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Development of
Visualization Models for
the Correlations Between
Synthetic Jet Fuels
Hydrocarbon Structure
and their Properties
Elfatih Elmalik, Jahanur Rahman, Nimir Elbashir
Texas A&M University at Qatar
2012 American Institute of Chemical Engineers Annual Meeting
Pittsburgh, PA
October 31st, 2012

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Outline
Introduction
Project Structure
Blending Studies
Statistical Analysis
Visualization Development
Summary

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Oil
Gas
Coal
Hydro
Nuclear
Renewable
Total Primary Energy: 4 EJ/year
Potentials for natural gas to play a
major role in the “Energy Market”
0 5 10 15 20 25 30
Russia
Iran
Qatar
Saudi Arabia
United Arab Emirates
United States
Algeria
Nigeria
Venezuela
Iraq
Indonesia
Australia
Malaysia
Rest of the world
Total Reserve 6,607 tcf

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Oil
Gas
Coal
Hydro
Nuclear
Renewable
Total Primary Energy: 4 EJ/year
Physical
1/600 volume
Natural Gas
Pipeline
LNG
GTL
Qatar’s aspiration to become the “World Gas Capital” led to the building the largest
GTL and LNG plants in the world.
Natural Gas Processing

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Dolfin Gas Project OryxGTL Plant
ExxonMobil LNG FacilitiesShell the Pearl GTL Plant

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Introduction – Energy Market
- Major producers and users are
located at great distances from
each other.
- Fuels must be transported
great distances.
- Due to transportation
concerns, liquid fuels are favored.
Figure 3: Major trade movements 2009 (Millions of tons)
[BP Statistical Review of world energy 2010]

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Cleaner Skies
Qatar Airways Makes Historic Journey With
First GTL Fueled Commercial Flight From
London Gatwick To Doha.
New Gas-to-Liquids Fuel offers Diversity of
Supply and better local Air Quality at busy
Airports.

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Consortium
 A unique collaboration
between industry and
academia partners.
 Each partner works on specific
topics and collaborate towards
the overall objective.
 The testing is split up as
follows:
Properties Testing Combustion Testing Performance Review
Technical Guidance
Funding Agencies

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Overview of TAMUQ Fuel Characterization Lab
 Built a world class research lab to
support the development of the Fuel
Technology Capabilities of Qatar for
Gas-to-Liquid (GTL) processes.

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Supercritical Fluid FTS Reactor
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Research Goals
 Work with industry & academia partners to
develop future synthetic jet fuels obtained via
Gas-to-Liquid [GTL] (i.e. Synthetic Paraffinic
Kerosene [SPK]).

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Experimental
Objectives:
 To develop correlation between the property and
the hydrocarbon structure
 Blending of GTL Kero with chemical solvents to alter
its physical properties
 To optimize the physical properties, so that they lie
within the limits imposed for Jet Fuels as given in
Table 2.
Property Min Max
Density (g/ml) 0.775 0.84
Flash Point (°C) 38
Freezing Point (°C) -47
Viscosity @ -20°C cSt 8
Heat Content (MJ/Kg) 42.8
Table 2: Jet Fuel Property Limits

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GTL Kerosene
Region of optimal
properties
Raza, Elmalik & Elbashir 2011. Perp. Fuel Chem. Div. 56; p. 431.
Property Min Max
Density (g/ml) 0.775 0.84
Property Min Max
Freezing Point
(°C)
-47
Property Min Max
Flash Point (°C) 38
Initial Assessment
n-Paraffin iso-Paraffin
cyclo-Paraffin

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Blending Strategy
• Aim to refine the compositional maps by starting with a broad mix
of blends
• A broad initial scope will allow for a better understanding of how
the linearity and non-linearity properties vary with n-paraffin, i-
paraffin and cylcoalkane content.
• The scope can then be narrowed towards the area of ultimate focus
by using neural network statistical analysis.
• The area of ultimate focus is fluid and will be constantly updated
after each batch of blends is tested.

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Density
• Strongly linear results observed
• Density strongly effected by the
cyclo-paraffin composition
• normal- and iso- paraffins have
low densities, less than the
aviation requirements
0
20
020406080
80
np
0
80
60
20
ip
40
20
40
0
np60
80
60
cp
80
40
20
0
0
20
40
6080 np
cp
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
0.86
GTL Kero
g/ml

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Freezing Point0
20
40
020406080
60
80
np
0
80
60
20
ip
40
20
40
0
np
60
80
60
cp
80
40
20
0
0
20
80
-70
-65
-60
-55
-50
-45
-40
-35
-30
-25
-20
GTL Kero
°C
• The use of other solvents causes significant
changes in the freezing point
• This indicates that carbon number may have a
larger influence on the freezing point than
previously discussed

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20
020406080
80
np
0
80
60
20
ip
40
20
40
0
np
60
80
60
cp
80
40
20
0
0
020406080
np
30
35
40
45
50
55
60
Flash Point
• Linear results observed
• Majority of points meet the target
flash point of 50 °C
°C
GTL Kero

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20
020406080
80
np
0
20
406080
np
0
80
60
20
ip
40
20
40
0
np
heatcontent
60
80
60
cp
80
40
20
0
42.5
43
43.5
44
44.5
Heat Content
• Mainly Linear Results observed
• Along the iso-paraffin axis there appears
to some non-linearity
• All areas meet the jet fuel limits for heat
content
GTL Kero
MJ/Kg

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Region of Optimum Properties
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Density Freezing point
Flash point Heat content
Overlap

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Artificial Neural Network
• Neural network analysis is used to develop a
link between input and output values.
• In this study the input values are the 3
compositions (technically 2 inputs since the
balance is 1), and the output values are the
properties.
• The network developed was trained using
the results from phase experimental data.
• The network was able to make strong
linkages between the inputs and outputs for
most of the properties.
• The model can be improved by increasing
the number of data points.
Neural Network Regression

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Results - Density
Density Results: ANN shows excellent predictability
g/mL
Experimental Results Neural Network Results
g/mL

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Results – Freezing Point
°C
Freezing Results: ANN shows excellent predictability
Experimental Results Neural Network Results
°C

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Aromatics in Jet Fuels
An experimental campaign concerned with evaluating
the role of aromatics was executed in two tracks as
follows:
Track 1: mono-aromatic (Toluene) was added to GTL-
Kero (SPK).
Track 2: mono-aromatic (Toluene) was added to the
previously established mixtures of n-, iso- and cyclo-
paraffins.
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Visualization
3-D neural network supports two types of analysis:
 Surface or area analysis (2-D analysis of the four surfaces of the
pyramid)
 Depth or volumetric analysis (3-D analysis or “slices” within the
pyramid)
Both are unique analysis tools, with the 3-D pyramid being
crucial in incorporating extra inputs.

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Skeleton of 3-D Pyramid

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Surface & Area Analysis

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ANN 3-D Visualization

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Summary and Future Work
 The methodology and the programing we developed as the
outcome of this research project will be extended to look at
different synthetic jet fuels compositions of different carbon
numbers.
 Visualize and identify the optimum composition of synthetic jet
fuels in the presence of aromatics.
 Our research efforts are directed towards finding replacement(s)
of these aromatics from the heavy hydrocarbons to minimize their
composition in jet fuels, and this 3-D visualization technique will
significantly improve our visualization of the experimental data
and reduce data analysis required to identify the optimum region
of composition.

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Acknowledgements
Collaborators
Willem Scholten
Ali Al-Sharshani
Dr. Joanna Bauldreay
Prof. Chris Wilson Dr. John Moran
Prof. Manfred Aigner
Dr. Patrick LeClercq
Paul Bogers
Funding Agencies:
Prof. Reza Sadr

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CHEMICAL ENGINEERING
PROGRAM
336F Texas A&M Engineering Building
Education City
PO Box 23874
Doha, Qatar
Tel. +974.423.0017
Fax +974.423.0065
chen@qatar.tamu.edu
http://chen.qatar.tamu.edu
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Thank
You!
Questions?