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Modeling Steam Cracking of Complex Hydrocarbons
Helge Dehandschutter
Promotoren: Prof. dr. ir. G.B. Marin
Prof. dr. lic. M.-F. Reyniers
Begeleider: Dr. ir. K. Van Geem
Scriptie ingediend tot het behalen van de academische graad van burgerlijk
scheikundig ingenieur
Academiejaar 2005-2006
Faculteit Ingenieurswetenschappen
Vakgroep Chemische Proceskunde en Technische Chemie Laboratorium voor Petrochemische Techniek
Directeur: Prof. Dr. Ir. G. B. Marin
Modeling Steam Cracking of Complex Hydrocarbons
Helge Dehandschutter
Promotoren: Prof. dr. ir. G.B. Marin
Prof. dr. lic. M.-F. Reyniers
Begeleider: Dr. ir. K. Van Geem
Scriptie ingediend tot het behalen van de academische graad van burgerlijk
scheikundig ingenieur
Academiejaar 2005-2006
Faculteit Ingenieurswetenschappen
Vakgroep Chemische Proceskunde en Technische Chemie Laboratorium voor Petrochemische Techniek
Directeur: Prof. Dr. Ir. G. B. Marin
Modeling Steam Cracking of Complex Hydrocarbons
Helge Dehandschutter
Scriptie ingediend tot het behalen van de academische graad van
burgerlijk scheikundig ingenieur
Academiejaar: 2005 – 2006
Promotoren: Prof. dr. ir. G. B. Marin en Prof. dr. lic. M.-F. Reyniers
Begeleider: Dr. ir. K. Van Geem
UNIVERSITEIT GENT
Faculteit Ingenieurswetenschappen
Vakgroep Chemische Proceskunde en Technische Chemie
Laboratorium voor Petrochemische Techniek Directeur: Prof. Dr. Ir. G. B. Marin
Abstract
The main objective of this thesis is the validation and the improvement of the fundamental
simulation models of Plehiers (1989) and Vercauteren (1991). The expansion in the petrochemical
industry, the continuing demands for ethylene and propylene, the varying feedstock availability,
and the rapidly changing market situation have brought and continue to bring research attention to
the modeling of the steam cracking process. In the past few decades step by step new and better
simulation models have been developed at the Laboratorium voor Petrochemische Techniek.
However, the cracking behavior of toluene is still not accurately described in the studied models,
as come forward in chapter 2. The reactions existing in the current reaction network disregard the
actual cracking mechanism of toluene in which the benzyl radical plays a key role. The steps taken
to eliminate this shortcoming are described in chapter 2 as well. By taking the real cracking
mechanism of toluene into account excellent simulation results for the benzene and toluene yields
are obtained. Furthermore, nowadays more and more heavy fractions (heavy naphtha, light gas oil
or vacuum gas oil) are used as feedstock for steam cracking. The reason is that the demand for
these fractions as fuel is becoming less and less important. This results in large remains of these
low cost fuels. It is of great importance that the simulation models also accurately predict the
product spectrum of these heavy fractions. In this respect the study of the cracking behavior of
several gas condensates is carried out. In chapter 3, the results of these cracking and decoking
experiments part of a new pilot campaign are discussed. Moreover, to use a simulation model, a
detailed composition of the feedstock is often required. In chapter 3, the method for analyzing the
feedstocks is described as well.
Keywords: thermal cracking, modeling, pilot plant experiments
___________________________________________________________________________________________
Krijgslaan 281 S5, B-9000 Gent (Belgium) tel. +32 (0)9 264 45 16 • fax +32 (0)9 264 49 99 • GSM +32 (0)475 83 91 11 •
e-mail: [email protected] http://allserv.ugent.be/tw12/
Opleidingscommissie Scheikunde
Verklaring in verband met de toegankelijkheid van de scriptie
Ondergetekende, Helge Dehandschutter
afgestudeerd aan de UGent in het academiejaar 2005 - 2006 en auteur van de scriptie met als
titel:
Modeling Steam Cracking of Complex Hydrocarbons
verklaart hierbij:
1. dat hij/zij geopteerd heeft voor de hierna aangestipte mogelijkheid in verband met de
consultatie van zijn/haar scriptie:
de scriptie mag steeds ter beschikking gesteld worden van elke aanvrager
de scriptie mag enkel ter beschikking gesteld worden met uitdrukkelijke, schriftelijke
goedkeuring van de auteur
de scriptie mag ter beschikking gesteld worden van een aanvrager na een wachttijd
van jaar
de scriptie mag nooit ter beschikking gesteld worden van een aanvrager
2. dat elke gebruiker te allen tijde gehouden is aan een correcte en volledige bronverwijzing
Gent, 16 juni 2006
FACULTEIT TOEGEPASTE WETENSCHAPPEN
Chemische Proceskunde en Technische Chemie Laboratorium voor Petrochemische Techniek
Directeur: Prof. Dr. Ir. Guy B. Marin
IK DANK:
voor het mogelijk maken en het
begeleiden van dit stukje wetenschap:
Prof. dr. ir. Marin
Prof. dr. ir. Reyniers
voor zijn enorme inzet, zijn enthousiasme,
zijn goede raad, zijn kennis en de leuke
samenwerking:
Kevin
voor het me wegwijs maken in de wondere
wereld van de pilootinstallatie:
Mister Wang en Michaël
voor de leuke sfeer aan de koffietafel:
iedereen van “Zwijnaarde”
voor de ontspannende middagen, avonden
en nachten:
mijn medecollegastudenten
voor hun diverse steun (want ook al
snappen ze niets van chemie, van de rest
begrijpen ze alles)
mijn ouders
Bjorn
voor de computerlogistiek
mijn broer, Bram
Modeling Steam Cracking of Complex Hydrocarbons
Helge Dehandschutter
Promotors: Prof.dr.lic. Reyniers M.F., Prof.dr.ir. Marin G.B. Coach: Dr.ir. Van Geem K.M.
Abstract: Fundamental simulation models are an indispensable
tool for the petrochemical industry. The development of an
extensive database of pilot plant experiments has made it possible
to validate the simulation models of Plehiers (1989)1 and
Vercauteren (1991)2 for a first time in a very systematic manner.
This validation reveals several shortcomings to both models.
Some of the shortcomings can be overcome by introducing new
reactions and species, e.g. reactions involving the benzyl radical.
However, this way of working does certainly not solve all the
problems encountered. Therefore a completely new single event
microkinetic model is developed by Van Geem (2006)3. This
model gives a good agreement for a wide range of pilot plant
experiments with light and heavier fractions. However the
database does not contain a lot of experiments with heavy
fractions. Nowadays more and more heavy fractions are used as
feedstock for steam cracking. In this respect the study of the
cracking behavior of several gas condensates is carried out. Eight
different gas condensates are cracked under identical conditions,
while for one feedstock also the process conditions have been
varied over a broad range. The detailed molecular composition of
one specific fraction is also determined.
Keywords steam cracking, modeling, pilot plant experiments
I. INTRODUCTION
Steam cracking of hydrocarbons is one of the main
processes in the petrochemical industry. In this process
hydrocarbon feedstocks ranging from light alkanes such as
ethane and propane up to complex mixtures such as naphthas
and heavy gas oils are cracked into commercially more
valuable products such as light olefins and aromatics. Steam
cracking is carried out in tubular reactors suspended in large
gas-fired furnaces at temperatures ranging from 600-900 °C.
The petrochemical industry is continually searching for
higher performance and increased selectivity to increase their
profit margins. In this search accurate simulation models have
become indispensable tools. Several fundamental simulation
models for steam cracking have been developed at the LPT.
However, the simulation results of some feedstocks are not
always as accurate as one desires. These shortcomings can be
partly explained by the absence of certain reaction pathways
and several important species. On the other hand, a new
optimization of the kinetic parameters of the reaction network
could also solve a lot of problems. These options are critically
evaluated in this work.
Another important aspect is the extension of the simulation
models to heavier feedstocks. Since the demand for heavier
fractions as fuel is becoming less and less important, the
interest of the petrochemical industry in these low-priced
fractions as feedstock for the ethylene production has
increased. Generally, as the feedstock gets heavier, the yield
H. Dehandschutter is with the Chemical Engineering Department, Ghent
University (UGent), Gent, Belgium. E-mail: [email protected]
.
of ethylene decreases and other products such as propylene,
butadiene and benzene become more significant. In order to
improve the reliability of the single event microkinetic model
for heavier feedstocks, the cracking behavior of gas
condensates is studied. Gas condensates are the liquid
condensate removed and recovered during the processing of
raw natural gas. These fractions show an approximate boiling
range between 50 and 350 °C.
II. RESULTS
A. Network improvement
1) Validation
To validate the fundamental simulation models of Plehiers
(1989)1 and Vercauteren (1991)2 the simulation results are
compared with experimental data from the experimental
database. In this database over 400 experiments obtained with
mare than 50 different feedstocks are gathered. An interface is
designed to make searching for data easy. One of the main
conclusions of this comparison is that the cracking behavior of
toluene is not accurately described. The reactions
implemented in the current reaction network disregard the
actual cracking mechanism of toluene in which the benzyl
radical plays a key role. The main reactions that appear during
the cracking of toluene are presented in Figure 1.
C-C & C-H scission of molecules and recombination
toluene benzyl + H
ethylbenzene benzyl + CH3
dibenzyl benzyl + benzyl
Hydrogen abstraction
H + toluene H2 + benzyl
CH3 + toluene CH4 + benzyl
C2H5 + toluene C2H6 + benzyl
Ipso addition
H + toluene benzene + CH3
Figure 1: Cracking mechanism of pure toluene 4
2) Adaptations
The reactions in Figure 1 are introduced in the reaction
network of Plehiers (1991)1. The optimization of the
independent kinetic parameters is performed using a
Rosenbrock optimization routine. The reaction rate
coefficients are fitted to experimental data from the pilot plant
set-up. The used pilot experiments include nineteen
experiments with different composition of the toluene-ethane
mixture and different conversions.
3) Conclusions
Introducing the benzyl radical leads to an improved
description of the cracking behavior of toluene in
toluene/ethane mixtures. However, for naphtha feedstocks the
accuracy of the simulated toluene yield remains poor. This is
not surprising because of the way the benzyl radical is treated
in the reaction network of Plehiers. Only a completely new
reaction network can overcome these problems. Furthermore,
a complete optimization of all the kinetic parameters of the
reaction network should be executed. In the new simulation
model of Van Geem (2006)³ all these considerations were
implemented, leading to an adequate simulation of the
benzene and toluene yields.
B. Pilot experiments
Next to extending the reaction network also the database
used for validation purposes should be extended with pilot
plant experiments carried out with heavy fractions. Therefore
several gas condensates are cracked in the pilot plant setup.
One feedstock was analyzed using both GC and GC-MS. The
calibration factors suggested by Dietz (1967) are used.5
However, Dietz does not specify calibration factors for all the
observed components in the fractions. For these components a
group contribution method developed by Dierickx et al.
(1986) is applied.6 These analyses show that the concerned
feedstocks consist mainly out of molecules with 4 to 14 carbon
atoms, with large amounts of n-alkanes, di- or trimethyl
substituted alkanes and aromatics (BTX).
1) Influence of the feedstock
Experiments with eight different gas condensates are carried
out under identical conditions. The results and main operation
conditions are given in Table 1. The feedstocks which produce
large quantities of ethylene and propylene also provide large
amounts of methane, ethane, butadiene, while aromatics and
naphthalenes are formed in less quantities.
2) Effect of the process conditions
For the 661 gas condensate both he steam dilution (0.3 and
1 kg/kg) and the coil outlet temperature (COT: 800 and
840°C) are varied. Table 2 indicates that an increased steam to
hydrocarbon ratio improves the yield of unsaturated products
such as acetylene, ethylene, propylene and butadiene.
Contrary, the production of BTX, fuel gas (naphthalene) and
saturated components such as methane, ethane and propane
decreases with increasing dilution. This is due to the fact that
at lower hydrocarbon partial pressures, monomolecular
reactions are kinetically favored compared with bimolecular
reactions.
This results in preferential occurrence of decomposition
reactions, while hydrogen abstractions are opposed. Higher
steam dilutions also decrease bimolecular formation of coke.
Table 2: Influence of the dilution and the COT on the cracking
behavior of feed 661 [cokes*: reactor + TLE + entrained]
Higher yields of ethylene are also obtained at higher COT,
as seen in Table 2. However, an increase of the COT leads to
higher coke formation rates as well.
III. CONCLUSIONS
The simulation results of the fundamental simulation models
for ethane/toluene mixtures are poor. Admitting the cracking
mechanism of toluene to the reaction network and optimizing
all the kinetic parameters leads to an accurate simulation of
the benzene and toluene yields for these experiments but does
not solve all the problems. Therefore a completely new single
event microkinetic model is developed by Van Geem (2006)³.
The pilot plant experiments with eight different gas
condensates show that these feedstocks behave similar to
naphtha feedstocks. Both the product spectrum and the coke
formation depend strongly on the processed feedstock. Studies
with varying operation conditions carried out with one
particular feedstock show that an increased steam dilution as
well as an increased COT positively influences the ethylene
yield. A lower total amount of coke is seen when the dilution
is increased and COT is decreased.
IV. REFERENCES
[1] Plehiers, PhD dissertation, UGent, 1989
[2] Vercauetern, PhD dissertation, UGent, 1991
[3] Van Geem K.M., PhD dissertation, UGent, 2006
[4] Bounaceur R., Scacchi G., Marquaire P.M., Domine F., Brevart O.,
Dessort D., Pradier B., Ind. & Eng. Chem. Res., 41(19), 2002, 4689-
4701.
[5] Dietz W.A., J. of G.C., February 1967, 68-71
[6] Dierickx J.L., Plehiers P.M., Froment G.F., J. of C., 362(2), 1986, 155-
174.
Feedstock 540 659 661 663 665 667 669 681
methane 13.13 13.2 11.03 11.34 11.25 13.3 13.21 13.01
ethylene 26.47 25.41 21.38 22.5 21.91 25.47 25.48 26.46
propylene 16.04 16.1 13.23 13.54 13.54 16.22 16.2 15.92
1,3-C4H6 5.2 5.09 4.92 4.97 4.94 5.37 5.35 5.23
benzene 7.01 5.37 7.55 7.33 7.14 5.18 5.04 6.43
toluene 3 3.01 6.15 6.11 5.92 2.91 2.87 2.72
naftalene 0.31 0.38 0.71 0.79 0.76 0.34 0.33 0.37
cokes* 13.58 6.48 7.45 4.26 5.49 8.19 5.37 8.48
Table 1: Main Product Yields and total amount of cokes [*: reactor + TLE + entrained] for steam cracking of eight different gas
condensates. Conditions: F = 4.0 kg h-1, δ: = 0.5 kg/kg; COT = 820 °C, COP = 0.17 MPa
dilution [kg/kg] 0.30 0.50 0.70 1.00 0.50 0.50
COT [°C] 820 820 820 820 800 840
methane 11.79 11.10 10.94 9.97 8.86 12.38
ethylene 21.20 21.40 22.72 22.02 18.45 23.37
propylene 12.64 13.30 13.67 12.88 12.88 12.40
propane 0.22 0.20 0.19 0.17 0.21 0.18
1,3-C4H6 4.30 5.00 5.00 4.84 4.50 4.46
benzene 7.81 7.50 6.88 6.45 6.00 7.93
toluene 6.42 6.00 5.84 5.59 6.47 6.00
naphthalene 1.15 0.73 0.65 0.56 0.66 0.93
cokes* 22.24 8.48 5.47 3.83 5.45 12.20
Table of Contents
Nederlandstalige samenvatting....................................................................................................i
1 Inleiding .......................................................................................................................i
2 Validatie en verbetering van simulatiemodellen voor stoomkraken...........................ii
2.1 Opbouw van het simulatiemodel.....................................................................ii
2.2 Vergelijking van de simulatieresultaten met experimentele gegevens ..........iii
2.3 Wijzigingen aan het reactienetwerk ................................................................v
2.4 Conclusie........................................................................................................vi
3 Stoomkraken van gascondensaten............................................................................viii
3.1 Analyse van de gascondensaten ...................................................................viii
3.2 Pilootexperimenten .........................................................................................x
4 Algemene conclusies.................................................................................................xv
Chapter 1 General Introduction .............................................................................................1
1.1 Introduction .................................................................................................................1
1.1.1 Industrial steam cracking process ...................................................................2
1.1.2 Factors affecting yield.....................................................................................4
1.1.3 Coke formation................................................................................................6
1.1.4 Environmental issues ......................................................................................7
1.2 Olefin production and market evolution .....................................................................7
1.3 Objective of this thesis ................................................................................................9
Chapter 2 Improvement of a Fundamental Simulation Model ...........................................12
2.1 Introduction ...............................................................................................................12
2.2 Reactor model ...........................................................................................................13
2.2.1 Reactor model equations ...............................................................................13
2.2.2 Solving the 1-dimensional reactor model equations .....................................15
2.3 Reaction network ......................................................................................................15
2.3.1 Primary network............................................................................................16
2.3.2 Secondary network........................................................................................16
2.4 Database ....................................................................................................................17
2.5 Comparison between experimental and simulated data ............................................19
2.5.1 C19 reaction network ....................................................................................19
2.5.2 C25 reaction network ....................................................................................28
2.5.3 Traced shortcomings .....................................................................................28
2.6 Working off the shortcoming....................................................................................30
2.6.1 Block.i ...........................................................................................................31
2.6.2 Net.i and PRC-files .......................................................................................32
2.6.3 For.i ...............................................................................................................34
2.7 Optimization..............................................................................................................35
2.8 Validation of the adaptations ....................................................................................36
2.9 Conclusion ................................................................................................................42
Chapter 3 Steam cracking of gas condensates ....................................................................43
3.1 Introduction ...............................................................................................................43
3.2 Description Pilot .......................................................................................................43
3.2.1 The feed section ............................................................................................43
3.2.2 The Furnace and the reactor..........................................................................45
3.2.3 The cooling section .......................................................................................46
3.2.4 The analysis section ......................................................................................47
3.3 Analysis of the feedstocks.........................................................................................51
3.3.1 Separation......................................................................................................51
3.3.2 Qualitative analysis .......................................................................................52
3.3.3 Quantitative analysis .....................................................................................56
3.3.4 Results ...........................................................................................................58
3.3.5 Alternatives for GC and GC-MS ..................................................................59
3.4 Pilot Plant Experiments.............................................................................................64
3.4.1 Experimental conditions................................................................................64
3.4.2 Effect of the feedstock on the product spectrum and coke deposition..........67
3.4.3 Effect of the process conditions on the cracking of Feed 661 ......................69
3.4.4 Conclusions ...................................................................................................79
Chapter 4 Conclusions & Future Work ..............................................................................80
Appendix A Overview Database ........................................................................................... 82
Appendix B Validation of the C25 reaction network ............................................................ 86
Appendix C Calculation of the standard molar entropy of the benzyl radical ...................... 91
Appendix D Calculation of the coefficients for the specific heat capacity ........................... 92
Appendix E Detailed composition of feedstock 667............................................................. 92
List of symbols
Roman symbols
Notation Explanation Dimension
aij contribution of group j in the calibration factor of component i -
A area m²
A frequency factor -
cp specific heat capacity J kg-1
K-1
Cn carbon number -
CF calibration factor -
E energy J
f friction factor -
Fm mass flow rate g s-1
F molar flow rate mol s-1
g weight function -
h specific enthalpy J kg-1
m mass kg
mol% mole fraction -
MW molecular weight g mol-1
n number
p pressure MPa
rv volumetric reaction rate mol m-3
s-1
s specific entropy J kg-1
K-1
S objective function -
t time s
tr retention time s
T temperature K
v velocity m s-1
V molar volume m³ mol-1
wt%i weight fraction -
q heat flux W
z charge C
Greek symbols
Notation Explanation Dimension
α conversion factor -
ρ specific density g m-3
σ number of single events -
υ stoechiometric coefficient -
Subscript
Notation Explanation
i component i
C2
CH3 CH3
ethane
CH2 CH2
ethylene
CH CH
acetylene
C3
CH3
CH3
propane
CH3
CH2
propylene
C4
CH3
CH3
butane
CH3
CH3
CH3
isobutane
CH2
CH3
1-butene
CH2
CH3
CH3
isobutene
CH3
CH3
2-butene
CH2
CH2
1,3-butadiene
C5
CPD
CH2
CH2
CH3
Isopentadiene
List of molecules
C6
cyclohexane cyclohexene benzene
C7CH3
toluene
C8
CH3
ethylbenzene
CH2
styrene
CH3
CH3
o-xylene
CH3
CH3
m-xylene
CH3 CH3
p-xylene
C9
indane indene
C10+
naphthalene phenanthrene pyrene
chrysene dibenzanthracene
acenaphthene
i
Nederlandstalige samenvatting
1 Inleiding
Het stoomkraken van koolwaterstoffen is een van de basisprocessen van de petrochemie. In
dit proces worden koolwaterstofvoedingen gaande van ethaan tot vacuüm gasolie bij hoge
temperaturen (750 tot 900 °C) onder toevoeging van stoom omgezet tot commercieel interessante
producten zoals ethyleen, propyleen en butadieën. Naast deze lichte olefines worden er eveneens
aromaten en zwaardere bijproducten gevormd. De stoomkraker vormt vaak het centrum van een
petrochemisch complex. De aardolieraffinage levert de voeding voor het stoomkraakproces,
terwijl de productstromen van de kraker gebruikt worden in stroomafwaartse productie-eenheden
zoals polyethyleen- en polypropyleenfabrieken. De vraag naar ethyleen, propyleen en hun
derivaten neemt vandaag de dag nog steeds sterk toe, voornamelijk door hun uitgebreide
toepassingen in de polymeerindustrie.
In de zoektocht van de chemische industrie naar de meest optimale uitvoering van het
krakingsproces spelen simulatiemodellen een onmisbare rol. Dergelijke modellen zijn
opgebouwd uit een reactormodel en een reactienetwerk. De laatste decennia zijn stap voor stap
nieuwe en betere simulatiemodellen ontwikkeld aan het Laboratorium voor Petrochemische
Techniek van de Universiteit Gent. Toch bezitten deze nog enkele tekortkomingen. Met name de
opbrengsten van propyleen, C4-olefines en de BTX fractie worden niet steeds even nauwkeurig
gesimuleerd. Ook zijn bepaalde belangrijke species zoals het benzyl radicaal nog niet beschouwd
in de oudere reactienetwerken van Plehiers (1989) en Vercauteren (1991). Zowel de validatie van
het huidige reactienetwerk als de stappen ondernomen om de vermelde tekortkoming van het
model weg te werken, worden beschreven in hoofdstuk 2.
Vandaag de dag worden is er ook een trend om steeds zwaardere voedingen te kraken. De
reden hiervoor is de afnemende vraag naar deze fracties als brandstof, wat resulteert in een
toenemend overschot aan de goedkope zware petroleumfracties. Het is uiteraard erg belangrijk
dat de simulatiemodellen ook voor deze voedingen accurate resultaten opleveren. Vanuit dit
standpunt zijn experimenten op zwaardere fracties dan ook uitermate interessant. De
ii
experimenten die in het kader van deze thesis op zwaardere gascondensaten werden uitgevoerd,
worden beschreven in hoofdstuk 3. Bovendien is ook getracht een gedetailleerde
voedingssamenstelling van de verschillende gascondensaten op te stellen. De manier waarop deze
accuraat kan bepaald worden, wordt ook in hoofdstuk 3 besproken.
2 Validatie en verbetering van simulatiemodellen voor stoomkraken
In de volgende paragrafen wordt de betrouwbaarheid van het fundamentele simulatiemodel
voor stoomkraken getest op basis van een brede waaier aan pilootexperimenten. Deze zijn
ondergebracht in een nieuw databaseprogramma ontwikkeld in Java. Deze uitgebreide database
van meer dan 400 experimenten maakt het mogelijk de belangrijkste tekortkomingen van de
huidige generatie simulatiemodellen op te sporen. De ondernomen stappen om deze
tekortkomingen weg te werken, worden eveneens besproken.
2.1 Opbouw van het simulatiemodel
Het fundamenteel simulatiemodel ontwikkeld aan het LPT is opgebouwd uit enerzijds een
reactormodel (modelvergelijkingen) en anderzijds een reactienetwerk dat de dominante
reactiepaden bundelt. Voor de simulatie van de reactorbuis wordt een 1-dimensionaal plug-flow
model gebruikt. Vermits het kraken een niet-isotherm, niet-isobaar en niet-adiabatisch proces is,
volgen de modelvergelijkingen uit de toepassing van de wetten van behoud van massa, impuls en
energie. Verschillende reactienetwerken werden in de loop der jaren ontwikkeld aan het LPT.
Plehiers (1989) creëerde het eerste netwerk, het zogenaamde `C19' reactienetwerk waarin
componenten met maximaal 19 koolstofatomen opgenomen zijn. Een paar jaar later ontwikkelde
Vercauteren (1991) de uitgebreidere ‘C25’ versie. Beide reactienetwerken bestaan voornamelijk
uit radicalaire reacties aangevuld met enkele globale reacties. In de reactienetwerken wordt
bovendien gebruik gemaakt van de zogenaamde β-µ regels. Twee types radicalen kunnen
onderscheiden worden: enerzijds de β radicalen, die hoofdzakelijk via bimoleculaire reacties
reageren, en aan de andere kant µ radicalen die voornamelijk optreden in monomoleculaire
reacties. In de reactienetwerken ontwikkeld door Plehiers (1989) en Vercauteren (1991) wordt
aangenomen dat de C5+-radicalen een zuiver µ karakter hebben, terwijl de C4--radicalen zowel
een µ als een β karakter vertonen. Het gedrag van deze radicalen worden dan ook apart
iii
beschreven, zodat het reactienetwerk onderverdeeld is in een primair (µ radicalen of C5+-
radicalen) en secundair (C4--radicalen) netwerk.
2.2 Vergelijking van de simulatieresultaten met experimentele gegevens
Om de betrouwbaarheid van de fundamentele simulatiemodellen van Plehiers (1989) en
Vercauteren (1991) na te gaan, worden de simulatieresultaten vergeleken met experimentele
gegevens uit de opgestelde databank. In het verleden werden reeds heel wat experimenten
uitgevoerd op de pilootinstallatie van het LPT. Deze experimenten werden gebundeld in een
databank. Voor de meer dan 400 experimenten met meer dan 50 voedingen werden de condities
en de experimenteel bepaalde productopbrengsten voor de belangrijkste producten opgeslagen.
Bovendien werd een interface ontworpen die het zoeken naar gegevens sterk vergemakkelijkt.
Deze interface werd opgesteld in Java. Het startscherm is weergegeven in onderstaande figuur.
Figuur 1: Startscherm van grafische interface voor de experimentele database van
pilootexperimenten
iv
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Experimente le Ethyleen opbrengst [wt%]
Ges
imu
lee
rde
Eth
yle
en
Op
bre
ng
st
[wt%
]
Figuur 2: Pariteitsdiagramma ethyleen (model Plehiers)
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40 50 60 70 80 90
Experime nte le Ethaan opbrengst [wt%]
Ge
sim
ule
erd
e E
tha
an
Op
bre
ng
st
[wt%
]
Figuur 3: Pariteitsdiagramma ethaan (model Plehiers)
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14 16
Experimente le Benzeen Opbrengst [wt%]
Ge
sim
ule
erd
e
Be
nze
en
Op
bre
ng
st
[wt%
]
Figuur 4: Pariteitsdiagramma benzeen (model Plehiers,)
0
2
4
6
8
10
0 2 4 6 8 10
Experimente le Tolueen Opbrengst [wt%]
Ge
sim
ule
erd
e T
olu
ee
n O
pb
ren
gs
t [
wt%
]
Figuur 5: Pariteitsdiagramma tolueen (model Plehiers)
Experimenten met
tolueen/ethaan mengsels
Experimenten met
tolueen/ethaan mengsels
v
In Figuren 2 tot 5 worden de pariteitsdiagramma’s voor ethyleen, ethaan, benzeen en tolueen
weergegeven. Deze werden verkregen met het C19 reactienetwerk van Plehiers (1991). Uit deze
figuren kan geconcludeerd worden dat het kraakgedrag van tolueen niet accuraat beschreven
wordt in het fundamenteel simulatiemodel. Uit Figuur 2 blijkt immers dat de ethyleenopbrengst
voor experimenten met tolueen/ethaanmengsels overschat wordt, terwijl de ethaanopbrengst
onderschat wordt (Figuur 3). In het bijzonder leveren de simulaties geen correcte tolueen- en
benzeenopbrengsten (zie Figuur 4 en Figuur 5). De resultaten verkregen met het reactienetwerk
van Vercauteren (1991) zijn echter nog aanzienlijk slechter. Niettemin is tolueen vaak een
component van aardoliefracties en bovendien één van de belangrijkste aromaten die in het
stoomkrakingsproces gevormd wordt. De belangrijkste reacties die optreden tijdens het kraken
van tolueen, worden voorgesteld in Figuur 6. De pyrolyse van tolueen verloopt via een aantal
radicalaire stappen waarin het benzylradicaal een centrale rol speelt.
2.3 Wijzigingen aan het reactienetwerk
Om het werkelijk kraakgedrag van tolueen in rekening te brengen, dienen de reacties uit Figuur 6,
evenals het benzylradicaal en het dibenzylmolecule aan het reactienetwerk toegevoegd te worden.
Het reactienetwerk wordt opgebouwd door verschillende bestanden: block.i, net.i, for.i en
verscheidene PRC-bestanden. Het bestand block.i bundelt de fysieke eigenschappen van alle
moleculen en radicalen die in het reactienetwerk inbegrepen zijn. De fysische eigenschappen
horende bij het benzylradicaal en het dibenzylmolecule werden dan ook aan dit bestand
toegevoegd. Het volledige reactieschema zit vervat in verscheidene files. Enerzijds C4- reactie
netwerk, of zogenaamd β netwerk (Van Geem, 2006), dat wordt weergegeven in het net.i
bestand. Anderzijds het primaire netwerk dat in een aantal PRC-bestanden is opgeslagen. De
reacties weergegeven in Figuur 6 worden in net.i opgenomen. Deze reacties beschrijven immers
het reactiepad van een β radicaal. For.i bevat tot slot alle informatie nodig om de kinetische
parameters horende bij de reacties opgenomen in het reactienetwerk te berekenen. Bij deze
berekening wordt thermodynamische consistentie in rekening gebracht. Dit impliceert dat de
verhouding van de parameters van de voorwaartse en terugwaartse reactie gelijk moet zijn aan de
thermodynamische evenwichtscoëfficiënt. De snelheidscoëfficiënt voor een exotherme reactie
kan dus afgeleid worden uit de overeenkomstige endotherme reactie. Voor de acht reacties dienen
dan 16 kinetische parameters (van de endotherme reacties) geoptimaliseerd te worden. Zowel de
vi
literatuurwaarden als de waarden die na optimalisering verkregen werden, worden voorgesteld in
Tabel 1.
C-C en C-H scissies
tolueen benzyl + H
ethylbenzeen benzyl + CH3
1-fenylpropaan (C9 1aro) benzyl + C2H5
dibenzyl benzyl + benzyl
Waterstofabstractie
H2 + benzyl H + tolueen CH4 + benzyl CH3 + tolueen
C2H6 + benzyl C2H5 + tolueen
Additiereactie H + tolueen benzeen + CH3
Figuur 6: Krakingsmechanisme van tolueen (Bounaceur et al., 2002)
2.4 Conclusies
In Figuur 7 en Figuur 8 worden de pariteitsdiagramma’s voor tolueen weergeven voor
respectievelijk tolueen/ethaanvoedingen en naftavoedingen. Deze pariteitsdiagramma’s werden
verkregen met het aangepaste netwerk. De introductie van het benzylradicaal in het
reactienetwerk leidt dus tot betere simulatieresultaten van de tolueenopbrengsten voor
tolueen/ethaan mengels, maar levert slechtere simulatieresultaten op voor nafta voedingen. Dit is
echter niet verwonderlijk. Zo moeten niet alleen experimenten met tolueen/ethaan mengsels maar
ook experimenten uitgevoerd op naftas in rekening gebracht worden bij de optimalisering van de
kinetische parameters. Echter, de belangrijkste oorzaak voor de slechtere resultaten voor nafta
voedingen is dat in het primaire netwerk geen 100% rekening gehouden wordt met het β karakter
van het benzylradicaal. In het primaire netwerk wordt verondersteld dat het benzylradicaal enkel
betrokken is bij waterstofabstractiereacties en dit radicaal instantaan wordt omgezet tot tolueen.
Deze veronderstelling is uiteraard niet correct. Enkel wijzigingen aan het primair reactienetwerk
kunnen hier aan verhelpen. In het nieuwe single event microkinetisch model van Van Geem
(2006) is een dergelijke aanpak wel systematisch doorgevoerd en de simulatieresultaten tonen
aan dat op deze wijze het wel mogelijk is om goede simulatieresultaten te verkrijgen voor zowel
nafta als ethaan/tolueen mengsels. Een belangrijke reden voor de betere overeenkomst bij Van
Geem (2006) is ook dat een volledige optimalisatie van de belangrijkste kinetische parameters is
doorgevoerd.
vii
literatuurwaarden na optimalisatie
endotherme reacties EA log A EA log A
tolueen benzyl + H 267.6 12.49 306.3 12.5
ethylbenzeen benzyl + CH3 316.4 15.2 316.4 15.7
1-fenylpropaan (C9 1aro) benzyl + C2H5 297.4 15.0 297.4 15.2
dibenzyl benzyl + benzyl 260.0 15.5 260.0 15.9
H2 + benzyl H + tolueen 93.7 11.5 98.8 11
CH4 + benzyl CH3 + tolueen 87.5 9.9 88.4 9.4
C2H6 + benzyl C2H5 + tolueen 60.0 9.4 64.4 8.9
H + tolueen benzeen + CH3 62.8 9.2 64.3 8.9
Tabel 1: Kinetische parameters
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
Experimentele Tolueen Opbrengst [wt%]
Gesim
ule
erd
e T
olu
een
Op
bre
ng
st
[wt%
]
oude netwerk
aangepaste netwerk
Figuur 7: Pariteitsdiagramma’s van tolueen voor
tolueen/ethaan mengsels
0
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10
Experimentele Tolueen Opbrengst [wt%]
Gesim
ule
erd
e T
olu
een
Op
bre
ng
st
[w
t%]
oude netwerk
aangepas te netwerk
Figuur 8: Pariteitsdiagramma’s van tolueen voor nafta
voedingen
viii
3 Stoomkraken van gascondensaten
In dit hoofdstuk worden de experimenten uitgevoerd in het kader van een nieuwe
krakingcampagne besproken. Dit betreft experimenten met acht verschillende gascondensaten.
Gascondensaten zijn vloeibare fasen met een kooktraject tussen 50 en 350 °C, afkomstig van de
productie van aardgas.
3.1 Analyse van de gascondensaten
3.1.1 Toegepaste methode
Een gedetailleerde voedingssamenstelling van de verschillende voedingen werd verkregen
door combinatie van de informatie afkomstig uit gaschromatografie (GC) en uit
gaschromatografie – massaspectrometrische (GC-MS) analyse. Gaschromatografie werd gebruikt
om de mengsels te scheiden in hun individuele componenten. Eens deze geïsoleerd zijn, kunnen
de componenten afzonderlijk geïdentificeerd en gekwantificeerd worden. Voor de kwalitatieve
analyse werden verschillende bronnen geraadpleegd. Zo leverden de gedetailleerde
samenstellingen van vroeger bestudeerde nafta’s en informatie van Kovats retentie-indices reeds
heel wat informatie op. Vooral de resultaten van Celie (2004) en Van Hecke (2005) bleken erg
waardevol wegens het grote aantal overeenkomstige componenten aanwezig in de bestudeerde
fracties. Parallel met gaschromatografie werd voor de kwalitatieve analyse eveneens gebruik
gemaak van GC-MS. De interpretatie van de massaspectra leverde de identificatie van diverse
pieken op die in het chromatogram worden waargenomen. De kwantitatieve analyse van de
verschillende voedingen werd uitgevoerd met een gaschromatograaf. De piekoppervlakte in een
chromatogram is immers evenredig met de massafractie van de corresponderende component:
iii ACFM ⋅=
Voor de calibratiefactoren (CF) werden de waarden voorgesteld door Dietz (1967) aangenomen.
Dietz specificieert echter niet voor alle waargenomen componenten een calibratiefactor. Deze
calibratiefactoren werden berekend met de groepscontributiemethode van Dierickx et al. (1986).
ix
3.1.2 Voedingssamenstelling
De voedingssamenstelling van één van de acht gascondensaten (gascondensaat 667) werd
bepaald door combinatie van GC en GC-MS. Zo’n 95 wt% van de voeding kon geïdentificeerd
worden met behulp van het Kovats retentiesysteem en door de interpretatie van de massaspectra.
In Tabel 2 worden de PIONA-waarden van deze voeding voor de verschillende koolstofgetallen
weergegeven. Uit de analyse volgt dat voeding 667 hoofdzakelijk bestaat uit moleculen met 4 tot
14 koolstofatomen onder de vorm van n-alkanen, di- of trimethylgesubstitueerde alkanen. De
gedetailleerde voedingssamenstelling kan teruggevonden worden in appendix E.
P I O N A
C3 0.017 0,000 0,000 0,000 0,000 0.017
C4 1.620 0.213 0,000 0,000 0,000 1.833
C5 9.382 10.314 0.000 1.366 0.000 21.061
C6 6.167 9.898 0.000 2.956 0.981 20.002
C7 4.610 6.650 0.000 4.803 1.493 17.556
C8 3.152 5.974 0.000 2.496 2.526 14.149
C9 2.306 4.233 0.407 1.456 1.777 10.180
C10 1.733 2.309 0.000 0.061 0.615 4.719
C11 1.261 1.219 0.000 0.000 0.000 2.480
C12 0.916 0.130 0.000 0.000 0.000 1.046
C13 0.652 0.061 0.000 0.000 0.000 0.712
C14 0.454 0.128 0.000 0.000 0.000 0.582
C15 0.342 0.157 0.000 0.000 0.000 0.499
C16 0.222 0.000 0.000 0.000 0.000 0.222
C17 0.153 0.000 0.000 0.000 0.000 0.153
C18 0.085 0.000 0.000 0.000 0.000 0.085
C19 0.049 0.000 0.000 0.000 0.000 0.049
33.121 41.286 0.407 13.137 7.392 95.34416
Tabel 2: PIONA-waarden voor voeding 667
3.1.3 Alternatieven voor GC en GC-MS
De conventionele hoge resolutie gaschromatografie is in staat om meer dan 500 componenten
te scheiden. Deze techniek slaagt er echter niet in om alle individuele componenten van complexe
mengsels zoals aardoliefracties te scheiden (Bertoncini et al., 2005). Deze mengsels bestaande uit
duizenden componenten, vertonen in een chromatogram piekoverlap, waarbij verschillende
x
componenten zich verzamelen in één piek. Dit heeft echter grote gevolgen voor de identificatie
van de pieken, vooral wanneer een massaspectrometer als detector wordt gebruikt. Vaak is het
niet mogelijk om tussen isomeren en zelfs in enkele gevallen tussen naftenen en olefines
onderscheid te maken. Bovendien, wanneer twee componenten in een gaschromatogram
overlappen, wordt een samengesteld massaspectrum verkregen van de massaspectra van de
overlappende componenten. Het scheidingsvermogen van de GC bepaalt dus de betrouwbaarheid
van de identificatie van de koolwaterstoffen. Voor identificatie met 100% zekerheid worden
zuivere componenten vereist. Er dient opgemerkt te worden dat verschillende pieken
ongeïdentificeerd bleven wegens de beperktheid van de gebruikte bibliotheek.
Meer geschikte technieken om de samenstelling van complexe aardoliefracties te bestuderen
zijn multidimensionele gaschromatografie (MDGC) en “uitgebreide” tweedimensionale
gaschromatografie (GC x GC). In deze technieken worden na de eerst scheidingskolom nog extra
kolommen geplaatst. Hierbij heeft elke scheidingskolom een specifieke, stationaire fase en
vertoont dus een specifieke, moleculaire interactie tussen de stationaire fase en de opgeloste stof;
de kolommen zijn “orthogonaal”. Door de overdracht van het effluent van de ene kolom naar een
andere wordt een sterke toename in het scheidingsvermogen waargenomen. In MDGC worden
slechts delen van het effluent van de eerste scheidingskolom naar de tweede kolom geleid. In
geval van GC x GC daarentegen wordt al de beschikbare scheidingresolutie van beide kolommen
op alle pieken van het chromatogram toegepast.
3.2 Pilootexperimenten
Twee verschillende reeksen van experimenten werden uitgevoerd. In een eerste reeks werden
acht verschillende zware gascondensaten onder dezelfde instelcondities gekraakt. In een tweede
set experimenten is de invloed van de stoomdilutie en van de reactoruitlaattemperatuur (COT) op
het kraakgedrag van 1 welbepaalde voeding onderzocht. Hierbij werd de stoomdilutie gevarieerd
tussen 0.3 en 1 kg/kg, de COT tussen 800 en 840 °C.
3.2.1 Invloed van de voeding op het productenspectrum en de cokesvorming
In eerste serie experimenten werd het kraakgedrag van de 8 verschillende voedingen
onderzocht. De gemiddelde opbrengsten [wt%] voor de belangrijkste producten worden in Tabel
3 weergegeven. De propyleen op ethyleenverhouding (P/E verhouding), een maat voor de
xi
conversie (Golombok et al., 2004), zijn gelijkaardig voor de verschillende voedingen. De meest
adequate voedingen voor de productie van ethyleen zijn voedingen 540 en 681, terwijl de
voedingen 667 en 669 de grootste hoeveelheden propyleen produceren. Voedingen 661, 663 en
665 daarentegen leveren grote opbrengsten aan aromaten en naftaleen, maar lagere opbrengsten
aan ethyleen en propyleen. Voedingen 540, 659, 667, 669 en 681 die aanleiding geven tot de
grootste hoeveelheden ethyleen en propyleen, produceren ook de grootste hoeveelheden methaan,
ethaan, butadieën, 1-buteen en i-buteen, terwijl aromaten zoals tolueen, styreen en naftaleen in
kleinere hoeveelheden worden gevormd.
Voeding 540 659 661 663 665 667 669 681
P/E ratio 0.55 0.58 0.58 0.57 0.57 0.58 0.58 0.55
productopbrengsten [wt%]
methaan 13.13 13.20 11.03 11.34 11.25 13.30 13.21 13.01
ethyleen 26.47 25.41 21.38 22.50 21.91 25.47 25.48 26.46
ethaan 3.71 3.46 2.80 2.93 2.87 3.54 3.39 3.64
propyleen 16.04 16.10 13.23 13.54 13.54 16.22 16.20 15.92
propaan 0.29 0.27 0.20 0.20 0.20 0.28 0.27 0.28
1,3-C4H6 5.20 5.09 4.92 4.97 4.94 5.37 5.35 5.23
benzeen 7.01 5.37 7.55 7.33 7.14 5.18 5.04 6.43
tolueen 3.00 3.01 6.15 6.11 5.92 2.91 2.87 2.72
naftaleen 0.31 0.38 0.71 0.79 0.76 0.34 0.33 0.37
Tabel 3: De gemiddelde opbrengsten voor de belangrijkste producten van de verschillende voedingen
Na 6 uur kraken worden reactor en TLE apart ontkoold. Op die manier kunnen de hoeveelheid
cokes die tijdens het kraken op de reactorwand en in de TLE1 zijn afgezet afzonderlijk bepaald
worden. In de reactorbuis vindt cokesvorming plaats op een oppervlakte van 0.34 m²; in TLE1 op
een oppervlakte van 0.13 m². Uit Tabel 4 blijkt dat de grootste cokesvorming werd waargenomen
bij het kraken van voeding 540, in tegenstelling tot voeding 669 die de kleinste hoeveelheid
cokes produceert. Voeding 669 is dus een zeer adequate voeding voor het stoomkraakproces
onder de gebruikte omstandigheden. Deze voeding geeft immers aanleiding tot de productie van
grote hoeveelheden ethyleen en propyleen, terwijl een lage cokesvormingssnelheid wordt
waargenomen.
xii
Voeding
Cokereactor
[g]
CokeTLE
[g]
Cokefilter
[g]
Totale
hoeveelheid [g]
540 8.80 0.83 3.95 13.58
659 3.22 2.28 0.99 6.48
661 2.16 1.92 3.37 7.45
663 1.34 1.49 1.43 4.26
665 1.99 2.32 1.18 5.49
667 6.31 1.10 0.78 8.19
669 4.19 0.83 0.36 5.37
681 7.49 0.98 0.00 8.48
Tabel 4: hoeveelheid cokes afgezet op de reactor wand, in de TLE,
opgevangen in de filter en de totale hoeveelheid cokes
3.2.2 Invloed van de procescondities op het kraakgedrag van voeding 661
3.2.2.1 Invloed van de stoomdilutie
Tabel 5 illustreert de invloed van de stoomdilutie op de productopbrengsten. Hieruit kan
besloten worden dat een verhoogde stoomdilutie aanleiding geeft tot een hogere opbrengst aan
onverzadigde producten zoals ethyleen, propyleen en butadieën. De productie van BTX, fuel olie
(naftaleen) en verzadigde componenten zoals methaan, ethaan en propaan neemt echter af met
stijgende dilutie. De toenemende selectiviteit naar ethyleen en propyleen door toevoegen van
stoom is toe te schrijven aan het feit dat stoom de koolwaterstofpartieeldruk in de reactor
vermindert. Bij lagere koolwaterstofpartieeldrukken worden de monomoleculaire reacties
kinetisch bevoordeeld ten opzichte van bimoleculaire reacties. Decompositiereacties aan ethyl- en
propylradicalen zullen dus sneller doorgaan in tegenstelling tot de waterstofabstracties aan deze
radicalen die vertraagd worden. Er wordt dus een stijging in de ethyleen- en propyleenopbrengst
waargenomen, terwijl de ethaan- en propaanopbrengsten afnemen.
Tabel 6 geeft de hoeveelheid cokes afgezet op de reactor wand en in de TLE, de hoeveelheid
cokes opgevangen in de filter en de totale hoeveelheid cokes weer voor verschillende waarden
van de stoomdilutie. Bij toenemende stoomdilutie blijkt de totale cokesvorming te verminderen.
Inderdaad, door stoom toe te voegen vermindert de koolwaterstofpartieeldruk in de reactor en
TLE1 zodat bimoleculaire reacties kinetisch achteruitgesteld worden en dus ook de bimoleculaire
cokesvormingreacties. Het effect van verminderde cokesvorming door toevoeging van stoom
wordt waargenomen in de hoeveelheid cokes afgezet in de reactorbuis, de hoeveelheid cokes in
xiii
TLE1, evenals in de hoeveelheid cokes verzameld in de filter. Merk op dat de hoeveelheid
cokesafzetting in de reactorbuis niet steeg toen de stoomdilutie van 0.5 tot 0.3 verminderde. Dit
kan verklaard worden door de uitzonderlijke toename van de hoeveelheid cokes die door de filter
opgevangen werd.
Voeding 661 661 661 661
Dilutie [kg/kg] 0.30 0.50 0.70 1.00
P/E 0.54 0.58 0.56 0.55
productopbrengsten [wt%]
methaan 11.79 11.10 10.94 9.97
ethyleen 21.20 21.40 22.72 22.02
ethaan 3.39 2.84 2.57 2.13
propyleen 12.64 13.30 13.67 12.88
propaan 0.22 0.20 0.19 0.17
1,3-C4H6 4.30 5.00 5.00 4.84
benzeen 7.81 7.50 6.88 6.45
tolueen 6.42 6.00 5.84 5.59
naftaleen 1.15 0.73 0.65 0.56
Tabel 5: Invloed van de stoomdilutie op de productopbrengsten
feedstock
dilution
[kg/kg]
Cokecoil
[g]
CokeTLE
[g]
Entrained
[g]
Total coke
[g]
661 0.3 2.51 8.77 10.96 22.24
661 0.5 3.17 5.31 1.84 8.48
661 0.7 2.01 3.46 0 5.47
661 1.0 1.82 2.01 0 3.83
Tabel 6: Invloed van de stoomdilutie op de hoeveelheid cokes afgezet op de reactor wand, in de
TLE, opgevangen in de filter en de totale hoeveelheid cokes
Een toenemende stoomdilutie verhoogd dus de conversie van de voeding naar doelproducten
(ethyleen en propyleen), terwijl cokesvorming onderdrukt wordt. De optimale stoomdilutie wordt
gewoonlijk bepaald door een economische evaluatie, waarin de opbrengsttoenamen afgewogen
worden tegen hogere investering- en bedrijfskosten.
xiv
3.2.2.2 Invloed van de reactoruitlaattemperatuur (COT)
Voeding 661 661 661
COT [°C] 800 820 840
P/E 0.64 0.57 0.49
productopbrengsten [wt%]
methaan 8.86 11.03 12.38
ethyleen 18.45 21.38 23.37
ethaan 2.80 2.80 2.82
propyleen 12.88 13.23 12.40
propaan 0.21 0.20 0.18
benzeen 6.00 7.55 7.93
tolueen 6.47 6.15 6.00
naftaleen 0.66 0.71 0.93
Tabel 7: Invloed van de reactoruitlaatemperatuur op de productopbrengsten
Tabel 7 geeft de invloed van de COT op de belangrijkste productopbrengsten weer. Hogere
opbrengsten aan ethyleen worden dus ook verkregen met toenemende reactoruitlaattemperatuur.
Helaas leidt een verhoging van de COT eveneens tot een toename van de snelheid van
cokesvorming, zoals blijkt uit Tabel 8. Vandaag de dag worden ethyleenproducenten
geconfronteerd met een vraag die sneller toeneemt dan de capaciteit. Een methode om het aanbod
van hun kraker op te voeren is bij hogere temperaturen te werken (Nexant, 2003). Er zal aldus
een compromis gesloten moeten worden tussen de verhoogde opbrengsten aan het ethyleen en
propyleen bij hogere temperaturen en de toename van cokesvorming resulterend in een meer
regelmatige ontkoling van de stoomkraker.
feedstock COT [°C]
Cokecoil
[g]
CokeTLE
[g]
Entrained coke
[g]
Total coke
[g]
661-C 800 2.16 1.92 3.37 5.45
661-C 820 3.17 5.31 1.84 8.48
661-C 840 2.24 7.15 2.81 12.20
Tabel 8: Influence of COT on the coke formation
xv
4 Algemene conclusies
De vergelijking tussen de simulatieresultaten verkregen met de fundamentele
simulatiemodellen van Plehiers (1989) en Vercauteren (1991) en de experimentele gegevens uit
een opgestelde databank toonde aan dat het krakingsmechanisme van tolueen in beide modellen
niet accuraat beschreven wordt. De introductie van dit mechanisme in het reactienetwerk van
Plehiers leidde tot betere simulatieresultaten van de tolueenopbrengsten voor tolueen/ethaan
mengels, maar leverde slechte simulatieresultaten op voor naftavoedingen. Dit is echter niet
verrassend gezien de manier waarop het benzylradicaal in het reactienetwerk behandeld wordt.
Enkel fundamentele wijzigingen aan het reactienetwerk kunnen dit probleem verhelpen.
Bovendien zou een volledige optimalisatie van al de kinetische parameters uit het reactienetwerk
moeten worden uitgevoerd. Dit alles werd gerealiseerd in het nieuwe simulatiemodel van Van
Geem (2006).
De databank met pilootexperimenten uitgevoerd aan het LPT werd uitgebreid met
experimenten op gascondensaten. De voedingssamenstelling van één van de acht gascondensaten
(gascondensaat 667) werd bepaald door combinatie van GC en GC-MS. Uit de analyse volgt dat
voeding 667 vooral uit n-alkanen en di- of trimethylgesubstitueerde alkanen bestaat met 4 tot 14
koolstofatomen in de keten. De pilootexperimenten wijzen uit dat de gascondensaten zich in het
kraakproces gelijkaardig aan naftafracties gedragen. Zowel het productspectrum als de
cokesvorming is sterk afhankelijk van de voeding. Het krakingsgedrag wordt ook beïnvloed door
de condities. Zowel een toenemende stoomdilutie als een stijgende reactoruitlaattemperatuur
leiden tot een verhoogde ethyleenopbrengst. Een toenemende stoomdilutie vermindert bovendien
de cokesvorming, Deze uitbreiding van de experimentele databank met experimenten op
gascondensaten is slechts een eerste stap. Ondanks het toenemende gebruik van zwaardere
fracties als grondstof voor de ethyleenproductie, is het aantal experimenten uitgevoerd op zware
fracties aan het LPT slechts beperkt. In dit opzicht zijn vooral experimenten met gasolies erg
interessant.
1
Chapter 1
General introduction
1.1 Introduction
Steam cracking of hydrocarbons is one of the main processes in the petrochemical industry. In
this process hydrocarbon feedstocks ranging from light alkanes such as ethane and propane up to
complex mixtures such as naphthas and heavy gas oils are cracked into commercially more
valuable products such as light olefins and aromatics. Steam cracking is carried out in tubular
reactors suspended in large gas-fired furnaces at temperatures ranging from 600-900 °C. The
olefin plants often form the centerpiece of an entire petrochemical complex, as represented in
Figure 1-1. Refineries provide the cracking feed, while the effluent streams from the cracker are
used in downstream units, e.g. polyethylene and polypropylene units.
Figure 1-1: Situation of the steam cracking process in the petrochemical industry
(Van Geem, 2006)
2
1.1.1 Industrial steam cracking process
Generally, a steam cracking facility comprises two main sections: a hot section where the
feedstock is cracked and the effluent is conditioned, and a cold section that assures the separation
and the purification of the formed products. The hot section forms the heart of a steam cracker
and can be divided in three sections: the convection section, the transition section and the radiant
section. Figure 1-2 gives a schematic overview.
Figure 1-2: Typical cracking furnace configuration (European IPPC Bureau, 2000)
The hydrocarbon feedstock enters the hot section of the unit through the convection zone of
the furnace as a liquid. The feedstock is evaporated and preheated to 500-650 °C (depending on
3
the feedstock) by indirect contact with hot flue gas from the radiant section and by direct contact
with steam, which is also preheated in this zone. Steam is added to the feed to increase the
conversion to the most desired products and to suppress coke formation. The latter is based on the
reduction of the partial pressure of the hydrocarbons, which have a stronger effect on the
bimolecular reactions that destroy the olefins(De Kever, 2001)
After leaving the convection section the process gas enters the radiant section of the furnace,
in which most of the cracking occurs. During a short reaction time of 0.1 – 0.5 s, the hydrocarbon
feedstock is cracked into smaller products, such as ethylene and propylene. Since the conversion
of saturated hydrocarbons to olefins in the radiant tube is highly endothermic a high energy input
is needed. This heat is provided by radiation burners in the sidewalls or long flame burners in the
bottom of the furnace. The burners use essentially methane, a by-product of steam cracking, as
fuel. The temperature of the flue gases produced by the radiation burners in the firebox can be as
high as 1200 °C (Zimmermann and Walzl, 2002). The coil outlet temperature in industrial
furnaces varies between 750 and 900 °C according the feedstock processed. The heat generated
by the flue gas is recovered by passing it through the convection section. In this section the flue
gases flow along tubes through which cold media flow. The heat is used for preheating the
hydrocarbon feedstock, for the heating of feed water for steam production, for preheating the
dilution steam and for the generation of high-pressure steam. This high-pressure steam can
subsequently be used for the operation of turbines for electricity production.
The temperature of the cracked gas leaving the radiant coils ranges from 750 to 900 °C. Rapid
reduction of gas temperature to 500 °C is necessary to avoid losses of valuable products by
secondary reactions. This is accomplished by a transfer-line exchanger (TLE), which cools the
cracked gases and recovers most of the heat contained in the cracked gas as high-pressure steam.
The obtained conversions for ethane furnaces are commonly around 65 %. In naphtha the
propylene to ethylene ratios typically vary between 0.65 and 0.75 kg/kg (Zimmermann and
Walzl, 2002).
In the cold section of the cracking unit the separation and the purification of the product
stream is carried out. A first fractionation column separates the light fraction that contains the
desired components at the top as a gas stream, a fuel extract in the middle of the column and a
heavy residue at the bottom. At this point, the hydrocarbon gases from the top of the fractionation
column need to be liquefied for purification. Therefore, the gases are compressed to very high
4
pressures (3.8 MPa) and cooled to very low temperatures (-150 °C). The main downstream
processing steps of the separation train are the removal of the heat contained in the cracked gas,
condensation of water and heavy hydrocarbons, washing, drying, separation, and hydrogenation
of certain multiple unsaturated components. The choice of which units are used depend mainly on
the feedstock (light gas or complex hydrocarbon feedstock) and the product specifications. Figure
1-3 shows a schematic overview of an olefins plant.
Figure 1-3: Schematic overview of an olefins plant (European IPPC Bureau, 2000)
1.1.2 Factors affecting yield
The severity is the most significant operation variable in adjusting the yields from
hydrocarbon cracking and is a function of feedstock, temperature, residence time and partial
pressure (European IPPC Bureau, 2000). Examples of cracking severity indices are the methane
yield, the coil outlet temperature, the degree of feed gasification (e.g., C3 and lighter yield), yield
ratios (e.g., P/E ratio) and decomposition of model compounds. Van Geem et al. (2005) shows
that at least two severity indices are required to unambiguously characterize the product yields.
5
They suggest that the combination of the ethylene/ethane yield ratio and the methane yield
characterizes the complete product spectrum for a given feedstock. It is indeed evident that the
feedstock determines for a large part the obtained product yields. However, also several process
conditions (e.g. the cracking temperature, dilution, etc.) can either have a positive or a negative
effect on the desired product spectrum. In the next paragraphs the effect of the selected feedstock,
the set value of the dilution, the cracking temperature and the residence time is discussed.
Feedstock:
Different feedstocks produce different ethylene yields and ranges of products. Generally, as
the feedstock gets heavier, the yield of ethylene decreases and other products such as propylene,
butadiene and benzene become more significant, see Table 1-1. Heavier petroleum fractions such
as gas oil or vacuum gas oil (VGO) are also subject to an increased coke deposition resulting in a
more frequent shutdown of the steam cracker.
Feedstocks Product
Ethane Propane Butane naphtha gas oil VGO
Hydrogen (95 mol%) 8.8 2.3 1.6 1.5 0.9 0.8
Methane 6.3 27.5 22.0 17.2 11.2 8.8
Ethylene 77.8 42.0 40.0 33.6 26.0 20.5
Propylene 2.8 16.8 17.3 15.6 16.1 14.0
Butadiene 1.9 3.0 3.5 4.5 4.5 5.3
Other C4 0.7 1.3 6.8 4.2 4.8 6.3
C5-200 °C gasoline 1.7 6.6 7.1 18.7 18.4 19.3
Benzene 0.9 2.5 3.0 6.7 6.0 3.7
Toluene 0.1 0.5 0.8 3.4 2.9 2.9
C8 aromatics - - 0.4 1.8 2.2 1.9
Non-aromatics 0.7 3.6 2.9 6.8 7.3 10.8
Fuel oil - 0.5 1.7 4.7 18.1 25.0
Table 1-1: Influence of feedstock on steam cracking yields [% wt] (Chauvel and Lefebvre, 1989)
Cracking temperature:
Since cracking reactions are endothermic, maximum olefin production is realized at high
temperatures. Temperatures of 500 ºC cause the hydrocarbon chain to crack in the middle
6
forming high molecular weight olefins, whereas higher temperatures cause chains to crack at the
ends and form lower olefins (European IPPC Bureau, 2000). Higher temperatures also increase
the cracking rate and this allows shorter residence times or lower partial pressures.
Residence time:
Long residence times allow secondary reactions to form oligomers and coke, while short
residence times (a few hundred milliseconds) increase the light olefin selectivity. Note that the
effect of the residence time is strongly related to the effect of the temperature profile (Plehiers
and Froment, 1987).
Dilution:
The cracking reactions increase the number of moles. Hence, from the thermodynamic point
of view, cracking of hydrocarbons into olefins and hydrogen is favored at low pressure. Dilution
gas is therefore introduced (usually as steam) to reduce the hydrocarbon partial pressure. The
amount of steam used is normally expressed as the mass ratio of steam to hydrocarbon and
depends on the type of hydrocarbons fed. For the cracking of ethane, the steam dilution usually
amounts between 0.2 and 0.4 kg steam/kg ethane. For the cracking of higher hydrocarbons, the
dilution is located between 0.4 - 0.6 kg steam/kg hydrocarbon in general (Zimmermann and
Walzl, 2002).
1.1.3 Coke formation
Coke formation during steam cracking is a slow and complex phenomenon (Froment, 1990).
Under typical operating conditions the coke yield is in the order of 0.01 wt%. First, there is a
catalytic phase in which the properties of the tube skin material play an important role
(Figueiredo, 1989). Once the metal surface is covered with coke, a second heterogeneous, but
non-catalytic, mechanism dominates (Bennet and Price, 1981). At the operating conditions
prevailing in industrial cracking units, the largest amount of coke formed during the run length
results from the heterogeneous, non-catalytic coke formation (Reyniers et al., 1994). Because of
its accumulative nature, coke deposits build-up on reactor walls and influence the reactor
performance in a number of ways. First, because coke is a thermal insulator, it prevents efficient
heat transfer from the furnace firebox to the reacting gas within the reactor tubes (Nexant, 2003).
Therefore, the surface temperature of the coils has to be increased in order to obtain the desired
7
temperature of the reacting gas. This adversely affects the life time of the coil. Secondly, the
pressure drop is increased due to the reduction of the inner diameter of the coil upon coking
(Chan et al, 1998). Higher average pressures result in a decrease of the ethylene selectivity.
Thirdly, coking may lead to corrosion of the coil due to carbonization (Chan et al, 1998).
Furthermore, steam cracking units are typically constructed of heat resistant Fe-Ni-Cr alloys,
which promote the deposition of carbonaceous materials. As a consequence of these issues,
decoking of the reactor coils has to be carried out periodically resulting in loss of production and
related costs (Nexant, 2003). The formed coke is burned down with air and steam or pure steam.
1.1.4 Environmental issues
From environmental point of view steam cracking is not the ideal process for the production
of olefins. Steam cracking is the most energy-consuming process in the chemical industry (Ren et
al., 2006). Modern olefin plants operate highly energy efficient. Nevertheless, they are still
responsible for the emissions of large amounts of greenhouse gases, especially CO2. Carbon
dioxide is formed in the furnace where methane (byproduct of the steam cracking process) is
burned to produce the necessary heat for the endothermic steam cracking process. The steam
cracking process currently accounts for approximately 180-200 million tons of CO2 emissions
worldwide (Ren et al., 2006). The Kyoto protocol states that the emissions of CO2 should be
drastically reduced. Another issue becoming increasingly serious is NOx formation in the furnace.
The high temperatures of the flames, higher when coke deposition reduces the heat transfer
efficiency, lead to the formation of large amounts of thermal NOx, which are not in agreement
with the increasingly strict air regulations.
1.2 Olefin production and market evolution
Ethylene and propylene are key components in the chemical sector with typical applications
in the polymer industry. More than 50 % of ethylene is used in the production of polyethylene.
The primary use of polyethylene is in film applications for packaging, carrier bags and trash
liners. Ethylene oxide, ethylene dichloride and styrene are also significant ethylene consumers.
Ethylene oxide is used in disinfecting, sterilizing, and fumigating applications. However, most of
the ethylene oxide produced is converted into other derivatives, particularly ethylene glycol.
8
These derivatives are used in a variety of applications, such as engine antifreeze, heat transfer
fluids, synthetic (polyester) fibers, solvents, and plasticizers. Styrene monomer is used principally
in polystyrene for packaging and insulation, as well as in styrene butadiene rubber for tires and
footwear.
EBZ7
EDC14
EO/EG12
PE58
Others9
OA8
PO7
AN10
CU6
PP58
Others11
Figure 1-4: Derivatives from ethylene and propylene (Van Geem, 2006)
Propylene is mainly used to produce polypropylene. Other important products include acrylic
esters (via acrylic acid), phenol and acetone (via cumene), acrylonitrile fibres, butanol and
ethylhexanol (via butyraldehyde), and glycol (via propylene oxide).
Ethylene demand
0
20
40
60
80
100
120
140
160
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Vo
lum
e [
millio
n t
on
ne
s]
Polyethylene Ethylene oxide Ethylene dichloride Ethylbenzene Others
Figure 1-5: Ethylene demand (Eramo, 2005)
9
The ethylene and propylene demand experiences increased growth each year as a result of the
expanding market of products based on ethylene and propylene, as illustrated in Figure 1-5 and
Figure 1-6. Eramo (2005) expects the global ethylene demand growth to increase 4,5% during the
next 5 years as global economies continue to grow strongly for the rest of the decade.
Propylene demand
0
10
20
30
40
50
60
70
80
90
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Vo
lum
e [
millio
n t
on
ne
s]
Polypropylene Cumene Acrylonitrile oxo alcohols acrylic acid propylene oxide Others
Figure 1-6: Propylene demand (Eramo, 2005)
During the seventies, eighties and nineties the main focus of a steam cracking plant was to
maximize the production of ethylene. Recently in Europe and Asia propylene has become more
and more the desired product (Van Geem, 2006). In 2000-2005, average total propylene demand
growth was 4.5%/year compared to an average ethylene growth rate of 3.5%/ year. Propylene’s
largest derivate, polypropylene, tends to grow at rates slightly faster than ethylene’s largest
derivate, polyethylene (Eramo, 2005).
1.3 Objective of this thesis
The main objective of this thesis is the validation and the improvement of the fundamental
simulation models of Plehiers (1989) and Vercauteren (1991). Expansion in the petrochemical
10
industry, the continuing demands for ethylene and propylene, the varying feedstock availability,
and rapidly changing market situation have brought and continue to bring research attention to
the modeling of the steam cracking process. In the past few decades step by step, new and better
simulation models have been developed at the Laboratorium voor Petrochemische Techniek.
However, the simulation results of some feedstocks are not always as accurate as one desires. De
Roo (1998) and De Buck (1999) mentioned already inaccurate simulation results for important
components such as propylene, butadiene and toluene. These shortcomings of the steam cracking
simulation software can be partly explained by the absence of certain reaction pathways and
several important species. On the other hand, a new optimization of the kinetic parameters of the
reaction network is necessary. Furthermore, nowadays more and more heavy fractions (heavy
naphtha, light gas oil or vacuum gas oil) are used as feedstock for steam cracking. The reason is
that the demand for these fractions as fuel is becoming less and less important. This results in
large remains of these low cost fuels. It is of great importance that the simulation models also
accurately predict the product spectrum of these heavy fractions. In this respect the study of the
cracking behavior of heavy fractions is of great importance. Therefore in this work the cracking
behavior of several gas condensates are studied. A gas condensate is a by-product of the
processing of raw natural gas. It is the liquid condensate removed and recovered during the
processing of raw natural gas.
In chapter 2, the shortcomings of the fundamental simulation models of Plehiers and
Vercauteren are mapped. First, the general structure of such a simulation model is discussed.
Subsequently, the reliability of both simulation models is tested based on a wide range of pilot
plant data from a constructed experimental database. In this chapter, the steps taken to work off
the shortcomings of the steam cracking simulation models are described as well. This part
clarifies the build-up of the reaction network, the extension of the network with missing
components and reactions and the optimization of the kinetic parameters. In conclusion, the
improvements resulting from these alterations in the reaction network are verified. For this
purpose, pilot experiments are simulated with the adjusted reaction network.
In chapter 3, the cracking and decoking experiments carried out in the pilot plant set-up for
the steam cracking of hydrocarbons are discussed. In these experiments the cracking behavior of
eight gas condensates is examined. First, the operation of the pilot plant set-up is given. Then the
11
detailed molecular composition of the different gas condensates is determined. Finally, the pilot
experiments are studied and conclusions are drawn.
Chapter 4 overviews all the conclusions.
12
Chapter 2
Improvement of a Fundamental Simulation Model
2.1 Introduction
Expansion in the petrochemical industry, the continuous rising demands for ethylene and
propylene, the varying feedstock availability, and rapidly changing market situation have brought
and continue to bring research attention to the modeling of the steam cracking process. In the
search for higher performance and increased selectivity simulation models have become an
indispensable tool for the chemical industry. Generally, these simulation models consist of two
parts: on the one hand a solver that solves the reactor model equations, on the other hand the
reaction network and the physical properties of the considered species (Van Geem, 2006). The
feedstock composition, the reactor and furnace geometry and the operation conditions define the
boundary conditions of this complex problem. The general build-up of a fundamental simulation
model for steam cracking of hydrocarbons is shown in Figure 2-1. These models account for both
the chemical reactions and the physical transport phenomena.
Figure 2-1: Illustration of the general construction of a fundamental simulation model for
steam cracking of hydrocarbons (Van Geem, 2006)
13
A fundamental simulation model for steam cracking is an attractive tool for optimal feedstock
selecting, optimal reactor control and/or reactor design. However, developing such a model is not
straightforward. First, a large number of chemical reactions take place during steam cracking.
Accurate simulation of the processes requires accurate modeling of the dominant reaction
pathways in the process. Furthermore, the models have to be very flexible; they have to predict
the product yields under different operation conditions, different feedstock compositions and
various reactor and furnace configurations. In the past few decades step by step new and better
simulation models have been developed at the Laboratorium voor Petrochemische Techniek. In
the next paragraphs the general structure of such a simulation model is discussed in detail.
Subsequently, the reliability of these fundamental simulation models developed at the LPT is
tested based on a wide range of pilot plant data from the experimental database. Next, the steps
taken to eliminate these shortcomings are described. In conclusion, the improvements resulting
from these alterations in the reaction network are verified. For this purpose, pilot experiments are
simulated with the adjusted reaction network.
2.2 Reactor model
For the simulation of a steam cracking reactor a 1-dimensional plug-flow model is used. This
reactor model implicitly assumes that there is no mixing in the axial (flow) direction but perfect
mixing in the transverse direction(s). All resistance to heat transfer is then located in a thin
(laminar) film near the tube wall.
2.2.1 Reactor model equations
Since steam cracking is a non-isothermal, non-adiabatic and non-isobaric process, the 1-
dimensional model equations consist of the transport equations for mass, momentum and energy.
Consider an infinitesimal volume element with cross sectional surface area Ω, circumference ω
and length dz shown in Figure 2-2.
Figure 2-2: infinitesimal volume element
14
The steady state continuity equation for a component j in the process gas mixture over the
infinitesimal volume element is:
Ω
= ∑
=
rn
1k
kv,kj
jr
dz
dFυ [3-1]
with Fj = the molar flow rate of component j
rv,k = the reaction rate of reaction k
The energy equation is given by:
( )∑ ∑ ∆−Ω+=
j k
0
kkV,pjj HRq dz
dTc F fω [3-2]
with q = the heat flux to the process gas
cpj = the heat capacity of component j at temperature T
∆rHk = the reaction enthalpy of reaction k
The momentum equation accounting for friction and changes in momentum is given by:
0dz
dv v v
r d
f 2
dz
dp 2
bt
t=+
++ ραρ
π
ζα [3-3]
with pt = the total pressure
α = a conversion factor
f = the Fanning friction factor
ρ = the density of the gas mixture
v = the velocity of the gas mixture
The boundary conditions at the reactor inlet are:
00j0j p p TT CC === [3-4]
15
2.2.2 Solving the 1-dimensional reactor model equations
In order to simulate the reactor the set of continuity equations for the various process gas
species is solved simultaneously with the energy and momentum equations. Note that the last two
equations only have to be considered when, respectively, the temperature and/or pressure profile
are not imposed. The resulting set of equations forms a system of stiff non-linear first order
differential equations. The stiffness is caused by the large difference (several orders of
magnitude) of the eigenvalues related to the molecular species on the one hand and the radical
species on the other hand. To overcome the stiffness problem the numerical procedure presented
by Dente and Ranzi (1979) was applied in the older simulation models (Plehiers, 1989;
Vercauteren, 1991). In the new microkinetic model of Van Geem (2006) a stiff solver solver
DASSL (Li and Petzold, 1999) is implemented to solve all the differential equations
simultaneously. DASSL uses backward differentiation formula (BDF) methods to solve a system
of Differential Algebraic Equations (DAE) or Ordinary Differential Equations (ODE). More
details can be found in Van Geem (2006).
2.3 Reaction network
Different reaction networks have been generated over the years. Plehiers (1989) created the
first reaction network ‘C19 network’ in which components with up to 19 C-atoms are considered.
A few years later Vercauteren (1991) developed the more extensive C25 version. Both these
authors developed a reaction network based on the free-radical mechanism. According to Rice
and Herzfeld (1931, 1934), three important reaction families can be distinguished:
(1) carbon-carbon and carbon-hydrogen bond scissions in molecules without radical character
and the reverse radical-radical recombinations
(2) hydrogen abstraction reactions, both intra- and intermolecular (isomerization reactions are
intramolecular hydrogen abstractions)
(3) radical addition to olefins and the reverse β scission of radicals, both intra- and
intermolecular (cyclization reactions are intramolecular additions)
16
Two types of radicals can be distinguished: β radicals react mainly through bimolecular
reactions, while µ radicals are principally involved in monomolecular reactions. In the reaction
networks developed by Plehiers (1989) and Vercauteren (1991) C5+-radicals are considered to be
µ radicals and thus have a “pure” µ character (CRACKSIM manual, 1997). These radicals are
treated separately from the C4--radicals. Accordingly, the reaction network is subdivided into a
primary (µ radicals) and a secondary network.
2.3.1 Primary network
In the primary network, the formation and the reactions of C5+-radicals (µ-radicals) are
considered. Since they can only disappear through unimolecular reactions, the set of continuity
equations for these intermediate radicals is linear in their concentrations. Three groups of primary
reactions are distinguished (Vercauteren, 1991):
Carbon-carbon bond scissions
Hydrogen abstractions
Radical additions
The first two reaction types are the dominant pathways for the disappearance of the feed
molecules. Radical addition reactions are important disappearance pathways for the formed (di)-
olefins. The primary reactions are followed by isomerization and decomposition reactions of the
formed radicals until only olefins and C4--radicals remain. Olefins with more than five C-atoms
are regarded as feed components and undergo the same primary reactions as those specified
above. The formed C4--radicals and C4--olefins are treated in the secondary network. In the
primary reaction network cyclization reactions are also included. Cyclic radicals can be generated
when the free electron is located five or six places from a double bond. The formed cyclic
radicals are precursors for cyclic olefins and aromatic compounds (Kopinke et al., 1988).
2.3.2 Secondary network
The C4--species that are formed in the reaction schemes deduced for the C5+-components in
the primary network, are treated in a separate network, the ”secondary network” or “C4--
network”. These radicals show both a β-character and a µ-character. Accordingly, their reaction
17
scheme is more extended. The radical reactions are subdivided in six classes: C-C scission
reactions, hydrogen abstractions, additions, decompositions, isomerizations and recombinations.
Also some global reactions are considered in the reaction networks of Plehiers (1989) and
Vercauteren (1991). These are introduced to predict the formation of C5+-components starting
from lighter species. These global reactions are mainly condensation reactions,
(de)hydrogenations, (de)methylation and ring closure reactions. Some of these reactions are in
fact radical reactions, but they are proposed as an equivalent molecular one. In this way the
number of species and the number of reactions is reduced.
2.4 Database
The LPT pilot plant installation is a vital element for testing the simulation results obtained
with the fundamental simulation model. Indeed, to improve and extend the simulation model
experimental results on the pilot are indispensable. Over the years a lot of experiments have been
carried out on the LPT pilot plant installation using feedstocks with widely varying
characteristics, resulting in an extensive experimental database containing over 400 experiments
obtained with over 50 different feedstocks. The feedstocks range from light gasses, over naphthas
to VGO’s and even waxes. A compact overview of the experimental database is given in Table
2-1, while in appendix A more details are given about the different experiments. For these
experiments both the operation conditions and the measured product yields of the main products
are gathered and safely stored.
Feed HC flow
[kg/hr]
Dilution
[kg/kg]
COP
[bar]
COT
[°C]
number of
experiments
Light Feedstocks 2.1 – 5.2 0 -1.0 1.6 – 2.9 660 - 950 264
Naphthas 2.1 -6.5 0.2 – 1.5 1.6 – 2.5 700 - 930 158
Heavy Feedstocks 2.7 – 4.5 0.4 – 1.2 1.3 – 2.5 750 - 850 32
Table 2-1: Overview of the experimental database
18
A logically designed interface makes searching for data easy. This new version of the
database program is written in Java. An introductory window shows a lists of all the experiments.
A search engine offers the possibility to refine the search and more specify the experiments by
defining some of the parameters. Clicking on one of the experiments renders a window with an
overview of the most important data regarding that experiment, see Figure 2-3. These data
include conditions, reactor configuration and product yields of ethylene and propylene. The
hyperlink buttons in the window lead to more detailed information. In this way the composition
of the feedstock, the temperature and pressure profile along the reactor tube and more product
yields can be consulted.
Figure 2-3: Information gathered in the database for each experiment
19
2.5 Comparison between experimental and simulated data
To validate the fundamental simulation model the simulation results are compared with
experimental data from the experimental database. The 1-dimensional reactor model is used for
simulating the pilot plant reactor. Both the C19 reaction network and the C25 network are
examined. The program simulates the product yields by steam cracking based on the feedstock
composition, the reactor and furnace geometry and the operation conditions.
2.5.1 C19 reaction network
The C19 reaction network (Plehiers, 1989) considers compounds with up to 19 C-atoms.
Figures 2-3:2-17 show the parity plots obtained for the main products [hydrogen, methane,
acetylene, ethylene, ethane, propylene, propane, butadiene, 1-butene, iso-butene, iso-butane, n-
butane, cyclopentadiene, benzene and toluene]. In Figure 2-4 the parity plot of hydrogen is given.
Hydrogen is formed by hydrogen abstraction reactions of the hydrogen radical. As mentioned by
De Roo (1998) the hydrogen yield is often slightly underestimated, but in general reasonable
good simulation results are obtained.
Figure 2-5 shows the parity plot for the methane yield. Methane is a cracking product from
any component in the cracking mixture. Hence, differences found for the simulated methane yield
will also reflect on the yields of the other products. However, Figure 2-5 shows that the methane
yield is accurately simulated. The parity plot of acetylene is not as good as for methane, see
Figure 2-6. This is not unexpected considering the low yields for this product and taking into
account experimental inaccuracies.
The parity plot for ethylene is excellent, as seen in Figure 2-7. Even at severe cracking
conditions the ethylene yield remains accurately simulated. This is no surprise because the
parameters of the C19 network are fitted in such a way that the ethylene yield is accurately
simulated. However, several points show a large deviation of the first bisector. These points
correspond mainly with cracking experiments of toluene/ethane mixtures. Figure 2-8 shows that
overall the ethane yield is well simulated both at low and high conversions. Nevertheless, some
large deviations are found as well which can also be ascribed to ethane/toluene mixtures.
For propylene the parity plot is also relatively good although more deviations can be seen as
for ethylene, see Figure 2-9. This is because the propylene yield results from a balanced system
20
0
1
2
3
4
5
0 1 2 3 4 5
Experimental Hydrogen Yield [wt%]
Sim
ula
ted
Hy
dro
ge
n Y
ield
[w
t%]
Figure 2-4: Parity plot for the hydrogen yield
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Experimental Methane Yield [wt%]
Sim
ula
ted
Me
tha
ne
Yie
ld [
wt%
]
Figure 2-5: Parity plot for the methane yield
21
0
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5 3
Experimental Acethylene Yield [wt%]
Sim
ula
ted
Ac
eth
yle
ne
Yie
ld [
wt%
]
Figure 2-6: Parity plot for the acetylene yield
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Experimental Ethylene Yield [wt%]
Sim
ula
ted
Eth
yle
ne
Yie
ld [
wt%
]
Figure 2-7: Parity plot for the ethylene yield
Cracking experiments of
toluene/ethane mixtures
22
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40 50 60 70 80 90
Experimental Ethane Yield [wt%]
Sim
ula
ted
Eth
an
e Y
ield
[w
t%]
Figure 2-8: Parity plot for the ethane yield
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Experimental Propylene Yield [wt%]
Sim
ula
ted
Pro
py
len
e Y
ield
[w
t%]
Figure 2-9: Parity plot for the propylene yield
Cracking experiments of
toluene/ethane mixtures
23
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40 50 60 70 80 90
Experimental Propane Yield [wt%]
Sim
ula
ted
Pro
pa
ne
Yie
ld [
wt%
]
Figure 2-10: Parity plot for the propane yield
between addition reactions, β scission reactions and hydrogen abstractions (Van Damme et al.,
1984). A rate of production analysis shows that the β scission reactions forming propylene are
generally the most important reactions for propylene (Van Geem et al., 2006).
The parity plot for propane is excellent. The yield of propane during naphtha cracking
experiments remains low, i.e. lower then 1 wt%. Hence, values for the propane yield higher than
1 wt% correspond to experiments with propane in the cracking mixture. Figure 2-10 shows that
the cracking reactions of propane are accurately simulated both at low and high conversions. Also
the low yields corresponding to naphtha or gas oil cracking experiments are accurately simulated.
In Figure 2-11 the parity plot for butadiene is shown. This parity plot is far from perfect.
Significant deviations between the simulated and experimentally determined butadiene yields are
already mentioned by Vercauteren (1991). These problems can be allocated to the high severity
range of the conditions. At high severity the simulated butadiene yield is significantly higher than
the experimentally observed butadiene yield. Vercauteren stated that at high severities part of the
butadiene forms vinylacetylene. In the present work no species such as buta-1,3-dien-1-yl were
considered either, and thus no vinylacetylene is formed from butadiene according to the present
24
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8
Experimental Butadiene Yield [wt%]
Sim
ula
ted
Bu
tad
ien
e Y
ield
[w
t%]
Figure 2-11: Parity plot for the butadiene yield
0
1
2
3
4
5
0 1 2 3 4 5
Experimental 1-Butene Yield [wt%]
Sim
ula
ted
1-B
ute
ne
Yie
ld [
wt%
]
Figure 2-12: Parity plot for the 1-butene yield
25
0
5
10
15
20
0 5 10 15 20
Experimental i-Butene Yield [wt%]
Sim
ula
ted
i-B
ute
ne
Yie
ld [
wt%
]
Figure 2-13: Parity plot for the i-butene yield
0
10
20
30
40
50
0 10 20 30 40 50
Experimental i-Butane Yield [wt%]
Sim
ula
ted
i-B
uta
ne
Yie
ld [
wt%
]
Figure 2-14: Parity plot for the i-butane yield
26
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Experimental n-Butane Yield [wt%]
Sim
ula
ted
n-B
uta
ne
Yie
ld [
wt%
]
Figure 2-15: Parity plot for the n-butane yield
0
1
2
3
4
5
0 1 2 3 4 5
Experimental CPD Yield [wt%]
Sim
ula
ted
CP
D Y
ield
[w
t%]
Figure 2-16: Parity plot for the CPD yield
27
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14 16
Experimental Benzene Yield [wt%]
Sim
ula
ted
Be
nz
en
e Y
ield
[w
t%]
Figure 2-17: Parity plot for the benzene yield
0
2
4
6
8
10
0 2 4 6 8 10
Experimental Toluene Yield [wt%]
Sim
ula
ted
To
lue
ne
Yie
ld [
wt%
]
Figure 2-18: Parity plot for the toluene yield
28
network (Van Geem, 2006). It is clear that an extension with these type of species is important.
Figure 2-12 points out that the 1-butene yield is not accurately simulated. The yield of 1-
butene is overestimated. On the contrary, iso-butene is accurately simulated as can be seen in
Figure 2-13. Also for iso-butane and n-butane a good conformity between the experimental data
and the simulation results is obtained, see Figure 2-14 and Figure 2-15. Note that the high yields
of n-butane and iso-butane correspond to experiments with these components in the feedstock.
Apparently the conversion of these two light hydrocarbons is well described by the single event
microkinetic model simulation model.
The yields of heavy products such as cyclopentadiene (CPD), benzene and toluene are often
underestimated, i.e. Figures 2-15:2-17.
2.5.2 C25 reaction network
The C25 reaction network is examined as well. In this network, components with up to 25 C-
atoms are considered. In Figures 2-18:2-19, the corresponding parity plots for the ethylene and
propylene yield are shown. In these plots, a larger deviation of the first bisector is observed
compared to the parity plots acquired with the C19 reaction network. Moreover, the ethylene
yield is underestimated at high conversions. This trend can also be observed in the parity plots of
hydrogen, methane, acethylene, ethane, propane, butadiene, 1-butene, iso-butene, iso-butane, n-
butane, cyclopentadiene, benzene and toluene. These parity plots are assembled in Appendix B.
Hence, it can be concluded that the C25 reaction network provides systematically poorer results
then the C19 reaction network.
2.5.3 Traced shortcomings
One of the main conclusions of the previous comparison is that the cracking behavior of
toluene is not accurately described in the single event microkinetic model. Figure 2-7 shows that
the ethylene yield is overestimated for experiments with toluene/ethane mixtures, while the
ethane yield is underestimated (Figure 2-8). In particular, the simulation results give no reliable
toluene and benzene yields, see Figure 2-17 and Figure 2-18. Nonetheless, toluene is often an
important component of petroleum fractions. Furthermore, toluene is one of the major aromatics
formed in the steam cracking process. Its presence in the reactor coil will have an influence on
the product distributions.
29
0
5
10
15
20
25
30
35
40
45
0 5 10 15 20 25 30 35 40 45
Experimental Ethylene Yield [wt%]
Sim
ula
ted
Eth
yle
ne
Yie
ld [
wt%
]
Figure 2-19: Parity plot for the ethylene yield (C25 network)
0
5
10
15
20
25
30
35
40
45
0 5 10 15 20 25 30 35 40 45
Experimental Propylene Yield [wt%]
Sim
ula
ted
Pro
py
len
e Y
ield
[w
t%]
Figure 2-20: Parity plot for the propylene yield (C25 network)
30
The main reactions that appear during the cracking of toluene are presented in Scheme 2-1
(Bonaceur et al., 2002). Pyrolysis of toluene proceeds via a free-radical mechanism, producing
mainly benzene, methane, hydrogen and dibenzyl molecules. Radicals are formed via the scission
of toluene molecules in a hydrogen radical and a benzyl radical. The formation of benzene results
from an ipso addition reaction followed by the elimination of a methyl radical. The formation of
methane or hydrogen is explained by hydrogen abstraction reactions. Finally, the dibenzyl
compound is formed by a termination reaction. The benzyl radical plays a prominent role in these
reactions. However, this radical is not included in the reaction network. Also the dibenzyl
molecule has to be added to the reaction network.
Scission of molecules toluene benzyl + H
Hydrogen abstraction H + toluene H2 + benzyl
benzyl + H2 toluene + H
CH3 + toluene CH4 + benzyl
CH3 + H2 CH4 + H
H + CH4 H2 + CH3
benzyl + CH4 toluene + CH3
Ipso addition H + toluene benzene + CH3
Recombination benzyl + H toluene
benzyl + CH3 ethylbenzene
benzyl + benzyl dibenzyl
Scheme 2-1: Cracking mechanism of pure toluene (Bounaceur et al., 2002)
2.6 Working off the shortcoming
In this part, the steps taken to work off the traced shortcoming of the cracking simulation
model are described. In Figure 2-21, the general structure of the reaction network is presented.
The reaction network is generated based on several files: block.i, net.i, for.i and several PRC-
files. In the following paragraphs, the function of the different files is discussed as well as the
information added to these files in order to describe accurately the cracking behavior of toluene.
31
Figure 2-21: Structure of the reaction network
2.6.1 Block.i
Block.i contains the physical properties of all the molecules and radicals that are included in
the reaction network. All the components are represented by a specific identification number. For
the different species, the molecular weight, the standard molar enthalpy, the standard molar
entropy and coefficients for the calculation of the specific heat capacity are subsumed in this file.
The physical properties added to the Block.i file in order to define the benzyl radical and the
dibenzyl molecule are given in Table 2-2.
benzyl radical dibenzyl molecule
ID number 723 77
MW [kg/kmol] 91.13335 182.2667
hf0 [kcal/kmol] 49500 32410
sf0 [kcal/K/mol] 72.071 119.43
Cp,A -8.072379 -12.86
Cp,B 0.141067905 0.2733
Cp,C -1.03E-04 1.76E-04
Cp,D 2.99E-08 4.32E-08
Table 2-2: The physical information added for the benzyl radical
and the dibenzyl molecule
The physical properties are if possible collected from the NIST database and/or the Perry
handbook (Perry and Green, 1997). The standard molar entropy of the benzyl radical is calculated
using the HBI-method of Laidler (Lay et al., 1995). The coefficients in the expression for the
32
specific heat capacity are determined fitted based on ab initio values. These calculations can be
found in respectively appendix C and appendix D.
2.6.2 Net.i and PRC-files
The complete reaction network consists of several files. On the one hand the C4- reaction
network, or so-called β network (Van Geem, 2006), which is stored in the net.i file. On the other
hand the primary network, that is stored in a set of PRC-files. In the present work the focus will
be on the net.i file, because the reactions that should be added to the reaction network regard the
behavior of a β radical. In the net.i file the reactions are subdivided in seven classes: initiation,
hydrogen abstraction, addition, decomposition, isomerization, termination and molecular
reactions. For each reaction class, the reactions are defined in a similar way. An example is given
in Figure 2-22. The first line contains the stoechiometric coefficients of the involved species
(reactants and subsequent products) and the number of single events factor of the reaction. The
number of single events is equal to the number of energetic equivalent reaction paths from
reactant(s) to product(s) (Van Geem, 2006). On the second line, the involved reactants and the
products are represented by their identification number. Next, the location of the kinetic
parameters in for.i is specified (CRACKSIM manual, 1997).
1 1 -1 1
651 651 1 41 41 0 0 0 0
H (651) + H (651) H2 (1)
Figure 2-22: definition of a reaction
The present reactions in the model that describe the formation and disappearance of toluene
are given in Scheme 2-2. Comparison between the simulation results and a set of pilot plant
experiments shows that these reactions are insufficient to describe the cracking mechanism of
toluene. The global reactions in Scheme 2-2 completely disregard the free-radical mechanism of
the cracking of toluene, in which the benzyl radical plays a prominent role. When toluene is
cracked the main products are: benzene, methane, hydrogen and dibenzyl molecules (Bounaceur
et al., 2002). A literature survey about the cracking behavior of toluene shows that the radical
33
reactions given in Scheme 2-3 play a dominant role. Hence, these reactions are added to the
reaction network in the file net.i, while the global reactions are removed.
me-indene + H2 CH4 + indene
me-naphthalene + H2 CH4 + naphthalene
toluene + H2 CH4 + benzene
xylene + H2 CH4 + toluene
C9arool + H2 CH4 + styrene
styrene + C2H4 naphthalene + H2
benzene + C2H4 ethylbenzene
benzene + C2H4 styrene + H2
toluene + C2H4 indene + H2
benzene + benzene biphenyl + H2
styrene + H2 ethylbenzene
C9arool + H2 indene
Scheme 2-2: Present reactions in the reaction network
Scission of molecules toluene benzyl + H
Hydrogen abstraction H + toluene H2 + benzyl
CH3 + toluene CH4 + benzyl
C2H5 + toluene C2H6 + benzyl
benzyl + H2 toluene + H
benzyl + CH4 toluene + CH3
benzyl + C2H6 toluene + C2H5
Recombination
benzyl + H toluene
benzyl + CH3 ethylbenzene
benzyl + C2H5 1-phenylpropane
benzyl + benzyl dibenzyl
Ipso addition
H + toluene benzene + CH3
Scheme 2-3: The reactions added to the reaction network (Bounaceur et al., 2002)
34
2.6.3 For.i
For.i contains all the information necessary to calculate the reaction rate coefficients for the
reaction network. It is evident that the calculation of thousands of reaction rate coefficients by
means of experimental product distributions is an impossible task. The number of parameters
should be minimized to bring the significancy of a parameter not in danger. Hence for each
reaction class, a reference reaction is chosen. The activation energy and frequency factor of an
arbitrary reaction, which differs from the reference reaction in this class, is calculated by adding
contributions to the reference values which take into account the structural differences between
the mechanism of the considered reaction and that of the reference reaction (CRACKSIM manual,
1997):
∑∆+
n
1=i
iref EE=E [3-5]
σ logfA log=A logn
1=i
iref ++∑ [3-6]
with ∆E i = contribution to the activation energy
fi = contribution to the logarithm of the frequency factor
σ = number of single events
The rate coefficient of the reference reaction is defined by a single event. Each rate coefficient
has to be multiplied by the number of single events. This procedure permits the calculation of the
activation energy and the frequency factor of a great number of reactions with a limited number
of fundamental parameters (CRACKSIM manual, 1997).
An extra condition, for using the structural contributions to calculate the kinetic parameters, is
introduced by incorporating thermodynamic consistency of the reaction rate coefficients. The
latter implies that the ratio of the rate coefficients of the forward and the backward reaction
should be equal to the equilibrium coefficient. This allows a reduction of the number of
independent contributions. In the model for steam cracking, C-C and C-H scissions of molecules
and recombinations form a group of reversible reactions. The same is true for addition and
decomposition reactions. The group of the hydrogen abstractions as well as the isomerization
reactions (internal hydrogen abstractions) are mutual reversible. The reaction rate coefficient for
35
an exothermic reaction can then be deduced from the reversible endothermic reaction by means
of the following formulas (CRACKSIM manual, 1997):
( )[ ]R.T.HEE ffbf ν∆+∆−−=o
[3-7]
( ) ( )( )
′∆+
∆−−=
ln10
.TRln+1.
R.ln10
SA logA log ff
bf
νo
[3-8]
where R’ = R/100
f, b stands for respectively forward and backward reaction (respectively
exotherm and endotherm)
∆νf = the change in the total amount of moles in accordance with the forward
reaction
Considering forward and backward reactions the number of independent reactions belonging
to the cracking mechanism of toluene is reduced to eight. These endothermic reactions are
presented in Table 2-3, together with the corresponding kinetic parameters found in the literature
(Bonaceur et al., 2002).
endothermic reactions
EA
[kcal/mol] log A
H2 + benzyl H+ toluene 93.7 11.5
CH4 + benzyl CH3+ toluene 87.5 9.9
C2H6 + benzyl C2H5+ toluene 60.0 9.4
toluene benzyl + H 267.6 12.49
ethylbenzene benzyl + CH3 316.4 15.2
1-phenylpropane benzyl + C2H5 297.4 15.0
dibenzyl benzyl + benzyl 260.0 15.5
CH3+ benzene H+ toluene 62.8 9.2
Table 2-3: Kinetic parameters of the exothermic reactions of the toluene pyrolysis
2.7 Optimization
The optimization of the independent kinetic parameters (of the endothermic reactions) is
performed using a Rosenbrock optimization routine. This code determines the optimal values for
36
the kinetic parameters by minimizing an objective function S. The objective function is defined
by the following equation:
2
1
)~(1
n
N
n
nn yygN
S ∑=
−= [3-9]
with N = number of considered experiments
gn = weight function
ny~ = target output or the experimentally observed product yield
yn = actual output or the simulated product yield
The reaction rate coefficients are fitted to experimental data from the pilot plant set-up for the
steam cracking of hydrocarbons at the LPT. The used pilot experiments include five sets of
experiments carried out with different toluene-ethane mixtures. Furthermore, in each set the
conversion of the feedstock is varied by changing the temperature profile. This makes that the
total number of experiments used in the for fitting the reaction rate coefficients is equal to
nineteen. The literature values for the kinetic parameters (Table 2-3) are used as initial guesses.
These initial values are varied within a specified interval. For the pre-exponential factor this
interval is given by ] log A – 0.5, log A + 0.5 [, while the activation energy may vary between
(EA – 10% EA) and (EA + 10% EA). An adaptation of a value is only accepted when this leads to a
decrease of the objective function. A few hundred iteration steps are necessary to complete the
optimization. When the minimum value of the objective function is reached, one can assume that
the model is fitted through the experimental points and accordingly the optimal values for the
parameters are determined. Both the literature values and the values obtained after optimization
are presented in Table 2-4.
2.8 Validation of the adaptations
At first, in order to validate the adjustments, the pilot experiments of toluene-ethane mixtures
used for the optimization are simulated with the adjusted reaction network. In Figures 3-3:3-6 the
parity plots obtained with the ‘old’ network and the adjusted network are compared for the
products ethylene, ethane, benzene and toluene.
The parity plots for ethylene and ethane obtained with the adjusted reaction network are
excellent as seen in Figure 2-23 and Figure 2-24. Furthermore, Figure 2-25 and Figure 2-26 show
37
that the adjusted fundamental simulation model is able to accurately predict the benzene and
toluene yields. Hence, it can be concluded that the cracking behavior of ethane-toluene mixtures
is now accurately described. By taking the real cracking mechanism of toluene into consideration
a good agreement between the experimental data and the simulated data is found. The
adjustments to the reaction network are also validated by means of simulating experiments with
naphtha feedstocks which contain toluene. In Figures 2-27:2-28, the parity plots for the ethylene,
ethane, benzene and toluene yield are shown for experiments with naphtha feedstocks. As seen in
Figure 2-27, no alternations are recorded in the simulated ethylene yield by adding the reactions
of toluene cracking into the reaction network. Once again, an excellent parity plot for ethylene is
received. Also for ethane and benzene no real improvements are obtained, see Figure 2-28 and
Figure 2-29. However, the agreement between the simulated yields of toluene with the extended
reaction network and the experimental data is even poorer then before. This is no surprise. The
main reason is that in the primary reaction network the pure β character of the benzyl radical is
not correctly accounted for. In the primary reaction network the assumption is made that when a
benzyl radical is formed it immediately abstracts a hydrogen and forms toluene. This assumption
is incorrect because the benzyl radical can undergo many other reactions. The only solution to
overcome this problem is to build a completely new primary reaction network in combination
with an adjusted β network (the former C4- network). Recently, Van Geem (2006) has built such a
new reaction network. The parity plot for toluene in Figure 2.31 shows that this model is able to
simulate the toluene yield accurately for ethane/toluene mixtures and naphtha fractions.
literature optimization
endothermic reactions EA[kcal/mol] log A EA[kcal/mol] log A
H2 + benzyl H+ toluene 93.7 11.5 98.8 11
CH4 + benzyl CH3+ toluene 87.5 9.9 88.4 9.4
C2H6 + benzyl C2H5+ toluene 60.0 9.4 64.4 8.9
toluene benzyl + H 267.6 12.49 306.3 12.5
ethylbenzene benzyl + CH3 316.4 15.2 316.4 15.7
1-phenylpropane benzyl + C2H5 297.4 15.0 297.4 15.2
dibenzyl benzyl + benzyl 260.0 15.5 260.0 15.9
CH3+ benzene H+ toluene 62.8 9.2 64.3 8.9
Table 2-4: Kinetic parameters
38
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Experimental Ethylene Yield [wt%]
Sim
ula
ted
Eth
yle
ne
Yie
ld [
wt%
]
old network
adjusted network
Figure 2-23: Parity plots for the ethylene yield of toluene-ethane mixtures
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80
Experimental Ethane Yield [wt%]
Sim
ula
ted
Eth
an
e Y
ield
[w
t%]
old network
adjusted network
Figure 2-24: Parity plots for the ethane yield of toluene-ethane mixtures
39
0
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10
Experimental Benzene Yield [wt%]
Sim
ula
ted
Be
nz
en
e Y
ield
[w
t%]
old network
adjus ted network
Figure 2-25: Parity plots for the benzene yield of toluene-ethane mixtures
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
Experimental Toluene Yield [wt%]
Sim
ula
ted
To
lue
ne
Yie
ld [
wt%
]
old network
adjus ted network
Figure 2-26: Parity plots for the toluene yield of toluene-ethane mixtures
40
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Experimental Ethylene Yield [wt%]
Sim
ula
ted
Eth
yle
ne
Yie
ld [
wt%
]
old network
adjusted network
Figure 2-27: Parity plots for the ethylene yield of naphtha experiments
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5
Experimental Ethane Yield [wt%]
Sim
ula
ted
Eth
an
e Y
ield
[w
t%]
old network
adjus ted network
Figure 2-28: Parity plots for the ethane yield of naphtha experiments
41
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12 14
Experimental Benzene Yield [wt%]
Sim
ula
ted
Be
nz
en
e Y
ield
[w
t%]
old network
adjus ted network
Figure 2-29: Parity plots for the benzene yield of naphtha experiments
0
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10
Experimental Toluene Yield [wt%]
Sim
ula
ted
To
lue
ne
Yie
ld [
wt%
]
old network
adjus ted network
Figure 2-30: Parity plots for the toluene yield of naphtha experiments
42
0
2
4
6
8
10
0 2 4 6 8 10
Experimental Toluene Yield (wt%)
Sim
ula
ted
To
luen
e Y
ield
(w
t%)
Figure 2-31: Parity plots for the toluene yield of naphtha and toluene/ethane experiments
2.9 Conclusion
The comparison between the simulation results with experimental data from the experimental
database revealed several shortcomings to the fundamental simulation models of Plehiers and
Vercauteren. One of the main conclusions of the comparison is that the cracking behavior of
toluene is not accurately described in these models. Introduction of the reactions that appear
during the cracking of toluene has led to an improved description of the cracking behavior of
toluene in toluene/ethane mixtures. However, for naphtha feedstocks the accuracy of the
simulated toluene yield remains poor. This is not surprising. Indeed, not only pilot experiments of
toluene-ethane mixtures but also experiments with naphtha feedstocks should be taken into
account in the optimization of the kinetic parameters. However, the inadequate results are mainly
caused by the fact that the primary network does not completely recognize the β character of the
benzyl radical. In the primary network it is assumed that is radical is only involved in hydrogen
abstraction reactions and that, once it is formed, it is immediately converted into toluene. This
assumption is of course not correct. Only modifications to the primary reaction network can solve
this problem. In the new simulation model of Van Geem (2006) all these considerations were
implemented, leading to an adequate simulation of the benzene and toluene yields.
43
Chapter 3
Steam cracking of gas condensates
3.1 Introduction
In this chapter, the results of cracking and decoking experiments part of a new pilot campaign
are discussed. In these experiments the behavior of eight different gas condensates is examined
under identical conditions. Gas condensates are liquid fractions emerging from the production of
natural gas. These fractions, with an approximate boiling range between 50 and 350 °C, consist
mainly of molecules with 4 to 14 carbon atoms and are intended for use as a petrochemical
feedstock. First the pilot plant set-up for steam cracking of hydrocarbons is discussed. Then the
used feedstocks are analyzed using a combination of GC-MS and GC. Finally, the results of the
pilot plant experiments are discussed and conclusions are drawn.
3.2 Description Pilot
The pilot plant set-up for steam cracking of hydrocarbons at the Laboratorium voor
Petrochemische Techniek of Ghent University allows measurement of the kinetics of the cracking
reactions (Zajdlik et al. 2003) and of the coke deposition in both the radiant coil (Reyniers and
Froment, 1995) and the transfer line exchanger (TLE) (Dhuyvetter et al., 2001). Four main parts
can be distinguished: the feed section, the furnace with the reactor coil, the cooling section and
the analysis section. An overview of the pilot unit is given in Figure 3-1.
3.2.1 The feed section
The feed section regulates the supply of the different feeds to the reactor coil. The flow is
regulated by the pumping frequency of the pump. The mass flow instead of the volume flow of
all feeds is measured in order to avoid inaccuracies of volume dependence on temperature and
pressure. The measurement of the mass flow is carried out using an electronic balance on which
an intermediate barrel with the concerned feedstock is placed. Every minute, the weight indicated
44
Figure 3-1: Overview of the LPT pilot plant setup (Van Geem, 2006)
45
by the balance is send to the computer. The flow can be easily calculated from the weight
reduction per time interval. If the flow calculated by the computer deviates from the set point,
then the pumping frequency is changed. When the fluid level in the intermediate barrel is to low,
the feed is filled up from the storage barrels or gas cylinders.
The hydrocarbon feedstocks are mostly fed as liquids. Next to liquefied gasses also heavier
hydrocarbons such as vacuum gas oils or waxes can be used. These heavier hydrocarbons are
preheated and melted before they are pumped to the reactor via a heated pump. Gasses (ethane,
propane, n-butane, etc.) can be fed as well. Different types of feedstocks can be pumped through
the reactor simultaneously, which allows co-cracking of any feedstock. Steam (water) is added to
the reactor coil to reduce the hydrocarbon partial pressure and thus favor the formation of the
target products (ethylene and propylene) and suppress coke formation.
3.2.2 The Furnace and the reactor
The furnace, built of silica/alumina bricks (Li23), is 4 m long, 0.7 m wide, and 2.6 m high.
The wall thickness is 0.15 m. The furnace is divided into seven separate cells that can be fired
independently to set any type of temperature profile. Each cell contains twelve radiation burners
with exception of cell 1 which comprises six extra burners. The gas flow is regulated separately
for each cell using a control valve. The fuel pipes to each cell are provided with a check valve,
which stops the gas supply when backdraft of the flame occurs.
The reactor coil, constructed out of INCOLOY 800H, is placed in the center plane of the
furnace. The reactor coil is 12.4 m long and has an internal diameter of 9 mm. These dimensions
where chosen to achieve turbulent flow conditions in the coil with reasonable feed flow rates.
Twenty thermocouples and five manometers are located along the reactor coil to measure the
temperature and pressure of the reacting gas. Values for the temperature and the pressure are
uploaded to the process computer every minute and stored in a file. The reactor coil is fired by
means of ninety premixed gas burners, mounted with automatic fire checks and arranged on the
sidewalls in such a way that they provide a uniform heat distribution. Before entering the reaction
zone, the hydrocarbons and the water are preheated separately in cells 1 and 2 and mixed in a
mixer placed in cell 2. Cracking and coke deposition are considered to occur only in cells where
the temperature is higher then 600 °C. Accordingly, cracking reactions start in the third cell.
46
3.2.3 The cooling section
The temperature of the cracked gas leaving the furnace can range from 750 to 900 °C. Rapid
reduction of gas temperature to 500 °C is necessary to avoid losses of valuable products by
secondary reactions. This is accomplished in the cooling section. The cooling section consists of
two heat exchangers (TLE1 and TLE2), two condensors and a cyclone. TLE1 can be used to study
coke deposition under TLE conditions and can be by-passed according to the purpose of the
experiments. The TLE’s are designed to achieve turbulent flow conditions with effluent flow
rates typical for the pilot unit. Figure 3-2 shows that both the TLE’s consist of two concentric
tubes: the reactor effluent flows through the inner tube, while air, providing cooling of the
effluent, flows co-currently through the outer tube. Both air and the process gas enter at the top of
TLE1. Co-current flow of both streams was chosen since this provides a more uniform wall
temperature profile along the TLE as compared to counter-current flow. By adjusting the airflow
rate, the temperature profile of the process gas in TLE1 can be regulated. TLE1 can also be heated
to 900°C for decoking with air/steam mixtures.
air in
T
P
F
air out
T
TLE 1 TLE 2
C5
+-
an
aly
sis
T
T
from reactor
T1
T2
T3
T4
T
TNN22
370°C
430°C
490°C
710°C
830°C
150°C
850°C
330°C
T
air in
T
P
F
air out
T
TLE 1 TLE 2
C5
+-
an
aly
sis
T
T
from reactor
T1
T2
T3
T4
T
TNN22
370°C
430°C
490°C
710°C
830°C
150°C
850°C
330°C
T
Figure 3-2: Overview of the cooling section in the LPT pilot plant setup (Van Geem, 2006)
47
In TLE2 the process gas is further cooled to 150°C by means of cooling oil. This oil is cooled
in a secondary circuit with water. After TLE2 the heavy hydrocarbons part of the fuel oil fraction
are condensed in a first condenser. In a second condenser, the steam is condensed. Liquids
remaining in the effluent are removed by a cyclone. The condensed fractions in these three
different points are periodically collected.
3.2.4 The analysis section
The pilot plant is provided with an extended on-line analysis section which allows analyzing
C1 to C18 mixtures (boiling point ~ 400°C), including H2, CO, CO2. At the reactor outlet, the
injection of nitrogen provides an internal standard for the on-line analysis and contributes to a
certain extent to the quenching of the process gas. To analyze the cracking products four GC’s
are used: an Agilent GC 6890N using a flame ionization detector (FID) and a thermal
conductivity detector (TCD), an Interscience Fison GC 8340 using a TCD and two HP GC’s
5890 using a FID. Both Agilent 6890N and Interscience Fison 8340 are used for the C4- analysis.
The two HP GC’s 5890 are used for the C5+ analysis. Table 3-1 gives a schematic overview of the
conditions that are used. In the next paragraphs more details are given about the C4- analysis and
the C5+ analysis.
3.2.4.1 C4 –analysis and calibration
The C4--fraction refers to components with maximum 4 carbon atoms in their chain, namely
hydrogen, methane, ethane, ethylene, acetylene, propane, propylene, propadiene,
methylacetylene, n- and i-butane, 1- and i-butene, cis- and trans-2-butene and 1,3-butadiene.
The C2- sample is simultaneously analyzed on two GC’s, i.e. Agilent Technologies 6890 N
and Interscience Fison 8340. Nitrogen, carbon monoxide, carbon dioxide and hydrocarbons up to
C2 are in both GC’s detected by a TCD. Hydrogen is only detectable with the FID. The use of
two different units for the same analysis improves the reliability of the analysis results. The
hydrocarbons from C1 to C4 are analyzed with the Agilent 6890 N using a FID.
The sample for the C4- analysis is taken from the quenched outlet gas stream, separated from
higher hydrocarbons and water. An IR analyzer is used for continuous analysis of CO and CO2.
The IR analyzer can be used on-line during decoking and also during cracking experiments.
48
GC Agilent 6890 N
Column Molecular sieve 13X
(60-80 mesh)
Porapark N
(80-100 mesh)
Molecular sieve 5A
(80-100 mesh)
HP-PLOT Al2O3
(capillary column)
Separation H2 CO2, C2 N2, CH4, CO C1-C4
Dimension 1.8 m, 1/8″ 3 m, 1/8″ 3 m, 1/8″ 50m×0.53mm×15µm
Carrier gas N2 He He He
Flow rate (mℓ min-1
) 16 95 55 5.1
Detector TCD FID
Injection temperature (K) 398 473
Oven temperature (K) Programmed, 313 – 443
Detector temperature (K) 473 523
GC Interscience Fison 8340 HP 5890 - Series-II
Column Hayesep N
(80-100 mesh)
Carbosphere S5
(80-100 mesh)
HP-PONA
(capillary column)
Separation CO2, C2 N2, CH4, CO C1-C10
Dimension 2 m, 1/8″ 1.8 m, 1/8″ 50m×0.199mm×0.55µm
Carrier gas He He He
Flow rate (mℓ min-1
) 40 0.65
Detector TCD FID
Injection temperature (K) 383 523
Oven temperature (K) 328 Programmed, 233 - 523
Detector temperature (K) 428 573
Table 3-1: Gas chromatographic analysis conditions in the pilot plant set-up (Wang, 2006)
The peak surface area in a chromatogram is proportional to the quantity of the corresponding
component. The relation between the peak surface areas and the mass fractions of the
components is given by the following equation:
iii ACFwt ⋅=% [3-1]
with wt%i = mass fractions of component i
CFi = calibration factor
Ai = peak surface areas of component i
49
To determine the calibration factors (CF’s), a reference mixture is injected and the peak areas
of the components present in the mixture are determined. The CF’s of the different components
are calculated using equation [3-2]:
REF
REFi
iREFi CF
wtA
wtACF ⋅
⋅
⋅=
%
% [3-2]
where Ai = the peak surface area of component i
wt%i = the weight fraction of component i in the calibration mixture.
Methane is used as reference component. The latter implies that the calibration factor for this
component CFref is set equal to 1.
3.2.4.2 C5+-analysis and calibration
The C5+-fraction refers to components with at least 5 carbon atoms. These C5+ components in
the effluent are analyzed with HP 5890 Series II using a HP PONA capillary column and a FID.
Sampling for this fraction has to occur at high temperatures to avoid the condensation of the
high-boiling components. Hence, the sample for the C5+-analysis is taken after the outlet of TLE1
and after the injection of N2. At this location, the temperature of the gas is still more than 300 °C.
Determining the calibration factors for all the components of the C5+-fraction is almost
impossible since there are so many. To determine the calibration factors of this fraction, a group
contribution method developed by Dierickx et al. (1986) is applied. This group contribution
method allows the estimation of the CF’s with a minimum of experimental work. The principle is
explained in paragraph 3.3.3.1.
3.2.4.3 Analysis of the reactor effluent
Peak identification and integration is performed by a commercial integration package
(XChrom of Labsystems). To correlate the analysis of the three GC’s a precisely known amount
of N2 is added to the effluent as internal standard. This permits the calculation of the conversion
and product yields of the effluent components based on the mass flow rate of the effluent
components. The use of the reference components is illustrated in Figure 3-3. From the peak
areas of the TCD-channel, the experimentally determined calibration factors and the known
amount of nitrogen, the flows of hydrogen, methane, carbon monoxide and C2 hydrocarbons are
50
calculated. The calculated methane flow is used to determine the flows of the other components.
Since both nitrogen and methane are also detected on the Interscience Fisons GC 8340, the
calculated methane flow can be verified by the results obtained on the Agilent TCD channel.
C2CH4
C5
CH4 CO2N2
H2
C2H4CO C2H6 C2H2TCD1
TCD2
GC 1
GC 2+3
FIDC3 C4
C2CH4FID
C3 C4 C6 C18 (b.p. 400°C)C...
Figure 3-3: Interactions of the nitrogen internal standard with the yields of the products
measured on the different gas chromatographs
With these data, a product distribution in terms of weight percentages can be determined.
Since the feed flow rate is known, yields (kilograms of product per kilogram of hydrocarbon
feed) and a material balance can also be calculated. The product yields are calculated according
to the following equation:
100%0
×=F
Fwt
i
m
i [3-3]
where wt%i = the yield of compound i
Fmi = the mass flow rate of component i in effluent
F0 = the mass flow rate of the hydrocarbon feed.
51
3.3 Analysis of the feedstocks
In this paragraph the method for analyzing the feedstocks is described and discussed. A
detailed feedstock composition is acquired using information obtained from the GC
chromatogram, the Kovats retention indices and the GC-MS spectrum. The approach is applied
on one of the eight gas condensates and can be easily applied on the other 7 because these
mixtures have a similar chromatograms. Gas chromatography is used to separate mixtures of
chemicals into individual components. Once isolated, the components are evaluated individually.
The qualitative analysis uses results obtained by both GC and GC-MS analysis’s of the mixtures .
In GC, the individual components are identified using retention time data, such as the Kovats
retention index. In GC-MS, the interpretation of the mass spectra allows the identification of
various peaks observed in the chromatogram. The quantitative analysis of the different feedstocks
is performed by GC.
3.3.1 Separation
The GC separation is performed with a gas chromatograph (Type 5890, Series II) with a 100
m x 0.25 mm fused silica capillary column coated with a 0.5 µm film of HP-PONA. In Figure 3-4
a GC is schematically presented.
Figure 3-4: Flowsheet of a gas chromatograph
A column held in an oven creates 2 distinct separating forces, temperature and stationary
phase interactions. For steam cracking traditionally a PONA column is used. This column is
especially designed for separation of non-polar components, such as paraffins, olefins,
52
naphthenes and aromatics. The elution of the compounds of the sample thus happens gradually.
The detector generates an electronic signal whenever a component emerges from the column. A
higher concentration of a component leads to a stronger signal. The results are visualized in a
chromatogram. Here the intensity of the detector signal is shown as a function of the retention
time, i.e. the time between injection and elution. Ideally, each peak in the chromatogram
represents an individual compound that is separated from the sample mixture.
Several detectors are known to be used for gas chromatography. The FID (Flame Ionization
Detector) is the most widely used detector for routine analysis. The effluent from the column is
mixed with hydrogen and air and then ignited electrically. Most organic compounds then produce
ions and electrons that are collected by the electrodes to generate a signal. The detection by a
TCD (Thermal Conductivity Detector) is based on the difference in thermal conductivity between
the carrier gas and the components of the sample. The TCD is cheap, nonselective and has a
reasonable sensitivity. The thermal conductivity is measured with 4 resistors in a Wheatstone
bridge. One resistor is surrounded by the carrier gas (reference cell), while another resistor is
surrounded by the carrier gas and the eluted component. Thanks to this, the temperature and
consequently also the resistance of this second resistor will increase. This results in a voltage
drop measured with a potentiometer and resulting in a GC-peak.
Gas chromatography is widely used in refinery industry to analyze the composition of light
and middle distillates. It can identify and quantify most of the hydrocarbon components in the
gasoline range (<180°C) (Beens and Brinkman, 2000). However this technique has certain
limitations, especially when heavy petroleum fractions are used, because of the low volatility of
these molecules.
3.3.2 Qualitative analysis
3.3.2.1 Gas chromatography and Kovats retention indices
For the qualitative analysis of hydrocarbon mixtures several information sources are
consulted. First, the detailed analyses of previously studied fractions were used as guideline for
identification. In particular, the results from Van Hecke (2005) and Celie (2004) showed to be
valuable because of the large number of components present in the studied fractions. By
comparing both chromatograms, many components could be identified. The chromatographic
53
analyses were performed under the same circumstances as used by Van Hecke and Celie. The
operating conditions are specified in Table 3-2.
GC HP 5890, Series II
Injector temperature 250°C
Carrier gas flow 65 ml (helium)
Initial T Hold time Rate Final T Hold time
35°C 5 min 2°C/min 170°C 5 min
5°C/min 270°C 0 min
0°C/min 270°C 1 min
Table 3-2: Conditions for the analyses of the feedstock samples
To further eliminate the effects of instrument parameters on retention correlations in peak
identifications by GC the Kovats retention index system is applied. Kovats retention indices
(KRI) form a logarithmic scale on which the adjusted retention time of a peak is compared with
those of linear n-alkanes as reference compounds. These compounds were chosen because they
are non-polar, chemically inert and soluble in most common stationary phases. Hence, Kovats
retention indices give an indication of the sequence of elution of the different components. The
fundamental equation for the isothermal retention index is (Castello, 1999):
zrzr
zrxrx
tt
ttzKRI
,1,
,,
loglog
loglog100100
−
−+=
+
[3-4]
where x = the compound of interest
z = the carbon number of the n-alkane eluting prior to x
z +1 = the carbon number of the n-alkane eluting after x
tr = the retention time.
A drawback of the KRI defined in equation [3-4] is that the value changes with changing
temperature. Hence the method in its current form cannot be applied in a temperature-
programmed gas chromatography analysis. This problem is partially solved by the introduction of
the Van den Dool and Kratz formula (Castello, 1999) to calculate linear retention indices (LRI):
zrnzr
zrxrx
tt
ttnzLRI
,,
,,100100
−
−+=
+
[3-5]
with n = the difference in carbon number of the two n-alkanes that are taken as
references.
54
Another technique used parallel to GC for the qualitative analysis is mass spectrometry (MS).
This technique provides information on the identity of every individual component obtained by
chromatographic separation by taking advantage of the common fragmentation pathways for
individual substance classes. These fragmentation pathways are truly unique for a particular
chemical substance, similar to a fingerprint of a person. The interpretation of the mass spectra
and library search using the ChemSystem software allows the identification of various peaks
observed in the chromatogram.
3.3.2.2 Hyphenated GC-MS technique
Mass spectrometry (MS) is widely used as GC detector in the analysis of lighter petroleum
fractions. The main principles of MS are shown in Figure 3-5.
Figure 3-5: Schematic presentation of a mass spectrometer
The eluted compounds are bombarded with an accelerated beam of electrons in an ionization
chamber. In this manner, the specimen molecules are shattered into well-defined fragments upon
collision with the high voltage electrons. The resulting fragments are charged ions with a certain
mass. The M/Z (mass to charge) ratio represents the molecular weight of the fragment, since most
fragments have a charge +1. In the acceleration chamber the charged particle's velocity increases
55
due to the influence of an accelerating voltage. For one value of voltage only one mass
accelerates sufficiently to reach the detector. The accelerating voltage varies over such a range of
masses that all fragments reach the detector. The charged particles travel in a curved path towards
the detector. Next, a quadrupole (a group of 4 electromagnets) focuses the fragments through a
split into the detector. An electron multiplier is the most commonly applied detector. When an
individually charged particle collides with the detector surface, several electrons emit from the
detector surface. Next, these electrons accelerate towards a second surface, generating more
electrons, that bombard another surface. Each electron carries a charge. Finally multiple
collisions with multiple surfaces generate thousands of electrons which emit from the last
surface. The result is an amplification of the original charge through a cascade of electrons
arriving at the collector. At this point the instrument measures the charge and records the
fragment mass as the mass is proportional to the detected charge.
Operation of the GC-MS is computer controlled, with GC peaks automatically detected as
they emerge from the column. Each individual mass spectrum is directly recorded onto the hard
disk for subsequent analysis. In a mass spectrum, the x-axis represents the M/Z ratios. The y-axis
shows the signal intensity (abundance) for each of the fragments detected. The mass spectrum is
a fingerprint for the molecule and can be used to identify the compound. Figure 3-6 shows the
spectrum of n-decane.
Figure 3-6: mass spectrum of n-decane
GC-MS is a hyphenated analytical technique. A separation system (GC) and a detection
device (MS) are combined to form a single method for analyzing mixtures of chemicals. Because
56
the MS requires a low pressure for successful operation and the GC requires a positive flow
through the column, a special interface between the two instruments is needed. The prime
function of this interface is to remove the GC carrier gas (usually helium) while allowing the
sample to flow through to the MS.
3.3.3 Quantitative analysis
The quantitative analysis of the different feedstocks is performed by gas chromatography
(GC). The peak surface area in a chromatogram is proportional to the quantity of the
corresponding component. Hence, integration of the peaks observed in the chromatogram makes
it possible to obtain a quantitative analysis of the feedstocks. The peak surface areas and mass
fractions of the components are related via the calibration factors CFi:
iii ACFM ⋅= [3-1]
3.3.3.1 Calibration factors
The calibration factors for gas chromatographic analysis with Flame Ionization Detector
suggested by Dietz (1967) are used. For hydrocarbons, with some exceptions, the values of the
response factors are all approximately 1. Important exceptions are benzene (1.12) and toluene
(1.07). However, the article of Dietz does not specify calibration factors for all the observed
components in the naphtha fractions. For the group of components not mentioned in this article,
a group contribution method developed by Dierickx et al. (1986) is applied. According to this
method the calibration factors CFi of the components i are calculated using the correlation:
∑ ⋅=⋅
j
jijii naMWCF [3-6]
where MWi = the molecular weight of component i i
aj = the contribution of group j in the calibration factor of component i
nij = the number of groups of type j in component i
The different groups taken into consideration are shown in Table 3-3.
To determine the contribution of the different groups in Table 3-3 a mixture with a known
composition is injected and the peak areas of the components present in the mixture are
57
determined. Then the calibration factors of the different components are calculated using the
following equation:
REF
REFi
iREFi CF
wtA
wtACF ⋅
⋅
⋅=
%
% [3-2]
where Ai = the peak surface area of component i
wt%i = the weight fraction of component i in the calibration mixture
One component is assigned to be the reference component with calibration factor CFref equal to 1.
Based on the determined calibration factors and the molecular weight of the components of the
calibration mixture, a computer program calculates the group contribution values. Note that
contribution factors have to be determined for every new set of analysis conditions. For the
conditions presented in Table 3-2, the corresponding group contributions are shown in Table 3-3.
Nr. Group j aj Description
1 CH3− 4.712225 Methyl group in aliphatic chain
2 −CH2− 18.31468 Methylene group in aliphatic chain
3 >CH− 38.78157 Tertiary C-atom in aliphatic chain
4 >C< 37.61744 Quaternary C-atom in aliphatic chain
5 5-ring -32.59085 Additional contribution for 5-ring
6 6-ring -33.58785 Additional contribution for 6-ring
7 Carom−C 21.61251 Aromatic secondary C-atom
8 Carom−H 12.66696 Aromatic tertiary C-atom
Table 3-3: Groups and group contributions
3.3.3.2 Weight and mole fractions
The weight fractions of the different components are calculated using equation [3-7]:
unid
n
i
ii
ii
i
AACF
ACFwt
+⋅
⋅=
∑=1
100% [3-7]
with n = number of identified components
Aunid = sum of the surface areas of the unidentified peaks
58
This equation implies that the calibration factors of the unidentified components are assumed
to be 1. This assumption is very reasonable because the calibration factors for hydrocarbons are
all approximately 1 (Dietz, 1967). Taking into account the molecular weight of the components,
the mole fractions can be determined based on equation:
∑=
⋅
⋅
=n
i i
ii
i
ii
i
MW
ACF
MW
ACF
mol
1
100% [3-8]
For the calculation of the mole fractions, the unidentified peaks are not taken into account
because their molecular weight is unknown.
3.3.4 Results
P I O N A
C3 0.02 0,000 0,000 0,000 0,000 0.02
C4 1.62 0.21 0,000 0,000 0,000 1.83
C5 9.38 10.31 0.00 1.37 0.00 21.06
C6 6.17 9.90 0.00 2.96 0.98 20.00
C7 4.61 6.65 0.00 4.80 1.49 17.56
C8 3.15 5.97 0.00 2.50 2.53 14.15
C9 2.31 4.23 0.41 1.46 1.78 10.18
C10 1.73 2.31 0.00 0.06 0.62 4.72
C11 1.26 1.22 0.00 0.00 0.00 2.48
C12 0.92 0.13 0.00 0.00 0.00 1.05
C13 0.65 0.06 0.00 0.00 0.00 0.71
C14 0.45 0.13 0.00 0.00 0.00 0.58
C15 0.34 0.16 0.00 0.00 0.00 0.50
C16 0.22 0.00 0.00 0.00 0.00 0.22
C17 0.15 0.00 0.00 0.00 0.00 0.15
C18 0.09 0.00 0.00 0.00 0.00 0.09
C19 0.05 0.00 0.00 0.00 0.00 0.05
33.12 41.29 0.41 13.14 7.39 95.34
Table 3-4: Determined detailed PIONA analysis
One of the eight gas condensates (gas condensate 667) is analyzed using both GC and GC-
MS. Identification based on the retention times and the mass spectrum and integration of the
59
peaks of the chromatogram makes it possible to reconstruct the compositions of the different
feedstocks. The PIONA analysis is given in Fout! Verwijzingsbron niet gevonden.. The analysis
shows that the concerned feedstock consists mainly out of molecules with 4 to 14 carbon atoms,
with large amounts of n-alkanes, di- or trimethyl substituted alkanes. The detailed feedstock
composition can be found in appendix E. With respect to the components, gas condensates can be
described as naphthas with a small gas oil fraction. Indeed, a large number of components present
in the studied fraction has already been observed in previous studies of naphtha compositions.
But gas condensates show a small fraction of heavy components that are not found in naphtha
feedstocks.
3.3.5 Alternatives for GC and GC-MS
3.3.5.1 Shortcomings of GC and GC-MS
The GC separation is performed with a gas chromatograph (Type 5890, Series II) with a 100
m x 0.25 mm fused silica capillary column coated with a 0.5 µm film of HP-PONA. This
conventional gas chromatography (GC) using modern high resolution capillary columns offers
high peak capacity, which enables the separation of more than 500 components. However, it fails
to separate all the individual compounds from complex mixtures such as petroleum products
(Bertoncini et al., 2005). With many thousands of compounds present, a single column
chromatogram suffers from peak overlap, often to the point of completely merging the peaks into
one or more lumps. This has great consequences for the identification of the peaks, especially
when gas chromatography is combined with mass spectrometry.
When mass spectrometry is used for detection, it is frequently not possible to distinguish
between isomers or even, at times, between naphthenes and olefins. In Table 3-5 the eight most
important mass to charge ratios and the corresponding intensities of three isomers of
trimethylbenzene are represented. Especially the difference between 1,3,5-trimethylbenzene and
1,2,4-trimethylbenzene is very subtle. Indeed, these two isomers show the same m/z ratios and
can only be distinguished through small differences in the corresponding intensities. Moreover,
when two components overlap in a gas chromatogram, they may be deconvoluting the MS data.
In the case of peak overlap, a compound mass spectrum is obtained from the different species,
leading to a wrong interpretation. No difference can be made between the mass to charge ratios of
60
the concerned species. Thus, the accuracy of the GC determines mainly the reliability of the
obtained identification of hydrocarbons. In order to obtain identification with 100% certainty, a
pure component is required. Furthermore, several peaks remained unidentified because of the
limitations of the used library of spectra. The Wiley Library is a library for general purpose; the
amount of organic components above n-decane is limited.
m/z ratios 105 120 119 77 106 91 79 121 1,2,3-
trimethylbenzene intensities 100 54 12 10 9 8 7 6
m/z ratios 105 120 119 77 106 91 121 79 1,3,5-
trimethylbenzene intensities 100 62 15 11 9 8 6 6
m/z ratios 105 120 119 77 106 91 121 79 1,2,4-
trimethylbenzene intensities 100 59 15 10 9 8 6 6
Table 3-5: m/z-ratios and the corresponding intensities of trimethylbenzene-isomers
More suitable techniques to study the composition of complex petroleum fractions are
multidimensional gas chromatography (MDGC) and comprehensive two-dimensional gas
chromatography (GC x GC). These techniques have the potential to dramatically increase the
resolution power and can be applied successfully to extremely complex mixtures (Bertoncini et
al., 2005). Furthermore, moderately complex samples can be separated much faster than with
high-resolution one-dimensional gas chromatography.
3.3.5.2 Multidimensional (heart-cut) gas chromatography (MDGC)
In multidimensional gas chromatography (GC) several additional columns are coupled in
series to the primary column in which the first separation is performed. Each dimension of
separation is associated to a specific type of stationary phase and to a specific molecular
interaction developed between the stationary phase and the solute. By the transfer of selected cuts
from one column to another, the resolution between elution peak groups which are contained in
such cuts is improved. Figure 3-7 shows a multidimensional gas chromatogram. A narrow
fraction from the chromatogram, containing the specific peak of interest, is transferred to a
second different column, for a more extensive separation. This so-called heart-cut technique can
be applied to a few (similar) compounds, but when the number of analytes increases it soon
becomes impractical.
61
Figure 3-7: Heart-cut GC system (Bertoncini et al., 2005)
3.3.5.3 Comprehensive two-dimensional gas chromatography (GC x GC)
In MDGC, only a limited part of the effluent of the first separation column will be directed
towards the second column. This is not the case for comprehensive two-dimensional gas
chromatography (GC x GC). Comprehensive gas chromatography, as the name implies, applies
all the available resolving power of both columns to all the peaks in a sample. In comprehensive
GC×GC, the sample is first separated on a high-resolution capillary column in a programmed
temperature mode, see Figure 3.8.
Figure 3-8: Schematic diagram of GC×GC (Bertoncini et al., 2005)
62
Before entering the second-dimension column, the effluent from the first column is thermally
modulated. Thermal modulation serves two purposes, i.e. to ‘digitise’ the first-dimension
chromatogram and to focus the sample material in a series of sharp, equidistant chemical pulses
(i.e. each tiny, concentrated fraction from the first chromatogram) (Schoenmakers et al., 2000).
Two different modulation principles are currently being used. One is based on a moving heating
element or ‘sweeper’. The alternative system, developed by Kinghorn and Marriott, is a moving
cold-trap modulator (Beens and Brinkman, 2000). The chemical pulses created by the thermal
modulator serve as injections onto the second column. The dimensions of the latter are chosen in
such a way that they allow a very fast analysis. Each pulse is very rapidly separated on the
second-dimension column. Finally, the material exiting the second column is passed to the
detector to obtain a series of short second dimension chromatograms, one after another. Figure
3-8 gives a schematic diagram of GC×GC operation with a thermal modulator and equipped with
a FID detector.
Figure 3-9: 2D chromatogram plot of a GCxGC analysis
In a multidimensional separation, the two separation methods must be independent of one
another, i.e. orthogonal. This implies that the two columns must be operated in a way that they
retain compounds based on different mechanisms (Phillips and Xu, 1995). Furthermore, the
secondary instrument must make measurements fast enough to preserve the information
contained in the primary instrument signal. The secondary GC must generate at least one
63
complete chromatogram during the time required for a peak to elute from the primary GC column
(Phillips and Xu, 1995). Typical analysis times used today are on the order of 0.1 minutes or less
with very short (1-2m) narrow-bore (100-180 mm i.d.) dimensions and linear velocities
(Hinshaw, 2004). The primary columns are generally 15-30 m long, with an internal diameter of
0.25 mm and a film thickness in the range of 0.25 – 1.0 µm. These columns allow the generation
of peak widths in the second dimension on the order of 10-20 s. The first dimension columns
typically have a non-polar stationary phase, either 100% polydimethylsiloxane or 95/5%
methyl/phenyl siloxane. Due to the very fast separations, GCxGC also needs very fast detection
systems. Generally used are fast-FID (flame ionization detector), micro-ECD (electron capture
detector) and TOF-MS (time-of-flight mass spectrometer) (Alencastro, 2003).
GC-GC data are commonly presented in a complete two-dimensional form rather than as
individual secondary chromatograms to show all the information (Phillips and Xu, 1995). Figure
3-9 shows a two-dimensional gas chromatogram. The primary column retention time axis is
calibrated in minutes and the secondary is calibrated in seconds. Signal intensity is indicated by
color code as shown by the color bar. In Figure 3-10 the main principles of construction of the 2D
(or 3D) chromatogram is visualized.
Figure 3-10: A general visualisation of a two-dimensional GC chromatogram
(Dalluge et al., 2003)
64
3.4 Pilot Plant Experiments
In this paragraph, the cracking and decoking experiments carried out in the pilot plant set-up
for the steam cracking of hydrocarbons at the Laboratorium voor Petrochemische Techniek of
Ghent University are discussed. In appendix F an overview of the performed experiments is
given. Two sets of experiments can be distinguished. In a first set of experiments all the
concerned feedstocks are submitted to the cracking process. The same conditions are used for the
various experiments. In a second set of experiments, the influence of the dilution and the coil
outlet temperature (COT) on the steam cracking behavior of one particular feedstock is
examined.
3.4.1 Experimental conditions
During a cracking experiment, the following procedures are taken: the cracking coil is heated-
up under a steam flow of 4 kg h-1
untill the cracking temperature profile is reached. Then, the
flow rate of steam is set to the desired value for cracking and the concerned feedstock is
introduced. The introduction of the feedstock causes a decrease of temperature in the cracking
coil due to the endothermic nature of the cracking reactions. After about 20 minutes, the
temperature in the cracking coil reaches the preset value. From this time on samples for the
analyses can be taken. The cracking experiments last for 6 hours. During this period, a coke layer
gradually builds up on the reactor wall and in TLE1.
Decoking of the cracking coil and TLE1 is performed with a steam/air mixture. The amount of
coke deposited on the reactor wall and in the TLE1 during cracking is determined separately. First
the amount of coke in the reactor is burnt off and subsequently the coke deposited in TLE1 is
removed. During decoking of the cracking coil the TLE is disconnected and backflushed with a
nitrogen flow of 35.5% as shown in Figure 3-11. At the start of the procedure, the cracking coil is
heated to 1073 K under a nitrogen flow. Subsequently, steam is introduced. After 3 minutes, the
nitrogen flow is stopped, and air is added. Initially, no air flow rate is fed in order to avoid high
decoking rates, since a strong temperature rise could damage the reactor tubes. Next, the air flow
rate is increased while the steam flow rate is decreased. When most of the coke is removed from
the reactor (CO2 < 1 mol%) the temperature of the coil is increased to 1173 K to eliminate the
remaining coke particles. When practically all the coke are burnt off, the steam flow is stopped
65
and further decoking occurs in air only. The standard decoking time is 100 minutes. Once all the
coke in the reactor coil is removed, the connection between the reactor and TLE1 is re-established
and decoking of the coke in the TLE1 can be carried out. The same steps as used for decoking of
the reactor coil are taken. Since the coke in the reactor are already burnt off, the mixture of steam
and air can be fed via the reactor. The conditions used for decoking of the reactor coil and the
TLE1 are assembled in respectively Table 3-6 and Table 3-7.
During decoking, the coke are converted into CO and CO2 by oxidation. The obtained
amounts of CO and CO2 are measured with two non-dispersive infrared devices. Both values are
uploaded to the process computer every 10 seconds and stored in a file. During decoking the
volumetric flow rate is calculated using a vortex rotameter in order to know the absolute flow
rates of CO and CO2. With these flow rates the total amount of coke mcoke can be calculated using
the following equation:
( ) ( )∑
=
∆⋅
⋅+⋅=
N
n
nCOnCO
cokes tFF
m0
1244
1228
2 [3-9]
with (FCO)n = flow rate of CO in the time interval n [g/s]
(FCO2)n = flow rate of CO2 in the time interval n [g/s]
∆t = time interval [s]
N . ∆t = total burn off time [s]
Figure 3-11: nitrogen backflush
66
FH2O
[g/h]
FAir
[nl/h]
FN2
[nl/h] Cell 3 Cell 4 Cell 5 Cell 6 Cell 7 TLE bar Cyclone
Heating-up 0 0 828 1073 1073 1073 1073 1073 Discon / /
Pre-start 1008 0 828 1073 1073 1073 1073 1073 Discon / /
Start 1008 828 0 1073 1073 1073 1073 1073 Discon / /
CO2<1vol% 1008 828 0 1073 1173 1173 1173 1173 Discon / /
CO2<0.1vol% 0 828 0 1073 1173 1173 1173 1173 Discon / /
Table 3-6: Conditions for decoking of the reactor coil
FH2O
[g/h]
FAir
[nl/h]
FN2
[nl/h] Cell 3 Cell 4 Cell 5 Cell 6 Cell 7
TLE
in
TLE
Center
TLE
out bar Cyclone
Heating-up 0 0 828 1073 1173 1173 1173 1173 1073 1073 1073 623 623
Pre-start 1260 0 828 1073 1173 1173 1173 1173 1073 1073 1073 623 623
Start 1260 828 0 1073 1173 1173 1173 1173 1073 1073 1073 623 623
CO2<1vol% 1260 828 0 1073 1173 1173 1173 1173 1173 1173 1173 623 623
CO2<0.1vol% 0 828 0 1073 1173 1173 1173 1173 1173 1173 1173 623 623
Table 3-7: Conditions for decoking of the TLE1
67
3.4.2 Effect of the feedstock on the product spectrum and coke deposition
In this first set of experiments 8 gas condensates are cracked under identical conditions. Gas
condensates are liquid fractions emerging from the production of natural gas. With respect to the
process yields, they behave very similar to naphtha fractions. The conditions of the cracking
experiment are given in Table 3-8.
FH2O [g/h] 2000
FHC [g/h] 4000
COP [bar] 1.7
N2 (internal standard) 70%
Reactor cell 3 (CIT) 923
Reactor cell 4 993
Reactor cell 5 1043
Reactor cell 6 1083
Reactor cell 7 (COT) 1093
TLE inlet 698
TLE center 693
tem
per
atu
re p
rofi
le [
K]
TLE outlet 623
Table 3-8: Steam cracking conditions
For the different feedstocks the yields of the most important products are presented in Table
3-9. The ideal feedstocks for the production of ethylene by steam cracking are straight-chain
normal paraffins. Apart from ethane, propane and n-butane, it is unusual to find pure component
feeds. More producers are therefore obliged to crack heavier petroleum derivatives (light,
medium, heavy, full range naphtha, natural gas condensates and gas oil). Generally, as the
molecular weight of the feedstock increases, the yield of ethylene decreases and other products
such as butadiene and benzene are produced in important quantities. Heavier petroleum fractions
are also subject to more side reactions that produce tarry products and contain coke precursors, as
mentioned in chapter 1.
The obtained propylene to ethylene ratios (P/E ratio), a measure for the conversion of the
feedstock (Golombok et al., 2004), are similar for the different feedstocks. The most suited
feedstock for the production of ethylene under the used conditions are feedstocks 540 and
feedstock 681, while feedstocks 667 en 669 provide the largest quantities of propylene. By
contrast, feedstocks 661, 663 and 665 give higher yields of aromatics and naphthalene, and lower
68
yields of ethylene and propylene. The feedstocks 540, 659, 667, 669 and 681 which produce the
largest quantities of ethylene and propylene also provide the largest amounts of methane, ethane,
butadiene, 1-butene and i-butene, while styrene and xylenes are formed in less quantities.
Feed 540 659 661 663 665 667 669 681
P/E ratio 0.55 0.58 0.58 0.57 0.57 0.58 0.58 0.55
yields [wt%]
hydrogen 1.05 1.39 0.89 1.00 0.93 1.06 0.93 0.93
methane 13.13 13.20 11.03 11.34 11.25 13.30 13.21 13.01
acetylene 0.35 0.34 0.30 0.31 0.30 0.35 0.32 0.33
ethylene 26.47 25.41 21.38 22.50 21.91 25.47 25.48 26.46
ethane 3.71 3.46 2.80 2.93 2.87 3.54 3.39 3.64
propylene 16.04 16.10 13.23 13.54 13.54 16.22 16.20 15.92
propane 0.29 0.27 0.20 0.20 0.20 0.28 0.27 0.28
1,3-C4H6 5.20 5.09 4.92 4.97 4.94 5.37 5.35 5.23
1-butene 1.82 1.72 1.31 1.33 1.36 1.79 1.80 1.84
iso-butene 2.82 3.28 2.24 2.27 2.29 3.40 3.38 2.89
2-butene 0.56 0.60 0.37 0.50 0.51 0.63 0.49 0.57
iso-butane 0.13 0.12 0.10 0.09 0.09 0.10 0.10 0.10
n-butane 0.77 0.44 0.70 0.65 0.69 0.40 0.41 0.62
benzene 7.01 5.37 7.55 7.33 7.14 5.18 5.04 6.43
toluene 3.00 3.01 6.15 6.11 5.92 2.91 2.87 2.72
m-xylene 0.55 0.72 1.67 1.67 0.60 0.73 0.72 0.46
p-xylene 0.04 0.18 0.61 0.59 0.17 0.23 0.23 0.14
styrene 0.83 0.75 1.35 1.36 1.30 0.74 0.72 0.78
o-xylene 0.31 0.31 0.62 0.60 0.30 0.30 0.30 0.29
naphthalene 0.31 0.38 0.71 0.79 0.76 0.34 0.33 0.37
Table 3-9: Yields of the important products for the different feedstocks
An inherent problem associated with the construction materials used in ethylene plants is their
tendency to promote the formation of carbonaceous materials that accumulate in the reactor coil
as well as in the TLE. The accumulation of coke during the steam cracking process leads to a
decreased heat transfer, a reduction of the tube cross section, and an increased pressure drop
(Dhuyvetter et al., 2001). The loss of the furnace availability due to decoking, the decrease of the
olefin selectivity and the energy losses associated with the accumulation of coke on the reactor
wall have important negative consequences for the economics of the cracking process. During the
69
cracking experiments the deposition of coke in the reactor coil takes place on a surface area of
0,34 m² and in the TLE1 on a surface area of 0,13 m².
feed
Cokecoil
[g]
CokeTLE
[g]
Entrained coke
[g]
Total coke
[g]
540 8.80 0.83 3.95 13.58
659 3.22 2.28 0.99 6.48
661 2.16 1.92 3.37 7.45
663 1.34 1.49 1.43 4.26
665 1.99 2.32 1.18 5.49
667 6.31 1.10 0.78 8.19
669 4.19 0.83 0.36 5.37
681 7.49 0.98 0.00 8.48
Table 3-10: Amount of coke deposited in the reactor coil, TLE and collected
in the filter (entrained coke) for the different feedstock
In Table 3-10 the amount of the deposited coke in the reactor coil and in TLE1, the amount of
coke collected in the filter, and the total coke amount are given for the different feedstocks. The
filter is placed in the first condenser after the TLE2. As seen in Table 3-10, the largest coke
formation was observed in the pyrolysis of feedstock 540. On the contrary, feedstock 669
produces the smallest amount of coke. Thus, feedstock 669 is an adequate feed for steam
cracking at the used conditions since it provides large amounts of ethylene and propylene, while a
low coke formation rate is perceived. The more coke is formed in the coil, the lesser coke is
formed in the TLE and collected in the filter. However, a strong relationship between the amount
of coke deposited in de reactor coil, the amount deposit in the TLE1, and the amount collected in
the filter is not observed.
3.4.3 Effect of the process conditions on the cracking of Feed 661
In this set of experiments the influence of the dilution and the coil outlet temperature (COT)
on the steam cracking behavior of the feedstock 661 is examined. The steam dilution is altered
between 0.3 and 1 kg/kg, the COT between 800 and 840°C.
70
3.4.3.1 Influence of the dilution
In Table 3-11 the yields of the important products are presented for different values of the
steam dilution.
Feed 661
Dilution [kg/kg] 0.30 0.50 0.70 1.00
P/E 0.54 0.58 0.56 0.55
yields [wt%]
hydrogen 1.02 1.01 1.02 1.02
methane 11.79 11.10 10.94 9.97
acetylene 0.26 0.27 0.32 0.36
ethylene 21.20 21.40 22.72 22.02
ethane 3.39 2.84 2.57 2.13
propylene 12.64 13.30 13.67 12.88
propane 0.22 0.20 0.19 0.17
1,3-C4H6 4.30 5.00 5.00 4.84
1-butene 1.01 1.44 1.45 1.53
iso-butene 1.95 2.30 2.22 2.06
2-butene 0.44 0.44 0.49 0.45
iso-butane 0.03 0.09 0.09 0.09
n-butane 0.18 0.63 0.67 0.64
benzene 7.81 7.50 6.88 6.45
toluene 6.42 6.00 5.84 5.59
m-xylene 1.76 1.71 1.68 1.62
p-xylene 0.59 0.75 0.77 0.60
styrene 1.53 1.27 1.35 1.21
o-xylene 0.69 0.64 0.62 0.61
naphthalene 1.15 0.73 0.65 0.56
Table 3-11: Influence of the steam dilution on the product yields
From Table 3-11 it can be concluded that increased steam to hydrocarbon ratio improves the
yield of unsaturated products such as acetylene, ethylene, propylene and butadiene. Contrary, the
production of BTX, fuel gas (naphthalene) and saturated components such as methane, ethane
and propane decreases with increasing dilution. The total amounts of the observed C2 fraction,
the observed C3 fraction and the observed C4 fraction do not vary strongly, as seen in Figure
3-12. This trend is also oberved by Steiner (1961).
71
0
5
10
15
20
25
30
0.3 0.5 0.7 1
dilution [kg/kg]
yie
ld [
wt%
]
total C2 total C3 total C4
Figure 3-12: Total C2, total C3 and total C4 amounts
By adding steam the selectivity towards the light olefins (ethylene and propylene) increases.
This is due to the fact that dilution steam reduces the partial pressure of the hydrocarbons in the
reactor. At lower hydrocarbon partial pressures, monomolecular reactions are kinetically favored
compared with bimolecular reactions. Thus, decomposition reactions, reactions in which two
molecules of product are produced from one reactant, are favored. Furthermore, the hydrogen
abstractions of methyl-, ethyl- and propylradicals are opposed leading to a decrease of the
methane, ethane and propane yield. Thus, high steam dilution is desirable when the maximum
yield of lower olefins is the objective (Dekever, 2001). The ratio of steam to feed is usually
determined by an economic evaluation, considering yield improvement against higher investment
and operating costs. Typically, steam-to-feed weight ratios used in commercial practice range
0.2-0.5 for ethane, 0.3-0.5 for propane and 0.3-1 for naphtha or heavier liquid feeds (Miller,
1969).
A side effect of the steam dilution is steam reforming in which the hydrocarbons are
converted into hydrogen and carbon monoxide. Since CO acts as a temporary poison for the
catalysts used in downstream acetylene, methylacetylene and propadiene hydrogenation units
(e.g. Pd supported on alumina (Van Geem, 2006)), it must be expelled from the product stream
(Reyniers and Froment, 1995). Steam reforming is catalyzed by the tube surface constructed of
72
heat resistant Fe-Ni-Cr alloys. In industrial practice, additives are frequently used to control CO
production. The most widely used group of additives in commercial ethylene plants is based on
sulfur components (DHuyvetter et al., 2001). Sulfur components can either be already present in
the feedstock or are added during the process. In addition to reducing the CO production, sulfur
addition is believed to minimize the overall coking rate by suppressing the catalytic activity of
the reactor wall. Indeed, sulfur compounds add much more easily to the tube surface than the
hydrocarbons, resulting in a decrease of the disposable active places on the surface (competitive
chemisorption) (Dekever, 2001). Nevertheless, a constant asymptotic CO-production remains due
to the gasification of the present coke layer inside the tubes.
feedstock
dilution
[kg/kg]
Cokecoil
[g]
CokeTLE
[g]
Entrained
[g]
Total coke
[g]
661 0.3 2.51 8.77 10.96 22.24
661 0.5 3.17 5.31 1.84 8.48
661 0.7 2.01 3.46 0 5.47
661 1.0 1.82 2.01 0 3.83
Table 3-12: Influence of steam dilution on the coke formation
In Table 3-12 the amount of the deposited coke in the reactor coil and the TLE1, the amount
of coke collected in the filter and the total coke amount are given for different values of the steam
dilution. Table 3-12 indicates that higher steam dilutions have a negative effect on the coke
formation rates (total coke formation). Indeed, by adding steam the partial pressure of the
hydrocarbons in the reactor and the TLE1 decreases leading to suppression of bimolecular
reactions and thus to a decrease of the bimolecular coke formation. Steam is also reported to have
the scavenging effect of removing carbon from the coil to form carbon monoxide by the way of
steam-carbon reaction (Miller, 1969).
This effect of decreased coke formation by adding steam is observed in the amount of coke in
the reactor coil, the amount of coke in the TLE1, as well as in the amount of coke entrained in the
filter. Note that the amount of coke deposit in the reactor coil did not increase when the steam
dilution decreased from 0.5 to 0.3. This can be explained by the exceptional enlargement of the
coke collected in the filter.
73
3.4.3.2 Influence of the COT
Feed 661
COT [°C] 800 820 840
P/E 0.64 0.57 0.49
yields [wt%]
hydrogen 0.94 1.01 1.16
methane 8.86 11.10 12.38
acetylene 0.17 0.27 0.38
ethylene 18.45 21.40 23.37
ethane 2.80 2.84 2.82
propylene 12.88 13.30 12.40
propane 0.21 0.20 0.18
1,3-C4H6 4.50 5.00 4.46
1-butene 1.88 1.44 0.87
iso-butene 2.18 2.30 1.78
2-butene 0.56 0.44 0.36
iso-butane 0.03 0.09 0.07
n-butane 0.19 0.63 0.41
benzene 6.00 7.50 7.93
toluene 6.47 6.00 6.00
m-xylene 2.04 1.71 1.43
p-xylene 1.23 0.75 0.46
Styrene 1.08 1.27 1.50
o-xylene 0.78 0.64 0.62
naphthalene 0.66 0.73 0.93
Table 3-13: Influence of the COT on the product yields
Table 3-13 illustrates the influence of the COT on the yields of the important products. From
this table it can be concluded that as the temperature rises olefins and aromatics are formed in
larger quantities. By increasing the temperature, reactions with higher activation energy are
favored. The feedstock cannot be raised to the reaction temperature instantaneously in a furnace
tube. The temperature varies along the tube according to a certain profile, as represented in
Figure 3-13. The change in the slope occurring around 725 °C marks the beginning of the
cracking reactions. For representing the temperature profile in the reactor, the COT is used.
74
600
650
700
750
800
850
900
0 2 4 6 8 10 12 14
Distance [m]
Te
mp
era
ture
[°C
]
COT = 800°C COT = 820°C COT = 840°C
Figure 3-13: Temperature profiles in the reactor coil
8
9
10
11
12
13
14
780 800 820 840 860
COT [°C]
Meth
an
e y
ield
[w
t%]
Figure 3-14: Influence of the COT on the methane yield
75
Figure 3-14 shows the influence of the COT on the methane yield. As the COT increases, an
enhancement of the methane yield is observed. Methane is produced by hydrogen abstraction of
methyl radicals. The methyl radicals are derived from C-C scission reactiosn of molecules and
decomposition reactions. At higher temperatures more methyl radicals are formed. Moreover,
hydrogen abstractions are kinetically favored by increased temperature because of higher
activation energy. For those reasons an increase of the COT augments the methane yield.
Methane is also considered to be a good measure for the conversion of heavy fractions
(Golombok et al., 2001).
In Figure 3-15 and Figure 3-16 the influence of the COT on respectively the ethylene and
ethane yield is shown. An increased ethylene and a decreased ethane yield is observed when the
temperature rises from 800°C to 840°C. Ethylene is mainly formed by decomposition of ethyl
radicals, while ethane is formed by hydrogen abstraction of these last radicals. Since the
decomposition reaction of the ethyl radical has a higher activation energy, this reaction will be
kinetically favored at higher temperatures leading to higher ethylene yields and lower ethane
yields. Ethylene is also produced by decomposition of µ-radicals, which arise by hydrogen
abstraction on a primary C-atom. When hydrogen abstraction occurs on a secondary C-atom
other olefins such as propylene are formed. At high temperatures, the reactions that produce
ethylene are kinetically favored over those giving propylene, since the activation energy for
hydrogen abstraction of a primary C-atom is higher than that of the abstraction of a secondary
atom. This will result in a more rapid rise of the ethylene yield in comparison with the propylene
yield with increasing temperature. Indeed, at higher temperature levels the differences between
the ethylene and propylene yield increases, as can be deducted from Table 3-13. The
disappearing reactions for propylene are hydrogen abstraction and addition reactions. The
difference between the activation energy of the formation reactions and the activation energy of
the disappearing reactions is much smaller for propylene in comparison with ethylene. The net
results can be either positive or negative. When the temperature rises from 800°C to 820°C, the
formation reaction are favored, resulting in an increase of the propylene yield, see Figure 3-17. In
the second temperature interval, the net result is negative leading to a decrease of the propylene
yield.
76
15
17
19
21
23
25
780 800 820 840 860
COT [°C]
Eth
yle
ne y
ield
[w
t%]
Figure 3-15: Influence of the COT on the ethylene yield
2
3
780 800 820 840 860
COT [°C]
Eth
an
e y
ield
[w
t%]
Figure 3-16: Influence of the COT on the ethane yield
77
12
13
14
780 800 820 840 860
COT [°C]
Pro
pyle
ne y
ield
[w
t%]
Figure 3-17: Influence of the COT on the propylene yield
4
5
780 800 820 840 860
COT [°C]
Bu
tad
ien
e y
ield
[w
t%]
Figure 3-18: Influence of the COT on the butadiene yield
78
2
3
4
780 800 820 840 860
COT [°C]
Bu
ten
e y
ield
[w
t%]
Figure 3-19: Influence of the COT on the total butene yield
The influence of the COT on the butadiene and the total butene yield is represented in Figure
3-18 and Figure 3-19. Both butadiene and butenes are mainly formed out of the decomposition of
butenyl radicals. At low temperatures, the butenyl radical will undergo a hydrogen abstraction
resulting in the formation of butenes. At high temperatures on the other hand, the butenyl radicals
react by decomposition to butadiene. This effect of the COT on the butadiene and butene yield is
perceived when the temperature rises from 800°C to 820°C. The butadiene yield shows first an
increase as a function of the temperature because the species that are necessary for the formation
of the butenyl radical at high temperatures are much more present. However, from slightly before
820°C a decrease of the butadiene yield is observed since the disappearing of butadiene is
promoted by the strongly accumulated concentration of butadiene.
In Table 3-14 the amount of the deposited coke in the reactor coil and the TLE1, the amount
of coke collected in the filter and the total coke amount are given for different values of the coil
outlet temperature. The observed trend is that the total amount of coke increases with increasing
COT. Coke formation is just like cracking an endothermic process. Hence, an increase of the
temperature (COT) will shift the equilibrium in the direction of the coke formation due to Le
Châtelier’s principle.
79
feedstock COT [°C]
Cokecoil
[g]
CokeTLE
[g]
Entrained coke
[g]
Total coke
[g]
661-C 800 2.16 1.92 3.37 5.45
661-C 820 3.17 5.31 1.84 8.48
661-C 840 2.24 7.15 2.81 12.20
Table 3-14: Influence of COT on the coke formation
3.4.4 Conclusions
Under the used cracking conditions specified in Table 3-8 feedstocks 659 and 669 are the
most adequate feeds for steam cracking. Indeed, these feedstocks provide large amounts of
ethylene and propylene, while a low coke formation is perceived. The feedstocks which produce
large quantities of ethylene and propylene also provide large amounts of methane, ethane.
When the steam dilution is increased the conversion to target products (ethylene and
propylene) increases and coke formation is suppressed. The latter is based on the reduction of the
partial pressure of the hydrocarbons limiting the bimolecular reactions that destroy the olefins.
The optimal ratio of steam to feed is usually determined by an economic evaluation, considering
yield improvement against higher investment and operating costs.
Higher yields of ethylene and propylene are also obtained at higher coil outlet temperature.
However, an increase of the COT leads to higher coke formation rates as well. Nowadays,
ethylene producers are faced with a demand that is growing faster than capacity and one method
to boost the output is to run plants at higher COT’s (Nexant, 2003). Thus, an evaluation between
the increased ethylene en propylene yields at higher temperatures and the increased coke
deposition resulting in a more frequent shutdown of the steam cracker must be made.
80
Chapter 4
Conclusions & Future Work
The validation and the improvement of the fundamental simulation models of Plehiers (1989)
and Vercauteren (1991) is performed. The comparison between the simulation results with
experimental data from the experimental database revealed several shortcomings to both models.
In the database 400 experiments obtained with over 50 different feedstocks are gathered. An
interface is designed to make searching for data easy. One of the main conclusions of the
comparison is that the cracking behavior of toluene is not accurately described in the fundamental
simulation models of Plehiers and Vercauteren. The reactions implemented in the reaction
network disregard the actual cracking mechanism of toluene in which the benzyl radical plays a
key role. Introducing the benzyl radical in the simulation model of Plehiers led to an improved
description of the cracking behavior of toluene in toluene/ethane mixtures. However, for naphtha
feedstocks the accuracy of the simulated toluene yield remained poor. This is not surprising
because of the way the benzyl radical is treated in the reaction network of Plehiers. Only a
completely new reaction network can overcome these problems. Furthermore, a complete
optimization of all the kinetic parameters of the reaction network should be executed. In the new
simulation model of Van Geem (2006) all these considerations were implemented, leading to an
adequate simulation of the benzene and toluene yields.
Next to extending the reaction network also the database used for validation purposes is
extended with pilot plant experiments carried out with heavy fractions. In this respect the study of
the cracking behavior of several gas condensates is important. One feedstock composition was
determined using both GC and GC-MS. The analyses reveals that the concerned feedstocks
consist mainly out of molecules with 4 to 14 carbon atoms, with large amounts of n-alkanes, di-
or trimethyl substituted alkanes and aromatics (BTX). Pilot plant experiments show that gas
condensates behave similar to naphtha feedstocks. Both the product spectrum and the coke
formation depend strongly on the processed feedstock. Moreover, the operation conditions
strongly influences the cracking behavior. An increased steam to hydrocarbon ratio improves the
yield of unsaturated products such as acetylene, ethylene, propylene and butadiene. Contrary, the
81
production of BTX, fuel gas (naphthalene) and saturated components such as methane, ethane
and propane decreases with increasing dilution. Higher steam dilutions also decrease the
formation of coke. Higher yields of ethylene are also obtained at higher COT’s. However, an
increase of the COT leads to higher coke formation rates as well.
The extension of the experimental database with experiments carried out with gas condensates
is just a first step. The number of experiments carried out with heavy fractions is too limited for
validation purposes at this moment. Especially experiments with gasoils are in this respect of
particular importance. Because despite the increased use of these fractions as feedstock for the
production of ethylene only a few gas oils were ever cracked in the pilot plant reactor for which
data are available. Moreover, other types of feedstocks such as feeds containing large amounts of
olefins seem also very interesting. The same can be said of naphthenic-rich feedstocks and
aromatic feeds, such as feeds containing isopropylbenzene, ethylbenzene, and poly-aromatics
such as tetrahydronaphthalene could give a surplus to the database.
Although studying the cracking behavior of heavy fractions is undoubtfully important, fast
reconstruction of these complex fractions based on easily determinable commercial indices has a
much wither application area then just steam cracking. The detailed analysis of the gas
condensates makes it possible to easily extend the feedstock reconstruction program SimCo (Van
Geem, 2006a) to these fractions. Also for gas oil fractions a lot of progress can be made in this
area.
82
Appendix A Overview database
A.1 Light feedstock
Feed HC flow Dilution COP COT Number of
[kg/hr] [kg/kg HC] [bar] [°C] experiments
Ethane 2.1 0.6 1.9 750-880 10
2.5 0.6 1.9 800 1
2.8 0.0 1.5 930 1
2.9 0.7 1.9 800-850 5
3.0 0.0 1.7 850 1
3.0 0.4 2.0 800-890 4
3.8 0.3 1.9 790-860 3
4.2 0.3 2.4 850 1
4.2 0.4 1.9 950 1
4.2 0.4 2.9 870 1
4.2 0.5 1.9 845 1
n-butane 3.0 0.4 2.0 750-850 16
3.0 1.0 2.0 770-880 10
i-butane 3.0 0.4 1.7 830 1
3.0 0.6 1.7 830 1
3.0 1.0 1.9 750-890 12
n-hexane 3.0 0.4 2.0 800-820 3
n-heptane 3.0 0.0 2.0 860 1
3.0 0.3 2.0 890 1
3.0 0.4 2.0 890 1
3.0 0.7 2.0 775 1
n-decane 3.0 0.7 1.9 700-702 2
C6 mixture 3.8 0.5 1.7 830 1
Amoco iC6 3.0 0.4 2.0 770-790 3
83
KTI C6 4.8 0.4 1.7 800-860 14
AMOCO isoC7 mixture 3.0 0.4 2.0 770-825 2
Mix methane – ethane 3.0 0.0 1.7 820-850 4
Mix methane – ethane² 3.0 0.0 1.7 850 1
Mix methane – ethane³ 3.0 0.0 1.7 820 1
Mix methane – ethene – ethane 2.4 0.0 1.4 870-890 2
Mix methane – ethane – propane 3.0 0.0 1.7 850 1
Mix meth–ethane–propane–butane 3.2 0.0 1.7 848 1
Mix meth–ethane–propane–butane² 3.1 0.0 1.6 730-840 7
Mix ethane – ethane 4.1 0.3 2.0 860-880 8
Mix ethane – ethene² 3.1 0.4 1.3 825-880 6
3.1 0.4 2.0 840-860 3
Mix ethane – ethene – propane 4.2 0.3 2.0 860-890 12
Mix ethane – ethene – propane² 1.2 0.3 2.0 770-870 11
Mix ethane – propane 3.1 0.3 1.7 850 1
4.2 0.4 2.5 850-870 3
Mix ethane – propane² 4.0 0.3 2.9 820 1
4.0 0.5 2.9 840-860 2
Mix ethane – propane³ 3.8 0.3 1.9 880 1
3.8 0.5 1.9 880 1
Mix ethane – propane4 4.0 0.3 2.9 860 1
Mix ethane – propane5 5.2 0.2 2.0 660-960 23
Mix ethane – propane – butane 3.5 0.0 1.6 820 3
Mix ethane – propane – butane² 3.6 0.0 1.6 800-850 3
Mix ethane – propane – butane³ 2.8 0.0 1.6 790-850 6
Mix ethane – toluene (87-13 wt%) 3.3 0.4 2.0 800-890 4
Mix ethane – toluene (77-23 wt%) 3.6 0.4 2.0 800-890 4
Mix ethane – toluene (70-30 wt%) 3.8 0.4 2.0 800-890 4
Mix ethane – toluene (60-40 wt%) 4.1 0.4 2.0 800-890 4
Mix propane – propene 3.0 0.4 1.3 825-880 3
3.0 0.4 2.0 820-880 6
Mix i-butane – n-butane 3.0 0.5 1.8 850 2
3.0 1.0 1.9 730-870 14
Mix 1 Reyniers 3.0 0.0 1.7 850 1
Mix n-heptane – benzene 3.0 0.6 2.0 875 1
84
A.2 Naphtha
Feed HC flow Dilution COP COT Number of
[kg/hr] [kg/kg HC] [bar] [°C] experiments
Naphtha HDT 4.8 0.5 1.7 840-865 2
Naphtha ELF '96 4.8 0.5 1.6 845-865 2
Naphtha ELF2 '96 4.8 0.5 1.6 825-865 3
Naphtha IFP 2.1 0.8 1.9 790-900 5
3.2 0.4 1.9 700-930 27
4.3 0.2 1.9 710-920 30
Naphtha ELF '84/'85 4.0 0.25 1.7 800-860 6
5.0 0.19 1.9 740-830 7
Naphtha labofina 3.5 0.6 1.7 870 1
4.5 0.4 1.7 860-900 4
5.2 0.4 2.0 860 1
Naphtha Fina research 4.5 0.5 2.2 790-860 9
4.0 1.0 2.0 780-920 16
6.5 0.5 2.0 780-940 10
Naphtha Esso 3.3 0.4 2.2 815 1
4.0 0.48 2.1 810-830 4
Esso Hydrofine 4.0 0.3 1.8 852 1
5.0 0.0 1.8 820 2
Keroseen 3.0 0.8 2.0 775-825 2
3.0 0.8 2.5 680-850 3
3.0 1.5 2.1 760 1
Naphta Shell 4.0 0.6 2.0 810-860 17
AMOCO light naphtha 3.0 1.0 2.0 750-850 2
AMOCO heavy naphtha 3.0 1.0 2.0 780-830 2
85
A.3 Gas oil
Feed HC flow Dilution COP COT Number of
[kg/hr] [kg/kg HC] [bar] [°C] experiments
OMV (AGO) 2.7 1.0 1.6 770-830 5
3.4 0.75 1.6 760-814 4
ATEC (HAGO) 2.0 1.0 1.3 790 1
2.0 1.0 2.0 790 1
Debutanized Natural GO 3.0 0.4 2.0 810-830 2
AGO ESSO/KOLN 2.4 0.8 2.5 775 1
2.6 1.2 2.0 810 1
VGO URBK 4.5 0.7 1.6 750-850 7
4.5 0.7 2.0 750-851 6
VGO fina Raffinaderij Antwerp 4.0 0.9 1.7 750-820 4
86
Appendix B Validation of the C25 reaction network
Figures B-1:B-8 show the parity plots obtained for the products hydrogen, methane,
acetylene, ethane, propane, butadiene, benzene and toluene obtained with the C25 reaction
network. In these plots, a larger deviation of the first bisector is observed compared to the parity
plots acquired with the C19 reaction network. (Figures 2-3:2-17). Hence, it can be concluded that
the C25 reaction network provides systematically poorer results then the C19 reaction network.
87
0
0.5
1
1.5
2
0 0.5 1 1.5 2
Experimental Hydrogen Yield [wt%]
Sim
ula
ted
Hy
dro
ge
n Y
ield
[w
t%]
Figure B- 1: Parity plot for the hydrogen yield
0
5
10
15
20
25
0 5 10 15 20 25
Experimental Methane Yield [wt%]
Sim
ula
ted
Me
tha
ne
Yie
ld [
wt%
]
Figure B- 2: Parity plot for the methane yield
88
0
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5 3
Experimental Acetylene Yield [wt%]
Sim
ula
ted
Ac
ety
len
e Y
ield
[w
t%]
Figure B- 3: Parity plot for the acetylene yield
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8
Experimental Ethane Yield [wt%]
Sim
ula
ted
Eth
an
e Y
ield
[w
t%]
Figure B- 4: Parity plot for the ethane yield
89
0
0.5
1
1.5
2
0 0.5 1 1.5 2
Experimental Propane Yield [wt%]
Sim
ula
ted
Pro
pa
ne
Yie
ld [
wt%
]
Figure B- 5: Parity plot for the propane yield
0
2
4
6
8
10
0 2 4 6 8 10
Experimental Butadiene Yield [wt%]
Sim
ula
ted
Bu
tad
ien
e Y
ield
[w
t%]
Figure B- 6: Parity plot for the butadiene yield
90
0
5
10
15
0 5 10 15
Experimental Benzene Yield [wt%]
Sim
ula
ted
Be
nz
en
e Y
ield
[w
t%]
Figure B- 7: Parity plot for the benzene yield
0
2
4
6
8
10
0 2 4 6 8 10
Experimental Toluene Yield [wt%]
Sim
ula
ted
To
lue
ne
Yie
ld [
wt%
]
Figure B- 8: Parity plot for the toluene yield
91
Appendix C Calculation of the standard molar entropy of the benzyl radical
The standard molar entropy of the benzyl radical is calculated using the HBI-method of Laidler
(Lay et al., 1995). This method presents an alternative approach, i.e. a single group, to estimate
the thermodynamic properties for a series of hydrocarbon free radicals. It shows that )(0
298 ⋅RS can
be determined if )(0
298 RHS and the bond strength for the R-H bond being broken to form the
radical and H atom are know since the molecular structure of a radical (R) is similar to that of
the corresponding stable molecule (RH). Indeed, the unpaired electron on the radical-centered
atom is replaced by a bond to a H atom in the stable molecule, while most of the atom sequence
and chemical bonds basically remain the same in the two species. If the differences in molecular
structure and the properties for R and RH are properly taken into account, one can calculate
)(0
298 ⋅RS values for R from properties of the corresponding RH parent plus increment values for
0
298S∆ that account for these changes (Lay et al., 1995):
0
298
0
298
0
298 )()( SRHSRS ∆+∆=⋅∆ [C-1]
The benzyl radical is formed via the elimination of H atom from toluene. Thus, the standard
molar entropy of the benzyl radical can be determined from the standard molar entropy of
toluene. The increment value for the benzyl radical amounts -4.739 J mole-1
K-1
(Lay et al., 1995).
Since the standard molar entropy of toluene is 76.81 J mole-1
K-1
(Perry and Green, 1997), the
standard molar entropy of the benzyl radical is 72.07 J mole-1
K-1
.
92
Appendix D Calculation of the coefficients for specific heat capacity of the
benzyl radical
The specific heat capacity is defined as the amount of heat per unit mass required to raise the
temperature by one degree Celsius. The specific heat capacity at constant pressure is function of
the temperature:
5432TFTETDTCTBAC p ⋅+⋅+⋅+⋅+⋅+= [D-1]
The coefficients in this expression are fitted based on ab initio values for toluene given in Table
D- 1. The values for the benzyl radical are derived from these values using the HBI-method (Lay
et al., 1995), as explained in appendix C.
T Cp(tolueen) ∆Cp(HBI) Cp(benzyl)
300 25.029 0.747 25.776
400 33.281 0.604 33.885
500 40.364 0.126 40.490
600 46.392 -0.422 45.970
800 55.747 -1.414 54.333
1000 62.271 -2.183 60.088
1013.25 62.626 -2.299 60.327
Table D- 1: Data for the calculation of the coefficients for specific heat capacity
The coefficients in equation [D-1] are determined by means of linear regression analysis. The
results of this analysis are found in Table D- 2.
A 25.77618
B 33.88516
C 40.4895
D 45.96964
E 54.33308
F 60.088
Table D- 2: The calculated coefficients for specific heat capacity
93
Appendix E Detailed composition of feedstock 667
nr name MW Area CF wt% mole%
1 propane 44.096 0.164 1.02 0.017 0.039
2 i-butane 58.123 2.747 0.75 0.213 0.370
3 n-butane 58.123 17.11 0.92 1.620 2.809
4 2,2-DiMe C3 72.150 1.12 0.91 0.105 0.147
5 i-pentane 72.150 103.865 0.95 10.208 14.258
6 n-pentane 72.150 94.544 0.96 9.382 13.103
7 Cy pentadiene 66.103 0.05 1.51 0.008 0.012
8 2,2-DiMe C4 86.177 5.819 0.96 0.577 0.675
9 CyC5 70.134 13.684 0.96 1.358 1.951
10 2,3-DiMe C4 86.177 46.511 0.97 4.660 5.449
11 3-Me C5 86.177 26.489 0.96 4.660 3.074
12 n-hexane 86.177 61.554 0.97 6.167 7.212
13 2,2-DiMe C5 100.203 2.931 0.98 0.297 0.298
14 Me CyC5 84.161 14.316 0.99 1.463 1.751
15 2,4-DiMe C5 100.203 4.681 0.98 0.474 0.476
16 2,2,3-TriMe C4 100.203 0.944 0.94 0.091 0.092
17 benzene 78.113 10.643 0.89 0.981 1.265
18 3,3-DiMe C5 100.203 1.453 0.97 0.146 0.146
19 CyC6 84.161 14.618 0.99 1.494 1.788
20 2-Me C6 100.203 24.165 0.98 2.445 2.459
21 2,3-DiMe C5 100.203 5.901 1.01 0.615 0.619
22 1,1-DiMe CyC5 98.188 2.264 0.97 0.227 0.233
23 3-Me C6 100.203 23.776 0.98 2.406 2.419
24 1,c2-DiMe CyC5 98.188 3.334 1.00 0.344 0.353
25 1,t3-DiMe CyC5 98.188 3.148 1.00 0.325 0.333
26 1,c3-DiMe CyC5 98.188 1.468 1.00 0.151 0.155
27 1,t2-DiMe CyC5 98.188 5.28 0.99 0.539 0.554
28 n-heptane 100.203 44.67 1.00 4.610 4.636
29 Me CyC6 98.188 31.472 0.99 3.216 3.300
30 1,1,3-TriMe CyC5 112.214 1.625 0.96 0.161 0.145
31 Et CyC5 98.188 1.718 1.00 0.177 0.182
32 2,5-DiMe C6 114.230 4.771 0.99 0.487 0.430
33 2,4-DiMe C6 114.230 4.439 1.01 0.463 0.408
34 1,t2,c4-TriMe CyC5 112.214 1.727 1.02 0.182 0.163
35 3,3-DiMe C6 114.230 1.219 1.02 0.129 0.114
36 1,t2,c3-TriMe C5 112.214 1.513 1.00 0.156 0.140
37 2,3,4-TriMe C5 114.230 0.125 1.01 0.013 0.011
38 toluene 92.140 15.48 0.93 1.493 1.633
39 2,3-DiMe C6 114.230 2.457 1.01 0.256 0.226
40 2-Me,3-Et C5 114.230 1.003 1.02 0.106 0.093
41 2-Me C7 114.230 17.066 1.03 1.816 1.602
94
nr name MW Area CF wt% mole%
42 4-Me C7 114.230 5.844 0.98 0.591 0.522
43 3,4-DiMe C6 114.230 1.222 1.01 0.127 0.112
44 1,c3-DiMe CyC6 112.214 0.122 1.00 0.013 0.011
45 3-Me C7 114.230 13.943 0.99 1.425 1.257
46 3-Et C6 114.230 1.889 1.00 0.195 0.172
47 1,c2,t3-TriMe CyC5 112.214 8.508 1.02 0.896 0.805
48 1,t4-DiMe CyC6 112.214 3.825 1.01 0.399 0.358
49 1,1-DiMe CyC6 112.214 1.356 0.97 0.136 0.122
50 2,2,4-TriMe C6 128.257 0.471 1.01 0.049 0.039
51 1-Me,c3-Et CyC5 112.214 0.423 1.00 0.044 0.039
52 1-Me,t3-Et CyC5 112.214 0.359 1.03 0.038 0.034
53 1-Me,t2-Et CyC5 112.214 0.721 0.99 0.074 0.066
54 1-Me,1-Et CyC5 112.214 0.165 1.09 0.019 0.017
55 1,2-DiMe CyC6 112.214 3.326 1.00 0.343 0.308
56 n-octane 114.230 29.63 1.03 3.152 2.781
57 1,c4-DiMe CyC6 112.214 2.139 1.00 0.221 0.198
58 i-propyl CyC5 126.241 0.263 1.03 0.028 0.022
59 2,2-DiMe C7 128.257 0.035 1.05 0.004 0.003
60 2,4-DiMe C7 128.257 0.156 0.96 0.015 0.012
61 2,3,4-TriMe C6 128.257 0.357 1.04 0.038 0.030
62 N8 112.214 1.017 1.00 0.105 0.094
63 I9 128.257 2.041 1.00 0.211 0.165
64 2,3,5-TriMe C6 128.257 0.986 1.04 0.106 0.083
65 c1,c3,c5-TriMe CyC6 126.241 0.034 0.98 0.003 0.003
66 2,2,3-TriMe C6 128.257 9.488 1.00 0.979 0.769
67 n-propyl CyC5 126.241 3.147 1.03 0.335 0.267
68 Et benzene 106.167 4.409 0.97 0.442 0.419
69 3,5-DiMe C7 128.257 1.853 0.96 0.184 0.144
70 3,3-DiMe C7 128.257 0.092 1.00 0.009 0.007
71 I10 142.284 0.223 1.00 0.023 0.016
72 2,5-DiMe C7 128.257 0.176 0.96 0.017 0.014
73 1,1,3-TriMe CyC6 126.241 0.103 1.06 0.011 0.009
74 1,2,4-TriMe CyC6 126.241 2.312 0.98 0.233 0.186
75 2,3-DiMe C7 128.257 2.219 0.96 0.220 0.173
76 unknown 0.155 1.00 0.016 0.000
77 1,1,4-TriMe CyC6 126.241 0.181 1.06 0.020 0.016
78 m-xylene 106.167 12.401 0.96 1.231 1.168
79 p-xylene 106.167 3.342 1.00 0.345 0.327
80 unknown 0.417 1.00 0.043 0.000
81 4-Et C7 128.257 0.523 1.01 0.054 0.043
82 3,4-DiMe C7 128.257 0.907 0.96 0.090 0.071
83 unknown 0.382 1.00 0.039 0.000
84 4-Me C8 128.257 5.877 1.01 0.610 0.479
85 2-Me C8 128.257 7.128 1.01 0.740 0.581
86 3-Et C7 128.257 1.239 1.01 0.129 0.101
87 3-Me C8 128.257 8.323 1.01 0.864 0.679
88 3,3-DiEt C5 128.257 0.148 1.05 0.016 0.013
89 o-xylene 106.167 5.03 0.98 0.509 0.483
95
nr name MW Area CF wt% mole%
90 1,1,2-TriMe CyC6 126.241 0.714 0.99 0.073 0.058
91 3,5-DiMe-3-Heptene 126.241 2.375 1.12 0.274 0.219
92 2,4-DiMe-3-Heptene 126.241 1.156 1.12 0.133 0.106
93 Et-Me CyC6 126.241 0.219 1.02 0.023 0.018
94 n-nonane 128.257 21.902 1.02 2.306 1.812
95 1-Me,t4-Et CyC6 126.241 1.579 1.02 0.166 0.133
96 1-Me,c4-Et CyC6 126.241 0.475 1.04 0.051 0.041
97 i-propyl benzene 120.194 0.453 1.03 0.048 0.040
98 i-propyl benzene b 120.194 0.466 1.03 0.050 0.042
99 i-propyl CyC6 126.241 0.757 1.02 0.080 0.064
100 i-propyl CyC6 b 126.241 0.901 1.02 0.095 0.076
101 2,2-DiMe C8 142.284 1.68 1.06 0.184 0.130
102 unknown 0.6 1.00 0.062 0.000
103 unknown 0.411 1.00 0.042 0.000
104 2,4-DiMe C8 142.284 1.959 0.99 0.199 0.141
105 unknown 2.393 1.00 0.247 0.000
106 unknown 0.2 1.00 0.021 0.000
107 2,5-DiMe C8 142.284 1.003 0.99 0.102 0.072
108 3,3-DiMe C8 142.284 0.778 1.06 0.085 0.060
109 2,6-DiMe C8 142.284 3.231 0.99 0.329 0.233
110 unknown 0.518 1.00 0.053 0.000
111 unknown 0.268 1.00 0.028 0.000
112 n-propyl benzene 120.194 1.933 0.99 0.198 0.166
113 3,6-DiMe C8 142.284 1.156 0.99 0.118 0.083
114 unknown 0.229 1.00 0.024 0.000
115 n-propyl CyC6 126.241 2.296 1.07 0.254 0.202
116 1-Me,2-Et CyC6 126.241 1.009 1.07 0.111 0.089
117 1-Me,3-Et benzene 120.194 2.087 0.99 0.213 0.179
118 1-Me,2-Et benzene 120.194 3.903 0.98 0.395 0.331
119 unknown 1.73 1.00 0.179 0.000
120 4-Me C9 142.284 4.014 1.03 0.425 0.301
121 2-Me C9 142.284 3.84 1.03 0.407 0.288
122 unknown 0.536 1.00 0.055 0.000
123 unknown 0.56 1.00 0.058 0.000
124 3-Me C9 142.284 4.118 1.03 0.436 0.309
125 N10 140.268 0.333 1.00 0.034 0.025
126 N10b 140.268 0.26 1.00 0.027 0.019
127 1,2,4-TriMe benzene 120.194 7.059 1.03 0.751 0.630
128 unknown 0.305 1.00 0.031 0.000
129 unknown 0.979 1.00 0.101 0.000
130 unknown 0.173 1.00 0.018 0.000
131 unknown 0.25 1.00 0.026 0.000
132 unknown 0.298 1.00 0.031 0.000
133 unknown 0.27 1.00 0.028 0.000
134 n-decaan 142.284 15.739 1.07 1.733 1.227
135 unknown 0.195 1.00 0.020 0.000
136 1,2,3-TriMe benzene 120.194 1.318 1.02 0.139 0.116
96
nr name MW Area CF wt% mole%
137 unknown 0.389 1.00 0.040 0.000
138 unknown 0.16 1.00 0.017 0.000
139 unknown 0.305 1.00 0.031 0.000
140 unknown 0.872 1.00 0.090 0.000
141 unknown 0.305 1.00 0.031 0.000
142 unknown 0.153 1.00 0.016 0.000
143 unknown 0.516 1.00 0.053 0.000
144 3,5-DiMe C9 156.311 2.419 1.01 0.251 0.162
145 unknown 0.998 1.00 0.103 0.000
146 unknown 1.494 1.00 0.154 0.000
147 unknown 0.33 1.00 0.034 0.000
148 1-Me,3-prop benzene 134.222 1.279 1.03 0.136 0.102
149 1-Me,2-prop benzene 134.222 1.581 1.03 0.168 0.126
150 unknown 0.242 1.00 0.025 0.000
151 unknown 0.678 1.00 0.070 0.000
152 unknown 0.36 1.00 0.037 0.000
153 Me(1-MeEt) benzene 134.222 1.421 2.12 0.311 0.234
154 unknown 0.535 1.00 0.055 0.000
155 5-Me C10 156.311 2.064 1.06 0.225 0.145
156 4-Me C10 156.311 1.792 1.06 0.196 0.126
157 2-Me C10 156.311 2.495 1.06 0.272 0.176
158 2,6-DiMe C9 156.311 0.647 1.01 0.067 0.043
159 3,7-DiMe C9 156.311 1.994 1.01 0.207 0.134
160 unknown 0.603 1.00 0.062 0.000
161 unknown 0.253 1.00 0.026 0.000
162 unknown 0.26 1.00 0.027 0.000
163 unknown 0.423 1.00 0.044 0.000
164 unknown 0.277 1.00 0.029 0.000
165 unknown 0.37 1.00 0.038 0.000
166 unknown 0.221 1.00 0.023 0.000
167 unknown 0.527 1.00 0.054 0.000
168 n-undecane 156.311 11.304 1.08 1.261 0.813
169 unknown 0.144 1.00 0.015 0.000
170 unknown 0.347 1.00 0.036 0.000
171 unknown 0.426 1.00 0.044 0.000
172 unknown 0.18 1.00 0.019 0.000
173 unknown 0.148 1.00 0.015 0.000
174 unknown 0.313 1.00 0.032 0.000
175 unknown 0.449 1.00 0.046 0.000
176 unknown 0.499 1.00 0.051 0.000
177 unknown 0.175 1.00 0.018 0.000
178 unknown 0.731 1.00 0.075 0.000
179 unknown 0.182 1.00 0.019 0.000
180 unknown 1.393 1.00 0.144 0.000
181 unknown 0.172 1.00 0.018 0.000
182 unknown 0.824 1.00 0.085 0.000
183 unknown 0.458 1.00 0.047 0.000
97
nr name MW Area CF wt% mole%
184 unknown 0.156 1.00 0.016 0.000
185 unknown 0.695 1.00 0.072 0.000
186 2,5-DiMe C10 170.340 1.086 1.02 0.115 0.068
187 5-Me C11 170.340 1.161 1.06 0.127 0.075
188 4-Me C11 170.340 1.182 1.06 0.129 0.076
189 2-Me C11 170.340 1.525 1.06 0.167 0.099
190 unknown 0.289 1.00 0.030 0.000
191 3-Me C11 170.340 1.187 1.06 0.130 0.077
192 unknown 0.188 1.00 0.019 0.000
193 unknown 0.323 1.00 0.033 0.000
194 unknown 0.36 1.00 0.037 0.000
195 unknown 0.395 1.00 0.041 0.000
196 n-dodecane 170.339 8.128 1.09 0.916 0.542
197 unknown 0.315 1.00 0.033 0.000
198 unknown 0.185 1.00 0.019 0.000
199 3,6-DiMe C11 184.366 1.782 1.04 0.191 0.104
200 unknown 0.13 1.00 0.013 0.000
201 2,3,7-TriMe C10 184.366 0.366 1.01 0.038 0.021
202 unknown 0.175 1.00 0.018 0.000
203 unknown 0.275 1.00 0.028 0.000
204 unknown 0.258 1.00 0.027 0.000
205 unknown 0.318 1.00 0.033 0.000
206 unknown 0.217 1.00 0.022 0.000
207 unknown 0.276 1.00 0.028 0.000
208 2,3-DiMe C11 184.366 1.062 1.04 0.114 0.062
209 unknown 0.749 1.00 0.077 0.000
210 unknown 0.752 1.00 0.078 0.000
211 2-Me C12 184.366 1.064 1.07 0.117 0.064
212 unknown 0.18 1.00 0.019 0.000
213 3-Me C12 184.366 0.902 1.07 0.100 0.054
214 unknown 0.88 1.00 0.091 0.000
215 unknown 0.11 1.00 0.011 0.000
216 2,6-DiMe C11 184.366 0.566 1.04 0.061 0.033
217 unknown 0.284 1.00 0.029 0.000
218 unknown 0.252 1.00 0.026 0.000
219 unknown 0.247 1.00 0.025 0.000
220 n-tridecane 184.366 5.734 1.10 0.652 0.356
221 unknown 0.259 1.00 0.027 0.000
222 unknown 0.201 1.00 0.021 0.000
223 unknown 0.484 1.00 0.050 0.000
224 unknown 0.132 1.00 0.014 0.000
225 unknown 0.243 1.00 0.025 0.000
226 unknown 0.127 1.00 0.013 0.000
227 unknown 0.529 1.00 0.055 0.000
228 unknown 0.408 1.00 0.042 0.000
229 unknown 0.512 1.00 0.053 0.000
230 unknown 0.508 1.00 0.052 0.000
98
nr name MW Area CF wt% mole%
231 2-Me C13 198.393 0.485 1.08 0.054 0.027
232 2,6,10-TriMe C12 198.393 0.692 1.04 0.074 0.038
233 n-tetradecane 198.393 3.963 1.11 0.454 0.230
234 4,8-DiMe C13 212.420 0.525 1.06 0.058 0.027
235 unknown 0.222 1.00 0.023 0.000
236 unknown 0.52 1.00 0.054 0.000
237 unknown 0.22 1.00 0.023 0.000
238 unknown 0.232 1.00 0.024 0.000
239 unknown 0.219 1.00 0.023 0.000
240 4-Me C14 212.420 0.887 1.09 0.100 0.047
241 unknown 0.292 1.00 0.030 0.000
242 unknown 0.12 1.00 0.012 0.000
243 n-pentadecane 212.420 2.969 1.12 0.342 0.162
244 unknown 0.242 1.00 0.025 0.000
245 unknown 0.138 1.00 0.014 0.000
246 unknown 0.353 1.00 0.036 0.000
247 unknown 0.199 1.00 0.021 0.000
248 unknown 0.205 1.00 0.021 0.000
249 unknown 0.191 1.00 0.020 0.000
250 n-hexadecane 226.447 1.918 1.12 0.222 0.099
251 unknown 0.184 1.00 0.019 0.000
252 unknown 0.468 1.00 0.048 0.000
253 unknown 0.932 1.00 0.096 0.000
254 unknown 0.299 1.00 0.031 0.000
255 n-heptadecane 240.474 1.315 1.13 0.153 0.064
256 unknown 0.437 1.00 0.045 0.000
257 n-octadecane 254.501 0.726 1.13 0.085 0.034
258 unknown 0.936 1.00 0.097 0.000
259 unknown 0.422 1.00 0.044 0.000
260 n-nonadecane 268.528 0.418 1.14 0.049 0.018
99.903
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