modeling steam cracking of complex hydrocarbons helge...

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



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:


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



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


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


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


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


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


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 + 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


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


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


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


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


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


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


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


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


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-


m/z ratios 105 120 119 77 106 91 121 79 1,2,4-


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 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



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



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


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


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


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


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


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


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


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


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

References

Beens J., Brinkman U.A.T., The role of gas chromatography in compositional analyses in the

petroleum industry, Trends in Analytical Chemistry, 19, 4, 2000, 260-275.

Bennet M.J., Price J.B. A physical and chemical examination of an ethane steam cracker: coke

and of the underlying pyrolysis tube, J. Mater. Sci., 16, 170-188, 1981.

Bertoncini F., Vendeuvre C., Thiébaut D., Interest and applications of multidimensional gas

chromatography for trace analysis in the petroleum industry, 60, 5, 2005, 937-950.

Bounaceur R., Scacchi G., Marquaire P.M., Domine F., Brevart O., Dessort D., Pradier B.,

Inhibiting effect of tetralin on the pyrolytic decomposition of hexadecane. Comparison with

toluene, Ind. & Eng. Chem. Res., 41, 19, 2002, 4689-4701.

Castello G., Retention index systems : alternatives to the n-alkanes as calibration standards,

Journal of Chromatography, 842, 1, 1999, 51-64.

Chan K.Y, Inal F., Senkan S., Suppression of Coke Formation in the Steam Cracking of Alkanes:

Ethane and Propane., Ind. & Eng. Chem. Res., 37, 3, 1998, 901-907.

Chauvel A. and Lefebvre G., Petrochemical Processes, Part 1: Synthesis Gas Derivatives and

Major Hydrocarbons, Editions Technip, Paris, 1989.

Chemical Kinetics Database, Standard Reference Database 17, Version 7.0 (Web Version),

Release 1.3. NIST 2005.

Dalluge J., Beens J., and Brinkman U.A.T., Comprehensive two-dimensional gas

chromatography: A powerful and versatile analytical tool, Journal of Chromatography A.,

1000, 1-2, 2003, 69-108.

De Buck J. Kinetische Studie van Cokesvorming bij het Thermisch Kraken van

Koolwaterstoffen, Master thesis, Ghent University, 1999.

De Kever D., Experimentele studie van de thermische kraking van koolwaterstoffen., Master

thesis, Ghent University, 2001,152p.

Dente M., Ranzi E. Detailed Prediction of Olefin Yields from Hydrocarbon Pyrolysis through a

Fundamental Simulation Program SPYRO, Comp. Chem. Eng., 3, 61, 1979.

De Roo T. Modellering van Stroming, Modellering van Cokesvorming in de Warmtewisselaar

stroomafwaarts van een Thermische Kraker, Master thesis, Ghent University, 1998.

Dierickx J.L., Plehiers P.M., Froment G.F., Online Gas-Chromatographic Analysis of

Hydrocarbon Effluents – Callibration Factors and Their Correlation, Journal of

Chromatography, 362, 2, 1986, 155-174.

Dietz W.A., Response Factors for Gas Chromatographic Analyses, Journal of Gas

Chromatography, February 1967, 68-71

Donsì F., Oxidative dehydrogenation of ethane in short contact time reactors, Universita degli

studii di Napoli, 2002, 194p.

Eramo M., Ethylene and propylene demand will experience increased growth in 2005-10, Oil &

Gas Journal, 103, 45, 2005, 52-60.

European IPPC bureau, Best Available Techniques in the Large Volume Organic Chemical

Industry, 2000, World Trade Center Sevilla, 394p

Figueiredo J.L. Filamentous Carbon. Erdöl und Kohle-Erdgas-Petrochemie, 42, 294-297, 1989

Froment, G. F., Coke formation in the steam cracking of hydrocarbons, Rev. Chem. Eng., 6, 4,

1990, 294-327

Goldstein R.F., The Petroleum Chemicals Industry, 1st edition, John Wiley & Sons (New York),

1950.

Golombok, M.; Van der Bijl, J.; Korngoor, M. Severity Parameters for Steam Cracking. Ind. &

Eng. Chem. Res., 40, 2001, 470-472.

Hinshaw J.V., Comprehensive two-dimensional gas chromatography, LC and GC Europe, 2004,

202-207.

Kopinke F.D., Zimmerman G., Nowak S. On the Mechanism of Coke Formation in Steam

Cracking – Conclusions Obtained from Tracer Experiments, Carbon, 26, 117-124, 1988.

Lay T.H. , Bozzelli J.W., Dean A.M. and Ritter E.R., Hydrogen Atom Bond Increments for

Calculation of Thermodynamic Properties of Hydrocarbon Radical Species, J. Phys. Chem., 99

1995, 14514-14527.

Li S., Petzold L.R. Design of New DASPK for Sensitivity Analysis, UCSB Technical report,

1999.

Miller S.A., Ethylene and its Industrial Derivatives, Ernest Benn Limited (London), 1969.

Perry R.H. and Green D.W., Perry's chemical engineers' handbook (7th edition), McGraw-Hill,

1997, 2640.

Phillips J.B., Xu J., Comprehensive multi-dimensional gas chromatography, Journal of

Chromatography A, 703, -, 1995, 327-334.

Plehiers, P.M.; Froment, G.F. The Reversed Split Coil improves Ethylene Yield. Oil & Gas

Journal. 1987, 85 (33), 41-48

Plehiers P.M. Rigoureuze Modellen voor de Simulatie van Fornuizen voor de Thermische

Kraking van Lichte Koolwaterstoffen, Ph.D. dissertation, Ghent University, 1989.

Ren T., Patel M., Block K. Olefins from Conventional and Heavy Feedstocks: Energy use in

Steam Cracking and Alternative Processes, Energy, 31, 2006, 425-451.

Reyniers G.C., Froment G.F, Kopinke F.D., Zimmerman G. Coke modeling in the thermal

cracking of hydrocarbons. 4. Modeling of coke formation in naphtha cracking, Ind. Eng.

Chem. Res., 33, 2584-2590, 1994.

Rice F.O. The Thermal Decomposition of Organic Compounds from the Standpoint of Free

Radicals, J. Am. Chem. Soc., 53, 1959, 1931.

Rice F.O., Herzfeld K.F. The Thermal Decomposition of Organic Compounds from the

Standpoint of Free Radica1s. VI. The Mechanism of Some Chain Reactions, J. Am. Chem.

Soc., 56, 284, 1934.

Schoenmakers P.J., Oomen J., Blomberg J., Genuit W., Van Velzen G., Comparison of

comprehensive two-dimensional gas chromatography and gas chromatography – mass

spectrometry for the characterization of complex hydrocarbon mixtures, Journal of

Chromatography A, 892, 1-2, 2000, 29-46.

Steiner H., Introduction to Petroleum Chemicals, Pergamon press (New York), 1961.

Van Damme P.S., Willems P.A., Froment G.F. Temperature, not Time, Controls Steam Cracking

Yields, Oil & Gas Journal., 68-73, 1984.

Vercauteren C. Rigoureuze Kinetische Schema’s voor de Thermische Kraking van

Koolwaterstoffen, PhD dissertation, UGent, 1991.

Van Geem K., Reyniers M.F., Marin G.B., Two Severity Indices for Scale-Up of Steam Cracking

Coils, Ind. & Eng. Chem. Res., 44, 10, 2005, 3402-3411.

Van Geem K.M., Single event microkinetic modeling for steam cracking of hydrocarbons, PhD

dissertation, UGent, 2006.

Van Geem K.M., Reyniers M.F., Marin G.B., Song J., Mattheu D.M., Green W.H. Automatic

Network generation using RMG for Steam Cracking of n-Hexane, AIChE Journal, 52, 718-

730, 2006.

Zimmermann H., Walzl E., Ethylene, Ullmann’s Encyclopedia of Industrial Chemistry, 2002.

modeling steam cracking of complex hydrocarbons helge...

Documents