Elbashir AIChE 2012 - Visulaization

CHEMICAL ENGINEERING PROGRAM

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


Outline

Introduction

Project Structure

Blending Studies

Statistical Analysis

Visualization Development

Summary


Oil

Gas

Coal

Hydro

Nuclear

Renewable

Total Primary Energy: 4 EJ/year

Potentials for natural gas to play amajor 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


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


Dolfin Gas Project OryxGTL Plant

ExxonMobil LNG FacilitiesShell the Pearl GTL Plant


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]


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.


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

http://www.dlr.de/

http://www.dlr.de/


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.


Supercritical Fluid FTS Reactor

10


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]).


ExperimentalObjectives:

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


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


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.


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

40

60

80

020406080

0

20

40

60

80

np

cp ip

0

80

60

20

ip

40

20

40

0

np60

80

60

cp

80

40

20

0

0

20

40

60

80

020406080

0

20

40

60

80

np

cp ip

0.7

0.72

0.74

0.76

0.78

0.8

0.82

0.84

0.86

GTL Kero

g/ml


Freezing Point 0

20

40

60

80

020406080

0

20

40

60

80

np

cp ip

0

80

60

20

ip

40

20

40

0

np60

80

60

cp

80

40

20

0

0

20

40

60

80

020406080

0

20

40

60

80

np

cp ip

-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


0

20

40

60

80

020406080

0

20

40

60

80

np

cp ip

0

80

60

20

ip40

20

40

0

np60

80

60

cp

80

40

20

0

0

20

40

60

80

020406080

0

20

40

60

80

np

cp ip

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


0

20

40

60

80

020406080

0

20

40

60

80

heatcontent

np

cp ip

0

20

40

60

80

020406080

0

20

40

60

80

heatcontent

np

cp ip

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


Region of Optimum Properties

19

Density Freezing point

Flash point Heat content

Overlap


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


Results - Density

Density Results: ANN shows excellent predictability

g/mL

Experimental Results Neural Network Results

g/mL


Results – Freezing Point

°C

Freezing Results: ANN shows excellent predictability

Experimental Results Neural Network Results

°C


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.

23


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.


Skeleton of 3-D Pyramid


Surface & Area Analysis


ANN 3-D Visualization


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.


Acknowledgements Collaborators

Willem ScholtenAli Al-SharshaniDr. Joanna Bauldreay

Prof. Chris Wilson Dr. John Moran

Prof. Manfred AignerDr. Patrick LeClercq

Paul Bogers

Funding Agencies:

Prof. Reza Sadr

http://www.dlr.de/

http://www.dlr.de/



336F Texas A&M Engineering Building

Education City

PO Box 23874

Doha, Qatar

Tel. +974.423.0017

Fax +974.423.0065

[email protected]

http://chen.qatar.tamu.edu

30

Thank You!

Questions?

Elbashir AIChE 2012 - Visulaization

Documents

Transcript of Elbashir AIChE 2012 - Visulaization