seal. It became evident

that the length of the extension tube had aversive effects to the signal intensity though this was

necessary to use a flange and gland capable of pressures up to 40 bar. The availability of a sight

glass made it possible to make a beautiful picture for the cover of this thesis.

Figure 52 DN40 PN40 sight glass with flanges.

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Secondly, a low pressure setup was made from generic stainless steel tubing as can be seen in

Figure 53. A Teflon ferrule was used to create a leak tight seal and be able to slightly pressurize the

setup. The use of stainless steel fittings would have made this setup also capable to maintain 40

bar, although it was decided against this because stainless steel ferrules irrevocably connect to the

extension tube. The use of generic compression fittings made it possible to use a much shorter

extension tube and collect spectra also at a lower pressure.

Figure 53 Low pressure measurement setup, on the left the pressure and temperature measurements are fitted between the in- and outlet valves, on the right side the extension tube and optical probe are installed.

The use of these measurement cells made it possible to reproduce the process composition,

although not the process conditions. The real gas may be turbulent, whereas it is relatively stagnant

in the measurement cell. Also the temperature of the measurement gas is uncontrolled. In

paragraph 5.4 the wake frequency of the probe was discussed, the measurement cell is unsuitable

for such studies. To measure natural gas at specific pressure and compositions these measurement

setups are adequate.

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6 S IGNAL TO SPECTRUM

When the instrumentation is optimized physically, it is time to overthink the further process. How

to convert a raw signal into a spectrum? What kind of calibration can be used? or how to adjust the

spectrum for the measured background? Some of these questions were inexplicitly answered

before, to properly compare the instrumentation. For instance, all shown data did not include any

cosmic rays, and some was also corrected for the background and dark current. This chapter is

focused on the operation of Raman technology, specifically how raw detector counts are processed

into a spectrum.

6.1 S IGNAL CALIBRATION

6.1 .1 Wavelength

The adjustment of the spectrometer pixel numbers to the actual measured wavelength is what is

here referred to as the wavelength calibration. Instead of an external standard sample an external

neon light is connected and the signal recorded, see Figure 54. This spectrum is what correlates the

pixel numbers to the measured wavelength.

Figure 54 Mean of 39 spectra recorded of Neon emission light, measured directly on HSC spectrometer, 1000 ms observation time, slit width 50 µm.

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The emission peaks of Neon are well defined, which means they are narrow (small FWHM) and

symmetrical peaks. Also the exact wavelengths of these emission lines are well known from

literature101,102. When Ultra Violet Products, the manufacturer of pen-ray® calibration sources, was

asked for an update of their 1997 ‘rare gas lamp spectra’ we were informed this was the most recent

update. The reproducibility of the neon emission spectrum reassures us that comparable calibration

can be obtained. All of the above make that a neon light is a well-established method for

wavelength calibration.

Table 7 shows selected peaks from the recorded Neon emission spectra with the assigned Neon

emission line. For comprehensibility also the FWHM is listed to illustrate the resolution of the

spectrometer which on these calibration lines has an average theoretical resolving power of 826.

From the pixel and assigned emission lines a calibration line is fitted, see Figure 55, correlating the

pixel numbers to absolute wavelengths. Whereas a linear line might be expected, a polynomial fit

rendered a better R-squared value, which might indicate the spectrometer components are not as

perfect as their digital twins.

Pixel no. Intensity (Counts) Assigned emission line (nm) FWHM (pixel) FWHM (nm)

307 2.37 * 104 585.25 5 0.9

363 2.26 * 104 594.48 4 0.7

456 2.81 * 104 609.62 5 0.8

485 3.48 * 104 614.31 4 0.6

562 3.08 * 104 626.65 5 0.8

648 3.82 * 104 640.23 6 0.9

716 3.47 * 104 650.65 5 0.7

828 3.79 * 104 667.83 5 0.7

997 3.63 * 104 692.95 6 0.9 Table 7 Peak table of selected peaks for calibration of HSC spectrometer. Peaks are defined with their top pixel, intensity, assigned emission line and measured FWHM, 1000 ms observation time, slit width 50 µm.

Practically the calibration of the spectrometer with an external light source is slightly inconvenient.

Neon light can be rather intense and for our used settings multiple light attenuators in series had to

be used to prevent detector saturation. Also the collection fiber from the probe needs to be

disconnected to connect the fiber from the Neon light to the spectrometer entrance. When it

becomes clear whether or not the unit is to be field-calibrated, the coupling of these fibers should

be optimized. It may be possible to configure the fiber layout in a way the laser and Neon light are

parallel light sources.

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Figure 55 A 2nd degree polynomial calibration line fit for HSC spectrometer, R-squared >0.9999.

6.1 .2 Raman shi ft

The calibration of the spectrometer for the measurement of a Raman spectrum was done with use

of Equation 4. Hereby it should be noted the precision of the calibration depends directly on the

specified precision of the laser, for this laser52 +/- 0.3 nm. For this study the specified laser

wavelength, 532.1 was used in the calibration of the HSC spectra, whereas a measured laser

wavelength could possibly provide a more precise Raman shift calibration.

Equation 4 Calculation of Raman shift.

To prevent the Raman shift calibration drift, the setup for Raman measurements would include the

use of the laser, and a direct correlation of wavenumber to pixel with the use of a reference sample

with known Raman spectrum. It may be possible to incorporate crystalline silicon, which has a sharp

peak at 520 cm-1, into the measurement system. Together with an available methane peak this

would provide a rudimentary calibration range. Such a method would be rather complex, and

depend on the performance of the setup as a whole to provide ample resolution. The advantage

would be such calibration would inherently account for the laser, probe, and spectrometer.

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6.1 .3 Intens ity

Intensity calibration of the detector may be necessary depending on the application and

experimental setup. As was discussed in paragraph 5.8.4, the two main detector types are CCD and

CMOS. Contrary to a CMOS sensor a CCD reads the intensity by the use of a shift register method

so the same electronics is used for each pixel. All pixels on a CCD therefore have approximately

equal readout noise, which are often additionally averaged by using binned pixel columns. In CMOS

detectors each pixel has separate electronics which can influence individual intensity and noise,

such detectors would also require pixel by pixel intensity calibration100. The used setup is equipped

with a CCD style, binned column, detector and is not specifically intensity calibrated.

Another reason to calibrate a detector would be the sensitivity of the pixels related to the measured

wavelength. Figure 47 showed different quantum efficiencies depending on different wavelengths

for multiple detectors. With the use of white light, a source with equal amount of light of each

wavelength, one may then account for the relative intensity differences. White light measurements

of the CCD detector with the sun as light source did show a limited trend. No certified white light

source was available, and it would only be possible to calibrate the sensitivity to the sun. A possible

unwanted effect of such gain adjustment of the pixels is that the noise also is amplified. Therefore

due to the limited influence, lack of proper calibration standard, and the subsequent experiments

with noise filtering, it was decided not to specifically calibrate the sensitivity to account for the

measured wavelength.

6.2 S IGNAL PREPARATION

6.2.1 Number of data points

Before any signal preparation is started the total number of data points should be considered. In

paragraph 5.8.2 it was described how mostly an observation time of one seconds was used. The

spectrum quality may be increased when the observation time is increased, also when this is done

mathematically. Figure 56 shows how the last few readouts as vectors may be selected to form a

data matrix together. With such a setup the data can be partially updated every observation time,

although data is included from previous measurements to provide sufficient spectrum quality.

The combination of multiple spectra into a single dataset is generally referred to as to flatten the

data matrix. The main reason for this operation would be that the following calculation would be

more intensive with more data points, increasing the calculation time, although no increase in

accuracy is expected. To flatten the dataset essentially means to reduce the number of spectra to

form a single spectrum. Where 5 spectra form a matrix [number of pixels, number of spectra] after

flattening it forms a vector with the length of the number of pixels. The operation should not be

confused with vectorization of the matrix, whereby all columns are transposed upon one another. It

is essential that his reduction of the number of data points is done carefully to prevent loss of data.

To decrease response time of the system it may be wise to multiply each spectrum with a weight

factor prior to flattening the dataset. The response time of industrial sensors is often quantified as

the t95, 95% of the step change in seconds. More readouts may then be used for signal stabilization

because 95% of the weight factor will determine the t95 time.

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Figure 56 Schematic representation of how multiple readouts might be used for signal processing.

To sum or average the signal per pixel are the most common operations that are performed to

arrive at a single spectrum. With the use of a computer and sufficient significant numbers this would

not make any difference in the accuracy of the spectrum. Examples are given in Figure 57 and Figure

58 for the sum and mean of the spectra. The number of observations relate to the readout noise

although practically this is hardly perceivable. The flattened spectra of 250 ms observation time has

20 measurements, where the 1000 ms spectra is built from 5 measurements. Where an increased

observation time would decrease the relative shot noise, by flattening multiple measurements the

overall noise is reduced. There is no notable advantage of either method, most depends on the

further analysis. For this study averaging the spectra was applied so that the intensity remains

comparable for similar observation times.

No normalization was done on any of the spectra mainly to keep the counts comparable during the

development process. Also the question would arise which signal to normalize the spectrum to? The

neon calibration source might prove to be a stable intensity, but this would subject the signal to an

equally external calibration as the ADC in the spectrometer. The spectrum does not contain any

suitable stable peaks because all peaks are from measurable components which vary in

concentration. A solution for normalization may lie with a non-interfering internal standard as

discussed in paragraph 6.1.2.

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Figure 57 Comparison of various observation times of the same setup, Observation time sum of 5 sec, laser power 156 mW, sample pressure 1 Bar, sample natural gas low calorific, measured on ULS spectrometer, dark corrected.

Figure 58 Comparison of various observation times of the same setup, Observation time mean of 5 sec, laser power 156 mW, sample pressure 1 Bar, sample natural gas low calorific, measured on ULS spectrometer, dark corrected.

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6.2.2 Cosmic ray detection

Cosmic rays are charged particles, protons, and atomic nuclei from outside the solar system that

radiate the earth. Photosensitive equipment is able to detect these charged particles due to their

operating mechanism. Unfortunately these cosmic rays therefore disturb the recorded spectra. The

sudden and intense signal can be properly detected by comparison of the spectra, those without

cosmic rays can be used to detect those with a spiked pixel. In Figure 59 one can see what a cosmic

ray looks like; a single pixel with a signal much higher than the surrounding pixels; and much higher

than previous spectra. The spectra are not dark or background corrected, therefore the background

fluorescence from the probe window impurities and the Raman signals from the fiber can be clearly

seen. By comparing the measurement to a set limit one can flag and count the cosmic rays.

Figure 59 Various spectra of helium with cosmic rays, not background corrected. 1000 ms observation time, slit width 50 µm. ULS spectrometer with 19cm extension tube was used.

Next comes the question of what to do with a spectrum once the cosmic ray has been detected, ISO

16269-4 might prove insightful about outlier detection and treatment. To keep the data would be

preferred in some applications, such as a research about cosmic rays. To omit the data the following

arguments are posed: 1. The outlier can be clearly detected with high statistical power, the chance

of a false positive is low, it would be relatively certain only the cosmic rays are omitted. 2. The

cosmic rays are completely independent from both the sample and the instrumentation, therefore

no influence is expected on the further data set. For the application of natural gas, cosmic rays are

of little or no value and can be considered misinformation.

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One may choose to omit the pixel from the spectrum, and continue with the remaining data points.

Although this would be most puritan, where only the bad measurement is omitted, the loss of data

would create problems with following calculations. For example, the dark light correction is a matrix

operation, in this case the matrices would not be of equal size. For all matrix operations the

algorithms would have to be adjusted to accommodate empty matrix positions or various vector

lengths. This task would not be impossible or too complex, although it is not straightforward and

therefore prone to error.

It might be tempting to remove the cosmic ray and insert an ‘educated guess’-value to continue

calculation. The educated guess might be the average of the surrounding, or of previously gathered,

pixel readings. This operation would definitely make the spectrum appear better because the spike

is gone, though the dataset has become untrue. In further signal processing steps, for instance

moving average and Savitzky-Golay filters, this data point will be used to adjust the surrounding

data points. The untrue data will spread to the surrounding pixels. In addition, when univariate

modeling is used the ‘made up’ data will also be part of the results. Adding data, even if it is made

from real observed values, is a mathematical trick which will impact the results.

One may otherwise choose to omit the complete spectra from the dataset, and collect another.

With the use of short observation times the chance of observing a cosmic ray is reduced.

Undoubtedly the mathematical operation simplifies in case only same sized matrices are inserted.

The deletion of complete spectra can only be done because of the random nature of cosmic

radiation. Errors resulting from omitting the spectra, if any, will even out over time. In the

performed experiments it was decided to omit the spectra with a measurable cosmic ray signal, the

resulted time loss of one observation time is deemed acceptable.

6.2.3 Dark current correct ion

Dark light is an indigenous signal to the detector, see paragraph 5.8.4, that has a clear influence on

the spectrum. Figure 60 expands upon Figure 48 with spectra from dark measurements taken with

different spectrometers and different observation times. Before this signal can be used we first take

a closer look at the dark measurement itself, then the impact upon the sample measurement is

illustrated.

The different dark measurements in Figure 60 are all remarkably different. Firstly it was noted the

Ultra Low Straylight (ULS) integrated spectrometer13 measures negative intensity with an

observation time of one second. This is illogical and indicates possibly random response in the

detector electronics where negative numbers are generated. Secondly it was noted the difference

between the intensity from consecutive pixels is higher for the ULS compared to the HERO sensline

(HSC) spectrometer94. That is the dark current, or readout noise, for the HSC detector is more

repeatable per pixel for the HSC. Lastly it was demonstrated by comparison of the measurements

with a 5 second observation time that the dark noise is less for the HSC spectrometer. Based upon

this data it can be said the HSC spectrometer has a superior signal quality in no light conditions.

Dark reading correction should be performed on all measurements103 by subtracting the dark

reading from the actual measurements. Hereby the signal should be returned to the baseline for

pixels without a signal and the peaks will have a height starting from zero counts. Most elementary

the dark noise of the detector is then eliminated from the further calculations.

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Figure 60 Dark readings of ULS and HSC spectrometers with 1 and 5 second observation times.

Figure 61 Spectra of Helium with and without dark correction. Measured on ULS, short extension tube, 1 second observation time, 50 µm slit width.

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In Figure 61 the effect of the dark correction can be clearly seen, from an unsteady line the spectrum

then shows clear peaks and baseline with much better S/N. As discussed in the previous paragraph

the dark measurement of the ULS spectrometer is illogical, since negative measurements are

found. A similar problem arises when the dark measurement apparently measures a higher light

intensity than the sample, resulting in negative intensity on the sample spectrum. It may be that

this is an insignificant part of the spectrum and the negative values will be discarded otherwise a

complete spectrum transposition may be necessary.

6.2.4 Background correction

In the perfect world, experimental setups do not have a background signal. In the performed

experiments background peaks were found from multiple sources. In Figure 59 the background is

dominated by the fluorescence from the impurities in the probe window. A few similar peaks, 443

cm-1, 615 cm-1, 806 cm-1, and 1439 cm-1, were found with a different spectrometer and probe

window, see Figure 61. These background signals might be from interference from the probe optics,

or from the fiber material. The signal around 200 cm-1 and lower is considered a laser artifact. To

completely eliminate any background signal, high quality materials and many experiments to

determine the precise cause for each peak would be needed.

Figure 62 Spectra background correction treatment of Low Calorific Natural Gas, HSC spectrometer mean of 5 measurements with 1 second observation time, 50 µm slit, laser power 156 mW, sample pressure 1 Bar.

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The background measurement is made with helium, a Raman inactive gas, and is thus free from

sample related Raman signals. Any Rayleigh scattering and probe-laser-fiber interactions would still

be measured. For practical purposes the background can be subtracted from the measured spectra,

much like the dark noise. A more advanced method may subtract the measured background and

optimize the baseline to zero with a robust fit. In this experiment a combination was used of a

background subtraction, a robust parabolic fit, and a translation to make all readings positive. A

visual representation of these steps is shown in Figure 62 where the final spectrum is formed.

Figure 63 Flowchart for the dark and background correction of sample spectra.

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The background correction sequence described in Figure 63 shows which steps were performed on

the cleared of cosmic rays and flattened spectra in order to obtain a background corrected

spectrum. The subtraction of the dark spectrum was already covered in the previous paragraph, but

is included for comprehensibility. Below the steps are further commented.

Before the background spectrum is subtracted it was subjected to a scaling. This operation will also

correct for laser instability although this was not expected to be necessary based on the laser

intensity stability experiments. The background stability was later investigated more closely and

shown to vary, see Figure 64. The influence of the background is not equal over all channels, the

scaled background does not have a complete overlay of the baseline, see Figure 62, where the

background is equal in the low and high shift, in the middle range the scaled background is not a

perfect fit. In case the instability comes from the laser intensity a comparable increase for all

channels would be expected.

During the experiment special care was taken to exclude external influences; for instance: the

instrument air tubing was flushed for sample homogeneity, the lights were mostly dimmed and not

altered during the experiment, the door was locked to prevent unauthorized disturbances, and the

spectrometer had already been switched on overnight. The background influence over time could

still be an effect of flow or sample quality, since the experiment is performed with instrument air the

compressor could run or not, or from other interference in the light path such as the optical

alignment or temporary fouling from soft parts. Still scaling the background may be considered the

appropriate action, another form of scaling would be normalization of all spectra based on an

internal standard.

Figure 64 Intensity of selected pixels followed in time during measurement of instrument air. ULS spectrometer 5 second observation time, 50 µm slit, laser power 156 mW, sample pressure 1 Bar.

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It was decided to do a robust fit with a polynomic function over the baseline and subtract the fitted

curve from the spectrum to straighten the baseline back to 0 counts. The spectrum up to 621 cm-1

was excluded from the fit so that a better fit was obtained. The sample will always have a peak at

2900 cm-1 for C-H stretch vibrations, it was possible to exclude this range from the baseline fit also,

although this had little influence because of the robust nature of the fit. After a final translation

where all counts are made ≥0 the final spectrum is found with a stable baseline.

6.2.5 Noise f i l ters

Moving average and Savitzky-Golay filters can be used to reduce noise and increase the quality of a

single spectrum. The quality of the spectrum would be mainly superficial, because when such a filter

is applied to a set of spectra information will be lost. This has to do with the inherent functionality of

the algorithms. Both filters have a so-called window on which they perform their function, the

window then moves one data point forward and the function is applied again. This process is

repeated until the whole length of the signal vector has been filtered from noise. The discussed

filters correlate the data point to its neighboring data points.

The main parameter to be set in the moving average filter, also known as boxcar averaging, is the so

called boxcar width104 and can be compared to the window parameter in a Savitzky-Golay filter, see

Equation 5. The window size has to be uneven, so that the data point upon which the filter is applied

is in the middle.

𝑊𝑖𝑛𝑑𝑜𝑤 𝑠𝑖𝑧𝑒 = (2 ∗ 𝐵𝑜𝑥𝑐𝑎𝑟 𝑤𝑖𝑑𝑡ℎ) + 1

Equation 5 relation between window size and boxcar width

The inherent noise from the HSC spectrometer is limited, though a possible improvement may be

possible. Figure 65 and Figure 66 show the spectra from paragraph 6.2.4 subjected to a moving

average and Savitzky-Golay filter, respectively. Both experiments show the window size of 11 to be

too wide, for the moving average a clear stumping of the C-H stretch peak can be observed, and for

the Savitzky-Golay filter artefacts are formed around the base of the peaks. The ideal window size

for both filters when applied on a single spectrum shall be between 3 and 7.

Although filters are widely used in spectroscopy to reveal signal trends that may not be clear from a

noisy spectrum. This stability and optical cleanliness of the spectrum has to be paid with the

method sensitivity when further numeric evaluation is performed. When taking into consideration

the product of the developing analyzer is not a spectrum, but a composition or quality parameters,

the filters should not be used.

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Figure 65 Filtered spectra of low calorific natural gas (1barg) made with spectrometer 'HSC' 1 sec observation time mean of 5 measurements, dark and background corrected.

Figure 66 Filtered spectra of low calorific natural gas (1barg) made with spectrometer 'HSC' 1 sec observation time mean of 5 measurements, dark and background corrected.

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6.3 RESULTS AFTER SIGNAL PREPARATION Both available gas standards, high and low calorific natural gas were measured and subjected to the

pre-processing. The figures and table in this paragraph show the measured spectra and the

components with assigned peaks. All low pressure measurements were taken with the use of the

HSC spectrometer, with an optical probe connected with a 600 µm round collection fiber and 6 cm

extension tube, comparably the best available setup.

It was specifically chosen to display the spectra of a mean of 150 measurements with a one second

observation time, because the ethane peak at 1000 cm-1 would not be visible with fewer

measurements, see Figure 67 until Figure 69. Many components could not be assigned to a clear

peak, though both running up to 2963 cm-1 and following after 3026 cm-1 a series of peaks is

measured. Peaks at 1072 cm-1, and 1470 cm-1 were found, though could also not be properly

assigned. At higher pressure with the ULS setup also the peaks for hydrogen at 570 cm-1, propane at

862 cm-1, butane at 792 cm-1, and carbon dioxide at 1286 and 1396 cm-1 could be assigned, see

Figure 70.

The results in Table 8 exemplify the difficulty of the measurement, the sensitivity is inadequate to

measure all components separately in low concentration ranges. The data consistency is low, as was

discussed before it seems the background fluctuates, this can be clearly seen in the comparison

where the spectra have different heights. Possibly the unclarified background fluctuations also

cause the broad peak at 1470 cm-1. That this peak is high for the high calorific gas also may not be a

coincidence, the heavier hydrocarbons and carbon dioxide all have one or more predicted peaks in

this range. The broad peak could also be a convolution of many smaller peaks.

Figure 67 Spectra with full background correction treatment of high and low calorific Natural Gas, HSC spectrometer mean of 1505 measurements with 1 second observation time, 50 µm slit, laser power 156 mW, sample pressure 1 Bar.

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A peak is found in both gasses at 2592 cm-1, although none was predicted in the theoretical

calculations, Petrov and Matrosov assign this peak to an overtone of the asymmetric bend of

methane105. The strong signal on 3020 cm-1 is assigned to a antisymmetric stretch of the methane.

Either one of these peaks could be selected as an internal standard for Raman shift calibration.

The difference between the HSC and the ULS spectrometer seems to be relatively constant around

7-8 cm-1. Although it may be easy to start doubting the calibration, it is unlikely new neon emission

lines have been discovered. Hansen et al. explain a part of the difference106, where the HSC

measurements are done at 1 barg, the ULS measurements are done at 35 barg. The surrounding gas

and the pressure can influence the peaks of the molecules.

Though differences can be measured at a low pressure on the main methane vibrations, the

presumed ethane signal, and on nitrogen, 2923 cm-1, 2963 cm-1, and 2338 cm-1, respectively. These

data points may provide an estimate of the key properties of the sample, even though no

composition can be made from such data.

Figure 68 Spectra with full background correction treatment of high and low calorific Natural Gas, HSC spectrometer mean of 1505 measurements with 1 second observation time, 50 µm slit, laser power 156 mW, sample pressure 1 Bar, zoomed in.

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Figure 69 Spectra with full background correction treatment of high and low calorific Natural Gas, HSC spectrometer mean of 1505 measurements with 1 second observation time, 50 µm slit, laser power 156 mW, sample pressure 1 Bar, zoomed in.

# Component Concentration in (Mol%) Assigned peak (cm-1)

Low Calorific High calorific HSC @ 1 bar ULS @ 35 bar

1 Methane 98.0513 83.672 1545, 2592, 2923, 3020

1538, 2592, 2918, 3020

2 Ethane 1.507 7.007 1002, 2963 991, 2956

3 Propane 0.1 1.501 862

4 n-Butane 0.01 0.302

5 2-Methylpropane 0.01 0.305 792

6 n-Pentane 0.0013 0.041

7 2-Methylbutane 0.0011 0.04

8 2,2-Dimethylpropane 0.0011 0.041

9 n-Hexane 0.0012 0.04

41 Hydrogen 0.001 0.021 570

52 Nitrogen 0.211 5.529 2338 2330

54 Carbon dioxide 0.105 1.501 1286, 1396 Table 8 Composition of available gas standards with ISO6976 component number and their assigned peaks

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Figure 70 Spectrum of High calorific natural gas, ULS spectrometer, 50 µm slit, laser power 156 mW, sample pressure approx. 35 bar, 19 cm extension tube, 1 second observation time, mean of 120 measurements, background corrected, SG filtered 2nd power polynomial window size 13.

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6.4 S IGNAL ANALYSIS BY MODELLING Data modeling, also known as chemometrics or advanced algorithms, has become more popular

over the years wherever increased computer power and data handling capacities became available.

Software packages for the construction of chemometric models or spectrum interpretation are

available on the market, among others; Avasoft, MATlab, LABview, Unscrambler, and Origin. The

development of an analyzer for this application is no exception and even depends upon these

computations to generate the desired results. Although no model has been made as a part of this

study, some thoughts and ideas are bundled for future reference.

6.4.1 Univariate model

Peak table analysis is one of the most known and forms of correlating a spectrum or chromatogram

to a composition. Regularly speaking one would either directly integrate the peak from the baseline

or fit a Gaussian or Lorenz peak over the signal in case of co-eluding or interfering peaks to

determine the area. The calibration comes in the form of a table where the area of the peak is

matched with a concentration. Although this is a very concise description of the peak analysis

process, an example is given in Figure 71, many people who have worked with chromatography or

used the isotope analysis function on a mass spectrometer used this functionality.

To be able to completely separate all components and calculate the results via a peak table hardly

seems possible. The resolution of the spectrometer is insufficient to separate all peaks, and at the

moment it is unclear how much of the C-H stretch vibration exactly can be assigned to methane or

other components. This type of data analysis is straightforward and widely accepted for industrial

analysis, therefore it would be the preferred method, alas this is not yet possible.

Figure 71 Flowchart for peak table analysis

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6.4.2 Multivar iate models

Multivariate modelling is what is usually meant with chemometrics, and to produce useful results

from the spectra a multivariate method could well possibly be used. Some restrictions have to be

followed to succeed in finding the correct calibration matrices, upon which also successive

experiments should be focused.

Firstly the number of samples taken would have to outnumber the measured wavelengths to obtain

a proper calibration matrix. With 1024 pixels this would be quite the challenge and it might prove to

be practical to cut out the baseline. Other methods to neglect useless parameters include: ridge

regression, lasso, and least angle regression.

Secondly co-linearity in X would be a problem during the matrix inversion. This means the spectra

of the individual components should not be linear combinations of each other, they need an

individual characteristic. With the current setup and a sample pressure of 1 bar this does not

comply, peaks in the 500 to 1000 cm-1 range should be measured whereby this restriction is fulfilled.

An interesting note for the analysis of natural gas is the current calculation guidelines from the

International Standards Organization in document ISO 6976. The calculation of caloric value, gas

density, and Wobbe index is there coupled to the input of the gas composition. From a current point

of view this is very logical, a GC measures only concentrations and computer power used to be

limited. Figure 72 shows what the calculation flowchart would look like for a multivariate model.

Figure 72 Suggested flowchart for the calculation of composition and key parameters with the use of a multivariate model and ISO 6976 calculation

With the use of multivariate modeling it is possible to calculate the sample key parameters, such as:

Wobbe index, superior calorific value, and methane number directly from the spectral data, see

Figure 73. Because the key parameters are dependent on the composition only the parameters can

be calculated because of the co-linearity restriction in multivariate modelling. Another possibility is

shown in Figure 74 where the ISO 6976 is used as a restriction algorithm to limit the possible

compositions only to match to the predicted key parameters. Both figures are meant to illustrate

multivariate modelling has many possibilities, of which only the direct calculation of key

parameters, and restriction algorithms are highlighted.

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Figure 73 Suggested flowchart for the calculation of sample key parameters with the use of a multivariate model

Figure 74 Suggested flowchart for the calculation of composition and key parameters with the use of a multivariate model and reverse ISO 6976 calculation as restriction algorithm

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7 DISCUSSION AND CONCLUSION

Imported natural gas and its use on the Dutch market provides an analytical challenge for processes

in which natural gas is used. To maintain operational robustness independent from the gas

composition, is the innovation drive upon which this thesis is based. During this research no method

was proven to work for the determination of the complete composition or parameters of the

sample. Though the results are such that improvements can be suggested and there is reason to

believe the technology can be helpful for the analysis of natural gas in a process environment.

Important properties for which the Raman technology is selected are the possibilities for an in situ

and static design, and the possible speed of analysis. Each of these properties are essential to take

advantage of for a competitive application. The weaknesses inherent to the Raman application are

the sensitivity and selectivity. The in situ approach is new with respect to the quantification of

natural gas, and provides a way to measure the sample at a high pressure which is good for the

signal intensity. The nature of the scattering event and the sample itself cannot be changed,

therefore the design of the instrumentation has a crucial role.

At the start of the light path there has to be a properly well-defined laser light source. A higher laser

power makes it possible to decrease the observation time, since the intensity of the signal is

proportional to the incident light. Drawback is that the laser power might not be increased without

consequences. It is possible to use a laser light source to ignite an explosive mixture66, therefore it is

unwise to use too high light irradiance while measuring natural gas. To improve the method in

sensitivity a laser with shorter emission wavelength may be selected, although that would also

decrease the selectivity since the Raman scatter would be within a smaller wavelength range.

Another factor to consider is that the parts and filters for the used wavelength, 532 nm, are widely

available. Taking this into account one could say the laser wavelength was correctly selected.

High impact improvements can be suggested for the immersion probe design, simulations showed

significant increase in signal when reflections were used for signal gain. To boost the signal intensity

with the use of a mirror was not a new idea, already in the early 19th century candles have been

positioned in front of mirrors, in the form of girandoles, to increase the light efficiency. Possibly

because of the specific application of the optical probes, a patent was granted for the use of

reflective surfaces at the probe tip.

Other simulations showed interesting effects from characteristics of the lens, and the length of the

extension tube. Naturally the in situ application of the probe ensures the measurement is done at a

high pressure, which increases the signal proportional to the sample density, but also the refractive

properties. Robustness tests on the suggested probe tip design showed the change in density of the

sample compared to the instrumentation atmosphere, which would also influence the refractive

index around the lens, has a moderate effect on the intensity of the signal amplification. The

suggested immersion probe took as much of this into account to optimize efficiency while

maintaining compatibility with a supplied optical probe.

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The following step in the development of the designed optical probe would be to make a steel

prototype, and commence a test sequence. From the drawings multiple 3d-printed prototypes were

printed to properly visualize and double check measurements, though the plastics materials are less

than ideal for functional testing. To collect information from industrial manufacturers and patent

experts could possibly increase the suitability of the first prototype. Tests would have to include

Raman spectra recordings of the separate components of the probe, pressure testing, and

adjustment experiments.

Special interest was given to the interface of the probe to the spectrometer. The changeable slits

were tested and found to be optimal for 50 um. Also thought was given to the type of fiber, which

size would be optimal for the 50 um slit, and even multiple types were considered. During these

experiments it was thought the round to linear fiber would yield the best results, which was

confirmed by the manufacturer experiments and specially made data. Upon delivery the round to

linear fiber yielded less signal compared to the 600 um round fiber. This data is contradictory and

should be further investigated.

At the end of the light path various spectrometers were evaluated and compared, the ULS and the

HSC spectrometers. Both back thinned CCD detectors showed a good performance, spectra from

both spectrometers are reported throughout this thesis. It was found the HSC had better laser

dump alignment, and acceptable resolution. The found improvement of the S/N-ratio of the HSC

was mainly due to an improved dark noise characteristic. Also there were no abnormalities in the

intensities found with the HSC when measuring low light intensities, contrary to the ULS

spectrometer. Therefore it follows the HSC would be the better choice of spectrometer for further

developments.

The measurements in paragraph 6.3 where the data is presented in fully processed form is hard to

compare to the literature data, or even the theoretically predicted data. The methane peak,

measured at 1538-1545 cm-1, was predicted at 1511.6 cm-1, whereas literature values put it around

1535 cm-1. It appears the measured values more closely resemble the literature than the predicted

values. Same applies to nitrogen, measured at 2330-2338 cm-1, predicted at 2188.3 cm-1, while

reported in literature on 2331 cm-1. Also the predicted relative intensities of the peaks do not match

the measured spectra. What the theoretical spectra predicted successfully was that the differences

are very small between the components, which was confirmed by the practical experiments in

which overlaying peaks are suspected. For the development of a new application it has been more

helpful to search for literature spectra than it has been to calculate theoretical spectra.

The data processing has shown to have significant influence on the final spectra. Especially the dark

correction, Figure 61, has a very clear influence on the spectrum quality. Also the background

correction can be regarded as proper means to remove unwanted interferences from the spectrum.

These spectra are important for the development of a more bare bone method for the analysis of

the natural gas, peaks can be pin-pointed and sensitivities charted. To correct specific pixels for

their dark reading will provide stability for the subsequent chemometric model, whereas the

background correction will render a spectrum with diagnostic information of the setup. As was

described for the specific signal filtering, the new application does not necessarily need to produce a

spectrum, the goal is a numeric output of the composition and key parameters. The signals which

are used as input for the model should be both as close to the source and as good a measure for the

separate components as possible.

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In this study, value was supposed to be added by looking into the heavier hydrocarbons C4-C6. I

have been unable to find any application where these gasses were effectively quantified, and this

study is not different. The heavier components are generally available in lower concentrations,

which makes it harder to measure. Although a butane peak was assigned while measuring at 35 bar,

no quantification could be done. The development of more sensitive equipment would increase the

possibilities to include these components in the composition.

Once there is sufficient sensitive instrumentation and data available, the research may focus on the

development of algorithms for comprehensive application of Raman spectroscopy. Combinations

of the aforementioned application complemented with the analysis of trace components such as;

H2S, COS, THT, or Limonene would render useful results for process control. Other key properties

such as the dew point could be included in the model. Combined, these measurements and

calculations could lead to a single comprehensive analysis method for the online analysis of natural

gas.

The formulation of a calibration matrix should be done with multiple gas mixtures, and spectra of

the pure compounds for optimal accuracy. With the described instrumentation I would suggest

initially only a limited number of components is acknowledged by the method, those which can be

clearly measured at the set pressure. After cross validation and estimation of accuracy of the model,

it can then be expanded with additional components and key priorities. Analysis time in Raman

spectroscopy is dependent on the observation time and the calculation time. From the made

calculations it was found that the calculation time is unlikely to be a significant factor in the analysis

time if powerful computers are combined with efficient programming.

To indicate whether or not Raman spectroscopy will be able to replace gas chromatography for the

continuous analysis of natural gas composition it may be too early. Many optimization steps can still

be done and even from elementary data relatively useful models may be made. What was known on

forehand, that the sensitivity and selectivity of the measurement were the main weakness of the

method, has proved to be correct. Without significant increase in the signal strength it will be

impossible to measure the heavier hydrocarbons and trace components, a comprehensive analysis

approach is therefore currently hard to imagine.

Important measured signals for the further development are the methane, ethane, and nitrogen

peaks. These components each have one or more signals that can be used as an input for the

calculations or as calibration marker. Together these signals account for the major components in

natural gas and could form a basic determination of key properties, such as the calorific value or

Wobbe index. I would therefore like to conclude that Raman spectroscopy would be a viable

technology for the characterization of the key parameters of natural gas in a process application.

On a final note, I would like to thank Dr. Hooijschuur and the staff at Analytical solutions and

products for their support and the opportunity to work on this project with their materials and

equipment. Also I would like to thank Dr. Ariese for the scientific conversations which sparked my

creativity and interest in Raman technology.

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9 LISTS OF FIGURES , EQUATIONS, AND TABLES

9.1 F IGURES Cover picture: Experimental setup of measurement cell and optical probe in backscatter mode in

use with 532 nm laser.

Figure 1 H-gas map of the Netherlands, note the LNG-import dock labelled 'LNG'. Source:

gasunietransportservices.nl accessed 16-11-17 ................................................................................. 6

Figure 2 Schematic view of diffraction spectrometer including lightpaths ........................................ 7

Figure 3 Picture of opened spectrometer, Manufacturer: Avantes13 .................................................. 7

Figure 4 Schematic view of filtered band spectrometer .................................................................... 8

Figure 5 Schematic view of the Claus process for sulfur recovery, indication of feedforward and

feedback loop ................................................................................................................................... 9

Figure 6 Jablonski diagram showing energy states of different scattering events. ........................... 13

Figure 7 Molecular vibrations in a -CH2- group, LRTB: Symmetrical stretch, Asymmetrical stretch,

Scissoring, Rocking, Wagging, and Twisting. Whereas the arrows display the initial direction of the

vibrations on the plane of the paper, the + and – show the movement perpendicular to this plane. 14

Figure 8 ADF-Calculation output for methane ................................................................................ 19

Figure 9 Theoretically predicted spectrum of methane, data from ADF plotted in MatLab ............. 20

Figure 10 Overlay of Theoretically predicted spectra from the components in Groningen-gas

multiplied by their typical concentrations. ...................................................................................... 21

Figure 11 Theoretical absolute difference of spectra of natural gas from Groningen and Qatar, one

subtracted from the other. .............................................................................................................. 21

Figure 12 Schematic view of the experimental setup with the three main components; laser, optical

probe and spectrometer. ................................................................................................................ 22

Figure 13 Display of Coherent FieldMaxII-TO power meter software during setup of Laser clamp. 1

measurement per second................................................................................................................ 24

Figure 14 Typical display of Cobolt Samba laser control software. .................................................. 25

Figure 15 Various types of connectors, edited from source68. .......................................................... 26

Figure 16 Various Fiber Optical Probe Configurations. Left to right: Single-fiber with dichroic mirror,

two fiber flat tipped with separated excitation and collection fiber, six around one flat tipped, single

fiber with dichroic mirror and lens, two fiber beveled with separated excitation and collection fiber,

and six around one beveled tip. After Cooney et al. (1996)78. .......................................................... 27

Figure 17 Component overview of the factory standard optical probe, copied from80. .................... 27

Figure 18 3D rendering of an alternative optical probe with collimating lens, optical filters, long pass

dichroic mirror, parabolic collimating mirror, and collection fiber. .................................................. 28

Figure 19 Short and long pass dichroic mirror, the colors of the arrows indicate the relative

wavelength of the light beams ........................................................................................................ 29

Figure 20 Various commercially available (optical) probes and immersion tubes. Left top Immersion

probe made by Solvias84, Right top AirHead™ Gas-phase Raman Probe made by Kaiser Optical

Systems inc85, Left bottom Bioprocess in-line Raman Analyzer (probe only) made by Resolution

spectra Systems86, Right bottom Fiber Optic Raman Probes made by Wasatch Photonics87. .......... 30

Figure 21 Schematic cross section of a process pipeline showing: A Flanged tie in point, B Ideal

probe length, C Laminar flow profile, D pipe diameter. .................................................................... 31

Figure 22 Impression renderings of designed alternative probe. ...................................................... 31

Figure 23 Lens at the tip of the extension tube, light path illustrates chromatic aberration over the

length of the extension tube. .......................................................................................................... 32

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Figure 24 Simulated experimental setup. Left to right; Detector 2, r=1.75mm light guide, Detector 1,

r=1.75mm light guide, condenser lens, r=1.75mm light guide, Light source, reflective circular

surface. ............................................................................................................................................ 33

Figure 25 Schematic view of Raman active cones. ........................................................................... 33

Figure 26 Detector 2 Signal: 24593 hits, with reflector. ................................................................... 34

Figure 27 Detector 1 Signal: 12296 hits, without reflector. .............................................................. 34

Figure 28 Robustness test of Z-axis shift. illustration of rays when lens 5 mm too close. ................. 35

Figure 29 Robustness test of Z-axis shift. illustration of rays when lens 5 mm too far. .................... 35

Figure 30 Recorded signal recovery of detector 1 and 2 during Z-axis robustness test, simulated

experimental setup as in figure 24, 28-29. ....................................................................................... 35

Figure 31 Robustness test for lens tilt. Illustration of rays with 4° lens deviation. ............................ 36

Figure 32 Recorded signal recovery of detector 1 and 2 during lens tilt robustness test, simulated

experimental setup as in figure 24, 31. ............................................................................................ 36

Figure 33 Recorded intensity on detector 2 with 4° lens deviation, simulated experimental setup as

in figure 24, 31. ................................................................................................................................. 37

Figure 35 Schematic for illustration of the effective fiber to slit surface. ......................................... 39

Figure 36 Calculated effective surface depending on slit size and fiber diameter. ........................... 40

Figure 37 Ratio of the effective interface depending on slit size and fiber diameter. ....................... 40

Figure 38 Proposed lens system to increase light efficiency of fiber to slit interface. ....................... 41

Figure 39 Left top: round fiber 600 µm, left bottom; collection fiber to slit interface on round to

linear optical fiber 7 x 100 µm, right; Fiber to slit interface with light shining though. ..................... 42

Figure 40 Spectrum supplied by spectrometer manufacturer for comparison of round fibers to

Round To Linear (RTL) fibers. ......................................................................................................... 43

Figure 41 Comparison of optical probe and matching laser. Observation time 2 sec, average of 60

sec, laser power 60 mW, sample pressure 1 Bar, sample flow 50 mL/min, sample natural gas low

calorific, measured on ULS spectrometer, 50 µm slit, dark and background corrected. .................. 43

Figure 42 Schematic view of diffraction spectrometer including lightpaths. ................................... 44

Figure 43 Illustration of slit acceptance pyramid and effective focal length of a spectrometer.

Adapted from 96. ............................................................................................................................. 45

Figure 44 Photograph of a 100 µm exchangeable slit used in Avantes spectrometers. .................... 45

Figure 45 Overlay of various natural gas spectra with various slit widths. Observation time 1 sec,

average of 5 sec, laser power 156 mW, sample pressure 36 Bar, No sample flow, measured on ULS

spectrometer, dark and background corrected. .............................................................................. 46

Figure 46 Natural gas spectra with various slit widths. Observation time 2 sec, average of 60 sec,

laser power 60 mW, sample pressure 1 Bar, sample flow 50 mL/min, sample natural gas low calorific,

measured on ULS spectrometer with probe with round to linear collection fiber, dark and

background corrected. .................................................................................................................... 46

Figure 47 Different detector types with their Quantum Efficiency per Wavelength, copied from99.

Note the difference in quantum efficiency between the FI and BV detectors around 600 to 700 nm.

........................................................................................................................................................ 48

Figure 48 Dark noise reading of two independent spectrometers with observation time 1 second

averaged over 150 measurements. ................................................................................................. 49

Figure 49 Spectrum of cyclohexane measured with both ULS and HSC spectrometer. observation

time 1 s, average of n measurements, slit 50 µm, laser power 156mW. note the calibrations on the

spectrometers were not checked. ................................................................................................... 50


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Figure 50 Best practice CCD allignment used to function as high (1) and low (2) wavelenght pass

filters. ..............................................................................................................................................51

Figure 51 Overlay of 1s cyclohexane spectra made with ULS (higher peak) and HSC (less noise)

spectrometer, slit 50 µm, laser power 156mW. note the calibrations on the spectrometers were not

checked. ..........................................................................................................................................51

Figure 52 DN40 PN40 sight glass with flanges. ............................................................................... 52

Figure 53 Low pressure measurement setup, on the left the pressure and temperature

measurements are fitted between the in- and outlet valves, on the right side the extension tube and

optical probe are installed. .............................................................................................................. 53

Figure 54 Mean of 39 spectra recorded of Neon emission light, measured directly on HSC

spectrometer, 1000 ms observation time, slit width 50 µm. ............................................................ 54

Figure 55 A 2nd degree polynomial calibration line fit for HSC spectrometer, R-squared >0.9999. .. 56

Figure 56 Schematic representation of how multiple readouts might be used for signal processing.

........................................................................................................................................................ 58

Figure 57 Comparison of various observation times of the same setup, Observation time sum of 5

sec, laser power 156 mW, sample pressure 1 Bar, sample natural gas low calorific, measured on ULS

spectrometer, dark corrected. ........................................................................................................ 59

Figure 58 Comparison of various observation times of the same setup, Observation time mean of 5

sec, laser power 156 mW, sample pressure 1 Bar, sample natural gas low calorific, measured on ULS

spectrometer, dark corrected. ........................................................................................................ 59

Figure 59 Various spectra of helium with cosmic rays, not background corrected. 1000 ms

observation time, slit width 50 µm. ULS spectrometer with 19cm extension tube was used. .......... 60

Figure 60 Dark readings of ULS and HSC spectrometers with 1 and 5 second observation times. ... 62

Figure 61 Spectra of Helium with and without dark correction. Measured on ULS, short extension

tube, 1 second observation time, 50 µm slit width. ......................................................................... 62

Figure 62 Spectra background correction treatment of Low Calorific Natural Gas, HSC spectrometer

mean of 5 measurements with 1 second observation time, 50 µm slit, laser power 156 mW, sample

pressure 1 Bar. ................................................................................................................................ 63

Figure 63 Flowchart for the dark and background correction of sample spectra. ............................. 64

Figure 64 Intensity of selected pixels followed in time during measurement of instrument air. ULS

spectrometer 5 second observation time, 50 µm slit, laser power 156 mW, sample pressure 1 Bar. 65

Figure 65 Filtered spectra of low calorific natural gas (1barg) made with spectrometer 'HSC' 1 sec

observation time mean of 5 measurements, dark and background corrected. ................................ 67

Figure 66 Filtered spectra of low calorific natural gas (1barg) made with spectrometer 'HSC' 1 sec

observation time mean of 5 measurements, dark and background corrected. ................................ 67

Figure 67 Spectra with full background correction treatment of high and low calorific Natural Gas,

HSC spectrometer mean of 1505 measurements with 1 second observation time, 50 µm slit, laser

power 156 mW, sample pressure 1 Bar. ........................................................................................... 68



power 156 mW, sample pressure 1 Bar, zoomed in. ......................................................................... 69



power 156 mW, sample pressure 1 Bar, zoomed in. ......................................................................... 70

Figure 70 Spectrum of High calorific natural gas, ULS spectrometer, 50 µm slit, laser power 156 mW,

sample pressure approx. 35 bar, 19 cm extension tube, 1 second observation time, mean of 120

measurements, background corrected, SG filtered 2nd power polynomial window size 13. ............. 71

Figure 71 Flowchart for peak table analysis ..................................................................................... 72

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Figure 72 Suggested flowchart for the calculation of composition and key parameters with the use

of a multivariate model and ISO 6976 calculation ............................................................................ 73

Figure 73 Suggested flowchart for the calculation of sample key parameters with the use of a

multivariate model .......................................................................................................................... 74

Figure 74 Suggested flowchart for the calculation of composition and key parameters with the use

of a multivariate model and reverse ISO 6976 calculation as restriction algorithm ......................... 74

9.2 EQUATIONS Equation 1: Intensity of Raman scattering, equation reproduced from30 ..........................................15

Equation 2 f-number ....................................................................................................................... 38

Equation 3 Resolving power of a spectrograph as a function of measured wavelength and spectral

resolution. ....................................................................................................................................... 44

Equation 4 Calculation of Raman shift. ........................................................................................... 56

Equation 5 relation between window size and boxcar width ........................................................... 66

Equation 6 Wavenumber to frequency ........................................................................................... 114

Equation 7 Vibrational frequency of harmonic oscillation107 ........................................................... 114

Equation 8 Reduced mass of the attached body............................................................................. 114

9.3 TABLES Table 1: A comparison of the advantages and disadvantages of competitive techniques. ............... 12

Table 2 Definition of symbols and units in Equation 1 ......................................................................15

Table 3 main components in sales gas, with composition in volume % for NG from Groningen34

(Wobbe index of 43,7 MJ/m3) and LNG from Qatar (mixed to a Wobbe index of 54 MJ/m3 for the

Dutch market) ................................................................................................................................. 16

Table 4 Raman shift (cm-1) of most common components, data reproduced from Kiefer et al.

(2008)11 ............................................................................................................................................ 17

Table 5 Results of isotope vibrations approximation. ...................................................................... 18

Table 6 Properties of the lenses used for simulations and the simulation results expressed in

Recorded signal recovery ................................................................................................................ 38

Table 7 Peak table of selected peaks for calibration of HSC spectrometer. Peaks are defined with

their top pixel, intensity, assigned emission line and measured FWHM, 1000 ms observation time,

slit width 50 µm. ............................................................................................................................. 55

Table 8 Composition of available gas standards with ISO6976 component number and their

assigned peaks ................................................................................................................................ 70

Table 9 Definition of symbols and units ......................................................................................... 114

Table 10 Constants used in the calculation ..................................................................................... 114

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10 GLOSSARY OF TERMS

ADC Analog to Digital Converter

APC Angled Physical Contact

CCD Charged Coupled Device

CMOS Complementary metal–oxide–semiconductor

COS Carbonyl sulfide

DPSS Diode-pumped solid-state

FC/PC Ferrule Connector / Physical Contact

FOP Fiber Optic Probe

GC Gas chromatograph

GHV Gross Heating Value

GRIN Graded index lenses

HSC AvaSpec-HERO SensLine

H2S Dihydrogensulfide

HTVS High Throughput Virtual Slit

laser light amplification by stimulated emission of radiation

LCA Lens Collection Array

LIDT Laser Induced Damage Threshold

LNG Liquefied Natural Gas

MN Methane Number

NA Numeric aperture

Nd : YAG Neodymium-doped yttrium aluminium garnet

NG Natural Gas

SO2 Dioxidesulfide

TCD Thermal Conductivity Detector (Wheatstone bridge)

THT Tetrahydrothiophene

ULS Ultra Low Straylight

WI Wobbe Index

ZOS Zemax Optical Studio

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11 APPENDIX A

11.1 THEORETICALLY PREDICTED SPECTRA CALCULATED OF PURE COMPOUNDS

IN NATURAL GAS .

Frequency Intensity

1336.7 0.1614

1336.7 0.1614

1336.7 0.1614

1511.6 21.8519

1511.6 21.8519

2873.5 184.7118

3006.1 88.5961

3006.1 88.5961

3006.1 88.5961

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Frequency Intensity

449.5 0.0001

868.8 0.0000

868.8 0.0000

1024.8 9.9854

1218.0 1.2535

1218.0 1.2535

1389.5 0.0000

1399.4 2.4145

1463.5 0.0022

1463.5 0.0022

1474.4 31.5080

1474.4 31.5080

2888.4 312.7611

2889.3 0.0000

2965.3 200.7960

2965.3 200.7960

2992.2 0.0029

2992.2 0.0029

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Frequency Intensity Frequency Intensity

313.7 0.0480 1448.6 0.8146

348.2 0.0197 1453.7 31.3443

385.7 0.3470 1454.8 27.3170

787.8 0.1079 1465.3 17.4311

883.9 9.4074 1473.4 0.0008

931.0 0.0022 2888.3 2.9687

938.7 0.0844 2888.6 342.4716

1071.9 5.6512 2904.0 136.9812

1168.9 2.1138 2945.6 263.1771

1201.4 0.1769 2977.6 11.5346

1290.7 10.6149 2980.9 86.7664

1341.0 0.4424 2982.5 125.9715

1375.9 2.1174 2990.0 22.6059

1397.6 1.3458

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276.3 0.0043 1370.4 2.0103

320.4 0.1041 1401.4 0.6923

323.5 0.1364 1429.1 0.0104

376.3 0.3754 1449.1 30.0123

377.1 0.3577 1449.6 32.7945

444.2 0.3517 1461.4 9.1829

796.5 9.4525 1462.0 12.9247

922.6 1.3113 1478.4 0.0875

924.3 1.1943 2886.2 1.9641

948.0 0.0321 2886.4 1.9619

974.4 5.6931 2889.5 479.5218

975.5 5.5879 2909.9 198.2766

1174.7 5.1187 2975.5 14.2979

1175.1 5.1357 2975.7 22.1244

1191.7 1.3452 2976.3 3.1828

1321.7 7.5238 2977.4 166.8663

1322.2 7.7297 2977.8 177.1946

1369.3 2.0395 2984.2 130.5771

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138.1 0.0000 1385.1 2.8948

266.2 0.0000 1386.6 0.0001

321.2 0.0000 1444.0 12.5136

345.1 0.0326 1444.8 0.0092

441.4 3.0384 1460.5 0.0003

768.2 0.0000 1462.0 37.4642

834.6 0.3730 1463.1 36.5281

845.3 9.5841 1464.6 0.0024

968.9 0.0000 2887.9 0.0013

989.0 0.0000 2889.3 274.5519

1020.0 0.0000 2894.4 305.7213

1068.6 11.5244 2904.0 0.0005

1155.2 2.3147 2936.3 300.7652

1191.0 0.9303 2958.4 0.0001

1264.7 0.0004 2980.6 211.9068

1290.2 23.1798 2981.3 85.8808

1297.5 0.0000 2981.4 0.0367

1339.7 1.8892 2987.5 0.0001

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Frequency Intensity Frequency Intensity Frequency Intensity

129.8 0.0883 1040.0 4.2751 1460.7 11.5007

255.4 0.1035 1151.6 4.3076 1466.6 3.5273

282.1 0.0473 1166.3 5.0077 1477.5 9.6753

300.9 0.0234 1180.9 1.0852 2887.3 90.3306

315.1 0.3024 1260.8 6.4168 2893.5 53.1359

375.4 0.4612 1288.1 8.1087 2895.1 255.5832

425.2 0.5714 1317.8 10.1474 2898.7 262.4422

466.9 2.5174 1333.4 1.7446 2908.0 113.8994

763.1 6.9785 1365.8 2.6759 2948.4 80.9966

807.1 2.9552 1381.6 0.6959 2975.0 79.8666

910.6 5.4776 1390.5 1.6608 2977.2 70.8516

924.5 1.3135 1430.2 3.8621 2979.9 140.4677

959.1 3.3835 1447.4 29.8541 2982.0 106.2909

980.0 0.1021 1453.5 5.4630 2987.7 52.9624

1022.6 4.3251 1457.4 31.4193 2991.3 50.3546

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119.7 0.0463 1082.8 6.5680 1461.5 31.3842

158.1 0.1675 1143.6 1.9018 1462.4 14.3466

257.7 0.0565 1171.3 1.2373 1469.4 6.9294

324.2 0.0470 1240.7 1.2190 2888.6 140.2874

334.1 0.3459 1271.5 3.1556 2893.8 108.6538

350.6 0.9763 1289.1 22.2968 2895.9 187.9365

481.7 2.0747 1304.0 6.1880 2904.1 284.1219

760.0 0.8450 1330.1 2.5186 2907.2 3.0003

788.0 2.5215 1351.3 1.3902 2940.3 252.7406

853.7 6.7026 1382.6 1.4647 2950.0 97.6560

880.3 1.8157 1389.1 2.2859 2964.5 34.9106

922.5 3.4839 1438.4 14.0652 2980.8 114.9211

995.3 0.8188 1449.0 8.3872 2981.5 107.4534

1029.9 1.2382 1451.6 12.2275 2986.9 24.9988

1042.1 4.1822 1459.3 8.0901 2990.2 24.3743

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234.8 0.0007 942.6 0.0099 1474.2 0.0430

309.0 0.0009 1058.5 4.3251 1474.6 0.0413

314.0 0.0164 1058.8 4.2942 1475.5 0.0277

317.5 0.0116 1250.3 9.1476 2885.2 0.8837

337.9 0.9362 1250.6 9.0770 2885.4 0.9517

338.9 0.9328 1251.4 9.1697 2885.6 0.9283

420.8 0.0933 1365.9 1.1438 2892.6 574.6191

421.7 0.0949 1366.9 1.1582 2972.4 0.2133

424.5 0.0964 1367.7 1.1225 2972.7 1.5217

717.2 11.8528 1410.7 0.0060 2973.1 1.0783

917.8 8.2909 1430.5 0.0125 2973.6 24.9154

918.7 8.3985 1431.9 0.0209 2973.8 24.3224

919.5 8.3911 1432.9 0.0017 2976.6 227.8351

940.0 0.0288 1448.7 52.9106 2976.8 227.3577

941.6 0.0077 1450.3 52.9033 2977.3 227.8159

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108.6 0.0280 1303.8 3.8673

111.9 0.0291 1335.2 3.1810

155.2 0.1077 1348.7 1.4515

275.9 0.0658 1386.7 3.3316

290.6 0.0100 1389.4 0.6548

327.6 0.1915 1440.2 8.9739

333.2 2.0533 1443.9 5.2471

345.1 1.1329 1446.1 4.2587

534.3 0.6371 1453.7 18.5816

754.1 0.5634 1461.3 11.7924

774.4 1.1137 1462.6 15.8546

831.9 11.1239 1463.5 31.1794

840.6 1.3714 1465.0 10.0066

912.5 1.8007 2887.9 84.4142

916.4 1.5390 2888.6 200.5309

1014.3 2.1769 2893.2 47.6959

1020.6 0.1968 2899.5 466.5698

1044.2 1.3467 2906.3 1.5138

1048.2 1.1649 2908.4 21.0029

1078.4 14.7013 2938.9 303.1405

1141.2 2.2178 2941.6 92.9963

1169.7 1.7964 2962.2 54.3840

1229.1 0.3903 2965.1 6.6483

1260.3 0.4467 2980.3 148.3434

1280.8 7.0499 2980.8 107.1283

1286.6 19.4768 2986.2 10.8777

1302.4 15.2397 2987.4 14.7689

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Frequency Intensity

2188.3 17.6209

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Frequency Intensity

601.7 0.0000

606.5 0.0000

1182.0 17.7747

2112.5 0.0000

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Frequency Intensity

5069.6 115.2725

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11.2 COMBINED PREDICTED SPECTRA

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Frequency (cm-1)

Intensity (AU)

Component Frequency (cm-1)

Intensity (AU)

Component

108.6 0.0280 C6n 320.4 0.1041 C4i

111.9 0.0291 C6n 321.2 0.0000 C4n

119.7 0.0463 C5n 323.5 0.1364 C4i

129.8 0.0883 C5i 324.2 0.0470 C5n

138.1 0.0000 C4n 327.6 0.1915 C6n

155.2 0.1077 C6n 333.2 2.0533 C6n

158.1 0.1675 C5n 334.1 0.3459 C5n

234.8 0.0007 C5neo 337.9 0.9362 C5neo

255.4 0.1035 C5i 338.9 0.9328 C5neo

257.7 0.0565 C5n 345.1 0.0326 C4n

266.2 0.0000 C4n 345.1 1.1329 C6n

275.9 0.0658 C6n 348.2 0.0197 C3

276.3 0.0043 C4i 350.6 0.9763 C5n

282.1 0.0473 C5i 375.4 0.4612 C5i

290.6 0.0100 C6n 376.3 0.3754 C4i

300.9 0.0234 C5i 377.1 0.3577 C4i

309.0 0.0009 C5neo 385.7 0.3470 C3

313.7 0.0480 C3 420.8 0.0933 C5neo

314.0 0.0164 C5neo 421.7 0.0949 C5neo

315.1 0.3024 C5i 424.5 0.0964 C5neo

317.5 0.0116 C5neo 425.2 0.5714 C5i

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Frequency (cm-1)

Intensity (AU)


Intensity (AU)

Component

441.4 3.0384 C4n 959.1 3.3835 C5i

444.2 0.3517 C4i 968.9 0.0000 C4n

449.5 0.0001 C2 974.4 5.6931 C4i

466.9 2.5174 C5i 975.5 5.5879 C4i

481.7 2.0747 C5n 980.0 0.1021 C5i

534.3 0.6371 C6n 989.0 0.0000 C4n

601.7 0.0000 CO2 995.3 0.8188 C5n

606.5 0.0000 CO2 1014.3 2.1769 C6n

717.2 11.8528 C5neo 1020.0 0.0000 C4n

754.1 0.5634 C6n 1020.6 0.1968 C6n

760.0 0.8450 C5n 1022.6 4.3251 C5i

763.1 6.9785 C5i 1024.8 9.9854 C2

768.2 0.0000 C4n 1029.9 1.2382 C5n

774.4 1.1137 C6n 1040.0 4.2751 C5i

787.8 0.1079 C3 1042.1 4.1822 C5n

788.0 2.5215 C5n 1044.2 1.3467 C6n

796.5 9.4525 C4i 1048.2 1.1649 C6n

807.1 2.9552 C5i 1058.5 4.3251 C5neo

831.9 11.1239 C6n 1058.8 4.2942 C5neo

834.6 0.3730 C4n 1068.6 11.5244 C4n

840.6 1.3714 C6n 1071.9 5.6512 C3

845.3 9.5841 C4n 1078.4 14.7013 C6n

853.7 6.7026 C5n 1082.8 6.5680 C5n

868.8 0.0000 C2 1141.2 2.2178 C6n

868.8 0.0000 C2 1143.6 1.9018 C5n

880.3 1.8157 C5n 1151.6 4.3076 C5i

883.9 9.4074 C3 1155.2 2.3147 C4n

910.6 5.4776 C5i 1166.3 5.0077 C5i

912.5 1.8007 C6n 1168.9 2.1138 C3

916.4 1.5390 C6n 1169.7 1.7964 C6n

917.8 8.2909 C5neo 1171.3 1.2373 C5n

918.7 8.3985 C5neo 1174.7 5.1187 C4i

919.5 8.3911 C5neo 1175.1 5.1357 C4i

922.5 3.4839 C5n 1180.9 1.0852 C5i

922.6 1.3113 C4i 1182.0 17.7747 CO2

924.3 1.1943 C4i 1191.0 0.9303 C4n

924.5 1.3135 C5i 1191.7 1.3452 C4i

931.0 0.0022 C3 1201.4 0.1769 C3

938.7 0.0844 C3 1218.0 1.2535 C2

940.0 0.0288 C5neo 1218.0 1.2535 C2

941.6 0.0077 C5neo 1229.1 0.3903 C6n

942.6 0.0099 C5neo 1240.7 1.2190 C5n

948.0 0.0321 C4i 1250.3 9.1476 C5neo

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Frequency (cm-1)

Intensity (AU)


Intensity (AU)

Component

1250.6 9.0770 C5neo 1389.5 0.0000 C2

1251.4 9.1697 C5neo 1390.5 1.6608 C5i

1260.3 0.4467 C6n 1397.6 1.3458 C3

1260.8 6.4168 C5i 1399.4 2.4145 C2

1264.7 0.0004 C4n 1401.4 0.6923 C4i

1271.5 3.1556 C5n 1410.7 0.0060 C5neo

1280.8 7.0499 C6n 1429.1 0.0104 C4i

1286.6 19.4768 C6n 1430.2 3.8621 C5i

1288.1 8.1087 C5i 1430.5 0.0125 C5neo

1289.1 22.2968 C5n 1431.9 0.0209 C5neo

1290.2 23.1798 C4n 1432.9 0.0017 C5neo

1290.7 10.6149 C3 1438.4 14.0652 C5n

1297.5 0.0000 C4n 1440.2 8.9739 C6n

1302.4 15.2397 C6n 1443.9 5.2471 C6n

1303.8 3.8673 C6n 1444.0 12.5136 C4n

1304.0 6.1880 C5n 1444.8 0.0092 C4n

1317.8 10.1474 C5i 1446.1 4.2587 C6n

1321.7 7.5238 C4i 1447.4 29.8541 C5i

1322.2 7.7297 C4i 1448.6 0.8146 C3

1330.1 2.5186 C5n 1448.7 52.9106 C5neo

1333.4 1.7446 C5i 1449.0 8.3872 C5n

1335.2 3.1810 C6n 1449.1 30.0123 C4i

1336.7 0.1614 C1 1449.6 32.7945 C4i

1336.7 0.1614 C1 1450.3 52.9033 C5neo

1336.7 0.1614 C1 1451.6 12.2275 C5n

1339.7 1.8892 C4n 1453.5 5.4630 C5i

1341.0 0.4424 C3 1453.7 31.3443 C3

1348.7 1.4515 C6n 1453.7 18.5816 C6n

1351.3 1.3902 C5n 1454.8 27.3170 C5n

1365.8 2.6759 C5i 1457.4 31.4193 C4n

1365.9 1.1438 C5neo 1459.3 8.0901 C5i

1366.9 1.1582 C5neo 1460.5 0.0003 C6n

1367.7 1.1225 C5neo 1460.7 11.5007 C4i

1369.3 2.0395 C4i 1461.3 11.7924 C5n

1370.4 2.0103 C4i 1461.4 9.1829 C4n

1375.9 2.1174 C3 1461.5 31.3842 C4i

1381.6 0.6959 C5i 1462.0 37.4642 C5n

1382.6 1.4647 C5n 1462.0 12.9247 C6n

1385.1 2.8948 C4n 1462.4 14.3466 C4n

1386.6 0.0001 C4n 1462.6 15.8546 C2

1386.7 3.3316 C6n 1463.1 36.5281 C2

1389.1 2.2859 C5n 1463.5 0.0022 C6n

1389.4 0.6548 C6n 1463.5 0.0022 C4n

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Frequency (cm-1)

Intensity (AU)


Intensity (AU)

Component

1465.0 10.0066 C6n 2904.1 284.1219 C5n

1465.3 17.4311 C3 2906.3 1.5138 C6n

1466.6 3.5273 C5i 2907.2 3.0003 C5n

1469.4 6.9294 C5n 2908.0 113.8994 C5i

1473.4 0.0008 C3 2908.4 21.0029 C6n

1474.2 0.0430 C5neo 2909.9 198.2766 C4i

1474.4 31.5080 C2 2936.3 300.7652 C4n

1474.4 31.5080 C2 2938.9 303.1405 C6n

1474.6 0.0413 C5neo 2940.3 252.7406 C5n

1475.5 0.0277 C5neo 2941.6 92.9963 C6n

1477.5 9.6753 C5i 2945.6 263.1771 C3

1478.4 0.0875 C4i 2948.4 80.9966 C5i

1511.6 21.8519 C1 2950.0 97.6560 C5n

1511.6 21.8519 C1 2958.4 0.0001 C4n

2112.5 0.0000 CO2 2962.2 54.3840 C6n

2188.3 17.6209 N2 2964.5 34.9106 C5n

2873.5 184.7118 C1 2965.1 6.6483 C6n

2885.2 0.8837 C5neo 2965.3 200.7960 C2

2885.4 0.9517 C5neo 2965.3 200.7960 C2

2885.6 0.9283 C5neo 2972.4 0.2133 C5neo

2886.2 1.9641 C4i 2972.7 1.5217 C5neo

2886.4 1.9619 C4i 2973.1 1.0783 C5neo

2887.3 90.3306 C5i 2973.6 24.9154 C5neo

2887.9 0.0013 C4n 2973.8 24.3224 C5neo

2887.9 84.4142 C6n 2975.0 79.8666 C5i

2888.3 2.9687 C3 2975.5 14.2979 C4i

2888.4 312.7611 C2 2975.7 22.1244 C4i

2888.6 342.4716 C3 2976.3 3.1828 C4i

2888.6 200.5309 C5i 2976.6 227.8351 C3

2888.6 140.2874 C2 2976.8 227.3577 C6n

2889.3 0.0000 C4n 2977.2 70.8516 C5n

2889.3 274.5519 C4i 2977.3 227.8159 C5neo

2889.5 479.5218 C5neo 2977.4 166.8663 C5neo

2892.6 574.6191 C6n 2977.6 11.5346 C5i

2893.2 47.6959 C5i 2977.8 177.1946 C5neo

2893.5 53.1359 C5n 2979.9 140.4677 C4i

2893.8 108.6538 C4n 2980.3 148.3434 C3

2894.4 305.7213 C5i 2980.6 211.9068 C4i

2895.1 255.5832 C5n 2980.8 107.1283 C5i

2895.9 187.9365 C5i 2980.8 114.9211 C6n

2898.7 262.4422 C6n 2904.1 284.1219 C4n

2899.5 466.5698 C4n 2906.3 1.5138 C6n

2904.0 0.0005 C3 2907.2 3.0003 C5n

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Frequency (cm-1)

Intensity (AU)


Intensity (AU)

Component

2980.9 86.7664 C3 2987.7 52.9624 C5i

2981.3 85.8808 C4n 2990.0 22.6059 C3

2981.4 0.0367 C4n 2990.2 24.3743 C5n

2981.5 107.4534 C5n 2991.3 50.3546 C5i

2982.0 106.2909 C5i 2992.2 0.0029 C2

2982.5 125.9715 C3 2992.2 0.0029 C2

2984.2 130.5771 C4i 3006.1 88.5961 C1

2986.2 10.8777 C6n 3006.1 88.5961 C1

2986.9 24.9988 C5n 3006.1 88.5961 C1

2987.4 14.7689 C6n 5069.6 115.2725 H2

2987.5 0.0001 C4n

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11.3 COMBINED PREDICTED SPECTRA FROM TYPICAL COMPOSITIONS

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12 APPENDIX B

12.1 COMPOSITION OF GAS STANDARD ‘H IGH CALORIFIC NATURAL GAS ’

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12.2 KEY PARAMETERS OF GAS STANDARD ‘H IGH CALORIFIC NATURAL GAS ’

ISO 6976 Report Line # Conditions

1 Measurement temperature 15 C

2 Combustion temperature 15 C

3 Reference pressure 101.325 kPa

4 Normalization On 5

6 Results for the REAL DRY gas

7 Superior (gross) Calorific Value 38.67731 MJ/m3

8 1038.068 BTU/cf

9 9237.917 kcal/m3

10 Inferior (net) Calorific Value 34.93903 MJ/m3

11 937.7352 BTU/cf

12 8345.043 kcal/m3

13 Wobbe Index 47.86429 MJ/m3

14 1284.639 BTU/cf

15 11432.19 kcal/m3

16 Relative density (air=1) 0.652964 17 Gas density 0.800339 kg/m3

18 0.049964 lb/cf

19 Compressibility 'z' 0.997554 20 Molecular Weight 18.87758 g/mol

Comp # Component

Concentration in (Mol%)

1 Methane 83.672 2 Ethane 7.007 3 Propane 1.501 4 n-Butane 0.302 5 2-Methylpropane 0.305 6 n-Pentane 0.041 7 2-Methylbutane 0.04 8 2,2-Dimethylpropane 0.041 9 n-Hexane 0.04 41 Hydrogen 0.021 52 Nitrogen 5.529 54 Carbon dioxide 1.501

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12.3 COMPOSITION OF GAS STANDARD ‘LOW CALORIFIC NATURAL GAS ’

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12.4 KEY PARAMETERS OF GAS STANDARD ‘LOW CALORIFIC NATURAL GAS ’

ISO 6976 Report Line # Conditions 1 Measurement temperature 15 C

2 Combustion temperature 15 C

3 Reference pressure 101.325 kPa

4 Normalization On 5 6 Results for the REAL DRY gas 7 Superior (gross) Calorific Value 38.17201 MJ/m3

8 1024.506 BTU/cf

9 9117.228 kcal/m3

10 Inferior (net) Calorific Value 34.38394 MJ/m3

11 922.8371 BTU/cf

12 8212.463 kcal/m3

13 Wobbe Index 50.77159 MJ/m3

14 1362.669 BTU/cf

15 12126.59 kcal/m3

16 Relative density (air=1) 0.56526 17 Gas density 0.69284 kg/m3

18 0.043253 lb/cf

19 Compressibility 'z' 0.997928 20 Molecular Weight 16.34815 g/mol

Comp # Component

Concentration in (Mol%)

1 Methane 98.0513 2 Ethane 1.507 3 Propane 0.1 4 n-Butane 0.01 5 2-Methylpropane 0.01 6 n-Pentane 0.0013 7 2-Methylbutane 0.0011 8 2,2-Dimethylpropane 0.0011 9 n-Hexane 0.0012 41 Hydrogen 0.001 52 Nitrogen 0.211 54 Carbon dioxide 0.105

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13 APPENDIX C

13.1 FORMULAS AND CONSTANTS USED TO CALCULATE ISOTOPIC INFLUENCE

For the calculation of the isotopic influence on the spectra the below equations and constants were

used. Firstly the Force constant is calculated in the case the molecule consists of 12C and Hydrogen.

By using this force constant in calculations with Deuterium and 13C, assuming they have similar

bond strength, the expected wavenumber can be calculated.

𝜎 𝑐 = 𝑣

Equation 6 Wavenumber to frequency

𝑣𝑚 =1

2𝜋√

𝑘

𝜇

Equation 7 Vibrational frequency of harmonic oscillation107

𝜇 = 𝑚1𝑚2

𝑚1 + 𝑚2

Equation 8 Reduced mass of the attached body

Symbol Description Unit

𝑣𝑚 Vibrational Frequency s-1

𝑘 Force Constant N m-1

𝜇 Reduced mass of the attached body kg

𝑚𝑖 Mass of atom 𝑖 kg

𝜎 Wavenumber cm-1

Table 9 Definition of symbols and units

Description Value Unit

Conversion unit Unified atomic mass unit to Kilogram

1,66054*10-27

Hydrogen mass 1,00794 U

Deuterium mass 2,01410178 U

Carbon 12 mass 12,0107 U

Carbon 13 mass 13,003355 U

Conversion unit Wavenumber to meter

100

Speed of light (free space) 2,998*108 m s-1 Table 10 Constants used in the calculation

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14 APPENDIX D

14.1 SECOND MANUFACTURER COMPARISON OF SPECTROGRAPH

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14.2 SPECTROMETER DETECTOR SPECIF ICATIONS

Property HSC ULS

Manufacturer Avantes Avantes

Spectrometer type AvaSpec-HSC-TEC AvaSpec-ULS2048x64TEC-RS-USB2

Serial no. 1609077U1 1603034U1

Bandwidth (approximately) 534-696 nm 528 – 757 nm

Options installed Detector collection lens behind slit for in coupled vertical light component

Detector collection lens for >200μm fibers. 75 mm ultra-low straylight AvaBench

Installed slit 50 um 50 um

Detector Manufacturer Hamamatsu Hamamatsu

Detector technology Back-thinned CCD detector Back-thinned CCD detector

Detector typeno. S7031-1006S S11071-1106

Number of (effective) pixels (HxV)

1024x58 2048x64

Pixel size 24x24um 14x14um

Operation temperature (DegC)

0.0 TEC cooled 5.0 (3-stage cooled)

Data interface USB 2.0 USB 2.0

Control board AS7010 AS5216

Control software Avasoft / avaspec dll library / matlab

Avasoft / AS5612 dll library / matlab / avaspec dll library

raman spectroscopy for natural gas process applications · 2020-01-14 · content of natural gas is...

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