Methane conversion in a plasma reactor Dries Michiels Supervisors: Prof. dr. ir. Kevin Van Geem, Prof. dr. ir. Christophe Leys Counsellor: Anton Nikiforov Master's dissertation submitted in order to obtain the academic degree of Master of Science in Chemical Engineering Department of Chemical Engineering and Technical Chemistry Chair: Prof. dr. ir. Guy Marin Department of Applied Physics Chair: Prof. dr. ir. Christophe Leys Faculty of Engineering and Architecture Academic year 2015-2016
Transcript
Methane conversion in a plasma reactor
Dries Michiels
Supervisors: Prof. dr. ir. Kevin Van Geem, Prof. dr. ir. Christophe Leys
Counsellor: Anton Nikiforov Master's dissertation submitted in order to obtain the academic degree of
Master of Science in Chemical Engineering
Department of Chemical Engineering and Technical Chemistry
Chair: Prof. dr. ir. Guy Marin Department of Applied Physics Chair: Prof. dr. ir. Christophe Leys Faculty of Engineering and Architecture Academic year 2015-2016
Methane conversion in a plasma reactor
Dries Michiels
Supervisors: Prof. dr. ir. Kevin Van Geem, Prof. dr. ir. Christophe Leys
Counsellor: Anton Nikiforov Master's dissertation submitted in order to obtain the academic degree of
Master of Science in Chemical Engineering
Department of Chemical Engineering and Technical Chemistry
Chair: Prof. dr. ir. Guy Marin Department of Applied Physics Chair: Prof. dr. ir. Christophe Leys Faculty of Engineering and Architecture Academic year 2015-2016
FACULTY OF ENGINEERING AND ARCHITECTURE
Department of Chemical Engineering and Technical Chemistry
Laboratory for Chemical Technology
Director: Prof. Dr. Ir. Guy B. Marin
Laboratory for Chemical Technology
Declaration concerning the accessibility of the master thesis Undersigned, ...................................................................................................................................... Graduated from Ghent University, academic year 2015-2016 and is author of the master thesis with title: ......................................................................................................................................
The author(s) gives (give) permission to make this master dissertation available for consultation and to copy parts of this master dissertation for personal use. In the case of any other use, the copyright terms have to be respected, in particular with regard to the obligation to state expressly the source when quoting results from this master dissertation.
1/06/2016
PREFACE
The research presented here is primarily based on years of knowledge and expertise that has been
built up and passed on to both the department of chemical engineering/technical chemistry and the
department of applied physics. The thesis which that research has led is also the merit of many.
First and foremost, I want to thank both prof. dr. ir. Guy B. Marin and prof. dr. ir. Christophe Leys for
providing the facilities available at the Laboratory for Chemical Technology and the Technicum
respectively. In particular, the promoters of this master thesis, prof. dr. ir. Kevin Van Geem and prof.
dr. ir. Christophe Leys which offered the opportunity to take further steps in this field of research.
Not least this thesis is the merit of my continuous counsellor; dr. Anton Nikiforov who guided me
throughout this tough period of dedication. Without his support and enthusiasm this work was
undoubtedly only a fraction of what it eventually became.
Furthermore, I would like to thank Kristina Franz who was like a second coach for me. Even more so,
in no particular order: Hilal Ezgi Toraman, Nenad Ristic, Sri Bala Gorungantu, Steffen Symoens, Diana
Cristina Vargas Solis and Florence Vermeire deserve a token of gratitude for their, although mostly
short but helpful discussions. In addition, when one swirls in desperate times, Michaël Lottin and Wim
Rogiers where present to extrapolate their technical knowledge of the analyzer devices.
Furthermore, I would like to express my profound gratitude to my friends aka the thezyz boiz for a
splendid last master year in the thezyz kot.
Finally, I must express my very profound gratitude to my parents for providing me with unfailing
support and continuous encouragement throughout my years of study. This accomplishment would
not have been possible without them.
Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.
-- Marie Curie -- Nobel prize in chemistry (1911) and physics (1903)
Methane conversion in a plasma reactor
Dries Michiels
Coach: Anton Nikiforov
Promotors: Prof. dr. ir. Kevin Van Geem, Prof. dr. ir. Christophe Leys
Master's dissertation submitted in order to obtain the academic degree of
Master of Science in Chemical Engineering
Department of Chemical Engineering and Technical Chemistry
Chair: Prof. dr. ir. Guy Marin
Department of Applied Physics Chair: Prof. dr. ir. Christophe Leys
Faculty of Engineering and Architecture Academic year 2015-2016
SUMMARY
The mechanisms and main reaction pathways in non-thermal plasma methane reforming are
unraveled by observation and quantification of a batch process. Primarily, methane dissociation
reactions into neutral particles are bound to be the most significant initial reaction steps. The formed
neutrals subsequently recombine to form C2+ hydrocarbon products. Even more so, the product
components were quantified which led to the conclusion that hydrogen and ethane are most
prominent. Furthermore, conversion, selectivities and energy costs are reported. In order to extend
the gained knowledge and industrial applicability of this process, a fully-fledged continuity equation
based kinetic model is established for the purpose of scale-up discussions. The model is also validated
with the use of various deduced quantities from experimentally obtained data. The effect of reactor
pressure on the obtained results is also discussed throughout.
9. Eliasson, B., C.-j. Liu, and U. Kogelschatz, Direct
Conversion of Methane and Carbon Dioxide to
Higher Hydrocarbons Using Catalytic Dielectric-
Barrier Discharges with Zeolites. Industrial &
Engineering Chemistry Research, 2000. 39(5): p.
1221-1227.
10. Beloqui Redondo, A., E. Troussard, and J.A. van
Bokhoven, Non-oxidative methane conversion
assisted by corona discharge. Fuel Processing
Technology, 2012. 104: p. 265-270.
I
TABLE OF CONTENTS
List of Figures .......................................................................................................................................... IV
List of Tables ........................................................................................................................................... IX
List of Symbols and Abbreviations ......................................................................................................... XI
J.1 Front 1-20 ................................................................................................................................... 160
A.1 Front 21-… ................................................................................................................................. 160
A.2 Back A-I ...................................................................................................................................... 160
Figure 1-2: Main reaction pathways during plasma discharge in a methane atmosphere.7 .................. 2
Figure 1-3: Outline of the mater thesis presented in a tree like schematic. ........................................... 3
Figure 2-1: Outline of the literature review presented in a tree like schematic. .................................... 5
Figure 2-2: Fluidized bed H-SAPO-34 methanol to olefins process developed by UOP coupled with the
olefin cracking process developed by Total and UOP for increased olefin yield.17 ............................... 10
Figure 2-3: The energy distribution of electrons (or ions) in a plasma given by the Maxwell Boltzmann
distribution. N represents the number of electrons or ions. Graph 2 depicts a distribution with a higher
temperature than graph 1.30 ................................................................................................................. 13
Figure 2-4: Classification of plasmas based on the electron temperature and density. Other
characteristic plasma parameters such as the plasma parameter, electron Debye length and electron
plasma frequency are also shown.26 ..................................................................................................... 15
Figure 2-5: Voltage-current characteristic of electrical discharge in neon at 133.32 Pa, with two planar
electrodes separated by 50 cm. The region indicated by A to B is Townsend discharge. Corona discharge
is located at point B.29 ........................................................................................................................... 16
Figure 2-6: Various electron impact reactions for non-thermal plasma methane activation.40 ........... 18
Figure 2-7: Comparison of methane conversion and conversion per unit of specific energy density (SED)
for different plasmas.44 ......................................................................................................................... 20
Figure 2-8: Schematic of a dielectric-barrier discharge reactor.49 ........................................................ 21
Figure 2-9: Schematic of a corona discharge reactor.49 ........................................................................ 23
Figure 2-10: Schematic of a spark discharge reactor (left), picture of spark discharge plasma (right).49
Figure 3-16: The RGA consists of two detectors TCD and FID. Argon is used as a reference component
for the TCD which is subsequently used to quantify methane that is used as reference component in
the FID detector. ................................................................................................................................... 56
VI
Figure 4-1: Outline of the chapter concerning the results and discussion presented in a tree like
schematic. Red indicates the experiment and blue represents the abstracted quantities from the latter
Hydrogen gas manufacturing from hydrocarbon fuels is needed for a variety of applications. These
applications include fuel cells used in stationary electric power production and in vehicular propulsion.
Hydrogen gas is also needed for many industrial applications such as hydrogenation reactors in
refineries. There is a wide range of requirements on the hydrogen manufacturing systems: capacity,
purity of the hydrogen, etc. Plasma technology has potential advantages over conventional means of
manufacturing hydrogen. The shortcomings of the conventional reformers include the need for large-
scale plants, cost and deterioration of catalysts; size and mass requirements; limitations on rapid
response; and limitations on hydrogen production from heavy hydrocarbons.3-5
Chapter 1: Introduction 2
Current industrial material and energy conversion technology is based on thermochemical processes
including various catalytic reactions. Present industry scale technology and related science is well
established, but further improvements in energy efficiency and material saving are required. Drastic
reductions in CO2 emissions are also important with the growing concern for energy and environmental
issues. Green chemistry is a rapidly growing field of science and technology, and often emphasizes
renewable bioenergy, bioprocesses, and solar photo catalysis of water splitting and CO2 regeneration
to provide synthetic fuels. Plasma catalysis of hydrocarbon feedstock is also an important component
of innovative next generation green technologies that are expected to satisfy the needs for energy
saving, environment protection, and resource conservation.6
Figure 1-2: Main reaction pathways during plasma discharge in a methane atmosphere.7
Plasma technology is even more interesting when considering non-thermal plasmas. This type of
plasma operates at low gas temperature which results in a higher electron temperature and thus
chemical reactivity. The most prominent reactions are shown in Figure 1-2. These generated reactive
species are used to initiate chemical reactions at much lower temperatures than the corresponding
thermochemical reactions. Therefore, non-thermal plasma extends the operation window of existing
chemical conversion processes, which will ultimately enable better control over the process
parameters to save energy, resources and to protect the environment.6
Chapter 1: Introduction 3
1.1. OUTLINE OF THE THESIS
Now that a general introduction has been given concerning the topic of methane reforming in general
and why plasma can yield certain benefits, the outline and structure of the work is presented in a tree
like schematic shown in Figure 1-3.
Figure 1-3: Outline of the mater thesis presented in a tree like schematic.
In Chapter 2, a literature review is performed in order to summarize the state of the art methane
conversion technologies. The latter includes industrialized as well as niche techniques in development
such as plasma methane reforming. In Chapter 3, the experimental setup is discussed with a specific
focus on the analyzers utilized. Experimental methods in order to obtain data from the setup in a
consistent way are also introduced. Chapter 4 focuses on the results with methane as feed in a batch
plasma reactor for different experiments. These results will be discussed and interpreted. Chapter 5
deals with kinetic modelling of the plasma reforming process. Generally, a fully functional and
developed model is useful to assess the optimal operating conditions and possible upscale
opportunities. In Chapter 6, a global conclusion of the whole thesis is given. In Chapter 7 improvements
and future work is discussed, the latter two topics are tightly related and are therefore combined.
Thesis
Introduction Body
Literature Experimental
Results Modelling
Conclusion Future work
Chapter 1: Introduction 4
1.2. REFERENCES
1. (EIA), U. S. E. I. A., Shale gas extration numbers. 2. J. Dedeyne, J. A., B. Devocht, A. De Vylder, Y. Marien, M. Matton, L. Temmerman, B. Vernimmen, Techno-economic study of methane activated olefin production. 2014, 171. 3. Mac Rae, D. R., Plasma arc process systems, reactors, and applications. Plasma Chemistry and Plasma Processing 1989, 9, (1), 85S-118S. 4. Bromberg, L.; Cohn, D. R.; Rabinovich, A.; Alexeev, N., Plasma catalytic reforming of methane. International Journal of Hydrogen Energy 1999, 24, (12), 1131-1137. 5. Bromberg, L.; Cohn, D. R.; Rabinovich, A.; O'Brie, C.; Hochgreb, S., Plasma Reforming of Methane. Energy & Fuels 1998, 12, (1), 11-18. 6. Nozaki, T.; Okazaki, K., Innovative Methane Conversion Technology Using Atmospheric Pressure Non-thermal Plasma. Journal of the Japan Petroleum Institute 2011, 54, (3), 146-158. 7. Ravasio, S.; Cavallotti, C., Analysis of reactivity and energy efficiency of methane conversion through non thermal plasmas. Chemical Engineering Science 2012, 84, 580-590.
Chapter 2: State of the art methane conversion technologies 5
Chapter 2: STATE OF THE ART METHANE CONVERSION TECHNOLOGIES
Before discussing results and the experimental methods used to obtain data from the setup, it is a
common and a good practice to start with a more theoretical approach to the subject at hand. A few
topics are discussed ranging from present industrial methane conversion processes to some basic
theoretical derivations to understand collective properties of plasma. A tree like schematic which
represents the outline and structure of the literature review is shown in Figure 2-1.
Figure 2-1: Outline of the literature review presented in a tree like schematic.
First, conventional methane conversion technologies are discussed. These processes can be divided
and distinguished based on if they are direct or indirect. This distinguishes if the methane conversion
is conducted in one step (direct) or multiple steps (indirect).
Methane conversion
Conventional technologies
Direct
Indirect
Plasma as unconventional
technology
Theory of plasma
Definition of plasma
Plasma parameters
The Boltzmann equation
Classification of plasmas
Chemistry in plasma
Reactions
Catalysis
Reactor configurations
Dielectric-barrier discharge
Corona discharge
Spark discharge
Rotating and gliding arc
Chapter 2: State of the art methane conversion technologies 6
As the next subtopic, the theory of plasma is briefly introduced with some basic mathematical
derivations. The definition of plasma will be given and a more in-depth discussion is conducted on the
most important plasma parameters. With the use of these parameters it is possible to classify different
plasmas. From these plasma types, gas discharge is further elaborated due to the fact that this plasma
is used throughout this thesis. Probably the most important subsection of the literature review is the
section about plasma chemistry. Plasma chemistry is very complex with a lot of different reactions of
different natures occurring at the same time. Furthermore, the ability of plasma catalysis is highlighted
in conjunction with plasma as methane activator at low temperatures. The last section will deal with
the most common plasma reactor configuration used for methane conversion. They are discussed and
literature results are presented. These include typical parameters and chemical quantities in the
process such as the input power, conversion, selectivities, etc.
Chapter 2: State of the art methane conversion technologies 7
2.1. CONVENTIONAL METHANE CONVERSION TECHNOLOGIES
Direct conversion of methane has been extensively investigated. The reaction products are mostly the
highly desired ones in the likes of ethylene, methanol, benzene and hydrogen. To solve problems for
the wider use of natural gas, a lot of effort has been devoted to conversion processes that operate
efficiently. In what follows a classification is made based on if the conversion of methane is direct, i.e.
in one step or indirect i.e. more than one process is required to form the desired products from
methane.1
2.1.1. Conventional direct methane conversion
A direct synthesis route from methane is extremely interesting because of the reduced cost of
transportation and storage of intermediate products, and of course the need of only one
process/reactor. In what follows the most significant direct methane conversion processes are
discussed. These include oxidative coupling, aromatization and selective partial oxidation. On a side
note, it is important to realize that none of these processes have been commercialized yet.
2.1.1.1. Oxidative coupling
The most famous and well-known reaction for direct methane conversion is oxidative coupling. The
conversion results in ethylene and water for reaction products as shown in Equation 2-1.1
𝐶𝐻4 +1
2𝑂2 →
1
2𝐶2𝐻4 + 𝐻2𝑂 ∆𝐻
0 = −140 𝑘𝐽/𝑚𝑜𝑙 2-1
Early research from 1982 indicated that operating conditions varied between 500 °C and 1000 °C at
atmospheric pressure depending on the desired product composition. Catalysts are required, the most
active ones for C2 formation were oxides of metals Sn, Pb, Sb, Bi, Tl, Cd and Mn. However, for the
process to be classified as viable it was found that a catalyst for the oxidative coupling of methane
should have a yield of 16% to 30% with a selectivity larger than 80%. These economic analyses typically
assumed industry level conditions of pressures and undiluted feed streams. The problem here, at
laboratory-scale catalyst screening has focused almost exclusively on operation at atmospheric
pressure with dilute feed streams. Even under these highly desired conditions of lab-scale experiments,
none of the synthesized catalyst were able to achieve the commercially viable conversions and yields.
This indicates a theoretical limitation in the reaction of oxidative coupling of methane. Indeed,
researcher have found a theoretical upper bound for C2 yield of 28% under laboratory conditions. The
latter translates into the fact that existing catalysts are already bounded by the theoretical limitation.
As a consequence, oxidative coupling does not appear to be viable with current economics.2-6
2.1.1.2. Aromatization
Another reaction known as aromatization also converts methane as shown in Equation 2-2.
Chapter 2: State of the art methane conversion technologies 8
6𝐶𝐻4 ⇋ 𝐶6𝐻6 + 9𝐻2 ∆𝐻0 = 531 𝑘𝐽/𝑚𝑜𝑙 2-2
The latter equation is however a simplification of the real reaction network for there are multiple
reactions that are taking place simultaneously. The main products hereof are benzene, toluene and
xylene (BTX). When the reaction shown in Equation 2-2 takes place at a temperature of 700 °C on a
zeolite catalyst like Mo2C/ZSM-5 a 60-80% selectivity to benzene is achievable. BTX are characterized
by an easy separation from the unconverted methane which is an important advantage of this process.
The latter results in BTX at the desired purity for reprocessing in the chemical industry. As of till now,
this process sounds very promising however, the reaction is an equilibrium reaction which limits
methane conversion to 5% at 600 °C, 11% at 700 °C and 16% at 800 °C which is low. Corresponding
benzene yields of lower than 10% are no incentive to consider this process in the industry.1, 7, 8
2.1.1.3. Selective partial oxidation
Burning methane or fully oxidizing it is one way of ‘converting’ it into energy. However, when using a
transition metal catalyst to suppress full oxidation at a temperature of 400-1000 °C, it is possible to
produce mainly methanol and formaldehyde. Although, the suppression can never be ideal so there
will always be some H2 and CO produced as well as some ethylene from the oxidative coupling reaction.
The reaction products, methanol and formaldehyde can potentially be further processed in
petrochemical plants. Mostly, this process is coupled with the methanol to olefins process to obtain
ethylene.1, 9
2.1.2. Conventional indirect methane conversion
Indirect methane conversion basically consists out of two main steps. The first step is the conversion
of methane to a gas mixture of carbon monoxide and hydrogen gas also known as syngas. Processes
used for the latter are steam reforming, dry reforming and partial oxidation. Afterwards, this syngas is
converted with the suggested available technology to the desired hydrocarbons; like the methanol to
olefins process or Fischer-Tropsch.
2.1.2.1. Syngas production
Steam reforming is a process that is vastly optimized and implemented in the industry for the
production of syngas from methane. Methane is a very stable molecule in his tetrahedral form. This
indicates that methane has to be processed under very severe conditions; for example, a temperature
of 500-900°C where the yield of the syngas increases with reaction temperature. The production of
syngas starting from methane is favored because the investment cost for a coal based plant is three
times as large as for a natural gas-based plant. The reaction in a coal based plant is given in Equation
2-3.10
Chapter 2: State of the art methane conversion technologies 9
3𝐶 + 𝑂2 +𝐻2𝑂 → 𝐻2 + 3𝐶𝑂 ∆𝐻0 = −45.7 𝑘𝐽/𝑚𝑜𝑙 2-3
The overall reaction of methane reforming as shown in Equation 2-4 is highly endothermic. The
reaction takes place by contacting steam over a heated platinum or rhodium catalyst at high pressures
and temperatures of 500-900 °C to produce a high hydrogen containing mixture.10
𝐶𝐻4 + 𝐻2𝑂 → 𝐶𝑂 + 3𝐻2 ∆𝐻0 = 206 𝑘𝐽/𝑚𝑜𝑙 2-4
After the first reaction, an excess of steam is added to promote the water-gas shift reaction shown in
Equation 2-5, which increases the hydrogen content of the syngas.
𝐶𝑂 + 𝐻2𝑂 → 𝐶𝑂2 + 𝐻2 ∆𝐻0 = −41 𝑘𝐽/𝑚𝑜𝑙 2-5
For the total process, two reactors should be considered; a steam reformer and a shifter. During the
shift reaction, excess CO2 is produced that must be vented or unwanted side reactions such as the
formation of carbon are always plausible. The most important advantage of steam reforming is the no
oxygen requirement policy. A disadvantage however, is that there exists a necessary balance between
the heat generated by combustion and heat consumed by reaction which should hold throughout the
reactor. Flow rates and flow distributions are monitored and controlled rigorously.10, 11
Dry reforming is another way to produce syngas. The process uses two greenhouse gases it is
recognized as an environmental friendly process. Furthermore, a big advantage of this process is the
fact that it is able to produce CO:H2 in an almost 1:1 ratio. Of course this is not wanted if one uses this
processes primarily for its hydrogen content; like hydrocrackers in refineries. However, when one is
planning to use the syngas of this process for the formation of oxygenated compounds it is very
beneficial to have a 1:1 ratio. A drawback is the highly endothermic nature of the reaction as shown in
Equation 2-6 which results in high temperature conditions.12
𝐶𝐻4 + 𝐶𝑂2 → 2𝐶𝑂 + 2𝐻2 ∆𝐻0 = 247 𝑘𝐽/𝑚𝑜𝑙 2-6
Also, when operating with CO2 and CH4 as the only compounds or only CO2, carbon formation is to be
expected. Carbon deposition can be suppressed by using noble metals instead of Ni, which is ascribed
to a smaller dissolution of carbon into these noble metals.12
Partial oxidation is also a way of producing syngas. This reaction is shown in Equation 2-7, because the
reaction is only slightly exothermic it is possible to work in an adiabatic reactor. This is a clear
advantage over both steam reforming processes. When methane is fed together with oxygen over a
catalyst bed, a ratio of 2:1 H2:CO is obtained.13
𝐶𝐻4 +1
2𝑂2 → 2𝐻2 + 𝐶𝑂 ∆𝐻
0 = −36 𝑘𝐽/𝑚𝑜𝑙 2-7
Chapter 2: State of the art methane conversion technologies 10
On a further note, a near complete conversion of methane is possible in only a fraction of a second, 1
ms to be exact. This very low reaction time is a huge advantage in terms of reactor sizing compared to
the other syngas production processes.14
2.1.2.2. Syngas to olefins
The process where methanol is converted to gasoline was commercialized by Mobil on an H-ZSM-5
catalyst. However, further research indicated that H-SAPO-34 could be used to increase the olefin yield
of the process. This is due to the shape selectivity introduced by its chabazite structure of large cavities
connected by 8-rings. As a consequence, the selectivity to light olefins increases to up to 80%.
Moreover, it is possible to vary the conditions of the process to increase propylene yield; and in general
play with the product composition. A big disadvantage of H-SAPO-34 is that it cokes a lot faster than
H-ZSM-5, and thus requires near continuous regeneration. A specialized reactor design should thus be
taken into consideration that can effectively regenerate the catalyst. A fluidized bed reactor would be
the perfect candidate. UOP developed the H-SAPO-34 based MTO process, applying a low-pressure
fluidized-bed reactor designed to enable a very efficient temperature control and continuous
regeneration. Furthermore, ethylene and propylene selectivity could be augmented by combining the
UOP MTO process with an olefin cracking process (OCP) developed by Total Petrochemicals and UOP,
a schematic representation is given in Figure 2-2.15-17
Figure 2-2: Fluidized bed H-SAPO-34 methanol to olefins process developed by UOP coupled with the olefin cracking process
developed by Total and UOP for increased olefin yield.17
Methanol is converted into olefins over H-SAPO-34 in the reactor and is afterwards sent to a first
column that condenses water and higher olefins. These higher olefins are then cracked in the OCP
reactor. The C3- fraction is sent over the top of the column and the C4+ fraction is recycled to the reactor
or used in a subsequent step.
Chapter 2: State of the art methane conversion technologies 11
The process known as Fischer-Tropsch synthesis is a catalytic process for the conversion of syngas into
a mixture of products that can further be refined into lubricants and other petrochemicals. Most of
the products obtained from the distillation in refineries can also be produced from the Fischer-Tropsch
syncrude. However, this is rarely economically viable because of the relatively high capital investments
and complexity of the Fischer-Tropsch process. The application of Fischer-Tropsch inevitably consists
of three basic processing steps: syngas preparation, Fischer-Tropsch synthesis and product upgrading.
A distinct advantage for the Fischer-Tropsch process is its flexibility; by tuning process conditions it is
possible to obtain considerable different product compositions. For the production of gasoline and
light olefins, the Fischer-Tropsch process is operated at a high temperature of 330-350 °C (high
temperature Fischer-Tropsch, HTFT), on the contrary for the production of waxes and diesel fuel low
temperatures of 220-250 °C are used (low temperature Fischer-Tropsch, LTFT). The differences in
operating conditions are outlined in Table 2-1.18, 19
Table 2-1: Process conditions for both low temperature and high temperature Fischer-Tropsch synthesis.
LTFP HTFP
Temperature [°C] 220-250 330-350
Pressure [MPa] 2.5-4.5 2.5
CO+H2 conversion [%] 60-90 85
The main reaction in the synthesis is the hydrogenation of carbon monoxide to hydrocarbons on group
VIII metals. These metals include Ru, Co and Fe. There is still a lot of uncertainty about the reaction
mechanism, generally three theories have been developed. In the first proposed mechanism, the
carbon monoxide dissociates at the catalyst surface to carbon and oxygen atoms. Chemisorbed
hydrogen reacts with the carbon to form CHx species, which further combines to higher hydrocarbon
products. The chemical nature of the products are α-olefins and alkanes due to the present termination
reactions. In the second theory, carbon monoxide does not dissociate but is hydrogenated to form
oxymethylene species. The latter condenses on the catalyst surface to propagate the hydrocarbon
chain. For the last proposed theory, the carbon monoxide molecule does not dissociate but is inserted
in a catalyst hydrogen bond or a catalyst carbon bound. This mechanism is the most widely accepted
because insertion of carbon monoxide is known to occur in the presence of homogenous catalysts. The
evidence for heterogeneous systems is however, inconclusive.20-22
Chapter 2: State of the art methane conversion technologies 12
2.2. THEORY OF PLASMA
A general definition of plasma is proposed in this section. Following, the most important plasma
parameters are elaborated which include the electron temperature and the electron density. A small
paragraph is also devoted to the Boltzmann equation. Furthermore, gas discharge plasma generation
is discussed in more detail for it is used in this thesis.
2.2.1. Definition of plasma
-A plasma is a quasineutral gas of charged and neutral particles which exhibits collective behavior-23
-A gas which is at least partially ionized and contains particles of various types like: electrons, atoms,
ions and molecules. The plasma as a whole is electrically neutral-24
Consider the forces acting on a non-charged molecule say ordinary air. The molecule moves
undisturbed until it makes a collision with another molecule. These collisions are what control the
motion of the particle. This is a totally different story in plasma, which contains charged particles. As
these charges move around, they generate local concentrations of positive and negative charge. These
gradients result in electric fields. Motion of charges also generates electric currents and hence
magnetic fields. These fields affect the motion of other charged particles far way. By collective plasma
behavior it is understood that the motion of the particles not only depends on local conditions but also
on the state of the plasma in remote regions. Quasineutrality on the other hand, can be understood
when one considers that it is not physically possible for a positive charge to exist without its negative
counterpart present. The plasma is quasineutral, that is, neutral enough so that the density of negative
charge is approximately equal to the density of positive charge i.e. ne≈ni. But not so neutral that all the
electromagnetic forces vanish.23, 25-27
2.2.2. Plasma parameters
There are a lot of parameters used to define properties of plasma. Only the most common plasma
parameters are discussed here which are used throughout this work. The latter includes the electron
temperature and electron density. For more than just the basic plasma parameters, the reader is
referred to Appendix A for the definition of all plasma parameters in existence.
2.2.2.1. Mean energy – electron/ion temperature, Te, Ti
The kinetic energy of a particle with mass mp moving with a speed up equals Ep=1/2∙mpup². For an
assembly of N particles with different kinetic energies, the average energy of the assembly is given by
Eav=1/2N∙∑p=1Nmpup². There are however other ways of expressing the average energy with the use of
distribution theory. The most probable distribution of the velocities of the particles is the so-called
Chapter 2: State of the art methane conversion technologies 13
Maxwellian (or Maxwell-Boltzmann) distribution which is given by Equation 2-8 and is derived in
Appendix B.23, 28
𝑓(𝑢) = 𝐷𝑒−12𝑚𝑢²𝑘𝑏𝑇 2-8
Here f(u)du represents the number of particles per unit volume with a velocity in the range of u to
u+du with D a normalization factor. The Maxwell Boltzmann distribution can perfectly be expressed in
terms of kinetic energy by a simple substitution. The effect of the temperature on the distribution is
shown in Figure 2-3. The average energy of the particles can be calculated from f(u) which results in
Eav=1/2∙kbT for a one dimensional Maxwellian distribution and Eav=3/2∙kbT for a distribution considered
in three dimensions. The temperature of a gas in thermodynamic equilibrium can also be expressed in
units of energy and thus eV. Typically, the energy corresponding to kbT = 1 eV is used to denote the
temperature.28, 29
Figure 2-3: The energy distribution of electrons (or ions) in a plasma given by the Maxwell Boltzmann distribution. N represents
the number of electrons or ions. Graph 2 depicts a distribution with a higher temperature than graph 1.30
Sometimes equilibrium situations are not present. If that is the case, a true temperature cannot be
assigned, although the interpretation of temperature as the average energy is still valid. It should also
be noted that temperature is referring to a kinetic temperature i.e. the state of energy of particles. A
high value of the temperature does not necessarily mean a lot of heat, because the latter also depends
on other factors like the number of particles and the heat capacity. A plasma is also perfectly capable
of having several temperatures at the same time. This is because often, electrons and ions have
separate Maxwell Boltzmann distributions of different temperatures of respectively Te and Ti. A
difference between these two equilibria can arise because the collision rate among ions or electrons
themselves is larger than that between ions and electrons. This results in a distinction between thermal
plasmas (equilibrium plasma, hot plasma, Te=T) and non-thermal plasmas (non-equilibrium plasma,
Chapter 2: State of the art methane conversion technologies 31
2.5. CONCLUSION
Present methane conversion is carried out in an indirect manner i.e. methane to syngas and
subsequently converting syngas into the desired products. This means that methane is first converted
to syngas is mostly carried out by the steam reforming processes in the industry to date. The second
step is then either the methanol to olefins process or Fischer-Tropsch synthesis. Direct conversion is a
promising route for converting methane but it has not been industrialized yet. Unconventional
technologies however, have the potential of increasing energy efficiency drastically by working at
ambient temperatures. Plasma as methane conversion technology has been discussed throughout.
For the theory of plasma, it is important to realize that plasma is quasineutral and that it can be
characterized by plasma parameters. The most important plasma parameters are the electron
temperature and electron density. Furthermore, the transport of particles in a plasma can be described
by the Boltzmann equation. Gas discharge is an important plasma type which is specifically interesting
to create non-equilibrium plasma discharges.
Plasma chemistry is complex and generally consists of two networks, one with electron impact
collisions were the main purpose is to produce radicals that can subsequently react in the second
network. The second network consumes radicals and produces chemical substances i.e. the products
by means of recombination and other radical reactions.
Presently, the most common plasma reactor configurations are dielectric-barrier discharge, corona
discharge, spark discharge and gliding and rotating arc reactors. Each configuration has his distinct
feature that include: results, benefits and disadvantages.
Plasma methane reforming is a highly flexible process. The main reason for this is the amount of
process conditions and input values that can be tuned. These include the power input, feed flow rate,
electrode shape, electrode distance, power source used and many more. By optimizing all parameters
towards one product yield, it is possible to achieve industrial grade conversion and selectivity.
Upscaling techniques are still in active research and development for non-thermal plasma methane
reforming.
Chapter 2: State of the art methane conversion technologies 32
2.6. REFERENCES
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Chapter 2: State of the art methane conversion technologies 33
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Chapter 2: State of the art methane conversion technologies 34
50. Kogelschatz, U.; Eliasson, B.; Egli, W., From ozone generators to flat television screens: history and future potential of dielectric-barrier discharges. Pure and Applied Chemistry 1999, 71, (10), 1819-1828. 51. Jeong, H.-K.; Kim, S.-C.; Han, C.; Lee, H.; Song, H. K.; Na, B.-K., Conversion of methane to higher hydrocarbons in pulsed DC barrier discharge at atmospheric pressure. Korean Journal of Chemical Engineering 18, (2), 196-201. 52. Li, X.-S.; Zhu, A.-M.; Wang, K.-J.; Xu, Y.; Song, Z.-M., Methane conversion to C2 hydrocarbons and hydrogen in atmospheric non-thermal plasmas generated by different electric discharge techniques. Catalysis Today 2004, 98, (4), 617-624. 53. Liu, C.-J.; Xue, B.; Eliasson, B.; He, F.; Li, Y.; Xu, G.-H., Methane Conversion to Higher Hydrocarbons in the Presence of Carbon Dioxide Using Dielectric-Barrier Discharge Plasmas. Plasma Chemistry and Plasma Processing 2015, 21, (3), 301-310. 54. Kim, S.-S.; Kim, J.; Lee, H.; Na, B.-K.; Song, H. K., Methane conversion over nanostructured Pt/γAl2O3 catalysts in dielectric-barrier discharge. Korean Journal of Chemical Engineering 22, (4), 585-590. 55. Zhu, A.; Gong, W.; Zhang, X.; Zhang, B., Coupling of methane under pulse corona plasma (I). Science in China Series B: Chemistry 43, (2), 208-214. 56. Zhao, G.-B.; John, S.; Zhang, J.-J.; Wang, L.; Muknahallipatna, S.; Hamann, J. C.; Ackerman, J. F.; Argyle, M. D.; Plumb, O. A., Methane conversion in pulsed corona discharge reactors. Chemical Engineering Journal 2006, 125, (2), 67-79. 57. Beloqui Redondo, A.; Troussard, E.; van Bokhoven, J. A., Non-oxidative methane conversion assisted by corona discharge. Fuel Processing Technology 2012, 104, 265-270. 58. Aleksandrov, N. L.; Bazelyan, E. M., Ionization processes in spark discharge plasmas. Plasma Sources Science and Technology 1999, 8, (2), 285. 59. Xiao-Song, L.; Can-Kun, L.; Chuan, S.; Yong, X.; You-Nian, W.; Ai-Min, Z., Stable kilohertz spark discharges for high-efficiency conversion of methane to hydrogen and acetylene. Journal of Physics D: Applied Physics 2008, 41, (17), 175203. 60. Kado, S.; Sekine, Y.; Fujimoto, K., Direct synthesis of acetylene from methane by direct current pulse discharge. Chemical Communications 1999, (24), 2485-2486. 61. Parvulescu, V. I.; Magureanu, M.; Lukes, P., Plasma chemistry and catalysis in gases and liquids. John Wiley & Sons: 2012. 62. Lee, D. H.; Kim, K. T.; Cha, M. S.; Song, Y. H., Optimization scheme of a rotating gliding arc reactor for partial oxidation of methane. Proceedings of the Combustion Institute 2007, 31, (2), 3343-3351. 63. Rueangjitt, N.; Sreethawong, T.; Chavadej, S.; Sekiguchi, H., Non-Oxidative Reforming of Methane in a Mini-Gliding Arc Discharge Reactor: Effects of Feed Methane Concentration, Feed Flow Rate, Electrode Gap Distance, Residence Time, and Catalyst Distance. Plasma Chemistry and Plasma Processing 2011, 31, (4), 517-534. 64. Hwang, N.; Lee, J.; Lee, D.; Song, Y.-H., Interactive Phenomena of a Rotating Arc and a Premixed CH4 Flame. Plasma Chemistry and Plasma Processing 2012, 32, (2), 187-200. 65. Hu, S.; Wang, B.; Lv, Y.; Yan, W., Conversion of Methane to C2 Hydrocarbons and Hydrogen Using a Gliding Arc Reactor. Plasma Science and Technology 2013, 15, (6), 555.
Chapter 3: Experimental 35
Chapter 3: EXPERIMENTAL
This chapter is devoted to the aspect of the experiments. These aspects include the setup as well as
the methods used to obtain data from the setup in a consistent way. The outline of this chapter is
depicted in Figure 3-1.
Figure 3-1: Outline of the chapter concerning the setup and methods presented in a tree like schematic.
Frist, the general setup is discussed starting with a complete schematic of the setup. Here, all
equipment is discussed that do not include the analyzers; the power supply, manometers, multimeters,
valves, gas bottles, etc. Each analysis device is devoted one sections for their details. These analyzers
include the optical emission spectroscope, refinery gas analyzer, mass spectrometer, x-ray
photoelectron spectroscope and the scanning electron microscope. Secondly, the methods are
discussed. They provide a step wise paragraph that allows the reader to repeat experiments
consistently. The methods deal with data acquisition from the experimental setup. For each
experiment, specific subsections are dedicated to data processing of the obtained raw data into other
quantities.
Experimental
Setup
General setup
Analyzers details
Methods
Data acquisition
Data processing
Chapter 3: Experimental 36
3.1. SETUP
In Figure 3-2 the different equipment is highlighted. In what follows, each number is discussed in more
detail. Model numbers, general properties and a schematic will be included for the most important
devices available in the setup.
8
16
13
14
15
3
2
1
9
6
7
To ouside vent
MS
OES
1817
DC5
4
10
11
12
RGA7
off-line
B
A
F
SEM
XPS
Figure 3-2: Schematic of the setup. 1) pump 2) turbo-molecular pump 3) reactor valve 4) current indicator 5) voltage indicator
6) DC power supply 7) Tedlar bag 8) Reactor vessel 9) discharge plate 10) discharge pins 11) POM base plate 12) resistors 13)
In Equation 3-24, Vr is the volume of the reactor in m³, R is the ideal gas constant and T is the gas
temperature in K, pCH4,0 is the initial methane pressure in Pa also indicated as pressure P0 in Section
3.2.1, pAr is the pressure of argon in Pa which is equal to P2- P1 as indicated in Section 3.2.5, MCH4 and
MAr are the molar mass in g/mol of methane and argon respectively.
3.2.5.4. Conversion
The conversion of a reaction network is based on the limiting reactant A. For this work, methane is the
only reactant and thus also the limiting one. The conversion based on limiting reactant A is obtained
with the use of Equation 3-25.
𝑋𝐴(𝑡) =𝑛𝐴(𝑡 = 0) − 𝑛𝐴(𝑡)
𝑛𝐴(𝑡 = 0) 3-25
As already mentioned the conversion is based on methane (A=CH4). In the latter equation, XA(t) is the
conversion based on limiting reactant A at time t, nA(t=0) is the initial amount of limiting reactant A in
mol and nA(t) is the amount of limiting reactant in mol at time t. The initial amount of methane can be
calculated with the ideal gas law as indicated in Equation 3-26.
Chapter 3: Experimental 60
𝑝𝐶𝐻4,0𝑉𝑟 = 𝑛𝐶𝐻4,0𝑅𝑇 <=> 𝑛𝐶𝐻4,0 =𝑃0𝑉𝑟𝑅𝑇
3-26
Here, pCH4,0 is the initial methane pressure equal to P0, Vr the reactor volume in m³, nCH4,0 the initial
methane amount in mol, R the ideal gas constant and T the gas temperature in K. Even more so, from
the conversion, the cost of the conversion can be calculated with the use of Equation 2-16.
𝜂𝐴(𝑡) =𝑃∆𝑡
𝑁𝐴(𝑛𝐴(𝑡 = 0) − 𝑛𝐴(𝑡)) 3-27
For the latter equation, ηA(t) is the conversion cost of limiting reactant A at time t in J/mol, P is either
the total or plasma power depending on the required definition of ηA in W, Δt is the timeframe of
plasma operation in seconds, NA Avogadro’s number, nA(t=0) the initial amount of reactant A in mol
and nA(t) the amount of reactant A at time t in mol. The denominator represents the amount of
converted molecules. The power indicated in Equation 3-27 is calculated with the use of Equation 3-28.
𝑃𝑡 = 𝑈𝑡 ∙ 𝑖 𝑃𝑝 = 𝑈𝑝 ∙ 𝑖 3-28
If one wants to calculate the total power based conversion cost (which is prone to the plasma
generation setup), the total power Pt is calculated in W, Ut is the total voltage of the power supply in
V and i is the current in A. On the other hand if one searches the plasma power based conversion cost
(which is not prone of the plasma setup), Pp is calculated in W, Up is the plasma voltage in V (as
calculated in Section 3.2.4.1) and i is the current in A. The conversion energy costs are reported in
eV/molecule to be literature comparable.
3.2.5.5. Yield
The yield of a formed component i can be calculated with the use of Equation 3-29.
𝑌𝑖,𝐴(𝑡) =𝑛𝑖(𝑡) − 𝑛𝑖(𝑡 = 0)
𝑛𝐴(𝑡 = 0), 𝑖 ≠ 𝐴 3-29
In the latter equation, Yi,A(t) is the yield of component i based on limiting reactant A at time t, ni(t) is
the amount of moles of component i at time t in mol, ni(t=0) is the initial amount of component i in
mol and nA(t=0) is the initial amount of limiting reactant (methane) in mol. It should be noted that yield
is not defined for the reactant itself; in Equation 3-29, methane is omitted from the components i. The
initial amount of methane and the amounts of the other components at time t are calculated as
explained in Section 3.2.5.4 and 3.2.5.3 respectively. The other initial amounts of the components are
zero.
3.2.5.6. Selectivity
The selectivity of a formed component i can be calculated with the use of Equation 3-30.
Chapter 3: Experimental 61
𝑆𝑖,𝐴(𝑡) =𝑛𝑖(𝑡) − 𝑛𝑖(𝑡 = 0)
∑ 𝑛𝑖(𝑡)𝑙𝑖=1
, 𝑖 ≠ 𝐴 3-30
In the latter equation, Si,A(t) is the selectivity of component i based on limiting reactant A at time t, ni(t)
is the amount of component i in mol at time t and ni(t=0) is the initial amount of component i in mol.
It should be noted that selectivity is not defined for the reactant itself; in Equation 3-30, methane is
omitted from the components i. The initial amount of methane and the amounts of the other
components at time t are calculated as explained in Section 3.2.5.4 and 3.2.5.3 respectively. The other
initial amounts of the components are zero.
3.2.5.7. Energy cost
To calculate the energy costs of the produced components two values are needed. These values are
first, the mass of a component i in g and secondly the energy input in J or any other energy unit. The
energy input is calculated by multiplying the provided power by the time that this power is sustained
as shown in Equation 3-31. A distinction is made based on if the power is the total power or plasma
power, just like between the total and plasma voltage. This results in a total energy cost (dependent
on the electric configuration) and a plasma energy cost which is not dependent on the electric
configuration.
𝜂𝑖 =𝑚𝑖𝑃∆𝑡
, 𝑖 ≠ 𝐴 3-31
Here, ηi is the energy cost of component i in g/J which indicates how much energy is needed to produce
an amount of a component i in g, mi is the mass of component i in g, P is either the total power or
plasma power in W depending on the wanted reported energy cost as already explained in Section
3.2.5.4 and Δt is the time interval at which the plasma is active in s which in this case is equal to half
an hour or 1800 s. Energy costs of the produced components are reported in g/kWh to be literature
comparable.
3.2.6. Mass spectrometry – 1
After the emptying and filling procedure of the reactor as explained in Section 3.2.1, the mass
spectrometer is switched on and monitoring on the client computer is conducted. The mass
spectrometer is now in the on state for one hour which conclude one experimental run. For the first
20 minutes the plasma is maintained in the off state. The latter is done because for the first few
minutes excessive ionization takes place which leads to insignificant results. After these 20 minutes of
‘stabilization’ the pressure is noted down from manometer (14) and the power supply is turned on for
30 minutes i.e. plasma is present with a constant current of 1 mA to observe the effect of reaction on
the components. Afterwards, the voltage is noted down with multimeter (5). After 30 minutes of
reaction, the pressure is measured and logged from manometer (14) and the power supply is turned
Chapter 3: Experimental 62
off for 10 more minutes i.e. no plasma. The latter results in the stabilization of the mass spectrometer
sampling after reaction. To conclude this one-hour experiment, the final pressure is noted down from
manometer (14). For the high pressure components in the reactor (methane) the Faraday cup detector
is used while the product species are detected on the SCEM detector.
3.2.6.1. Component rates
From the mass spectrometer raw data, rates of the components are deduced by linear fitting. For the
first 20 minutes (really only the last 10 minutes of these 20 minutes because of excessive ionization),
the rate of pumping is measured because there are no reactions taking place. When the power supply
is switched on at 20 minutes i.e. plasma is present, the rate of sampling and reaction is measured.
From the latter, the net effect of reaction can be calculated. A similar strategy as in Section 3.2.2 is
utilized as shown in Equation 3-32.
𝑑𝑝
𝑑𝑡𝑟,𝑖,𝑀𝑆=𝑑𝑝
𝑑𝑡𝑜𝑛,𝑖,𝑀𝑆+ |𝑑𝑝
𝑑𝑡𝑜𝑓𝑓,𝑖,𝑀𝑆| 3-32
In the latter equation, dp/dtr,i,MS is the net effect of the reaction on the pressure of component i
observed in the mass spectrometer in Pa/s, dp/dton,I,MS is the effect of reaction and mass spectrometer
sampling on the pressure of component i observed in the mass spectrometer in Pa/s which is obtained
from the linear fitting between 20-50 minutes and lastly dp/dtoff,i,MS is the pressure decrease by the
mass spectrometer observed in the mass spectrometer in Pa/s which is obtained from linear fitting on
the first portion between 10-20 minutes of the curve of component i. Furthermore, a scaling factor is
calculated. The scaling factor is used to convert the observed pressures in the mass spectrometer to
pressure in the reactor as shown in Equation 3-33.
𝑑𝑝
𝑑𝑡𝑟,𝑖= 𝛾
𝑑𝑝
𝑑𝑡𝑟,𝑖,𝑀𝑆 3-33
𝛾 = 𝑝𝐶𝐻4,𝑟𝑒𝑎𝑐𝑡𝑜𝑟𝑝𝐶𝐻4,𝑀𝑆
3-34
Here, α is scaling factor calculated for the three reference pressures, pCH4,reactor is the reactor pressure
of methane at 20 minutes in Pa, pCH4,MS is the mass spectrometer pressure of methane at 20 minutes
in Pa. The scaling factor is used in Equation 3-33 where dp/dtr,i is the effect of reaction on pressure in
the reactor for component i in Pa/s, γ the scaling factor defined in Equation 3-34 and dp/dtr,i,MS the
effect of reaction on the pressure in the mass spectrometer for component i in Pa/s. The pressure rates
in the reactor can subsequently be converted from Pa/s to mol/s with Equation 3-35.
𝑑𝑛
𝑑𝑡𝑟,𝑖=𝑑(𝑝𝑉𝑟𝑅𝑇)
𝑑𝑡 𝑟=𝑉𝑟𝑅𝑇
𝑑𝑝
𝑑𝑡𝑟,𝑖 3-35
Chapter 3: Experimental 63
In the latter equations, dn/dtr,i represents the reaction rate of component i in the reactor in mol/s, Vr
the reactor volume in m³, R the ideal gas constant, T the gas temperature in K and dp/dtr is the
calculated net effect of reaction of component i on the pressure in Pa/s. The rates obtained are
quantitative indication of the slopes. They cannot be compared between components for the device is
not calibrated. However, with respect to the different pressures they are comparable.
3.2.7. X-ray photoelectron spectroscopy
After all experimental runs, the reactor is opened from the top and carbon samples are collected in
cylindrical specimen glass tubes. These samples are subsequently analyzed in the x-ray photoelectron
spectroscopy device to obtain the atomic amounts of the specimen. A pressure below 10-6 Pa is
maintained in the device. Survey scans (C1s, O1s and Si2p) are recorded with a pass energy of 117.4
eV and 23.5 eV respectively. The result is the atomic composition (at%) of the sample surface.
3.2.8. Scanning electron microscope
After all experimental runs, the reactor is opened from the top and carbon samples are collected in
cylindrical specimen glass tubes. These samples are subsequently analyzed under the SEM to check
the morphology of the unknown deposition. Three different resolutions are used, first a low resolution
to capture the bigger picture and afterwards a medium and high resolution to provide sufficient detail
of the surface morphology.
Chapter 3: Experimental 64
3.3. CONCLUSION
Throughout this chapter, the discussions handle the experimental setup and methods. Special focus is
devoted to the details in the methods so that an external person can repeat the experiment at hand
with ease. The plasma used during experiments is of glow discharge nature. Generally, it is important
that first the reactor is pumped to a very low pressure to prevent any contamination for the next
experimental run. The latter is of course a common routine before each experiment. For the pressure
versus time experiment, the pressure is noted down on a periodical basis of 10 minutes. The latter is
done first for the power supply off and afterwards on. The current is fixed at 5 mA when plasma is
present. From the pressure versus time characteristics, it is possible to obtain the global rate and with
some assumptions the initial hydrogen gas production rate. The initial hydrogen gas production rate is
used to validate the kinetic model. For optical emission spectrometry, the plasma was switched on and
with an optical emission spectroscope the active species in the reactor are detected. The current is
fixed at 4.5 mA when the power supply is switched on. Furthermore, by fitting of the experimentally
obtained spectra in a spectral simulation tool such as LIFBASE, the gas and vibrational temperature are
obtained. For the current versus voltage experiment, the current is noted down while varying the
voltage from the power supply. The current is limited to 4.5 mA during gas discharge. The latter
obtained data points results in the deduction of the reduced electric field which is used in conjunction
with the gas temperature in a Boltzmann equation solver (BOLSIG+) to result in the electron
temperature and mobility. These values are subsequently used to obtain the electron density of the
gas discharge. For refinery gas analysis experiments, the concept of a Tedlar bag for off-site analysis is
deployed. By sampling the reactor after 30 minutes of reaction with a fixed plasma current of 1 mA
and subsequently integrating the peak areas of the components from the analyzer, absolute
concentrations are obtained. From these concentrations; mole fractions, conversion, conversion cost,
yield, selectivity and energy efficiency are deduced. Pressure measurements at specific moments
during the experiment are necessary for data processing. For mass spectrometry, specific
measurements of pressure are conducted. Also, the device itself which samples the reactor
continuously results in a detector response versus time characteristic for each component. The current
is limited to 1 mA during plasma discharge. From these graphs, component rates are established to
qualitatively describe the effect of pressure on the component rates. The SEM and XPS experiments
are easiest to implement. After carefully obtaining the samples from the reactor, they are analyzed in
both devices. This results in the atomic composition and surface morphology of the deposition in the
reactor.
Chapter 3: Experimental 65
3.4. REFERENCES
1. Edwards, T-STATION 75 TURBOMOLECULAR PUMPING STATION Product Datasheet. 2. Incorporated, G. H. V., ER Series 300 Watt Regulated High Voltage DC Power Supplies Product Datasheet. 3. Sigma-Aldrich, 1L Tedlar PLV Gas Sampling Bag w/Thermogreen LB-2 Septa Product datasheet. 4. Edwards, APG100 Active pirani vacuum gauge Product Datasheet. 5. Thyracont, VD81 Compact Vacuum Meter Product Datasheet. 6. Bronkhorst, Datasheet F-201CV Mass Flow Controllers for Gases. 4. 7. Bronkhorst, EL-FLOW Base series Mass Flow Controllers. 2015, 36. 8. Liquide, A., Methane data sheet. In Air Liquide's Gas Encyclopedia, pp 273-304. 9. Optics, O., S2000 Spectrometer Data Sheet. 10. Optics, O., Operating Manual and User's Guide S2000 Miniature Fiber Optic Spectrometers and Accessories. 11. Solutions, G. A., Fast Refinery Gas Analyser Datasheet. 12. Analytical, H., Hiden HPR-30 Series Process Gas Anayzers. 6. 13. ULVAC-PHI, PHI 5000 VersaProbe II Brochure. 14. JEOL, JSM-6010PLUS series scanning electron microscope datasheet. 15. J. Luque, D. R. C., LIFBASE: Database and Spectral Simulation Program (version1.5). SRI International 1999, 20. 16. Hagelaar, G. J. M.; Pitchford, L. C., Solving the Boltzmann equation to obtain electron transport coefficients and rate coefficients for fluid models. Plasma Sources Science and Technology 2005, 14, (4), 722.
Chapter 4: Results and discussion 66
Chapter 4: RESULTS AND DISCUSSION
In this chapter, the experimental results are presented and discussed. The quantities that can be
calculated from these results are also given in their respective subsections. All of the discussed data
and quantities are obtained by application of the methods outlined in Section 3.2. A tree like overview
of this chapter is depicted in Figure 4-1.
Figure 4-1: Outline of the chapter concerning the results and discussion presented in a tree like schematic. Red indicates the
experiment and blue represents the abstracted quantities from the latter experiment.
First it is observed if there is actually methane conversion going on in this brand new setup with a
simple pressure versus time experiment. From here it has become evident that there is some kind of
pressure dependence on reaction taking place. It is interesting to see how this pressure dependence
is translated into chemistry and therefore optical emission spectroscopy is conducted. From the latter
is has been observed that there is some sort of radical distribution that changes with pressure. In order
to characterize parameters that can alter this distribution a current versus voltage experiment is
conducted. From here on out, a refinery gas analyzer is used for product identification and
quantification. Following this, a more qualitative discussion in how these products are established is
researched with mass spectroscopy. At the end some deposition was found in the reactor for which x-
ray photoelectron spectroscopy is used to quantify and a scanning electron microscope is used to
observe the surface morphology.
Experimental
Pressure versus time
Mass spectrometer sampling rate
Effect of reaction
Optical emission spectroscopy
Active species
Gas temperature (Tg)
Vibrational temperature
(Tvib)
Current versus voltage
Plasma voltage drop (Up)
Reduced electric field (Ered)
BOLSIG+ (µe,Te)
Electron density (ne)
Gas analysis
Components
Mole fractions (xi)
Conversion (X)
Selecitivty (Si)
Yield (Yi)
Mass spectrometry
Components in time
X-ray photoelectron spectroscopy
Deposition composition
Scanning electron
microscope
Deposition morphology
Chapter 4: Results and discussion 67
4.1. PRESSURE VERSUS TIME – 4
In this section, the pressure versus time characteristics are discussed. These have been obtained for
both the plasma on and off which results in a differentiation between reactions occurring or not. The
difference between these two is clearly brought forward and elaborated on. From the latter, initial
hydrogen production rates are also elaborated.
4.1.1. Data
Due to the nature of the setup, the mass spectrometer samples continuously which results in a reactor
pressure drop in function of time. Data from the experiments at three different pressures i.e. 55 kPa,
75 kPa and 95 kPa are discussed and visualized for each reference pressure. To start, the raw data is
plotted in Figure 4-2 for the first reference pressure of 55 kPa.
Figure 4-2: Pressure in function of time for plasma off and on for a reference pressure of 55 kPa. The total power equals
22.5 W.
At first, when plasma is not present a linear decreasing trend is observed due to the mass spectrometer
sampling. The pressure decreased by 0.6 kPa over a timeframe of 60 minutes. This is in big contrast
when reactions are taking place. A linear trend is not observed at all here. Furthermore, the pressure
increased by 0.47 kPa over a reaction time of 60 minutes. The latter indicates the significant presence
of dissociation reactions. This is also to be expected, as plasma results in methane dissociation into
neutral fragments such as CH3, CH2, CH and H. Furthermore, the input power equals 22.5 W.
54.0
54.2
54.4
54.6
54.8
55.0
55.2
55.4
55.6
55.8
0 10 20 30 40 50 60
Pre
ssu
re, p
[kP
a]
Time [min]
Off
On
Chapter 4: Results and discussion 68
Figure 4-3: Pressure in function of time for plasma off and on for a reference pressure of 75 kPa. The total power equals
23.1 W.
The data for the second reference pressure is plotted in Figure 4-3. Here, a pressure decrease of 1.1
kPa is observed when no plasma is present. A small deviation from a true linear trend at around 30
minutes is observed. The pressure drop characteristic for the plasma off is thus to be said, quasilinear.
This is probably caused by the sensitivity of the manometer. Even more so, the pressure drop is
significantly higher than at the lower reference pressure of 55 kPa. The latter might indicate that the
mass spectrometer sampling rate is augmented for higher pressures. Furthermore, the pressure drop
in the reactor due to the sampling is significantly higher than when the plasma is active i.e. reactions
occurring which then equals 0.75 kPa. The input power is equal to 23.1 W which has increased in
comparison to the lower reference pressure of 55 kPa. This could be explained due to the fact that for
a fixed current at a higher pressure more molecules in the discharge gap are present.
Figure 4-4: Pressure in function of time for plasma off and on for a reference pressure of 95 kPa. The total power equals
24.9 W.
74.0
74.2
74.4
74.6
74.8
75.0
75.2
75.4
0 10 20 30 40 50 60
Pre
ssu
re, p
[kP
a]
Time [min]
Off
On
93.0
93.5
94.0
94.5
95.0
95.5
96.0
0 10 20 30 40 50 60
Pre
ssu
re, p
[kP
a]
Time [min]
Off
On
Chapter 4: Results and discussion 69
The data for the last and highest reference pressure of 95 kPa is plotted in Figure 4-4. Overall a pressure
drop of 1.5 kPa is observed when the plasma is left in the off state. A quasilinear trend is identified for
the pressure versus time characteristic with a small deviation at 30 minutes of reaction. The latter is
possibly caused by the sensitivity of the manometer. The pressure drop has again increased in
comparison to the other reference pressures of 75 kPa and 55 kPa respectively. When plasma is
present and reactions are occurring, the pressure drop decreased to 0.44 kPa which again insists on
the presence of dissociation reactions. At the start, the pressure rises significantly indicating the
importance of dissociation reactions at the start of the batch. The power input for the last reference
pressure equals 24.9 W. The latter has again observed an increase in comparison to the first and second
reference pressure due to an increased molecule count in the discharge gap.
In general, the pressure drop or thus sampling rate is dependent on the pressure. The higher pressure
pushes in a manner of speaking more molecules in the mass spectrometer which results in a larger
pressure drop in the reactor over time. Furthermore, the effect of reaction is more spoken out at the
lowest reference pressure of 55 kPa possibly due to an increased production of CH radicals which
results in the release of three H atoms. At the higher reference pressure it is expected that CH3 species
are primarily formed.
4.1.2. Global rate
From the obtained data points, it is possible to calculate the derivative of the pressure in between two
time stamps. This value is then corrected in order to obtain the net effect of reaction on pressure as
explained in Section 3.2.2.1. This net effect can be recalculated to global reaction rates and are
summarized in Table 4-1.
Table 4-1: Global reaction rates for three reference pressures. The powers equals 22.5 W, 23.1 W and 24.9 W for the respective
reference pressures of 55 kPa, 75 kPa and 95 kPa.
Reference pressure [kPa] 55 75 95
Time [min] Global reaction rate, dn/dtr [mol/s]
0 5.49E-6 2.26E-6 9.68E-6
10 4.36E-6 3.07E-6 5.49E-6
20 2.74E-6 -1.94E-6 -1.78E-6
30 2.26E-6 1.45E-6 4.03E-6
40 1.29E-6 1.61E-7 -1.61E-7
50 1.13E-6 6.45E-7 -1.61E-7
Chapter 4: Results and discussion 70
In Table 4-1, it is evident that the net effect of reaction on the reactor pressure is highest in magnitude
at the start of the batch. This is logical because in a batch reactor initial reactions are the most
important. These initial reactions are primarily dissociation reactions of methane to neutral radicals
and thus bring forth an increase of pressure. They are shown in Equation 4-1 to 4-3.
𝐶𝐻4 + 𝑒− → 𝐶𝐻3
. + 𝐻. + 𝑒− 4-1
𝐶𝐻4 + 𝑒− → 𝐶𝐻2
. + 2𝐻. + 𝑒− 4-2
𝐶𝐻4 + 𝑒− → 𝐶𝐻. + 3𝐻. + 𝑒− 4-3
One can assume that for the first time interval (10 minutes) primarily dissociation of methane is taking
place which results in an estimate of the hydrogen gas production rate of all initial dissociation
reactions combined. The latter values are highlighted in bolt in Table 4-1 and are used to validate the
kinetic model in Section 5.3.2.
Chapter 4: Results and discussion 71
4.2. OPTICAL EMISSION SPECTROSCOPY - 4
For each reference pressure, the spectrum is discussed which contains peaks of the active species in
the reactor. After spectra acquisition, they are subsequently used in LIFBASE for fitting which results
in the gas and vibrational temperature of the gas. The plasma current for this experiment is fixed at
4.5 mA.
4.2.1. Data
In this section the obtained spectra are discussed starting with the lowest reference pressure. In
general, for every spectrum, the wavelength is limited between 200 nm and 900 nm due to the
limitations of the spectroscope. The spectrum of the reactor contents at 55 kPa is shown in Figure 4-5.
Figure 4-5: Optical emission spectrum of the active species in the reactor for a reference pressure of 55 kPa. The total power
equals 16.6 W.
The peak at wavelength 315 nm represent the CH peak of electron band B-X. At 390 nm electron band
C-X is visible of the same methylidyne component. Furthermore, at 430 nm the highest peak is
observed which is the A-X electron band of the CH spectrum. Continuing over the wavelength of the
spectrum, between 450-550 nm, a small but wide increase is visible, this is the Swan band which
contains emission of C2 species. Even more so, between 600 nm and 850 mm the high pressure band
is present which consists of many different C2 component spectra bands that are impossible to
distinguish due to their tight packing and overlap. A lower initial methane pressure results in formed
species to have a higher probability of colliding with electrons and other radicals. The latter results in
a relatively more significant CH peak in comparison to the C2 emission bands. Even more so, the
electron temperature is lower at this pressure because the electrons lose less energy due to ion
collisions. Methyl species are always produced as this is the initial reaction of the mechanism, however
0
50
100
150
200
250
300
350
400
450
200 300 400 500 600 700 800 900
Inte
nsi
ty, I
[co
un
ts]
Wavelength, λ [nm]
Swan band
CH (A-X)
CH (C-X)
CH (B-X)
High pressure band
Chapter 4: Results and discussion 72
CH3 is more likely to further react to CH2 and CH in comparison to other reference pressure. The higher
electron temperature also results in high energetic electrons for the production of these lower
hydrogen containing radicals. Input power for this pressure equals 16.6 W.1
Figure 4-6: Optical emission spectrum of the active species in the reactor for a reference pressure of 75 kPa. The total power
equals 18.6 W.
The spectrum of the reactor contents at 75 kPa is shown in Figure 4-6. The peak at wavelength 315 nm
represent the CH peak of electron band B-X. At 390 nm another peak is observed, now for another
electron band transition of the same methylidyne component i.e. C-X. Furthermore, on 430 nm the
biggest peak is visible which is the A-X electron band transitions of the CH spectrum. Continuing over
the wavelength spectrum, between 450-550 nm a wide increase is visible, this is the Swan band which
is established by emission of C2 species. Even more so, between 600 nm and 850 mm the high pressure
band is now clearly visible which has drastically increased in relative importance with respect to the
CH peaks. The Swan band also observed a relative increase. The high pressure band consist of many
different C2 component emission bands that are impossible to distinguish due to their overlap and tight
packing. At a higher pressure, formed radicals are more likely to collide with methane which results in
the formation of C2 species. Even more so, CH intensity has decreased in relative importance indicating
that CH3 and possibly CH2 reacts before being able to form CH species as mentioned earlier. Input
power for this pressure equals 18.6 W.1
0
50
100
150
200
250
300
350
400
200 300 400 500 600 700 800 900
Inte
nis
ity,
I [c
ou
nts
]
Wavelength, λ [nm]
CH (B-X)CH (C-X)
CH (A-X)
Swan band
High pressure band
Chapter 4: Results and discussion 73
Figure 4-7: Optical emission spectrum of the active species in the reactor for a reference pressure of 95 kPa. The total power
equals 20 W.
The spectrum of the reactor contents at 95 kPa is shown in Figure 4-7. The peak at wavelength 315 nm
represent the primary CH peak of electron band transition B-X. AT 390 nm another CH peak is observed
but now for another electron band transition i.e. C-X. Continuing over the wavelength spectrum, at
430 nm the biggest peak is observed which is the A-X electron band transition of methylidyne.
Furthermore, between 450-550 nm, a small but wide increase is visible, this is the Swan band which
represents emission of C2 species. Even more so, between 600 nm and 850 nm the high pressure band
increases above the relative importance of the CH peaks which is in contrast to the relative importance
at other pressures. The high pressure band consists of many different C2 component bands that are
impossible to distinguish due to their overlap and tight spacing. As already indicated, the Swan and the
high pressure band have again increased in importance in comparison to the CH peaks. This
phenomenon can be assigned to the probability of collisions at this higher pressure. CH species will be
present in a lower amount due to the high probability of collision between CH3 and CH2 with methane
that results in C2 active species. Even more so, at this pressure the electron temperature is the lowest
which also helps in the lower production of CH and CH2 radicals. The input power for this pressure
equals 20.0 W.1
The difference in absolute intensity between the spectra at different pressures can be caused by a
changing discharge volume which results in an overall decrease or increase in intensity. Even a small
different probe position could result in the latter effect. Furthermore, the detection of methyl species
is not possible with optical emission spectroscopy because it falls in the infrared spectrum. A laser
absorption technique is required to detect CH3 species in an active discharge.1
0
50
100
150
200
250
200 300 400 500 600 700 800 900
Inte
nsi
ty, I
[co
un
ts]
Wavelength, λ [nm]
High pressure band
CH (A-X)
CH (B-X) CH (C-X)
Swan band
Chapter 4: Results and discussion 74
4.2.2. Fitting of the CH (A-X) peak
In this section, the experimentally obtained spectra are fitted to obtain the gas and vibrational
temperature. Starting with the lowest reference pressure of 55 kPa, the simulated and adjusted
experimental CH (A-X) peak used for fitting are shown in Figure 4-8.
Figure 4-8: LIFBASE fit of the adjusted experimental spectrum against the simulated spectrum for a reference pressure of
55 kPa. The fit is executed on the CH (A-X) peak.
The adjusted experimental spectrum is obtained by isolating the CH (A-X) peak between 410 nm and
450 nm from the complete spectrum discussed in Section 4.2.1. Furthermore, the spectrum is
multiplied by 1.4 and a baseline correction of 27% is used. No wavelength correction is needed. As a
last step the spectrum is smoothed which results in the experimental peak shown in Figure 4-8. The
simulated spectrum is calculated in LIFBASE as explained in Section 3.2.3.1. The fit results in a gas
temperature of 1500 K and a vibrational temperature of 6000 K. The correlation coefficient of the fit
equals 0.943
Figure 4-9: LIFBASE fit of the adjusted experimental spectrum against the simulated spectrum for a reference pressure of
75 kPa. The fit is executed on the CH (A-X) peak.
0
20
40
60
80
100
120
410 420 430 440 450
Inte
nsi
ty, I
[o
un
ts]
Wavelength, λ [nm]
Sim
Exp
0
20
40
60
80
100
120
410 420 430 440 450
Inte
nsi
ty, I
[co
un
ts]
Wavelength, λ [nm]
Sim
Exp
Chapter 4: Results and discussion 75
The simulated and experimental CH (A-X) peak for a reference pressure of 75 kPa are shown in Figure
4-9. The adjusted experimental spectrum displayed in the latter figure is obtained by isolating the CH
(A-X) peak between 410 nm and 450 nm from the complete spectrum discussed in Section 4.2.1.
Furthermore, the peak is multiplied with a factor of 1.25 and a baseline correction of 20% is used. No
wavelength correction is needed. As a last step the spectrum is smoothed which results in the
experimental peak shown in Figure 4-10. The fit results in a gas temperature of 1750 K and a vibrational
temperature of 6000 K. The correlation coefficient of the fit equals 0.978.
Figure 4-10: LIFBASE fit of the adjusted experimental spectrum against the simulated spectrum for a reference pressure of
95 kPa. The fit is executed on the CH (A-X) peak.
The simulated and adjusted experimental CH (A-X) peak for a reference pressure of 95 kPa are shown
in Figure 4-10. The adjusted experimental spectrum is obtained by isolating the CH (A-X) peak between
410 nm and 450 nm from the complete spectrum discussed in Section 4.2.1. Furthermore, the peak
has been multiplied with a factor of 1.4 and a baseline correction of 28% is used. No wavelength
correction is needed. As a last step the spectrum is smoothed which results in the experimental peak
shown in Figure 4-10. The fit results in a gas temperature of 2000 K and a vibrational temperature of
6000 K. The correlation coefficient of the fit equals 0.947.
In general, it should be noted that the obtained gas temperatures are high. Which is in contradiction
with the whole story about non-thermal plasma and their low gas temperature. This is a limitation of
the optical emission spectroscopy method to measure the gas temperature with. The center of the
plasma discharge is highly likely to contain a small portion of thermal plasma. The region around this
portion of plasma is completely non-thermal from nature with a gas temperature close to room
temperature. However, thermal plasma emits much more light than a non-thermal plasma which
results in the relatively high observed temperatures.
0
20
40
60
80
100
120
410 420 430 440 450
Inte
nsi
ty, I
[co
un
ts]
Wavelengthn, λ [nm]
Sim
Exp
Chapter 4: Results and discussion 76
4.3. CURRENT VERSUS VOLTAGE – 4
First the raw data is discussed which is directly obtained from the setup. Form the latter, all the
different calculated quantities follow. A lower boundary in the current is present because below a
minimum voltage it is not possible to generate plasma between the discharge pins and plate. The latter
results in a lower boundary of the current. A maximum value is observed by a user controlled limitation
on the current of 4.5 mA.
4.3.1. Data
Experiments have been conducted for each reference pressure as explained in Section 3.2.4. The data
points of the measurements can be found in Table E-1 in Appendix E. This data is visualized in Figure
4-11 for three reference pressures.
Figure 4-11: Current in function of total voltage for three reference pressures.
As seen in Figure 4-11, the effect of pressure on the current in the circuit is only distinguishable at
higher values of the total voltage. For the same current, a higher total voltage is observed for a higher
pressure. The latter can be ascribed by the fact that more molecules are present in the discharge gap
at these higher pressures. On the other hand, when looking at a constant total voltage, a higher current
is realized for a lower pressure. This is caused by a further development in the plasma which can carry
a higher current due to a higher ionization. Furthermore, at the start of the current versus voltage
characteristic, there isn’t a lot of difference between the pressures. Possibly by a very low development
of the plasma at all three reference pressures. Also, it should be pointed out that the current
characteristic from Figure 2-5 in Section 2.2.4.1 has a few similarities close to the transition of glow to
arc discharge.
0
1
2
3
4
5
0 1 2 3 4 5 6 7
Cu
rren
t, i
[mA
]
Total voltage, Ut [kV]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 77
4.3.2. Plasma voltage
From the experimental data outlined in the previous section it is easy to calculate the plasma voltage
drop which is a function of the total voltage and current in the circuit as explained in Section 3.2.4.1.
The calculated data points of the plasma voltage drop are given in Table E-2 in Appendix E. The plasma
voltage drop in function of the total voltage data is plotted in Figure 4-12.
Figure 4-12: Plasma voltage in function of total voltage for three reference pressures.
From Figure 4-12, it is clear that for constant total voltage a higher plasma voltage is realized for a
higher pressure. The latter can be ascribed that at a higher pressure, the plasma is not developed as
much as at a lower pressure and thus cannot carry a current. The lower current capacity at the higher
pressure results in a lower voltage over the resistor thus realizing a higher plasma voltage.
4.3.3. Reduced electric field
From the previously calculated plasma voltage it is finally possible to transition to the reduced electric
field as explained in Section 3.2.4.2. The calculated data points of the reduced electric field are given
in Table E-3 in Appendix E. These values are shown as a function of total voltage in Figure 4-13.
0
1
2
3
4
5
0 1 2 3 4 5 6 7
Pla
sma
volt
age,
Up
[kV
]
Total voltage, Ut [kV]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 78
Figure 4-13: Reduced electric field in function of total voltage for three reference pressures.
In Figure 4-13, the reduced electric field is shown in function of the total voltage. When considering
the previous results i.e. the plasma voltage which is higher for higher pressures at a constant total
voltage one expects the reduced electric field to undergo the same trend. However, from the
definition, the gas particle density is also a function of pressure and one can clearly tell that this takes
the upper hand in the reduced electric field calculations. The reduced electric field is lower for a higher
pressure. As previously stated, the electron temperature generally increases with an increasing value
of the reduced electric field so this value is important. The reduced electric field also has a significant
influence on the radical distribution as explained in Section 2.3.
4.3.4. BOLSIG+
From the calculated reduced electric field and the fitted gas temperature in LIFEBASE, it is possible to
obtain the electron temperature and mobility from BOLSIG+ which solves the Boltzmann equation
iteratively as explained in Section 3.2.4.3 and Appendix C. First the obtained electron mobility is
discussed. The raw data points of the electron mobility obtained from BOLSIG+ are given in Table E-4
contained in Appendix E. To start, the electron mobility versus the total voltage is plotted in Figure
4-14.
0
5
10
15
20
25
0 1 2 3 4 5 6 7
Red
uce
d e
lect
ric
fiel
d, E
red
[Td
]
Total voltage, Ut [kV]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 79
Figure 4-14: Electron mobility in function of total voltage for three reference pressures.
As one can see in Figure 4-14, for a higher pressure a higher electron mobility is obtained for a constant
value of the total voltage. At around 3.5 kV, this behavior changes at a total voltage of 3.75 kV were
now the lower pressure results in the highest electron mobility. Now the electron temperature is
discussed. The output data points for the electron temperature from BOLSIG+ are given in Table E-5
contained in Appendix E. Figure 4-15 shows the trend between the electron temperature and total
voltage.
Figure 4-15: Electron temperature in function of total voltage for three reference pressures.
From Figure 4-15, it is clear that the electron temperature, or the average energy of the electrons in
the plasma is higher for a lower pressure. As already brought forward a few times, the electron
temperature is a causal function of the reduced electric field. This can be understood that, primarily,
a higher plasma voltage results in more energy input to the electron. And secondly, a lower gas particle
density results in less collisions of the electrons with gas molecules. The lower amount of collisions on
its turn, results in less energy loss to these gas molecules form the electrons. The reactor pressure is
thus also important to control the reduced electric field value.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 1 2 3 4 5 6 7
Elec
tro
n m
ob
ility
, μe
[m²/
V/s
]
Total voltage, Ut [kV]
55 kPa
75 kPa
95 kPa
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0 1 2 3 4 5 6 7
Elec
tro
n t
emp
erat
ure
, Te
[eV
]
Total voltage, Ut [kV]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 80
4.3.5. Electron density
Now every value is known to calculate the electron density which is an input of the kinetic model
discussed in Chapter 5. The electron density is obtained as explained in Section 3.2.4.4. The calculated
data points of the electron density are given in Table E-6 in Appendix E furthermore, the electron
density is plotted as a function of the total voltage in Figure 4-16.
Figure 4-16: Electron density in function of total voltage for three reference pressures.
Visible in Figure 4-16 is the hefty increase in electron density for a relatively small increase in total
voltage. The latter indicates that for a higher total voltage over the setup, the total ionization and thus
the presence of electrons have been increased. Furthermore, this effect is augmented for a lower
pressure. The electron density is dependent on too many values for a good interpretation.
The electron density together with the electron temperature are important parameters to control the
chemical reactivity of a plasma. A higher electron density i.e. more electrons per unit volume, of course
results in more electron impact reactions and thus radical and ion production rates. On the other hand,
a higher electron temperature results in a higher average kinetic energy of the electrons. This energy
is beneficial for highly activated reactions such as the formation of CH and C species. Electron impact
reaction rate laws observe an exponential dependence in the electron temperature while only linear
in the electron density.
1.0E+12
5.1E+13
1.0E+14
1.5E+14
2.0E+14
2.5E+14
3.0E+14
0 1 2 3 4 5 6 7
Elec
tro
n d
ensi
ty, n
e[1
/m³]
Total voltage, Ut [kV]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 81
4.4. REFINERY GAS ANALYSIS – 1
In this sub section of the experiments, the gas analysis results are interpreted and discussed. One is
directly pointed to the fact that the plasma discharge in the reactor is in the order of cm³ while the
reactor itself is in the order of l. The latter coupled with a low power input between 1-5 W results in
low absolute values. However, when one takes a closer look at the energy efficiencies needed for
conversion and product production another story develops.
4.4.1. Data
The raw data from the RGA are the chromatograms and subsequently the integrated peak surface
areas of the detected components. This data is however not useful as such and is thus also not
discussed. It is only after the processing of the raw data that useful information is obtained. The
pressure measurements during the method as explained in Section 3.2.5 are given in Table F-1 and the
raw data of the peak integrations from the chromatograms is given in Table F-2 in Appendix F.
4.4.2. Calibration
For the calibration, again the raw data is not discussed and is summarized in Table F-3 found in
Appendix F. However, after processing of the raw data, response factors are obtained per component
for each detector. The response factors of the TCD and the FID are given in Table 4-2 and Table 4-3
respectively. Argon is used as an internal standard for the TCD detector while methane is used for the
FID detector.
Table 4-2: C2- response factors for the TCD detector in the refinery gas analyzer.
The yields in the mixture after 30 minutes of reaction are calculated as indicated in Section 3.2.5.5 and
are visualized in Figure 4-20. From the latter figure, it is observed that the yield of hydrogen is highest
for the highest pressure just like ethane. Furthermore, one can say that ethylene and acetylene yield
are significantly lower. The fact that acetylene has a low yield is a positive given. Acetylene production
with the use of plasma is already industrialized but never witnessed a breakthrough.
Figure 4-20: Product yields after 30 minutes of reaction for three reference pressures. The total power equals 3.51 W, 3.87 W
and 4.38 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively.
4.4.7. Selectivity
The selectivities in the mixture after 30 minutes of reaction are calculated as indicated in Section
3.2.5.6. They are depicted in Figure 4-21. First of all, the selectivity towards hydrogen and ethane are
the most promising components for the reforming of methane in a plasma reactor. Definitely the
hydrogen production could be an interesting case to further investigate as modern day hydrogen
production from methane is conducted at high temperatures.
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
H2 C2H2 C2H4 C2H6
Yiel
d, Y
i[%
]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 87
Figure 4-21: Product selectivities after 30 minutes of reaction for three reference pressures. The total power equals 3.51 W,
3.87 W and 4.38 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively.
4.4.8. Energy cost
The energy costs in the mixture based on the total power after 30 minutes of reaction are calculated
as indicated in Section 3.2.5.7. Moreover, they are visualized in Figure 4-22. The energy costs indicate
the energy required to produce an amount of mass of that component. One can clearly see that the
energy costs of ethane are most promising for non-thermal plasma methane reforming. Energy costs
of hydrogen gas are quiet large due to the low mass of the molecule. Mole wise, hydrogen was in the
same order of magnitude for ethane.
Figure 4-22: Product energy costs based on the total power after 30 minutes of reaction for three reference pressures. The
total power equals 3.51 W, 3.87 W and 4.38 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively.
The energy costs in the mixture based on the plasma power after 30 minutes of reaction are calculated
as indicated in Section 3.2.5.7. Furthermore, they are visualized in Figure 4-23. From the latter, the
same trend is observed; ethane energy costs are the best while the energy cost of hydrogen is
0%
10%
20%
30%
40%
50%
60%
H2 C2H2 C2H4 C2H6
Sele
ctiv
ity,
Si[%
]
55 kPa
75 kPa
95 kPa
0
5
10
15
20
25
30
H2 C2H2 C2H4 C2H6
Ener
gy c
ost
, µ [
g/kW
h]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 88
surprisingly low. The energy costs based on the plasma power are specifically reported to be
comparable with literature results.
Figure 4-23: Product energy costs based on the plasma power after 30 minutes of reaction for three reference pressures. The
total power equals 3.51 W, 3.87 W and 4.38 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively.
In Table 4-8, the product energy cost are compared. These values are called ‘costs’ but a higher value
actually represents a better case. As one can see, the max product energy cost for ethane is much
larger than literature reported results, possibly caused by the high mass of ethane or overestimation
of the experiments.
Table 4-8: Comparison between different reported product energy costs (higher is better).
This work DBD3 Beloqui4
Product energy cost [g/kWh] 1-45 1-6 0.04
0
5
10
15
20
25
30
35
40
45
50
H2 C2H2 C2H4 C2H6
Ener
gy c
ost
, µ [
g/kW
h]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 89
4.5. MASS SPECTROMETRY – 1
Due to immense ionization at the start of each experiment, the first 10 minutes are obsolete and thus
omitted. Furthermore, at 20 minutes of the one hour experimental run the plasma is switched on for
30 minutes. A constant current of 1 mA is used. After the latter reaction time the reactor and mass
spectrometer are given 10 minutes to stabilize by switching the plasma off. In the following, the mass
spectrometer detector responses are discussed with their linear regressions also plotted.
4.5.1. Data
The raw data from mass spectrometry runs are the detector response in function of time. For each
component in Section 4.4, the detector response will be given for three reference pressures.
Furthermore, on every graph the linear fits will be shown which are utilized to calculate the rates of
the components. The logged pressures as explained in the method of this experiments i.e. Section 3.2.6
are summarized in Table G-1 found in Appendix G. To start, the mass spectrometer run for methane is
given in Figure 4-24.
Figure 4-24: Methane in function of time during a one hour experimental run for three reference pressures. The total power
equals 3.35 W, 3.78 W and 4.38 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively. Linear fits are also
plotted on top of the original in black.
From the latter figure it is obvious that the methane signal decreases when the plasma is switched on
at 20 minutes. However, the rate is lower than when no plasma is present which is curious. This could
be caused by the production of characteristic mass fragments from C2 components which results in an
augmentation of the detected ‘methane’. Furthermore, the effect is more spoken out at a lower
pressure due the fact that when methane reacts away at that pressure, fraction wise, it is a lot larger
than at a higher pressure. The next component is hydrogen gas and is shown in Figure 4-25.
5.0E-04
6.0E-04
7.0E-04
8.0E-04
9.0E-04
1.0E-03
1.1E-03
10 20 30 40 50 60
Pre
ssu
re, p
MS
[Pa]
Time [min]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 90
Figure 4-25: Hydrogen gas in function of time during a one hour experimental run for three reference pressures. The total
power equals 3.35 W, 3.78 W and 4.38 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively. Linear fits are
also plotted on top of the original in black.
For the hydrogen gas, as clearly visible from Figure 4-25, a steep increase at 20 minutes is observed
due to the plasma. Furthermore, a clear stabilization at 50 minutes is observed when the plasma is
again switched off and the production of hydrogen comes to a halt. For the quantification of the rates,
they are given in the next section of this experiment. The next figures will deal with the produced C2
components starting with acetylene shown in Figure 4-26.
Figure 4-26: Acetylene in function of time during a one hour experimental run for three reference pressures. The total input
power equals 3.12 W, 3.87 W and 4.05 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively. Linear fits are
also plotted on top of the original in black.
5.0E-06
7.0E-06
9.0E-06
1.1E-05
1.3E-05
1.5E-05
10 20 30 40 50 60
Pre
ssu
re, p
MS
[Pa]
Time [min]
55 kPA
75 kPa
95 kPa
8.5E-07
1.0E-06
1.2E-06
1.3E-06
1.5E-06
1.6E-06
10 20 30 40 50 60
Pre
ssu
re, p
MS
[Pa]
Time [min]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 91
From Figure 4-26, acetylene increases clearly from the moment the plasma is turned on. Furthermore,
the acetylene detector signal stabilizes when the plasma is turned off. Hydrogen is expected to increase
the most, however possibly due to a different detector sensitivity towards carbon components this is
not visible from the figures. The overlapping carbon fragments in the mass spectrometer might also
cause the relatively strong increases of C2 components with respect to hydrogen gas. Next up is
ethylene as shown in Figure 4-27.
Figure 4-27: Ethylene in function of time during a one hour experimental run for three reference pressures. The total input
power equals 3.12 W, 3.87 W and 4.05 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively. Linear fits are
also plotted on top of the original in black.
Again, as expected, the ethylene detector signal increases in function of time when the plasma is
turned on at 20 minutes. And of course it is expected that the signal stabilizes when the plasma is
turned off at 50 minutes indicating the halt of the production of product components. A special case
is at the reference pressure of 95 kPa is visible. Here, the excessive ionization has not yet stabilized.
Last but not least ethane is taken under the loop in Figure 4-28. Ethylene in general is not favored in
plasma reforming. However, the amount formed is easily detected. At the reference pressure of 55
kPa ethylene is not formed at all. The detector response keeps dropping until it has been stabilized
while for the other pressures a clear increase is observed.
4.0E-06
5.0E-06
6.0E-06
7.0E-06
8.0E-06
9.0E-06
10 20 30 40 50 60
Pre
ssu
re, p
MS
[Pa]
Time [min]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 92
Figure 4-28: Ethane in function of time during a one hour experimental run for three reference pressures. The total input power
equals 3.12 W, 3.87 W and 4.05 W for a reference pressure of 55 kPa, 75 kPa and 95 kPa respectively. Linear fits are also
plotted on top of the original in black.
Ethane is expected to increase the most at the highest pressure due to the presence of methane and
the primary production of CH3 radicals. The latter is clearly visible in the figure; ethane hardly increase
at 55 kPa while at 950 this is a significant increase. The ethane signal also increases drastically at the
middle pressure, where due to the relatively low electron temperature CH3 species are still very
significant. Ione should however take into account that some masses show overlap, for example the
C2 components can sometimes result in the same fractions of molecules being detected.
It should be noted that the results obtained with the mass spectrometer are in perfect correspondence
with the results obtained in Section 4.4. The fractions of the partial pressures in the mass spectrometer
are comparable with the obtained absolute concentrations. The latter indicates the consistency of the
observed trends and values.
4.5.2. Component rates
From the linear fits visualized in the previous section as black lines, the component rates are calculated
as explained in Section 3.2.6.1. They are summarized in Table 4-9. It is evident that methane decreases
due to reaction while the other components increase their amount due to production. The component
rates can only be compared between pressures for this device has not been calibrated.
1.0E-07
1.4E-07
1.8E-07
2.2E-07
2.6E-07
3.0E-07
10 20 30 40 50 60
Pre
ssu
re, p
MS
[Pa]
Time [min]
55 kPa
75 kPa
95 kPa
Chapter 4: Results and discussion 93
Table 4-9: Component production rates for three reference pressures. The total power equals 3.35 W, 3.78 W, 4.38 W for the
CH4 and H2 rates, and 3.12 W, 3.87 W, 4.05 W for the C2 component for a respective reference pressure of 55 kPa, 75 kPa and
95 kPa.
Reference pressure [kPa] 55 75 95
Component Component net production rate [mol/s]
CH4 -4.38E-04 -3.70E-04 -1.93E-04
H2 5.40E-05 6.98E-05 9.99E-05
C2H2 1.13E-05 1.72E-05 1.12E-05
C2H4 1.10E-04 6.32E-05 2.42E-05
C2H6 2.54E-06 3.82E-06 9.12E-06
Generally, it is seen from Table 4-9 that methane is converted at a faster rate for a lower pressure. The
electron temperature does infect influence the conversion more than the pressure, as well known from
the rate law dependences. Furthermore, acetylene and ethylene net production rates are highest for
a lower pressure which indicates the importance of a higher electron temperature at this pressure.
This results in the formation of CH and CH2 radicals. Hydrogen gas production rates are highest for the
higher pressure which is probably caused by more methyl radical formation and thus hydrogen. This is
also visible in the production rate of ethane which is higher for a higher pressure.
Chapter 4: Results and discussion 94
4.6. X-RAY PHOTOELECTRON SPECTROSCOPY
During the period of this master thesis a lot of experiments were conducted in the reactor. At the end,
some sort of deposition occurred on the discharge pins. Also during reaction flickering is observed in
the plasma discharge. This flickering is probably caused by the burning or incineration of carbon. An x-
ray photoelectron spectroscopy analysis is conducted to quantify the elements in the unknown
deposition found in the reactor.
Table 4-10: XPS composition results from the unknown deposition in the reactor.
Component C (1s) O (1s) Si (2s)
Atomic percentage [%] 90.15 9.56 0.29
As can be seen in Table 4-10, the primary atom of the unknown deposition is carbon so it is in fact
carbon deposition that is taking place in the reactor. Furthermore, a relatively high oxygen content is
observed. The latter can be ascribed by the periodical maintenance of the reactor. During maintenance
the reactor is opened and oxygen is entering the reactor which results in fast oxidation of this carbon
deposition. Also, because XPS is a surface technique it does not penetrate the sample very deep. The
latter indicates that this oxygen has possibly been deposited after the carbon. Silicon is also present,
however in insignificant amounts. Formation of this carbon is perfectly preventable with correct
process design for the amount was so insignificant.
Chapter 4: Results and discussion 95
4.7. SCANNING ELECTRON MICROSCOPE
Now that it is clear that the unknown deposition is in fact carbon. It is interesting to observe the surface
morphology under a scanning electron microscope. Starting with a low resolution to get an image of
the bigger picture and working upwards in more detail. In total three resolutions are used. Also, the
sample is very brittle, after touching the tube shaped samples break immediately.
Figure 4-29: Low resolution (x25) SEM image of the carbon deposition in the reactor.
As one can see in Figure 4-29, the carbon deposition is in the form of long thin tubes. These tubes are
very fragile, some broken of parts of tubes are clearly visible. For the medium resolution shown in
Figure 4-29, charging of the sample had some negative influence on the contrast.
Figure 4-30: Medium resolution (x850) SEM image of the carbon deposition in the reactor.
Chapter 4: Results and discussion 96
Mostly conductive samples are gold plated to prevent this phenomenon. However, due to the very
fragile structure of the deposition this was inhuman to attempt. As one can see the base of the
deposition (top right corner) is wider than the head of the deposition (left bottom corner). The latter
might be explained by the fact that the most prominent carbon deposition is occurring at the discharge
pin themselves where the wider base of the carbon tube is located. From Figure 4-31, the surface
morphology is very irregular. The latter indicating that possibly carbon is not deposited in a uniform
way.
Figure 4-31: High resolution (x5000) SEM image of the carbon deposition in the reactor.
Chapter 4: Results and discussion 97
4.8. CONCLUSION
In general one can say that plasma methane reforming has been observed with quite some different
experiments conducted with the methods explained in Chapter 3.
In the pressure versus time experiment, a clear pressure decrease was observed by the continuous
sampling of the mass spectrometer. When the power supply was switched on i.e. plasma present, the
pressure characteristic changed drastically which indicated the presence of dissociation reactions.
For optical emission spectroscopy, the active species were identified and significantly present. CH
peaks of different electron bands were observed with a larger relative importance at lower pressures.
The Swan and high pressure band increased in importance relative to the CH peaks with increasing
pressure. Furthermore, gas and vibrational temperatures have been obtained by fitting; 1500 K, 1750
K and 2000 K for reference pressures 55 kPa, 75 kPa and 95 kPa respectively.
From the current versus voltage experiment, it was observed that the current increases for increasing
power supply voltage. A higher electron temperature is observed for a lower pressure indicating that
for a lower pressure, more energetic electrons are present. Even more so, a higher electron density
was obtained for a higher total voltage and thus power. The electron density and temperature are
important plasma parameters which controls the chemical reactivity of a plasma.
From the gas analysis experiments, hydrogen and ethane are the most prominent products as expected
in plasma methane reforming processes that utilize non-equilibrium plasma. Plasma power based
conversion costs were found to be: 40.5 eV/molecule, 29.6 eV/molecule and 8.5 eV/molecule for a
reference pressure of 55 kPa, 75 kPa and 95 kPa respectively. Selectivities are reported as follows:
hydrogen gas <40.8%, acetylene <10.5%, ethylene <27% and ethane <54.8%.
From the mass spectrometer runs it has been observed that methane drops in function of time due to
reaction. This effect is augmented for a higher pressure. The product components clearly increase in
mass spectrometer pressure during the experiment, indicating their formation. Ethane and hydrogen
gas are the most prominent components that are formed.
The scanning electron microscope and x-ray photoelectron spectroscopy experiments had one
conclusion in general; the deposition was carbon where its creation is found to occur in a non-uniform
way. The formed mass of carbon was however so insignificant that with proper process design this is
completely preventable.
Chapter 4: Results and discussion 98
4.9. REFERENCES
1. McNally, J. R., The Identification of Molecular Spectra. By R. W. B. Pearse and A. G. Gaydon. The Journal of Physical Chemistry 1951, 55, (5), 758-759. 2. Li, X.-S.; Zhu, A.-M.; Wang, K.-J.; Xu, Y.; Song, Z.-M., Methane conversion to C2 hydrocarbons and hydrogen in atmospheric non-thermal plasmas generated by different electric discharge techniques. Catalysis Today 2004, 98, (4), 617-624. 3. Eliasson, B.; Liu, C.-j.; Kogelschatz, U., Direct Conversion of Methane and Carbon Dioxide to Higher Hydrocarbons Using Catalytic Dielectric-Barrier Discharges with Zeolites. Industrial & Engineering Chemistry Research 2000, 39, (5), 1221-1227. 4. Beloqui Redondo, A.; Troussard, E.; van Bokhoven, J. A., Non-oxidative methane conversion assisted by corona discharge. Fuel Processing Technology 2012, 104, 265-270.
Chapter 5: Kinetic modelling 99
Chapter 5: KINETIC MODELLING
In this chapter the kinetic model is discussed. Like any other simulation one needs an input and a model
that converts this input to an output as shown in Figure 5-1. The model implementation is discussed
throughout, ranging from model inputs to obtained output. Model assumptions are also elaborated
for they are important to understand the model limitations.
Figure 5-1: Outline of the chapter concerning kinetic modelling presented in a tree like schematic.
The input consists of values such as the electron temperature, electron density, plasma volume,
reactor volume, reaction time and gas temperature. Furthermore, kinetic data is necessary to calculate
the rate coefficients of the various reactions taken up in the model. It is discussed how electron impact
reaction coefficients can be calculated. The model itself is a full implementation of the continuity
equation on the reactor at hand for each component. The model equations are thus developed with
respect to a batch reactor and consist of a reaction and accumulation term. Last but not least, the
output consists of the moles of the various components in function of time. From these moles in
function of time various quantities can be defined such as the conversion, selectivity, mole fractions
and more.
Simulation
Input
Kinetic data
Simulation inputs
Model
Continuity equation
Batch reactor
Output
Moles
Other quantities
Chapter 5: Kinetic modelling 100
5.1. INPUT
Kinetic data for general chemical reactions such as the recombination of various radicals is readily
available in kinetic databases such as the one from NIST. The most recent entry from the kinetic data
base is taken up in the model. Furthermore, the reaction network is limited to C2- components because
less is more. Primarily though, because the experiments in Section 4.4 of the refinery gas analysis have
only been calibrated for C2- components. All general reactions that are taken into account in the model
are shown in Table 5-1 (electron impact reactions are considered later).1
Table 5-1: General reactions with their kinetic data acquired from the kinetic database available on NIST.1
Reaction Pre-exp factor
[m³/molecules/s]
T-coefficient
[-]
Activation energy
[kJ/mol]
𝐶𝐻. + 𝐶𝐻. → 𝐶2𝐻2 1.99E-16 0 0
𝐶𝐻3. + 𝐶𝐻3
. → 𝐶2𝐻6 1.03E-16 -1.1 1.33
𝐶𝐻4 + 𝐻. → 𝐶𝐻3
. +𝐻2 4.36E-19 3.16 36.63
𝐶𝐻4 + 𝐶𝐻2. → 𝐶𝐻3
. + 𝐶𝐻3. 7.14E-18 0.41 41.99
𝐶𝐻4 + 𝐶𝐻. → 𝐶2𝐻4 + 𝐻 1.7E-17 0 0
𝐶𝐻3. + 𝐶𝐻3
. → 𝐶2𝐻4 + 𝐻2 1.66E-14 0 134
𝐶𝐻3. + 𝐶𝐻2
. → 𝐶2𝐻4 +𝐻. 7.01E-17 0 0
𝐶𝐻2. +𝐻2 → 𝐶𝐻3
. + 𝐻. 5E-21 0 0
𝐶𝐻. + 𝐻2 → 𝐻. + 𝐶𝐻2
. 3.11E-16 0 13.72
𝐶𝐻2. + 𝐶𝐻2
. → 𝐶2𝐻2 + 𝐻2 5.3E-17 0 0
Reaction kinetics obtained from the NIST kinetic database are utilized to calculate the rate coefficients
of general reactions. These rate coefficients are calculated with a modified Arrhenius expression
shown in Equation 5-1.
𝑘 = 𝐴 (𝑇
298 𝐾)𝛼
𝑒−𝐸𝑎𝑅𝑇 5-1
In the latter equation, A is the pre-exponential factor in m³/mol/s, T is the gas temperature in K, α is
an exponent to correct the pre-exponential factor for its temperature dependence and Ea is the
activation energy of the reaction in J/mol.
Furthermore, reactions taking care of radical production are electron impact dissociation reactions.
Obtaining kinetic data for these reactions is not straight forward because they are dependent on the
plasma discharge characteristics. They can be correctly determined with the use of collision theory
(Appendix D) that utilizes dissociation cross sections and the distribution of the kinetic energy of
Chapter 5: Kinetic modelling 101
electrons (assumed Maxwellian, with an electron temperature Te). During plasma discharge, this
distribution changes as a function of time described by the Boltzmann equation. To capture this effect
one would need to implement a self-updating Boltzmann equation solver in the kinetic model. The
latter is not easy at all as explained in Appendix C. To continue the modelling of the plasma reforming
process, it is assumed that the distribution of the kinetic energy of the electrons is constant and thus
a constant electron temperature can be assumed. To continue the discussion about obtaining kinetic
data for electron impact reactions, the use and acquisition of a cross section will be enlightened
however not conducted.2
In the previous years, distinct experiments have been conducted in order to obtain total dissociation
cross sections of methane into neutral fragments such as radicals. Methyl and methylene cross sections
are readily available from articles as shown in Figure 5-2.3, 4
Figure 5-2: Total dissociation cross sections for CH3 and CH2 radicals from methane. Non-specified cross sections are the
combination of all formed dissociative species.4
Now that the electron temperatures and the dissociation cross sections are known, collision theory
comes in handy. With the use of Equation 5-2, it is possible to calculate the production rates of CH3
and CH2 radicals. For more information about collision theory the reader is referred to Appendix D.
��(𝑇) = (8𝑘𝑏𝑇
𝜋𝜇)1/2∫ 𝑆𝑟(𝐸)
𝐸𝑒−𝐸𝑅𝑇
𝑅𝑇(𝑑𝐸
𝑅𝑇)
+∞
0
5-2
By fitting the rate coefficients that are function of the electron temperature as shown in Equation 5-2
to the Arrhenius expression, a pre-exponential factor, temperature coefficient as well as an activation
energy are obtained. The latter is mostly done to obtain the same Arrhenius type expression to
Chapter 5: Kinetic modelling 102
calculate the rate coefficients of all reactions. The fitting and cross section integration has not been
performed in this work. However, a similar approach would have been followed as outlined above.
This approach is widely accepted and other research groups such as of Hontao et al. have already
conducted the latter calculations. Furthermore, the kinetic data from Cavallotti et al. is in the same
order of magnitude and is more correct because steam reforming reactions are omitted in their
calculation approach. The kinetic data from Cavallotti et al. is utilized in the modelling of plasma
methane reforming in this thesis as shown in Table 5-2.2, 5
Table 5-2: Electron impact dissociation reactions with their kinetic data obtained by fitting as explained above.5
Reaction Pre-exponential factor
[m³/mol/s]
T-coefficient
[-]
Activation energy
[eV]
𝐶𝐻4 + 𝑒− → 𝐶𝐻3
. +𝐻. + 𝑒− 1.6E12 -0.989 20
𝐶𝐻4 + 𝑒− → 𝐶𝐻2
. + 𝐻2 + 𝑒− 2.7E11 -0.989 20
𝐶𝐻4 + 𝑒− → 𝐶𝐻. + 𝐻2 + 𝐻
. + 𝑒− 1.44E11 -0.989 20
𝐶𝐻4 + 𝑒− → 𝐶 + 2𝐻2 + 𝑒
− 1.2E8 -0.989 20
𝐻2 + 𝑒− → 2𝐻. + 𝑒− 3.8E12 -1.984 25
Reaction kinetics obtained from Cavallotti et al. are utilized to calculate the rate coefficients of the
electron impact dissociation reactions. These rate coefficients are calculated with a modified Arrhenius
expression shown in Equation 5-3.
𝑘 = 𝐴𝑇𝑒𝛼𝑒−𝐸𝑎𝑇𝑒 5-3
In Equation 5-3, A is the pre-exponential factor of in m³/mol/s, Te is the electron temperature of the
plasma in eV, α is an exponent to correct the pre-exponential factor for its temperature dependence
and Ea is the activation energy of the reaction in eV. As one can see, by fitting, a simple and well known
Arrhenius type expression is obtained.
5.1.1. Assumptions and simulation inputs
Primarily, the electron temperature in the plasma is considered constant as explained above. A
Boltzmann equation solver is not implemented. However, BOLSIG+ is used to obtain kinetic data for
electron impact dissociation reactions. The electron temperature is an input constant for the model.
An analogy can be made with the gas temperature. The gas temperature is also assumed constant; the
energy balance is thus not taken into account. The gas temperature is an input constant for the model.
The latter is not a bad assumption as the plasma is of non-thermal nature and thus Te>>T. Due to the
latter, equilibrium is also omitted.
Chapter 5: Kinetic modelling 103
Secondly, the electron density in the plasma is considered constant. The electron density is an input
constant for the model. Normally one would include electron impact ionization reactions in the model.
By utilizing the quasi neutrality of plasma, the electron density can be calculated from the total
concentration of produced ions.
Thirdly, at the start of the batch, only methane is present. The latter indicates that the initial pressure
of methane is equal to the total pressure in the reactor. The initial methane pressure is an input for
the model. Of course the batch time should also be given to indicate the length of the batch reactor
simulation.
Last but not least, the volumes of the reactor and the plasma discharge are also necessary inputs.
These are used to define where each reaction is taking place. Electron impact dissociation reactions
are defined on the plasma volume (electrons need to be present). General reactions are defined on
the whole reactor volume.
Chapter 5: Kinetic modelling 104
5.2. MODEL
In order to model the reactor, batch reactor equations are used as indicated in Equation 5-4. Starting
with a simple model and only taking into account the continuity equation. A few assumptions are
utilized which include constant values for a few variables. These variables include the gas temperature,
electron temperature, plasma volume, reactor volume and the electron density.
𝑑𝑛𝑖𝑑𝑡= 𝑅𝑖 , 𝑖 = 1. . 𝑙 5-4
In the latter equation, ni is the number of moles of component i in mol, Ri is the net production rate of
component i in mol/s. In the model a total number of l components are taken into account. The
production rates can be expressed as indicated in Equation 5-5.
𝑅𝑖 =∑ 𝑣𝑖𝑗𝑟𝑗𝑉𝑝 +∑ 𝑣𝑖𝑗𝑟𝑗𝑉𝑟𝑁𝐺𝑅
𝑗=1
𝑁𝑃𝑅
𝑗=1, 𝑖 = 1. . 𝑙 5-5
In the latter equations, νij indicates the stoichiometric coefficients of reaction rate rj to or from
component i, rj indicates the reaction rate of reaction j in mol/m³/s and NPR and NGR represent the
number of plasma reactions (reactions that require an electron) and respectively general reactions
that are contributing to the production and consumption of component i. Furthermore, the plasma
reactions can only occur in the plasma volume Vp and are thus multiplied by that volume in m³. On the
other hand, general reactions take place in the whole reactor volume Vr and are thus multiplied by
that volume in m³. Reaction rates rj are calculated using collision theory as generally indicated in
Equation 5-6. If one needs refreshment of collision theory the reader is referred to Appendix D.
𝑟𝑗 = 𝑘𝑗𝐶𝐴𝐶𝐵 5-6
In this equation, kj is the rate coefficient of reaction j in m³/mol/s, rate coefficient kj of reaction j is
calculated based upon if the reactions are general reactions or electron impact reactions as outlined
in Section 5.1, CA and CB are the respective concentrations of reagents A and B of reaction j in mol/m³.
The concentrations are all based on the reactor volume.
After solving the batch reactor model equations with an ordinary differential equation solver, the
moles of the components in function of batch time are known, ni(t). A robust solver is used from
Sundials computation. Their CVODE solver offers variable-order, variable step multistep methods. They
also include the Adams-Moulton formulas for non-stiff problems while also offering backward
differentiation formulas for a stiff set of ODE’s. A stiff solver is necessary because the radical species
are explicitly taken into account by their mass balance instead of the quasi steady state assumption.
The latter approach is shown in Equation 5-7. Furthermore, for a set of ODEs, initial conditions are
Chapter 5: Kinetic modelling 105
required before integration. These initial conditions are given in Equation 5-8. The initial amount of
methane follows from the ideal gas law as explained in previous sections.6
of products in the end of the batch are obtained. Ethane and hydrogen gas are the most prominent
components while ethylene is only present in a significantly lower amount.
Figure 5-8: Total and partial pressures of the different components at the end of the simulation for a varying plasma volume.
A small discussion for the used reactor configuration discussed in Chapter 3 is now conducted in the
light of a varying plasma volume. In the present reactor configuration, the electron temperature is not
so much different from the base value of 2 eV. Even more so, the electron density is calculated higher
for the real setup than what is used as standard value in the model. This indicates that the present
reactor setup used to obtain experimental data is limited by the plasma volume and not the electron
temperature or density. Limited in the sense of being able to obtain more significant conversion and
yield values. A plasma of even 10% the reactor volume can lead to a conversion of 10% under the same
conditions as seen from Figure 5-7. The ratio between the plasma and reactor volume in the present
reactor configuration equals 0.006% which is in big contrast to what is needed and is observed to be a
clear bottleneck of the present setup. Even more so, the ratio of the plasma volume to the reactor
volume is smaller than the observed conversion from Section 4.4.5.
5.3.1.4. Reactor volume
Here, the effect of the reactor volume on conversion and partial pressure of the feed and products is
examined. The effect on conversion is shown in Figure 5-9. A higher reactor volume results in a lower
overall conversion. The latter is easily understood, for the same amount of methane in a larger volume,
the methane concentration decreases significantly and thus also their rates which they are dependent
upon. No inflection point is visible, indeed, a linear relation with the reactor volume is present in the
rate laws in the form of concentration. One should note that, an increasing reactor volume does
however, increase the rates of the general reactions such as radical recombination’s.
0
20000
40000
60000
80000
100000
120000
0 2 4 6 8 10
Pre
ssu
re, p
[P
a]
Plasma volume, Vp [l]
Methane
Hydrogen gas
Acetylene
Ethylene
Ethane
Total pressure
Chapter 5: Kinetic modelling 111
Figure 5-9: Conversion at the end of the simulation for a varying reactor volume.
Secondly, the effect of the reactor volume on the partial pressures of the components is shown in
Figure 5-10. The latter indicates that a larger reactor volume decreases the total and partial pressures
of the products. Even more so no inflection point is present which indicates again that the reactor
volume has a linear relation with the rate laws as elaborated earlier. Furthermore, due to the model
implementation, i.e. the general reactions are defined on the reactor volume, a lower amount of
intermediate species is expected at higher reactor volumes. However, not too high of a reactor volume
were the concentrations are too low for which the concentration effect would take the upper hand for
they are present twice in the dissociation rate laws.
Figure 5-10: Total and partial pressures of the different components at the end of the simulation for a varying reactor volume.
5.3.1.5. Methane pressure
In this section the last parameter is analyzed which is the initial methane pressure. First the effect on
conversion is discussed as shown in Figure 5-11. An increase in conversion is obtained for a higher
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 20 40 60 80 100
Co
nve
rsio
n, X
[%
]
Reactor volume, Vr [l]
0
20000
40000
60000
80000
100000
120000
0 20 40 60 80 100
Pre
ssu
re, p
[P
a]
Reactor volume, Vr [l]
Methane
Hydrogen gas
Acetylene
Ethylene
Ethane
Total
Chapter 5: Kinetic modelling 112
initial methane pressure. The latter is ascribed to collision theory, a higher pressure results in a higher
concentration and thus a higher chance of reactions that convert methane. The effect of this is mostly
pronounced at low pressures where the rate is limited by the concentration or thus the initial methane
pressure.
Figure 5-11: Conversion at the end of the simulation for a varying initial methane pressure.
Furthermore, the effect on the partial pressures is shown in Figure 5-12. This indicates that of course
the partial pressures of the components are in the order of magnitude of the initial start pressure of
methane. Even more so, a quasi linear trend is observed. The latter figure indicates a weak pressure
dependence of the model which could be caused by the limited number of reactions that are taken
into account.
Figure 5-12: Total and partial pressures of the different components at the end of the simulation for a varying initial methane
pressure.
0%
5%
10%
15%
20%
25%
0 200 400 600 800 1000
Co
nve
rsio
n, X
[%
]
Pressure, pCH4,0 [Pa]
0
200
400
600
800
1000
1200
0 200 400 600 800 1000
Pre
ssu
re, p
[P
a]
Pressure, pCH4,0 [Pa]
Methane
Hydrogen gas
Acetylene
Ethylene
Ethane
Total pressure
Chapter 5: Kinetic modelling 113
5.3.2. Validation of the kinetic model
In this section of the kinetic modelling output, the experiments conducted in Section 4.4 are plotted
against simulated values for three reference pressures. The input values of the model for these three
simulations are calculated with linear interpolation on the data outlined in the previous chapter. The
electron temperature is obtained from Section 4.3.4 and the electron density from Section 4.3.5. These
values are all interpolated for a current of 4 mA with four discharge pins (which is the same as for one
discharge pin at 1 mA). For the runs, a different initial methane pressure is chosen equal to the
reference pressures. A gas temperature of 300 K is utilized. The plasma volume equals 1.6 cm³ and the
reactor volume 24 l respectively. The simulation inputs are summarized in Table I-1 contained in
Appendix I. First, the initial hydrogen production rates from the pressure versus time experiment are
compared. Secondly, the simulated mass percentages are compared to the experimentally obtained
values. The comparison is subsequently followed by the obtained conversion and product selectivities.
Furthermore, it should be reminded that from Section 4.4, the experimental data points are over and
underestimated for a higher and lower reference pressure respectively.
5.3.2.1. Initial hydrogen production rate
From the pressure versus time experiments conducted and discussed in Section 4.1, the experimental
initial hydrogen gas production rate can be deduced. Furthermore, from the model the amount of
hydrogen gas in function of time is known. Hence, the initial hydrogen gas production rate can be
established by the slope of a linear fit on the same time interval as the experiment. Furthermore, the
experimental values are adjusted for taking into account the difference in discharge pins (four in the
pressure versus time experiment and one in the simulation). The latter data is summarized in Table
5-3.
Table 5-3: Simulated and experimental initial hydrogen gas production rates for three reference pressures.
Reference pressure [kPa] 55 75 95
Source Initial hydrogen production rate [mol/s]
Simulated 5.01E-07 7.94E-07 7.37E-07
Experimental 5.70E-07 1.37E-06 2.42E-06
Here, the initial hydrogen rates are very well predicted from an order of magnitude point of view. The
model is not developed enough to predict trends and absolute values. Even more so, not enough
quantitative experiments have been conducted to clearly establish different case studies in the process
of model validation.
Chapter 5: Kinetic modelling 114
5.3.2.2. Mass percentage
The experimental and simulated values of the mass percentages are shown for three reference
pressures in the coming figures, first the 55 kPa reference pressure is discussed given in Figure 5-13. It
is observed that the mass percentages are predicted in the same order of magnitude as the
experiments which is a good start. Secondly, the hydrogen gas mass percentages are really on point
with its experimental value. Ethane is however, undershooting the predicted value by quite a bit.
Furthermore, the model predicts that ethylene is more significant than acetylene for which almost
zero production is observed. The experiments confirm the exact opposite which is more logical.
Ethylene is really not preferred and the model predicts that acetylene weight fraction is near zero. This
hick-up could be caused by faulty kinetic data. Another way of looking at this comparison is from a
model point of view. As stated earlier it is possible that the absolute experimental values are
underestimated for the lower reference pressure which actually could result in a significant and correct
model if these values were higher.
Figure 5-13: Simulated mass percentage in function of time versus experimental mass percentages after 30 minutes of reaction
for a reference pressure of 55 kPa.
The comparison for the next reference pressure of 75 kPa is shown in Figure 5-14. Here, the ethane
predicted end value overshoots the experimental data point. The latter was also the case for a
reference pressure of 55 kPa and can possibly be caused by the experimental data. Furthermore, the
simulated acetylene and ethylene mass percentages are again reversely predicted in comparison to
the experiments, this could be caused by either the simplicity of the model or faulty kinetic data.
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0 300 600 900 1200 1500 1800
Mas
s p
erce
nta
ge, w
t i[%
]
Time [s]
Hydrogen gas sim
Acetylene sim
Ethylene sim
Ethane sim
Hydrogen gas exp
Acetylene exp
Ethylene exp
Ethane exp
Chapter 5: Kinetic modelling 115
Figure 5-14: Simulated mass percentages in function of time versus experimental mass percentages after 30 minutes of
reaction for a reference pressure of 75 kPa.
The simulated and experimental mass percentages for the last reference pressure are shown in Figure
5-15. The simulated value of ethane and ethylene are undershooting their experimental value by quite
a bit. This could be caused from an experimental point of view, as it was expected that absolute
concentrations obtained at higher pressure are overestimating their real value. Furthermore, at this
pressure the hydrogen simulated mass percentage is predicted the best. In general, some trends are
predicted and some are not, the order of magnitudes are however correct and in-line with the
experiments.
Figure 5-15: Simulated mass percentages in function of time versus experimental mass percentages after 30 minutes of
reaction for a reference pressure of 95 kPa.
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0 300 600 900 1200 1500 1800
Mas
s p
erce
nta
ge, w
t i[%
]
Time [s]
Hydrogen gas sim
Acetylene sim
Ethylene sim
Ethane sim
Hydrogen gas exp
Acetylene exp
Ethylene exp
Ethane exp
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.45%
0 300 600 900 1200 1500 1800
Mas
s p
erce
nta
ge, w
t i[%
]
Time [s]
Hydrogen gas sim
Acetylene sim
Ethylene sim
Ethane sim
Hydrogen gas exp
Acetylene exp
Ethylene exp
Ethane exp
Chapter 5: Kinetic modelling 116
5.3.2.3. Conversion
In this section the simulated conversion is compared with the experimentally obtained conversions
shown in Figure 5-16. It is observed that the conversion is predicted perfectly in relative proportion for
the first two reference pressures. The absolute values are not exactly falling on top of each other but
the order of magnitude is still predicted correctly. As previously indicated, experimental values are
likely to be over and under estimated for a higher and lower pressure respectively. Taking this into
account results in the experiments coming closer towards the simulated values.
Figure 5-16: Simulated conversion in function of time versus experimental conversion after 30 minutes of reaction for three
reference pressures.
5.3.2.4. Selectivity
As a last comparison between model and experiments, the selectivities are taken under the loop and
for the first reference pressure, the comparison is shown in Figure 5-17. Here, it is observed that the
selectivities do not change much in time which could be caused by determined significance of the
kinetic data utilized. Even more so, ethylene and acetylene selectivities are in the good order of
magnitude, however inversed like the previously observed mass percentages. Ethane and hydrogen
gas selectivities are also in the correct order of magnitude however inversed with experiments.
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0 300 600 900 1200 1500 1800
Co
nve
rsio
n, X
[%
]
Time [s]
55 kPa sim
75 kPa sim
95 kPa sim
55 kPa exp
75 kPa exp
95 kPa exp
Chapter 5: Kinetic modelling 117
Figure 5-17: Simulated selectivities in function of time versus experimental selectivities after 30 minutes of reaction for a
reference pressure of 55 kPa.
The selectivities for the second reference pressure of 75 kPa are shown in Figure 5-18. Here, the
selectivities of the experiments for ethane and hydrogen gas are quite on the low side compared to
the simulated values. Acetylene and ethylene selectivities from the simulation are reversely predicted,
possibly due to faulty kinetic parameters, bad quantitative analysis or limited model reactions taken
into account.
Figure 5-18: Simulated selectivities in function of time versus experimental selectivities after 30 minutes of reaction for a
reference pressure of 75 kPa.
The selectivity comparison for the last reference pressure of 95 kPa is shown in Figure 5-19. This case
is by far the worst case in terms of correspondence of the experiments with the simulations. However,
0%
10%
20%
30%
40%
50%
60%
0 300 600 900 1200 1500 1800
Sele
ctiv
ity,
Si[%
]
Time [s]
Hydrogen gas sim
Acetylene sim
Ethylene sim
Ethane sim
Hydrogen gas exp
Acetylene exp
Ethylene exp
Ethane exp
0%
10%
20%
30%
40%
50%
60%
0 300 600 900 1200 1500 1800
Sele
ctiv
ity,
Si[%
]
Time [s]
Hydrogen gas sim
Acetylene sim
Ethylene sim
Ethane sim
Hydrogen gas exp
Acetylene exp
Ethylene exp
Ethane exp
Chapter 5: Kinetic modelling 118
they are still predicted by the model in the correct order of magnitude which his as already pointed
out a good basis to further develop and optimize this model.
Figure 5-19: Simulated selectivities in function of time versus experimental selectivities after 30 minutes of reaction for a
reference pressure of 95 kPa.
0%
10%
20%
30%
40%
50%
60%
0 300 600 900 1200 1500 1800
Sele
ctiv
ity,
Si[%
]
Time [s]
Hydrogen gas sim
Acetylene sim
Ethylene sim
Ethane sim
Hydrogen gas exp
Acetylene exp
Ethylene exp
Ethane exp
Chapter 5: Kinetic modelling 119
5.4. CONCLUSION
As mentioned in the introduction of this chapter in order to simulate an input and a model to covert
this input to an output are required. Primarily kinetic data for the general and electron dissociation
reactions are required to calculate their rate coefficients. For general reactions, the data is obtained
from a kinetic data base available on NIST. The electron impact dissociation data is obtained by fitting
integrated radical production cross sections to an Arrhenius type equation. Furthermore, simulation
inputs are necessary which describe the operating conditions of the simulated plasma batch reactor
such as the plasma temperature and density. In order to model a real life phenomenon, assumptions
are required and important to be elaborated because they determine the limit of the model.
The model itself is a fully fledged version of the continuity equation developed on a batch reactor
which means that a reaction and accumulation term are present. A stiff ODE solver is utilized in order
to deal with the continuity equations of the radical species. A constant electron temperature and
density are assumed in terms of plasma characteristics. Furthermore, electron impact reactions are
defined in the plasma volume while general reactions are defined in the reactor volume.
The model is used for two case studies. The first case is a general purpose sensitivity analysis which
indicates the most sensitive parameters. They were subsequently the electron temperature, electron
density and plasma volume in that order. It was found that the plasma volume is the limiting value in
the reactor configuration utilized. The second study dealt with the validity of the kinetic model in
comparison to the experiments. The big conclusion hereof is that the values are all predicted in the
same order of magnitude which is a good baseline to further develop the model. Even more so, some
trends are perfectly described and other are not. It is also extremely important to realize that only one
experiment has been conducted for each reference pressure. From the latter it is not enough proof to
say that the model is not working. Even more so, it was observed from the gas analysis section that
the values are likely to be over and underestimated for a higher and lower reference pressure
respectively. When taking into account the latter, the results of the comparisons are good for a
reference pressure of 55 kPa and 75 kPa.
Chapter 5: Kinetic modelling 120
5.5. REFERENCES
1. NIST Chemical Kinetics Database. http://kinetics.nist.gov/kinetics/index.jsp 2. Zheng, H.; Liu, Q., Kinetic Study of Nonequilibrium Plasma-Assisted Methane Steam Reforming. Mathematical Problems in Engineering 2014, 2014, 10. 3. Morgan, W. L., A critical evaluation of low-energy electron impact cross sections for plasma processing modeling. II: Cl4, SiH4, and CH4. Plasma Chemistry and Plasma Processing 12, (4), 477-493. 4. Song, M.-Y.; Yoon, J.-S.; Cho, H.; Itikawa, Y.; Karwasz, G. P.; Kokoouline, V.; Nakamura, Y.; Tennyson, J., Cross Sections for Electron Collisions with Methane. Journal of Physical and Chemical Reference Data 2015, 44, (2), 023101. 5. Ravasio, S.; Cavallotti, C., Analysis of reactivity and energy efficiency of methane conversion through non thermal plasmas. Chemical Engineering Science 2012, 84, 580-590. 6. Alan Hindmarsh, R. S., User Documentation for CVODE v2.8.2. 164.
Table F-3: Integrated peak areas of the components in the calibration mixture for three runs.
Run number 1 2 3
Detector Component Surface area [mi]
TCD H2 10514920 10517430 10462440
TCD CO2 1478515 1342568 970020
TCD C2H4 3803021 3806873 3804420
TCD C2H6 451072 451272 450740
TCD C2H2 577406 573122 570498
TCD N2 1425248 1421662 1416639
TCD CH4 438412 335572 556212
TCD CO 561696 539608 543760
TCD Ar 2616265 2595828 2672523
FID CH4 4251398 4119608 4126247
FID C2H6 113308 1099434 1102570
FID C2H4 94477839 9149273 9167290
FID C2H2 371076 351116 359047
Table F-4: Calculated mass percentages from average peak areas of three runs for three reference pressures.
Reference pressure [kPa] 55 75 95
Component Mass percentage [%]
Ar 71.462% 54.104% 29.788%
H2 0.002% 0.003% 0.007%
CH4 29.138% 79.817% 222.928%
C2H2 0.005% 0.011% 0.027%
C2H4 0.002% 0.003% 0.139%
C2H6 0.036% 0.069% 0.273%
Appendix G: Data mass spectrometry 149
Appendix G: DATA MASS SPECTROMETRY
Table G-1: Logged pressures during different mass spectrometer experiments for three different reference pressures.
Reference pressure [kPa] 55 75 95
Components run CH4+H2 C2 CH4+H2 C2 CH4+H2 C2
Pressure measurement number Pressure [kPa]
P0 55.0 55.0 75.0 75.0 95.0 95.0
P1 54.7 54.7 74.6 74.6 94.4 94.3
P2 54.4 54.5 74.1 74.0 92.9 92.3
P3 54.2 54.3 73.7 73.5 92.8 91.7
Appendix H: Python source code 150
Appendix H: PYTHON SOURCE CODE
The model is implemented in the best scientific language to date; Python. More specifically, the Python
3.5 64-bit compiler was utilized. A small note, a few extra packages were used. Assimulo which contains
the genius solver from Sundials. The latter package uses Scipy, Numpy and the intel MKL library.
Matpotlib is a great package for plotting results and data. And last but not least, xlsxwriter which is the
perfect module to directly write the results in excel files.
H.1 DATA.PY
#kinetic data #[A,alpha,Ea]=[preexponential factor, temperature exponent, activation energy] #units of data [m³/mol/s, - , eV] #k=A*Te^n*exp(-Ea/Te) (for electron impact reactions) (in correct SI units) E1=[1.6e12,-0.989,25] #CH4 + e -> CH3 + H + e E2=[2.7e11,-0.989,25] #CH4 + e -> CH2 + H2 + e E3=[1.4e11,-0.989,25] #CH4 + e -> CH + H + H2 + e E4=[1.2e8,-0.989,25] #CH4 + e -> C + 2H2 + e E5=[3.8e12,-1.984,30] #H2 + e -> 2H + e #k=A*(T/298)^n*exp(-Ea/R/T) (for chemical reactions) (in correct SI units) #units of data [m³/molecules/s, - , J/mol] R1=[1.99e-16,0,0] #CH + CH -> C2H2 R2=[1.03e-16,-1.1,1.33e3] #CH3 + CH3 -> C2H6 R3=[4.36e-19,3.16,36.63e3] #CH4 + H -> CH3 + H2 R4=[7.14e-18,0,41.99e3] #CH4 + CH2 -> 2CH3 R5=[1.7e-17,0,0] #CH4 + CH -> C2H4 + H R6=[1.66e-14,0,134e3] #CH3 + CH3 -> C2H4 + H2 R7=[7.01e-17,0,0] #CH3 + CH2 -> C2H4 + H R8=[5e-21,0,0] #CH2 + H2 -> CH3 + H R9=[3.11e-16,0,13.72e3] #CH + H2 -> H + CH2 R10=[5.3e-17,0,0] #CH2 + CH2 -> C2H2 + H2 #thermodynamic data #reaction enthalpy [J/mol] HE1=0 #CH4 + e -> CH3 + H + e HE2=0 #CH4 + e -> CH2 + H2 + e HE3=0 #CH4 + e -> CH + H + H2 + e HE4=0 #CH4 + e -> C + 2H2 + e HE5=0 #H2 + e -> 2H + e HR1=(-2*594.13+227.4)*1e3 #CH + CH -> C2H2 HR2=(-83.7)*1e3 #CH3 + CH3 -> C2H6 HR3=(--74.87-216)*1e3 #CH4 + H -> CH3 + H2 HR4=(-386.39--74.87)*1e3 #CH4 + CH2 -> 2CH3 HR5=(-594.13--74.87+216+52.4)* #kinetic data #[A,alpha,Ea]=[preexponential factor, temperature exponent, activation energy] #units of data [m³/mol/s, - , eV] #k=A*Te^n*exp(-Ea/Te) (for electron impact reactions) (in correct SI units) E1=[1.6e12,-0.989,20] #CH4 + e -> CH3 + H + e E2=[2.7e11,-0.989,20] #CH4 + e -> CH2 + H2 + e E3=[1.4e11,-0.989,20] #CH4 + e -> CH + H + H2 + e E4=[1.2e8,-0.989,20] #CH4 + e -> C + 2H2 + e
Appendix H: Python source code 151
E5=[3.8e12,-1.984,25] #H2 + e -> 2H + e #k=A*(T/298)^n*exp(-Ea/R/T) (for chemical reactions) (in correct SI units) #units of data [m³/molecules/s, - , J/mol] R1=[1.99e-16,0,0] #CH + CH -> C2H2 R2=[1.03e-16,-1.1,1.33e3] #CH3 + CH3 -> C2H6 R3=[4.36e-19,3.16,36.63e3] #CH4 + H -> CH3 + H2 R4=[7.14e-18,0,41.99e3] #CH4 + CH2 -> 2CH3 R5=[1.7e-17,0,0] #CH4 + CH -> C2H4 + H R6=[1.66e-14,0,134e3] #CH3 + CH3 -> C2H4 + H2 R7=[7.01e-17,0,0] #CH3 + CH2 -> C2H4 + H R8=[5e-21,0,0] #CH2 + H2 -> CH3 + H R9=[3.11e-16,0,13.72e3] #CH + H2 -> H + CH2 R10=[5.3e-17,0,0] #CH2 + CH2 -> C2H2 + H2 #thermodynamic data #reaction enthalpy [J/mol] HE1=0 #CH4 + e -> CH3 + H + e HE2=0 #CH4 + e -> CH2 + H2 + e HE3=0 #CH4 + e -> CH + H + H2 + e HE4=0 #CH4 + e -> C + 2H2 + e HE5=0 #H2 + e -> 2H + e HR1=(-2*594.13+227.4)*1e3 #CH + CH -> C2H2 HR2=(-83.7)*1e3 #CH3 + CH3 -> C2H6 HR3=(--74.87-216)*1e3 #CH4 + H -> CH3 + H2 HR4=(-386.39--74.87)*1e3 #CH4 + CH2 -> 2CH3 HR5=(-594.13--74.87+216+52.4)*1e3 #CH4 + CH -> C2H4 + H HR6=(52.4)*1e3 #CH3 + CH3 -> C2H4 + H2 HR7=(-386.39+216+52.4)*1e3 #CH3 + CH2 -> C2H4 + H HR8=(-386.39+216)*1e3 #CH2 + H2 -> CH3 + H HR9=(+386.39-594.13+216)*1e3 #CH + H2 -> H + CH2 HR10=(-386.39+227.4)*1e3 #CH2 + CH2 -> C2H2 + H2 #reaction entropy [J/K/mol] SE1=0 #CH4 + e -> CH3 + H + e SE2=0 #CH4 + e -> CH2 + H2 + e SE3=0 #CH4 + e -> CH + H + H2 + e SE4=0 #CH4 + e -> C + 2H2 + e SE5=0 #H2 + e -> 2H + e SR1=-2*183.04+200.8 #CH + CH -> C2H2 SR2=229.5 #CH3 + CH3 -> C2H6 SR3=+130.6-186.2 #CH4 + H -> CH3 + H2 SR4=-193.93-186.2 #CH4 + CH2 -> 2CH3 SR5=-183.04-186.2+219.5 #CH4 + CH -> C2H4 + H SR6=+130.6+219.5 #CH3 + CH3 -> C2H4 + H2 SR7=-193.93+219.5 #CH3 + CH2 -> C2H4 + H SR8=-193.93-130.6 #CH2 + H2 -> CH3 + H SR9=-183.04+193.93-130.6 #CH + H2 -> H + CH2 SR10=-2*193.93+130.6+200.8 #CH2 + CH2 -> C2H2 + H2
H.2 MODEL.PY
from math import exp from data import * R=8.3144598 #ideal gas constant [J/K/mole] NA=6.022140857e23 #number of Avogadro [particles/mole] #definition of chemical rate coefficient def chemrate(kineticdata,T): return NA*kineticdata[0]*((T/298)**kineticdata[1])*exp(-kineticdata[2]/R/T) #definition of physical rate coefficient def physrate(kineticdata,Te):
Appendix H: Python source code 152
return kineticdata[0]*(Te**kineticdata[1])*exp(-kineticdata[2]/Te) #definition of equilibrium constant def equilibrium(reactionenthalpy,reactionentropy,T): return exp(-(reactionenthalpy-T*reactionentropy)/R/T) #definition of the right hand side ordinary differential equations def model(t,equations): #equation indices #[vplasma,vreactor,Te,T, Ce ,CH4,H,C,CH,CH2,CH3,H2,C2H2,C2H4,C2H6] #[ 0 , 1 ,2 ,3, 4 , 5 ,6,7, 8, 9 , 10,11, 12 , 13 , 14 ] #forward rate equations rE1f=(equations[0]*physrate(E1,equations[2])*(equations[5]/equations[1])*equations[4]) rE2f=(equations[0]*physrate(E2,equations[2])*(equations[5]/equations[1])*equations[4]) rE3f=(equations[0]*physrate(E3,equations[2])*(equations[5]/equations[1])*equations[4]) rE4f=(equations[0]*physrate(E4,equations[2])*(equations[5]/equations[1])*equations[4]) rE5f=(equations[0]*physrate(E5,equations[2])*(equations[11]/equations[1])*equations[4]) rR1f=(equations[1]*chemrate(R1,equations[3])*(equations[8]/equations[1])*(equations[8]/equations[1])) rR2f=(equations[1]*chemrate(R2,equations[3])*(equations[10]/equations[1])*(equations[10]/equations[1])) rR3f=(equations[1]*chemrate(R3,equations[3])*(equations[5]/equations[1])*(equations[6]/equations[1])) rR4f=(equations[1]*chemrate(R4,equations[3])*(equations[5]/equations[1])*(equations[9]/equations[1])) rR5f=(equations[1]*chemrate(R5,equations[3])*(equations[5]/equations[1])*(equations[8]/equations[1])) rR6f=(equations[1]*chemrate(R6,equations[3])*(equations[10]/equations[1])*(equations[10]/equations[1])) rR7f=(equations[1]*chemrate(R7,equations[3])*(equations[10]/equations[1])*(equations[9]/equations[1])) rR8f=(equations[1]*chemrate(R8,equations[3])*(equations[9]/equations[1])*(equations[11]/equations[1])) rR9f=(equations[1]*chemrate(R9,equations[3])*(equations[8]/equations[1])*(equations[11]/equations[1])) rR10f=(equations[1]*chemrate(R10,equations[3])*(equations[9]/equations[1])*(equations[9]/equations[1])) #mole balance equations, equations[x..end] dnCH4=(-rE1f-rE2f-rE3f-rE4f-rR3f-rR4f-rR5f) dnH=(+rE1f+rE3f+2*rE5f-rR3f+rR5f+rR7f+rR8f+rR9f) dnC=(+rE4f) dnCH=(+rE3f-2*rR1f-rR5f-rR9f) dnCH2=(+rE2f-rR4f-rR7f-rR8f+rR9f-2*rR10f)
from matplotlib import pyplot as plt from xlsxwriter import Workbook #plot function def plotdata(datalabels,dataunits,time,data): if data.ndim==1: if datalabels is not None: if dataunits is not None: plt.title(datalabels+' ['+dataunits+']') else: plt.title(datalabels) plt.plot(time,data) else: f, axarr = plt.subplots(len(datalabels)) for i in range(len(datalabels)): axarr[i].plot(time,data[:,i]) if datalabels is not None: if dataunits is not None: axarr[i].set_title(datalabels[i]+' ['+dataunits[i]+']') else: axarr[i].set_title(datalabels[i]+' [-]') plt.show() #print function def printdata(numberofpoints,datalabels,dataunits,time,data): timestep=int(len(data)/numberofpoints) if data.ndim==1:#1-dimensional data maxwidth=len(datalabels)+1 if datalabels is not None: print('Time'.center(maxwidth)+''.join(datalabels.center(maxwidth))) if dataunits is not None: print('s'.center(maxwidth)+''.join(dataunits.center(maxwidth))) if data is not None: for i in range(0,len(data),timestep): print(format(time[i],'.2f').center(maxwidth)+''.join(format(data[i],'.7f').center(maxwidth))) else:#multi-dimensional data maxwidth=max(len(datalabel) for datalabel in datalabels)+1 if datalabels is not None: print('Time'.center(maxwidth)+''.join(label.center(maxwidth) for label in datalabels)) if dataunits is not None:
Appendix H: Python source code 154
print('s'.center(maxwidth)+''.join(unit.center(maxwidth) for unit in dataunits)) if data is not None: for i in range(0,len(data),timestep): print(format(time[i],'.2f').center(maxwidth)+''.join(format(element,'.7f').center(maxwidth) for element in data[i])) print('\n') #write function for txt files def writetxt(directory,filename,datalabels,dataunits,time,data): f=open(filename,'w') if data.ndim==1:#1-dimensional data if datalabels is not None: f.write('Time,'+datalabels) f.write('\n') if dataunits is not None: f.write('s,'+dataunits) f.write('\n') if data is not None: for i in range(len(data)): f.write(str(time[i])+','+str(data[i])) f.write('\n') else:#multi-dimensional data if datalabels is not None: f.write('Time,'+','.join(label for label in datalabels)) f.write('\n') if dataunits is not None: f.write('s,'+','.join(unit for unit in dataunits)) f.write('\n') if data is not None: for i in range(len(data)): f.write(str(time[i])+','+','.join(str(element) for element in data[i])) f.write('\n') f.close() #write function for xlsx files def writexlsx(directory,filename,sheetnames,datalabels,dataunits,time,data): book=Workbook(filename,{'in_memory':True,'nan_inf_to_errors': True}) #witexlsx function inputs should be lists if type(sheetnames)==list and type(datalabels)==list and type(dataunits)==list and type(data)==list: if len(sheetnames)==1:#1-sheet sheet=book.add_worksheet(sheetnames[0]) if data.ndim==1: #1-dimensional data if dataunits[0] is not None: sheet.write(0,0,'s') sheet.write(0,1,dataunits[0]) if datalabels[0] is not None: sheet.write(1,0,'Time') sheet.write(1,1,datalabels[0]) for j in range(len(data[0])): sheet.write(j+2,0,time[j]) sheet.write(j+2,1,data[0][j]) else:#multi-dimensional data if dataunits[0] is not None: sheet.write(0,0,'s') for j in range(len(dataunits[0])): sheet.write(0,j+1,dataunits[0][j]) if datalabels[0] is not None: sheet.write(1,0,'Time')
Appendix H: Python source code 155
for j in range(len(datalabels[0])): sheet.write(1,j+1,datalabels[0][j]) if data[0] is not None: for j in range(len(data[0])): sheet.write(j+2,0,time[j]) for k in range(len(data[0][j])): sheet.write(j+2,k+1,data[0][j][k]) else:#multi-sheet for i in range(len(sheetnames)): sheet=book.add_worksheet(sheetnames[i]) if data[i].ndim==1:#1-dimensional data if dataunits[i] is not None: sheet.write(0,0,'s') sheet.write(0,1,dataunits[i][0]) if datalabels[i] is not None: sheet.write(1,0,'Time') sheet.write(1,1,datalabels[i][0]) if data[i] is not None: for j in range(len(data[i])): sheet.write(j+2,0,time[j]) sheet.write(j+2,1,data[i][j]) else:#multi-dimensional data if dataunits[i] is not None: sheet.write(0,0,'s') for j in range(len(dataunits[i])): sheet.write(0,j+1,dataunits[i][j]) if datalabels[i] is not None: sheet.write(1,0,'Time') for j in range(len(datalabels[i])): sheet.write(1,j+1,datalabels[i][j]) if data[i] is not None: for j in range(len(data[i])): sheet.write(j+2,0,time[j]) for k in range(len(data[i][j])): sheet.write(j+2,k+1,data[i][j][k]) else: print('Inputs should be a list, even if only one worksheet needs to be written') book.close()
H.4 MAIN.PY
from numpy import divide,c_,sum,multiply,seterr from assimulo.problem import Explicit_Problem from assimulo.solvers import CVode seterr(divide='ignore', invalid='ignore') #ignore division by zero R=8.3144598 #ideal gas constant [J/K/mole] NA=6.022140857e23 #number of Avogadro [particles/mol] #input, change simulation constants vplasma=1e-3 #volume of the plasma in the reactor [m³] vreactor=10e-3 #volume of the reactor [m³] Te=2 #electron temperature [eV] T=300 #gas temperature [K] pCH4=1e5 #initial methane pressure in the reactor [Pa] ne=1e14 #electron density [electrons/m³] <- alter this one treaction=1800 #reaction time [s] points=1000 #number of points for the simulation #output, change what you want as output plotresults=0
Appendix H: Python source code 156
printresults=0 writeresults=1 outputdirectory='output/simulation runs/950 mbar' #from here on you do no have to fill in anything else #moles to pressure conversion function def n2p(moles,volume,temperature): return moles*R*temperature/volume #pressure to moles conversion function def p2n(pressure,volume,temperature): return pressure*volume/R/temperature #define constant variables variables=['vplasma','vreactor','Te','T','Ce'] variablenames=['Volume plasma','Volume reactor','Electron temperature','Gas temperature','Electron density'] variableunits=['m³','m³','K','eV','1/m³'] Ce=(ne/NA) #electron concentration [mol/m³] initialvariables=[vplasma,vreactor,Te,T,Ce] if len(variables)!=len(variablenames): print('The number of variables does not match the number of variable names') input('Press enter to exit') exit() elif len(variables)!=len(variableunits): print('The number of variables does not match the number of variable units') input('Press enter to exit') exit() elif len(variables)!=len(initialvariables): print('The number of variables does not match the number of initial variable boundary conditions') input('Press enter to exit') exit() else: print('There are',len(initialvariables),'variables kept constant during simulation') print(variables) print('Values of the constant variables:') print(variableunits) print(initialvariables) #define components components=['CH4','H','C','CH','CH2','CH3','H2','C2H2','C2H4','C2H6'] componentnames=['Methane','Hydrogen','Carbon','Methylidyne','Methylene','Methyl','Hydrogen gas','Acetylene','Ethylene','Ethane'] molarmasses=[16.0139,1.0008,12.0107,13.0115,14.0123,15.0131,2.0016,26.023,28.0246,30.0262] initialcomponents=[p2n(pCH4,vreactor,T),0,0,0,0,0,0,0,0,0] if len(components)!=len(componentnames): print('\nThe number of components does not match the number of component names') input('Press enter to exit') exit() elif len(components)!=len(molarmasses): print('\nThe number of components does not match the number of component masses') input('Press enter to exit') exit() elif len(components)!=len(initialcomponents): print('\nThe number of components does not match the number of initial component boundary conditions') input('Press enter to exit') exit() else:
Appendix H: Python source code 157
print('\nThere are',len(initialcomponents),'different species taken into account in the model') print(components) print('Initial amounts of the components [mol]:') print(initialcomponents) print('\n') #solving the model equations initialvalues=initialvariables+initialcomponents from model import model problem = Explicit_Problem(model,initialvalues,0) solver=CVode(problem) solver.atol=1e-14 solver.rtol=1e-14 time,solution=solver.simulate(treaction,points) variables=solution[:,0:len(initialvariables)] moles=solution[:,len(initialvariables):len(initialvalues)] totalmole=sum(moles,axis=1) moles=c_[moles,totalmole] #post processing with moles conversion=divide(moles[0][0]-moles[:,0],moles[0][0]) print('\nAfter',time[-1],'s of reaction time, methane conversion equals:',conversion[-1],'\n') yields=divide(moles[:,1:-1]-moles[0,1:-1],moles[0][0]) selectivities=divide(moles[:,1:-1]-moles[0,1:-1],sum(moles[:,1:-1],axis=1)[:,None]) pressures=n2p(moles,vreactor,T) molefractions=divide(moles[:,:-1],moles[:,-1][:,None]) concentrations=divide(moles[:,:-1],variables[:,1][:,None]) totalconcentration=sum(concentrations,axis=1) concentrations=c_[concentrations,totalconcentration] #post processing with masses masses=multiply(moles[:,:-1],molarmasses) totalmass=sum(masses,axis=1) masses=c_[masses,totalmass] massfractions=divide(masses[:,:-1],totalmass[:,None]) #define units and names for output functions componentnames=componentnames+['Total'] moleunits=['mol']*len(moles[0]) concentrationunits=['mol/m³']*len(concentrations[0]) molefractionunits=['mol%']*len(molefractions[0]) massunits=['g']*len(masses[0]) massfractionunits=['wt%']*len(massfractions[0]) pressureunits=['Pa']*len(pressures[0]) #plot conversion if plotresults==True: from output import plotdata plotdata(variablenames,variableunits,time,variables) plotdata('Conversion',None,time,conversion) plotdata(componentnames,moleunits,time,moles) plotdata(componentnames[1:-1],None,time,yields) plotdata(componentnames[1:-1],None,time,selectivities) plotdata(componentnames[:-1],molefractionunits,time,molefractions) #print results if printresults==True: from output import printdata printdata(10,variablenames,variableunits,time,variables) printdata(10,'Conversion',None,time,conversion)
Appendix H: Python source code 158
printdata(10,componentnames,moleunits,time,moles) printdata(10,componentnames[1:-1],None,time,yields) printdata(10,componentnames[1:-1],None,time,selectivities) printdata(10,componentnames[:-1],molefractionunits,time,molefractions) printdata(10,componentnames,concentrationunits,time,concentrations) printdata(10,componentnames,massunits,time,masses) printdata(10,componentnames[:-1],massfractionunits,time,massfractions) printdata(10,componentnames,pressureunits,time,pressures) #write results if writeresults==True: #create specified output directory dirs=outputdirectory.split('/') import os olddir=os.getcwd() for dir in dirs: if len(dir)!=0: if not os.path.exists(dir): os.mkdir(dir) os.chdir(dir) else:os.chdir(dir) #write results to txt files from output import writetxt writetxt(outputdirectory,'variables.txt',variablenames,variableunits,time,variables) writetxt(outputdirectory,'conversion.txt','Conversion',None,time,conversion) writetxt(outputdirectory,'moles.txt',componentnames,moleunits,time,moles) writetxt(outputdirectory,'yields.txt',componentnames[1:-1],None,time,yields) writetxt(outputdirectory,'selectivities.txt',componentnames[1:-1],None,time,selectivities) writetxt(outputdirectory,'concentrations.txt',componentnames,concentrationunits,time,concentrations) writetxt(outputdirectory,'molfractions.txt',componentnames[:-1],molefractionunits,time,molefractions) writetxt(outputdirectory,'masses.txt',componentnames,massunits,time,masses) writetxt(outputdirectory,'massfractions.txt',componentnames[:-1],massfractionunits,time,massfractions) writetxt(outputdirectory,'partialpressures.txt',componentnames,pressureunits,time,pressures) #define variables for writexlsx function + write xlsx sheetnames=['variables','conversion','moles','yields','selectivities','concentrations','molefractions','masses','massfractions','partialpressures'] sheetdatalabels=[variablenames,['Conversion'],componentnames,componentnames[1:-1],componentnames[1:-1],componentnames,componentnames[:-1],componentnames,componentnames[:-1],componentnames] sheetdataunits=[variableunits,[''],moleunits,[''], [''],concentrationunits,molefractionunits,massunits,massfractionunits,pressureunits] sheetdata=[variables,conversion,moles,yields,selectivities,concentrations,molefractions,masses,massfractions,pressures] from output import writexlsx writexlsx(outputdirectory,'summary.xlsx',sheetnames,sheetdatalabels,sheetdataunits,time,sheetdata) os.chdir(olddir)
Appendix I: Data kinetic modelling 159
Appendix I: DATA KINETIC MODELLING
Table I-1: Model input parameters for three reference pressure simulations.
Electron density [1/m³], ne 2.33E14 2.35E14 2.31E14
Reaction time [s], Δt 1800 1800 1800
Number of points 1000 1000 1000
Appendix J: Lab journal 160
Appendix J: LAB JOURNAL
In the next sections, the page numbers are given for each section contained in the lab journal. Front
indicates that the reader should start looking form the front cover of the lab journal. On the other
hand, back means that this information is given starting form the back of the lab journal.
J.1 FRONT 1-20
General notes are written down during the period of the thesis. The latter mostly includes small
discussions with my coach about the setup, methods and theory surrounding plasma.
A.1 FRONT 21-…
Dropbox layout of all data stored on the DVD with respect to everything used in the creation of this
master thesis.
A.2 BACK A-I
Experimental conditions for the experiments included in the report of this thesis.
Methane conversion in a plasma reactor
Dries Michiels
Supervisors: Prof. dr. ir. Kevin Van Geem, Prof. dr. ir. Christophe Leys
Counsellor: Anton Nikiforov Master's dissertation submitted in order to obtain the academic degree of
Master of Science in Chemical Engineering
Department of Chemical Engineering and Technical Chemistry
Chair: Prof. dr. ir. Guy Marin Department of Applied Physics Chair: Prof. dr. ir. Christophe Leys Faculty of Engineering and Architecture Academic year 2015-2016
