final project report - colfuturo 2012
TRANSCRIPT
Masters in Oil and Gas Engineering, |Rudith Andrea Porras Cifuentes
Beneficiario COLFUTURO 2012
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Vapor – Liquid Equilibria
Measurements and Modelling of LG
multi-component mixtures including
Methane, Ethane, Propane, and
Butane at cryogenic conditions
The University of Western Australia,
School of Mechanical and Chemical Engineering
In the research group of
Professor Eric May (Centre of Energy)
2012
Rudith Andrea Porras Cifuentes
Masters in Oil and Gas Engineering
9th November 2012, Perth
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Acknowledgement
The author would like to acknowledge to her Supervisor Winthrop Professor Eric May; for his
constant direction, support and guidance throughout the project. Further thanks go to Professor
Thomas Hughes his guidance during this project. Jerry Guo must be acknowledged for his extensive
assistance through the experimental part of the project.
Special thank you go to the author’s parents for all their supports. Final acknowledgment must go to
the Western Australian Energy Research Alliance, the Chevron Energy Technology Company and
the Australian research Council for their continued funding of this research project.
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List of figures
Figure 1. %orth West Shelf Project area ............................................................................................................. 9
Figure 2. L%G Trade Flows in 2009 ................................................................................................................... 2
Figure 3. Liquefied facility process schematic and the location of scrub column ............................................ 11
Figure 4 L%G Scrub column in Aspen HYSYS .............................................................................................. 15
Figure 5 Deviation in composition from SRK to PR EOS ................................................................................. 15
Figure 6. Schematic diagram of the VLE apparatus ........................................................................................ 17
Figure 7. Diagram of mixing apparatus ............................................................................................................ 22
Figure 8. Phase envelope for binary mixture and experimental pathways ....................................................... 22
Figure 9. Phase envelope for multicomponent mixture and experimental pathways ........................................ 24
Figure 10. Liquid mole fraction residuals using PR and SRK in HYSYS .......................................................... 27
Figure 11. Vapour mole fraction residuals using PR and SRK in HYSYS ........................................................ 27
Figure 12. Liquid mole fraction residuals using PR and SRK in HYSYS .......................................................... 27
Figure 13. Vapour mole fraction residuals using PR and SRK in HYSYS ........................................................ 27
Figure 14. Liquid mole fraction vs. Pressure for a comparison among literature data and experimental data at -29C . 28
Figure 15. Vapour mole fraction vs. Pressure for a comparison among literature data and experimental data at -29C 28
Figure 16. Comparison deviation mole fraction between PR and SRK for the isochore pathway, binary experiment .... 29
Figure 17. Comparison deviation mole fraction between PR and SRK for the isotherm pathway, binary experiment ... 29
Figure 18. Liquid Mole fraction residuals from GERG EOS, Isochore pathway ............................................. 29
Figure 19. Vapour Mole fraction residuals from GERG EOS, Isochore pathway ........................................... 30
Figure 20. Comparison deviation mole fraction among PR, SRK and GERG for the isochore pathway ......... 30
Figure 21. Liquid Mole fraction residuals from GERG EOS, Isotherm pathway\ ............................................ 31
Figure 22. Vapour Mole fraction residuals from GERG EOS, Isotherm pathway ............................................ 31
Figure 23. Comparison deviation mole fraction among PR, SRK and GERG for the isotherm pathway ......... 32
Figure 24. Comparison deviation mole fraction among PR, SRK and GERG for the isotherm pathway without
highest point in Pressure ................................................................................................................................... 32
Figure 25. Representative BIP regresion pathway ........................................................................................... 35
Figure 26. Standard error against number iterations ....................................................................................... 35
Figure 27. Deviation in liquid phase methane using multicomponent code ..................................................... 36
Figure 28. Deviation in liquid phase of methane when using tuned literature binary parameters ................... 37
Figure 29. Scrub column simulated using Aspen HYSYS .................................................................................. 38
Figure 30. Multi-component tuning starting from default BIP values .............................................................. 44
Figure 31. Aij parameters against number of iterations for tuning multi-component mixture starting from default BIPs values ...... 44
Figure 32. Diagram valves for filling and evacuating the equilibrium cell ...................................................... 48
Figure 33. Diagram of mixing apparatus .......................................................................................................... 51
Figure 34. Diagram for the VLE apparatus ...................................................................................................... 52
Figure 35. Rolsi Valve Diagram ....................................................................................................................... 53
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List of tables
Table 1. Destination of Australia's L%G exports in 2010 ................................................................................... 9
Table 2. Default BIP in HYSYS ......................................................................................................................... 21
Table 3. Summary of gravimetric mixture composition and uncertainties ........................................................ 23
Table 4. Summary of experimental VLE data for binary mixture C1+nC4 Isochore pathway .......................... 23
Table 5. Summary of experimental VLE data for the binary mixture C1+nC4 Isotherm pathway .................... 24
Table 6. Summary of gravimetric mixture composition and uncertainties ........................................................ 24
Table 7. Summary of VLE data for multi-component mixture Isochore pathway, liquid phase ........................ 25
Table 8. Summary of VLE for multi-component mixture Isochore pathway, vapour phase ............................ 25
Table 9. Summary of VLE data for multi-component mixture Isotherm pathway, liquid phase ...................... 26
Table 10. Summary of VLE data for multi-component mixture Isotherm pathway, vapour phase ................... 26
Table 11. Range in pressure and temperature for binary literature data ......................................................... 34
Table 12. Aijs for default Hysys values and tuned with standard errors absolute and relative ........................ 34
Table 13. Optimized parameters against tuned literature data ......................................................................... 36
Table 14. Comparison in mass flow when using default Aijs values and optimized ......................................... 38
Table 15. Response Procedures ........................................................................................................................ 54
Table 16. Description of resources required for the project ............................................................................. 55
Table 17. JSA for project ................................................................................................................................... 56
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omenclature
Aij Temperature independent binary interaction parameter
Bij Binary interaction parameter
BIP Binary interaction parameter
BWR Benedict-Webb-Rubin equation of state
C1 Methane
C2 Ethane
C3 Propane
C4 Butane
Cij Binary interaction parameter
EOS Equation of state
GERG Groupe European de Recherche Gaziere
GERG EOS Groupe European de Recherche Gaziere equations of state
L%G Liquefied %atural Gas
LPG Liquefied petroleum gases
mi Molar flow component i
%WS %orth West Shelf
P Pressure
PR Peng-Robinson equation of state
R Gas constant
Rms Root mean square
SRK Soave Redlich Kwong equation of state
T Temperature
u (xCi) Uncertainty of liquid composition
u (yCi) Uncertainty of vapour composition
V Volume
Ki, Ki=yi/xi Vapour liquid distribution ratio
VLE Vapour-liquid equilibria
xi Liquid mole fraction
yi Vapour mole fraction
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Contents
Acknowledgements ................................................................................................................................ 2
List of figures ......................................................................................................................................... 3
List of tables ........................................................................................................................................... 4
Nomenclature ......................................................................................................................................... 5
Executive Summary ......................................................................................................................... 6
1. Introduction ...................................................................................................................................... 7
Importance of Liquefied Gas Natural .............................................................................................. 8
Scrub Column ................................................................................................................................ 10
Problem Statement ......................................................................................................................... 11
2. Literature Review........................................................................................................................... 12
Introduction to Equations of State ................................................................................................. 12
Multi-parameter Equation of State ................................................................................................ 13
LNG Scrub Column Discrepancies ............................................................................................... 13
Tuning of Equations of State ........................................................................................................ 14
3. Experimental Equipment and Modelling Tool .............................................................................. 16
The VLE Apparatus ...................................................................................................................... 16
Mixing Apparatus ......................................................................................................................... 17
Gas Chromatograph System ......................................................................................................... 18
Tuning Code .................................................................................................................................. 19
BIPs in HYSYS........................................................................................................................ 19
Objective function ................................................................................................................... 20
Multi-component macro overview ........................................................................................... 20
4. Results and Discussion .................................................................................................................. 21
VLE experimental results ............................................................................................................. 21
Binary mixture results ............................................................................................................. 21
Multi-component mixture results ............................................................................................ 22
Comparison between experimental and prediction compositions ................................................ 25
Binary mixture results ............................................................................................................. 25
Multi-component mixture results ............................................................................................ 27
Tuning results................................................................................................................................. 31
Binary tuning results from literature ........................................................................................ 31
Multi-component mixture tuning results ................................................................................. 32
Scrub column results ...................................................................................................................... 35
5. Conclusions .................................................................................................................................... 37
Bibliography .................................................................................................................................. 39
A. Appendix ........................................................................................................................................ 41
Tuning results................................................................................................................................. 41
Standard Operating Procedures...................................................................................................... 42
Action plans ................................................................................................................................... 50
Resources ...................................................................................................................................... 51
Project Risks .................................................................................................................................. 51
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EXECUTIVE SUMMARY
Australia has got abundant resources of natural gas; this has a significant impact on the economy as
this industry is a direct and indirect source of employment, investment, government revenue and
development of technologies. Due to the geographical position of Australia, the only way to be part
of the market in gas natural is through the Liquefied Natural Gas (LNG) technology. An important
part of the LNG production is sold under contract basis to supply energy to Japan, China, South
Korea and Taiwan among other countries, this in conjunction with the important position in the
global LNG Trade market of Australia, being as the third largest LNG exporter in the Asia-Pacific
region and the fourth largest LNG exporter in the world make a further study about the improvement
of the actual technology with the aim of reducing the expenditure of the process (Australian
Government).
The construction of an LNG facility implies a considerable expenditure the capital, generally, the
process simulators are used to estimate the operating parameters of the liquefaction plant and to size
the equipment needed. Although, natural gas data has been widely studied under non-cryogenic
conditions, the availability of thermodynamic data for multi-component mixtures at high pressure,
and cryogenic temperatures is still poor (Laskowski L., 2008). Therefore, accurate vapour-liquid
equilibria (VLE), and calorific data for multi-component mixtures at LNG process conditions would
enhance the simulations.
The main purpose of this report is to provide VLE data for binary mixture consisted of methane and
butane and also a multi-component mixture; methane, ethane, propane and butane. Furthermore, a
comparison of these data with the predictions given by Aspen HYSYS software has made, using the
Peng-Robinson (PR), Soave Redlich Kwong (SRK) and the GERG equations of state (EOSs).
Moreover, an improvement in the performance of cubic EOSs in HYSYS is studied by anchoring
these data using a tuning code. Finally, analyse the impact of those finding in the LNG scrub column.
The experimental part of the project has completed using a cryogenic VLE apparatus, while the
modelling part has done by using a tuning code that interacts between HYSYS and MS Excel; it
optimizes an objective function by the modification of binary interaction parameters in the equation
of state.
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1. I#TRODUCTIO#
Gas processing is important to the Australian economy. Significant expenditure is made on the
capital involved in construction and operation of facilities, due to extreme conditions such as low
temperatures and high pressures. A major process operation in an LNG plant is the scrub column,
where the liquefied petroleum gases (LPG) and the heavier hydrocarbons liquids are separated from
the stream heading to the main cryogenic heat exchanger to avoid potential freezing in further
downstream processing. Often simulator predictions for the product streams of these columns deviate
significantly from actual product streams observed in operating plant data. (Kandil M., 2011)
Current LNG production systems are over-designed, due to unreliable predictions of the process
simulators. Process simulators have incorporated a wide range of hydrocarbons data at different
conditions, however there is a lack of data at the process conditions in the scrub columns and heat
exchangers for the production of LNG therefore; this unreliability has been overcome by over-
engineering, which is expensive in both capital and operational costs.
1.1 Importance of Liquefied Gas atural
Natural gas is created by two mechanisms. A biogenic gas generated by marshes, bogs, landfills in a
methanogenic reaction and the second one a thermogenic gas deeper in the earth, which is product of
buried organic material under high pressure (USGS, Science for a changing world). With increasing
global energy demand, natural gas has an important role in energy supply. Due to huge discoveries in
the North of Western Australia the gas market has expanded faster than those of other fossil fuels
(Economides., 2009). Natural gas continues to be the fuel of choice for many regions of the world to
generate electric power and in industrial use, because its relative low carbon intensity compared with
oil and coals. It is also an attractive option for those countries interested in decreasing greenhouse
emissions. (U.S. Energy Information Administration). However, natural gas is a more difficult
resource to harness than oil and coal. Developments of technologies such as LNG are the effective
way that natural gas can have a dominant role in the global energy supply.
The production of LNG is the only practical way Australia can participate in the international trade
of natural gas, as transportation pipelines are generally only built on the land and are impractical
across oceans. Australian gas reserves account about 8% of the world conventional gas reserves.
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Australia is the third largest LNG exporter in the Asia-Pacific region and the fourth largest LNG
exporter in the world, with exports of 18.9 million tonnes in 2011. The benefits from the LNG
industry are long-term employment, government revenue among others. (Australian Government).
The major resources have been identified offshore of North and Western Australia in the North West
Shelf (NWS) Project area with project fields such as Gorgon, Browse, Greater Sunrise, Pluto,
Wheatstone, Ichthys and Prelude as given in figure 1 (May, 2009)
Australia has sales contracts in place to supply LNG to China, Japan, South Korea, and Taiwan
among others. In 2010, 69% of LNG’s exports were to Japan as illustrated in Table 1. This data is
complemented with the figure 2, and the LNG trade flows.
Country Export volume (million tonnes)
Japan 13.28
China 3.92
South Korea 1.03
Taiwan 0.82
Other 0.06
Total 19.11
Table 1. Destination of Australia's L%G exports in 2010 (Department of Resources, 2010)
Figure 1. %orth West Shelf Project area
(May, 2009)
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Figure 2. L%G Trade Flows in 2009 (IEA)
1.2 Scrub Column
The LNG scrub column (de-methanizer) is the major interest in a train processing liquefied natural
gas, due to its operating conditions; low temperatures and elevated pressures with multi-component
hydrocarbon mixtures. It is used to separate the lighter components (C1 and C2) from the heavier
components (C3+) present in gas feed stream. (Laskowski L., 2008)
A schematic figure 3 is illustrating the liquefaction process and the location of the de-methanizer
column below.
The three major objectives of the scrub column within the process train are; (Laskowski L., 2008)
a. Preventing from freezing heavier components in the downstream Main Cryogenic Heat
Exchanger (MCHE).
b. Controlling the heating value of the LNG.
c. Controlling the hydrocarbon dew point of the LNG upon regasification to meet pipeline
transport specifications.
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Figure 3. Liquefied facility process schematic and the location of scrub column (Laskowski L., 2008)
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Previous work (Laskowski L., 2008) outlined discrepancies between two equation of state commonly
regarded as equivalent, Peng-Robinson (PR) and Soave Redlich Kowng (SRK), in the prediction of
equilibrium in the LNG scrub column. Those compositional discrepancies were significant for
butanes.
1.3 Problem Statement
The research aims are:
• Measure new VLE for a binary mixture (C1+nC4) and a multi-component hydrocarbon mixture
(C1 to C4)
• Compare experimental data with the predicted from simulators such as AspenTech HYSYS,
• Anchor and underline thermodynamic models to real data characteristic LNG fluids and
conditions,
• Analyse the impact of those finding in the scrub column.
New measured data from VLE experiments will be compared with the defaults HYSYS prediction
using Peng-Robinson and Soave Redlich Kwong EOSs.
With a nonlinear model which is coded in Visual Basic for Applications (VBA) in Microsoft Excel
tuning of composition of liquid and vapour with the absolute residuals.
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2. LITERATURE REVIEW
2.1 Introduction to Equations of State
An equation of state is described as an empirically-derived function which relates temperature,
pressure, density and composition, in mixtures, for a real fluid. (Assael M., 1996)
Equations of state are used in the prediction of thermodynamic properties of pure fluids and fluid
mixtures, because they provide a thermodynamically consistent route to the properties of gases and
liquid phases (Assael M., 1996). The most well-know application of EOS is in determination of
phase equilibrium conditions and properties.
In 1662, Boyle deduced based on experimentation on air, that a given temperature, the volume of a
fixed mass of gas is inversely proportional to its pressure. The effect of temperature was observed by
Charles (1787), later on Gay-Lussac (1802) found a linear dependence between volume and
temperature at constant pressure. Considering Dalton’s law of partial pressures a postulated in 1801,
suggested the relation given by equation 1 (Assael M., 1996).
= ∑ / (1)
Equation (1) could predict gas phase at low pressure and high temperature. Further experimentation
leaded Van der Waals EOS (1873), which was the first EOS capable of predicting gaseous and liquid
phases concurrently. Current equations of state models are simple empirical modifications of Van der
Waals EOS, which retain its basic cubic form. (Assael M., 1996)
= −
(2)
For hydrocarbon processing such as LNG production, the cubic EOS of Soave, Redlich and Kwong
(SRK) and Peng Robinson (PR) are the most used. Although, those EOS have some deficiencies,
some correction techniques are available (e.g. volume translation (Peneloux A., 1982), excess free
energy) (Wong D., 1992). Despite of deficiencies, the unmodified SRK and PR EOS are still
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recommended for predicting the VLE behaviour of multi-component mixtures of light hydrocarbons
(Laskowski L., 2008) (Valderrama J O, 2003)
2.2 Multi-parameter Equation of State
Complex equations of states expand on the cubic equations by non-linear multi-parameter models
which offer improved accuracy (Assael M., 1996). A commonly used multi-parameter EOS is the
Han-Starling modified Benedict-Webb-Rubin (BWR) equation
= + − − + − !" + ##$ + %& 1 + ()*+−( (3)
The BWR EOS is a truncated virial equation that allows for pressure calculation from a density
polynomial, additional temperature dependent coefficients and an exponential term that account
truncated virial series. (Assael M., 1996). Han and Stirling updated the original version by including
generalised coefficients based on acentric factors and critical constants for pure fluids and combining
rules for mixtures.
Multi-parameter EOSs are based on a wide range of reliable thermodynamic data for a specific
number of fluids. In 2004, the Groupe European de Recherche Gaziere (GERG) introduced a new
multi-parameter EOS for natural gas mixtures (up to 18 components) which has a range of
temperatures from -183C to 177C and pressures to 35000kPa; with uncertainties for VLE properties
of 1-5%. (Kunz O., 2007)
The GERG-2004_XT08 EOS is an important advance in the description of thermodynamic natural
gas mixtures, although its computational complexity is incompatible with the iterative algorithms;
those which are incorporated into current process simulators. (Laskowski L., 2008)
2.3 Scrub Column Discrepancies
Laskowski outlined a comparison between two commonly EOSs; Peng-Robinson and Soave Redlich
Kwong using a simple simulation of the LNG scrub column, as represented in figure 5 (Laskowski
L., 2008), by the use of Aspen HYSYS from AspenTech (Laskowski L., 2008). These EOSs that
under non-cryogenic conditions are considered as equivalent (Ryan B. , 2011), however, the results
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pointed to a significant deviation between the predictions of them. The results are illustrated in figure
4 and 5 (Laskowski L., 2008). Figure 4 outlined the relative change in mole fraction for the liquid
bottoms and the distillates product.
Figure 4 L%G Scrub column in Aspen HYSYS Figure 5Deviation in composition from SRK to
PR EOS (Laskowski L., 2008)
2.4 Tuning of Equations of State
The binary interaction parameters (BIP) are empirically based; they can be modified or altered for
optimizing accuracy in thermodynamic prediction; although this solution is not new with several
methods presented within literature (Ashour I., 1996) (Paunaovic R.S., 1981) (Englezos P., 1989)
(Shibata S.K., 1989).
The BIP are defined according to equation below, where Aij value is temperature independent;
conversely the Bij and Cij. Szanjnkienig (2009) found that the binary system prediction ability is
highly improved by the Aij parameter, but the Bij one is not statistically justifiable, therefore the
optimization should focus purely on the first parameter. (Ryan, 2011)
, = - + - + .- (4)
The measured VLE data is tuned by the variation of binary interaction parameters by minimising an
objective function, which is an equation that add deviation between calculated and predicted
Product Compositions: From SRK to PR EOS
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
C1 C2 C3 iC4 nC4 iC5 nC5 nC6 nC7
Component
Relative Change in Mole Fraction
Vapor
Liquid
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experimental values for a selected equilibrium property (Ashour I., 1996). The default BIP values of
the Peng-Robinson EOS are used as starting point for the optimization.
Some of the objective functions are given by equations (5) and (6), for absolute and relative error
respectively.
/ = ∑ 0,232 − 0,4567489 (5)
/ = ∑ :;.=>?=:;,@>ABC@D:;,@>ABC@D
9 (6)
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3. EXPERIME#TAL EQUIPME#T A#D MODELLI#G TOOL
Detailed standard operation procedures are given in the Appendix for the equipments used in the
research.
3.1 The VLE Apparatus
The VLE cryogenic experimental apparatus was designed and builded by Kandil et al. in 2010. It can
operates within a temperature range of 77 to 373K (-196 to 100ºC) and pressures ranging from 0.5 to
20,000 kPa. (Kandil M., 2011) The experimental data was collected using equipment illustrated on
the figure 6;
Figure 6. Schematic diagram of the VLE apparatus (Kandil M., 2011)
The equipment consists in a stainless steel grade 316 vapour-liquid equilibrium cell of 60cm3 (EC),
with a 1 mm thick copper external lining to improve heat transfer and temperature uniformity
(Mohamed Kandil, 2010). The EC cell is positioned within an isothermal copper can which is
enclosed by a radiation shield, further positioned inside a steel vacuum cryogenic Dewar (CRY)
connected to an automatic liquid nitrogen pump (LNP).
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The lid of the cell was fitted with a custom cryogenically compatible fill valve (V1) which is
operated by a control motor (M2) when occupying and evacuating the EC. The measurement of
pressure is done by a Kulite pressure transducer (PK). (Kandil M., 2011)
Samples are measured through two capillary tubes mounted on the cell’s lid. The vapour capillary
(VV) measure the vapour phase in the cell, with length 13 cm, while the liquid capillary (VV)
measures the liquid phase in the cell, and it extends nearly to the bottom of the it, with a total length
of 20cm. (Mohamed Kandil, 2010). Both capillaries finish through the cell lid into a specialised
Rapid On-Line Sampler Injector (ROLSI) electromagnetic solenoid valve. (Kandil M., 2011)
A helium gas line is connected to each of the ROLSI samples valves, and it carries the liquid or
vapour phase through the Gas Chromatograph (GC). The GC is equipped with two capillary columns
and two flame ionization detectors for vapour (FID V) and liquid (FID L) phases respectively.
3.2 Mixing Apparatus
Gas mixtures are prepared gravimetrically using a high pressure, 300 cm3 sample cylinder and a
1100 g electronic balance with a 0.001g resolution. To ensure homogeneity metallic balls are placed
into the vessel prior to mixture preparation. A schematic diagram is illustrated in Figure 7.
The system consists on a network of ¼ inch stainless steel 316 pipes (Askarian, 2012). Valves are
operated manually. The source of pure gas comes from “Gas from Bottles” section in figure 7.
According to the “Gas from Bottles” pressure an appropriate pressure regulator should be used; for
example if the loading gas has high pressure (e.g.Methane) then the high pressure regulator needs to
be used. Conversely, if the loading gas has low pressure (e.g. Propane) the low pressure regulator is
used. Details about the Standard Operation Procedure are given in the Appendix.
The sample experimental cylinder is connected to the mixing apparatus; both are evacuated to
standard pressure and further pumped into a vacuum. The sample cylinder is isolated, detached and
weighted to determine the control mass of the loading vessel. The component with the lowest vapour
pressure or the lowest cylinder pressure should be the first in being loaded. Between loadings of
components the whole system is flushed, at least twice, with the loading gas to reduce contamination.
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This procedure; flush, fill, weight and mix, is repeated for all the desired mixture components until
the total mass of the fluid is reached.
Figure 7. Diagram of mixing apparatus (Ryan B. , 2011)
3.3 Gas Chromatograph System
Liquid and vapour phases from the VLE cell are analysed by a Varian CP-3800 gas chromatograph
(GC). Helium gas carries the samples from the vapour and liquid phase to two separate capillary
columns, approximately 25m in length and 0.53mm in diameter, lined with an absorbent liquid
material (PoraPLOT Q) (Kandil M., 2011). PoraPLOT Q absorbs both samples components, the
selectivity of this absorbent differ for each component, therefore each component will have different
retention time. The separated components are fed through a thermal conductivity detector (TCD),
which is not used in this research as a result of the lack of non-burnable components within the
experimental mixture. (Ryan B. , 2011)
Once the samples have passed through the TCD, they are sent to two separate flame ionization
detectors (FID). The detectors analyse the changes between a pure sample gas, hydrogen and the
experimental samples. Hydrogen gas and intermittently spaced sample components are fed through
the FID oven, mixed with air and combusted in a flame jet. When each sample is burnt it reacts to
create an ionised sample which can be detected by the electrode and it is compared against the pure
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hydrogen response. Equation 7 illustrates the formation of organic based ions, using the combustion
of methane as an example, respectively. (Ryan B. , 2011)
.EF + E + G → .EGI +EG + EI + 2) (7)
Carbon atoms within the original organic compound are converted into electrically charged species,
which the detector response convert into an electronic signal and sent to the GC computer for
analysis. Once each component passes through the FIDs a peak is created within a voltage against
time plot. (Ryan B. , 2011)
Mole fraction of components in each phase are calculated using the detector response are (peak area)
with a calibration factor. Equation 8 is used for estimating the compositions. (Kandil M., 2011)
0 = K;L;KMLMIKNLNI⋯IKPLP (8)
Where Zi is the component phase composition, ki, kj, kh and kN are the response factors for each
component and Ai, Aj, Ah and AN are the integrated detector responses or area under the component
peaks. (Ryan B. , 2011)
3.4 Tuning Code
The modelling part of the project is done by using a tune code; which utilize the measured VLE data
and tune the most efficient equation of state to its results. The use of a macro has been previously
tested by several authors. (Szajnkienig, 2009) (McCallum, 2010) (Butler, 2007) (Ryan B. , 2011)
3.4.1 BIPs in HYSYS
Although the binary of interaction parameters (BIP) -Aij, Bij and Cij - are accessible in HYSYS; only
the first temperature independent Aij is assigned a non-zero value within HYSYS. Table 2 illustrates
the default Aij values in HYSYS.
, = - + - + .- (4)
C1 C2 C3 nC4
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C1 - 2.24E-03 6.83E-03 1.23E-02
C2 2.24E-03 - 1.26E-03 4.10E-03
C3 6.83E-03 1.26E-03 - 8.19E-04
nC4 1.23E-02 4.10E-03 8.19E-04 -
Table 2. Default BIP in HYSYS
3.4.2 Objective function
The updated version of the multi-component macro, through (Ryan B. , 2011), included the
possibility to calculate different objective functions. The selection of the objective function (Q)
allows the user to optimise the BIPs to the vapour mole fractions (yi), liquid mole fractions (xi) or the
vapour-liquid distribution ratio (Ki, Ki=yi/xi). The general objective function is given within equation
(9). (Ryan B. , 2011)
/ = ∑ ∑ 0,-232 − 0,-456748R-STUST (9)
Where Z is x, y or K, N corresponds to the number of data points and S equals the number of
components in the mixture.
3.4.3 Multi-component Macro Overview
The macro uses the Levenberg-Marquardt (LM) algorithm in an effort to minimise the objective
function by varying the binary interaction parameters. The coding required for the macro is situated
within the Visual Basics for Applications function of Microsoft Excel. The user needs to import the
experimental data - compositions, temperature and pressure - into the “VLE Data” tab, and then
complete the user form, and initiates the LM algorithm. Initially the program calculates the overall
compositions using a flash separator in HYSYS. Arbitrary molar flow rates are used for the flash
calculation. The resulting vapour and liquid mole fractions are then exported into excel for further
calculation. (Ryan B. , 2011)
The results of each iteration are stored within separate workbooks in the excel file. The “Setup” tab
contains the firstly calculated variables such as the residual values, distribution ratio residual and the
overall molar composition. “GradHessDelt” tab outlines the variables needed to calculate the LM
algorithm as well as the gradient vector and the hessian matrix. “RegressData” tab allows for the
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pathway of the BIP values and the associated standard errors to be stored. “FinDiffApprox” tab
details the finite different approximations which are needed to complete the gradient vector. The
“Results” and component residual plots are also automatically generated, which are graphically
represented in additional tabs. (Ryan B. , 2011)
4. RESULTS A#D DISCUSSIO#
4.1 VLE Experimental Results
The composition of the vapour and liquid phase for the binary and multi-component mixture were
collected along two pathways; an isochoric and isothermal one.
4.1.1 Binary mixture; Methane (C1) and butane (C4)
For the isochore pathway, the measurements were taken over a temperature range between -70ºC and
25ºC, with pressures from 4000 kPa up to 8800 kPa.
On the other hand the isotherm measurement was taken at -29ºC, with pressures from 1300 kPa up to
10000 kPa. The phase envelope as predicted by the VMG APR EOS, along both pathways are
illustrated within Figure 8 below.
Figure 8. Phase envelope for binary mixture and experimental pathways
0
2000
4000
6000
8000
10000
12000
-100 -80 -60 -40 -20 0 20
p [
kP
a]
T [C]
VMG APR EOS: 0.9353CH4+0.0647nC4H10
Bubble Point Dew Point Isochore Isotherm
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The measurements were repeated in order to confirm the accuracy of the measurements taken. The
gravimetric composition of the mixture is given in Table 3 it also includes the uncertainty associated
with the mixture preparation.
Composition Gravimetric
Mixture
Uncertainty
ZC1 0.9353 0.00007
ZnC4 0.0647 0.00007
Table 3. Summary of gravimetric mixture composition and uncertainties
Within Table 4 to 5, there is a summary of the vapour liquid equilibrium data for the binary mixture
with both pathways described above. The uncertainties of each measurement, u, represent the
standard deviation within each point. It must be noted that this uncertainty does not take into account
the associated with the GC calibrations. Each composition point is the average of approximately 4-6
points taken sequentially. This method of experimentation ensures reliable data collection and
therefore reliable results.
T [ºC] p [kPa] xC1 xnC4 yC1 ynC4 u(xC1) u(yC1)
25.0 8776.0 0.93358 0.06642 0.93439 0.06561 0.00082 0.00072
25.0 8598.3 0.93632 0.06368 0.93632 0.06368 0.00095 0.00095
0.0 7428.8 0.42463 0.57537 0.95175 0.04825 0.00307 0.00105
-10.0 6993.8 0.43307 0.56693 0.96429 0.03571 0.00403 0.00074
-20.0 6557.0 0.44797 0.55203 0.97351 0.02649 0.00314 0.00029
-29.0 6104.2 0.46007 0.53993 0.98033 0.01967 0.00391 0.00015
-30.0 6110.8 0.46461 0.53539 0.98116 0.01884 0.00392 0.00022
-40.0 5669.6 0.48802 0.51198 0.98742 0.01258 0.00308 0.00023
-40.0 5681.4 0.48923 0.51077 0.98530 0.01470 0.00311 0.00027
-50.0 5216.2 0.52004 0.47996 0.99251 0.00749 0.00294 0.00037
-60.0 4731.8 0.55939 0.44061 0.99150 0.00850 0.00470 0.00250
-70.0 4231.8 0.62166 0.37834 0.99636 0.00364 0.00294 0.00005
Table 4. Summary of experimental Vapour Liquid Equilibrium data for binary mixture C1+C4
Isochore pathway
T [ºC] p [kPa] xC1 xnC4 yC1 ynC4 u(xC1) u(yC1)
-29.0 1310.8 0.10705 0.89295 0.97108 0.02892 0.00160 0.00018
-29.0 3318.0 0.26685 0.73315 0.98245 0.01755 0.00378 0.00030
-29.0 5058.0 0.39500 0.60500 0.98448 0.01552 0.00238 0.00197
-29.0 6593.0 0.50231 0.49769 0.98148 0.01852 0.00419 0.00010
-29.0 8237.7 0.61796 0.38204 0.97478 0.02522 0.00554 0.00589
-29.1 9160.8 0.68639 0.31361 0.96014 0.03986 0.00273 0.00060
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-29.0 9913.4 0.75333 0.24667 0.94822 0.05178 0.00254 0.00090
-29.0 10131.8 0.77744 0.22256 0.94022 0.05978 0.00254 0.00090
Table 5. Summary of experimental Vapour Liquid Equilibria data for the binary mixture C1+C4 Isotherm
pathway
4.1.2 Multi-component mixture Methane, Ethane, Propane and Butane C1C2C3C4
For the isochore pathway, the measurements were taken over a temperature range between -70ºC and
25ºC, with pressures from 3800 kPa up to 10900 kPa.
On the other hand the isotherm measurement was taken at -30ºC, with pressures from 1600 kPa up to
8800 kPa. The phase envelope as predicted by the VMG APR EOS, along both pathways are
illustrated within Figure 9 below.
Figure 9. Phase envelope for multicomponent mixture and experimental pathways
The measurements were repeated in order to confirm the accuracy of the measurements taken. The
gravimetric composition of the mixture is given in Table 6 it also includes the uncertainty associated
with the mixture preparation.
Composition Gravimetric
Mixture
Uncertainty
ZC1 0.7800 0.00090
ZC2 0.1170 0.00008
ZC3 0.0500 0.00004
ZnC4 0.0520 0.00004
Table 6. Summary of gravimetric mixture composition and uncertainties
0
5000
10000
15000
-100 -80 -60 -40 -20 0 20
p [
kP
a]
T [C]
0.780CH4+0.117C2H6+0.050C3H8+0.052nC4H10
Bubble Point Dew Point Isochor Isotherm
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Within Table 7 to 10, there is a summary of the vapour liquid equilibrium data for the binary mixture
with both pathways described above. The uncertainties of each measurement, u, represent the
standard deviation within each point. It must be noted that this uncertainty does not take into account
the associated with the GC calibrations. Each composition point is the average of approximately 4-6
points taken sequentially.
T [ºC] p [kPa] xC1 xC2 xC3 xnC4 u(xC1) u(xC2) u(xC3) u(xnC4)
30.0 10947.0 0.77823 0.11677 0.05277 0.05223 0.00207 0.00269 0.00031 0.00030
30.0 10283.7 0.77702 0.11767 0.05351 0.05180 0.00231 0.00199 0.00029 0.00076
0.0 8421.3 0.49698 0.16998 0.13164 0.20140 0.00316 0.00207 0.00094 0.00201
-10.0 7820.5 0.50513 0.17790 0.13261 0.18436 0.00276 0.00204 0.00102 0.00178
-20.0 7222.9 0.52090 0.18489 0.13017 0.16404 0.00333 0.00216 0.00099 0.00230
-30.0 6583.5 0.53861 0.19015 0.12564 0.14560 0.00393 0.00202 0.00062 0.00265
-30.0 6315.3 0.51608 0.19560 0.13222 0.15609 0.00308 0.00215 0.00110 0.00205
-30.0 6308.4 0.51447 0.19596 0.13287 0.15671 0.00322 0.00213 0.00109 0.00233
-40.0 5936.1 0.55929 0.19182 0.11722 0.13167 0.00390 0.00213 0.00083 0.00313
-40.0 5687.2 0.53686 0.19937 0.12502 0.13875 0.00390 0.00228 0.00088 0.00247
-50.0 5255.3 0.58527 0.19157 0.10910 0.11406 0.00316 0.00247 0.00070 0.00140
-60.0 4575.0 0.61860 0.18542 0.09786 0.09812 0.00388 0.00232 0.00046 0.00198
-70.0 3882.2 0.64811 0.17682 0.08847 0.08660 0.00373 0.00247 0.00024 0.00150
Table 7. Summary of Vapour Liquid Equilibria data for multi-component mixture C1C2C3nC4 by Isochore
pathway, liquid phase
T [ºC] p [kPa] yC1 yC2 yC3 ynC4 u(yC1) u(yC2) u(yC3) u(ynC4)
30 10947 0.77737 0.11733 0.05323 0.05207 0.00277 0.00256 0.00035 0.00055
30 10283.7 0.77659 0.11733 0.05382 0.05227 0.00181 0.00163 0.00039 0.00069
0 8421.3 0.80793 0.11151 0.04489 0.03567 0.00212 0.00179 0.00039 0.00068
-10 7820.5 0.83452 0.10451 0.03675 0.02422 0.00199 0.00181 0.00036 0.00051
-20 7222.85 0.85962 0.09566 0.02885 0.01587 0.00186 0.00174 0.00026 0.00034
-30 6583.45 0.88319 0.08524 0.02164 0.00993 0.00195 0.00177 0.00025 0.00031
-30 6315.3 0.88406 0.08567 0.02123 0.00905 0.00178 0.00172 0.00006 0.00013
-30 6308.4 0.88350 0.08574 0.02136 0.00939 0.00194 0.00182 0.00010 0.00018
-40 5936.1 0.90447 0.07352 0.01585 0.00616 0.00138 0.00142 0.00009 0.00006
-40 5687.15 0.90477 0.07426 0.01538 0.00558 0.00184 0.00172 0.00009 0.00010
-50 5255.25 0.92492 0.06103 0.01069 0.00336 0.00153 0.00151 0.00014 0.00011
-60 4575 0.94539 0.04608 0.00676 0.00178 0.00074 0.00075 0.00018 0.00022
-70 3882.2 0.95989 0.03454 0.00431 0.00126 0.00050 0.00050 0.00004 0.00004
Table 8. Summary of Vapour Liquid Equilibria data for multi-component mixture C1C2C3nC4 by Isochore
pathway, vapour phase
T [ºC] p [kPa] xC1 xC2 xC3 xnC4 u(xC1) u(xC2) u(xC3) u(xnC4)
-30.0 1608.6 0.12245 0.13534 0.25337 0.48884 0.00157 0.00043 0.00330 0.00447
-30.0 2657.0 0.20927 0.15912 0.22941 0.40219 0.00205 0.00083 0.00262 0.00384
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-30.0 3411.0 0.27584 0.16754 0.21113 0.34549 0.00297 0.00106 0.00235 0.00424
-30.0 4356.0 0.34860 0.16875 0.18654 0.29610 0.00321 0.00167 0.00184 0.00336
-30.0 5417.7 0.43495 0.16351 0.15942 0.24212 0.00310 0.00164 0.00139 0.00295
-30.0 6308.4 0.51447 0.19596 0.13287 0.15671 0.00322 0.00213 0.00109 0.00233
-30.0 6565.6 0.53049 0.15084 0.12975 0.18892 0.00294 0.00173 0.00098 0.00219
-30.0 7713.7 0.63219 0.13139 0.09881 0.13761 0.00330 0.00183 0.00066 0.00221
-30.0 8288.2 0.69160 0.12520 0.08155 0.10166 0.00309 0.00181 0.00060 0.00193
-30.0 8778.7 0.75579 0.10260 0.06276 0.07885 0.00257 0.00184 0.00060 0.00115
Table 9. Summary of Vapour Liquid Equilibria data for multi-component mixture C1C2C3nC4 by Isotherm
pathway, liquid phase
T [ºC] p [kPa] yC1 yC2 yC3 ynC4 u(yC1) u(yC2) u(yC3) u(ynC4)
-30.0 1608.6 0.84976 0.09826 0.03706 0.01493 0.00166 0.00146 0.00015 0.00042
-30.0 2657.0 0.88099 0.08247 0.02635 0.01019 0.00172 0.00151 0.00011 0.00027
-30.0 3411.0 0.89146 0.07616 0.02307 0.00931 0.00144 0.00135 0.00032 0.00026
-30.0 4356.0 0.89941 0.07093 0.02101 0.00865 0.00139 0.00138 0.00029 0.00020
-30.0 5417.7 0.90315 0.06727 0.02022 0.00937 0.00157 0.00144 0.00007 0.00015
-30.0 6308.4 0.88350 0.08574 0.02136 0.00939 0.00194 0.00182 0.00010 0.00018
-30.0 6565.6 0.90189 0.06556 0.02107 0.01148 0.00144 0.00126 0.00006 0.00028
-30.0 7713.7 0.89389 0.06601 0.02377 0.01633 0.00157 0.00123 0.00020 0.00039
-30.0 8288.2 0.89418 0.06780 0.02284 0.01519 0.00271 0.00159 0.00072 0.00086
-30.0 8778.7 0.87891 0.06841 0.02823 0.02444 0.00216 0.00135 0.00046 0.00067
Table 10. Summary of Vapour Liquid Equilibria data for multi-component mixture C1C2C3nC4 by Isotherm
pathway, vapour phase
4.2 Comparison between experimental and prediction compositions
4.2.1 Binary mixture
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Figure 10. Liquid mole fraction residuals using PR and
SRK in HYSYS
Figure 11. Vapour mole fraction residuals using PR
and SRK in HYSYS
The PR and SRK predicted compositions were compared to the experimental VLE cell results. The
absolute residuals were calculated for both EOSs and are outlined in Figure 10 to 11 for the isochore
measurements and within Figure 12 to 13 for the isotherm pathway; zero value represents an
accurate prediction of the phase composition. Furthermore, there is an indication of the improved
vapour predictability compared to the liquid composition predictions, of at least a factor of four in
both pathways.
Figure 12. Liquid mole fraction residuals using PR and
SRK in HYSYS
Figure 13. Vapour mole fraction residuals using PR
and SRK in HYSYS
-0.030
-0.020
-0.010
0.000
0.010
0.020
0.030
-80.0 -60.0 -40.0 -20.0 0.0 20.0
x1
-x
1_
HY
SY
S
T [C]
Liquid composition deviation:
Isochore
nC4 (PR) C1 (PR) C1 (SRK) nC4 (SRK)
-0.005
-0.004
-0.003
-0.002
-0.001
0.000
0.001
0.002
0.003
0.004
0.005
-80.0 -60.0 -40.0 -20.0 0.0 20.0
x1
-x
1_
HY
SY
S
T [C]
Vapour composition deviation:
Isochore
nC4 (PR) C1 (PR) C1 (SRK) nC4 (SRK)
-0.080
-0.060
-0.040
-0.020
0.000
0.020
0.040
0.060
0.080
0.00 0.50 1.00
x1
-x
1_
HY
SY
S
XnC4
Liquid composition deviation: Isotherm
nC4 (PR) C1 (PR) C1 (SRK) nC4 (SRK)
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.00 0.02 0.04 0.06 0.08
y1
-y
1_
HY
SY
S
YnC4
Vapour composition deviation: Isotherm
nC4 (PR) C1 (PR) C1 (SRK) nC4 (SRK)
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These graphs show that the predictive ability for both EOSs varies only minimally for all the
components and across all the entire temperature range. It must be noted that a higher deviation is
present when temperatures are below -40ºC for the isochore measurement, while for the isotherm it
occurs at n-butane compositions below 0.5.
The isotherm measurement was compared against literature data at similar temperatures for the
liquid and vapour phases, as illustrated within Figure 14 and 15. From these graphs is outlined
presence of a lot of scatter in the measured literature data, it is shown that commercial softwares (e.g.
HYSYS, VMG) match them. However, there are greater deviations at around 244K. (May E, 2012)
Figure 14. Liquid mole fraction vs. Pressure for a comparison among literature data and experimental data at -29C
Figure 15. Vapour mole fraction vs. Pressure for a comparison among literature data and experimental data at -29C
A comparison between figure 16 and 17 outline a difference in the root mean square (rms) for the
binary isotherm and isochore measurements. A possible cause of those deviations is due to the
presence of higher deviation at low n-butane compositions.
0.0
2000.0
4000.0
6000.0
8000.0
10000.0
12000.0
14000.0
0.0000 0.2000 0.4000 0.6000 0.8000 1.0000
Pre
ssu
re [
kP
a]
x (nC4)
Methane - n-Butane
Expt277.59K VMGAPR HYSYSPR Expt244.3K VMGAPR
HYSYSPR Expt210.9K VMGAPR HYSYSPR UWA -29degC
0.0
5000.0
10000.0
15000.0
0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000
Pre
ssu
re [
kP
a]
y (nC4)
Methane - n-Butane
Expt277.59K VMGAPR HYSYSPR Expt244.3K VMGAPR
HYSYSPR Expt210.9K VMGAPR HYSYSPR UWA -29degC
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Figure 16. Comparison deviation mole fraction between
PR and SRK for the isochore pathway, binary
experiment
Figure 17. Comparison deviation mole fraction between
PR and SRK for the isotherm pathway, binary
experiment
4.2.2 Multi-component mixture
The experimental mole fraction for the liquid and vapour phases of the multi-component mixture was
compared against predictions of composition for PR and SRK in HYSYS, and GERG EOSs. The
absolute residuals were calculated for them are outlined in figure 18 to 19 for the isochore
measurements and within figure 21 to 22 for the isotherm pathway.
Figure 18. Liquid Mole fraction residuals from GERG EOS, Isochore pathway
0.000
0.002
0.004
0.006
0.008
0.010
xC1 xnC4 yC1 ynC4rms
mo
le f
ract
ion
de
via
tio
n o
f
EO
S f
rom
da
ta
Component
rms mole fraction deviations of EOS
from VLE data Isochore experiment
C1nC4
PR SRK
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
xC1 xnC4 yC1 ynC4rms
mo
le f
ract
ion
de
via
tio
n o
f
EO
S f
rom
da
ta
Component
rms mole fraction deviations of EOS from
VLE data Isotherm experiment C1nC4
PR SRK
-0.010
-0.005
0.000
0.005
0.010
0.015
-80 -60 -40 -20 0 20 40
xi
-x
i,G
ER
G
T [C]
Mole fraction deviations from GERG EOS: liquid phase
Isochore
C1 C2 C3 C4 HY-PR-C1 HY-PR-C2 HY-PR-C3 HY-PR-C4
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Figure 19. Vapour Mole fraction residuals from GERG EOS, Isochore pathway
Figure 20. Comparison deviation mole fraction among PR, SRK and GERG for the isochore pathway
Analysing figure 18 and 19 show differences among the EOSs are minimal however, the highest
deviation occurs in the methane composition for most of range of temperatures. In order to compare
more detailed the predictive ability of the EOSs a root mean square of each prediction within each
phase has been represented graphically in figure 20 for isochore experiment and within figure 23 for
the isotherm one.
Figure 22, illustrates overall the PR EOS has better prediction efficiency for the composition at the
experimental conditions tested. It must be noted that the GERG EOSs also gives better results
compared with the SRK EOS.
-0.008
-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
-80 -60 -40 -20 0 20
yi
-y
i,G
ER
G
T [C]
Mole fraction deviations from GERG EOS: vapor phase
C1 C2 C3 C4 HY-PR-C1 HY-PR-C2 HY-PR-C3 HY-PR-C4
0.000
0.002
0.004
0.006
0.008
0.010
0.012
x1 x2 x3 x4 y1 y2 y3 y4rms
mo
le f
ract
ion
de
via
tio
n
of
EO
S f
rom
da
ta
Component
rms mole fraction deviations of EOS from VLE data
Isochore experiment C1C2C3nC4
PR SRK GERG
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Figure 21. Liquid Mole fraction residuals from GERG EOS, Isotherm pathway\
Figure 22. Vapour Mole fraction residuals from GERG EOS, Isotherm pathway
-0.016
-0.012
-0.008
-0.004
0.000
0.004
0.008
0.012
0.016
0 2000 4000 6000 8000 10000
xi
-x
i,G
ER
G
P [kPa]
Mole fraction deviations from GERG EOS: liquid phase
C1 C2 C3 C4 HY-PR-C1 HY-PR-C2 HY-PR-C3 HY-PR-C4
-0.010
-0.006
-0.002
0.002
0.006
0.010
0 2000 4000 6000 8000 10000
xi
-x
i,G
ER
G
P [kPa]
Mole fraction deviations from GERG EOS: Vapour phase
C1 C2 C3 C4 HY-PR-C1 HY-PR-C2 HY-PR-C3 HY-PR-C4
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Figure 23. Comparison deviation mole fraction among PR, SRK and GERG for the isotherm pathway
Figure 24. Comparison deviation mole fraction among PR, SRK and GERG for the isotherm pathway without
highest point in Pressure
A comparison between figure 20 and 23 outline a difference in the root mean square (rms) for the
isotherm and isochore measurements. A possible cause of those deviations is due to the presence of
higher deviation at 8778kPa in the isotherm measurement, probably the higher deviation affect the
root mean square (rms) values. To analyse the effect of this point the Figure 24 was made; once this
point is discarded the figure 20 and 24 are comparable.
The root mean square plots for the binary and multi-component experiments outline an increased
performance of the models for the prediction of the vapour phases; overall the PR EOS has better
prediction for the composition and experimental conditions tested.
0.000
0.005
0.010
0.015
0.020
x1 x2 x3 x4 y1 y2 y3 y4
rms
mo
le f
ract
ion
de
via
tio
n o
f
EO
S f
rom
da
ta
Component
rms mole fraction deviations of EOS from VLE data
Isotherm experiment C1C2C3nC4
PR SRK GERG
0.000
0.002
0.004
0.006
0.008
0.010
x1 x2 x3 x4 y1 y2 y3 y4
rms
mo
le f
ract
ion
de
via
tio
n
of
EO
S f
rom
da
ta
Component
rms mole fraction deviations of EOS from VLE data
Isotherm experiment C1C2C3nC4 Without point 8778 kPa
PR SRK GERG
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4.3 Tuning results
With the aim of anchor and underline thermodynamic models to real data characteristic LNG fluids
and conditions; VLE experimental data was implemented into the multi-component macro with the
purpose of tuning the binary interaction parameters (BIP).
4.3.1 Binary tuning results from literature
Literature data for binary mixtures including methane, ethane, propane and n-butane, a summary of
the conditions of them is in table 11.
Comp 1 Comp 2 plo
kPa
phi
kPa
Tlo
K
Thi
K
Source
Methane Ethane 16 6657 111 280
Price, A. R. (1957)
Cosway, H. F.; Katz, D. L. (1959)
Ellington, R. T.; Eakin, B. E.; Parent, J. D.; Gami. D. C.; Bloomer, O. T.
(1959)
Skripka, V. G.; Nikitina, I. E.; Zhdanovich, L. A.; Sirotin, A. G.;
Ben'yaminovich, O. A. (1970)
Mulholland, K. L.(1970)
Wichterle, O.; Kobayashi, R. (1972)
Wilson, G. M. (1975)
Davalos, J.; Anderson, W. R.; Phelps, R. E.; Kidnay, A. J. (1976)
Miller, R. C.; Kidnay, A. J.; Hiza, M. J. (1977)
Gupta, M. K.; Gardner, G. C.; Hegarty, M. J.; Kidnay, A. J. (1980)
Gomes de Azevedo, E. J. S.; Calado, J. C. G. (1989)
Nixdorf, J.; Oellrich, L. R. (1997)
Raabe, G.; Janisch, J.; Koehler, J. (2001)
Janisch, J.; Raabe, G.; Kohler, J. (2007)
Methane Propane 172 9997 130 344
Sage, B. H.; Lacey, W. N.; Schaafsma, J. G. (1934)
Reamer, H. H.; Sage, B. H.; Lacey, W. N. (1950)
Akers, W. W.; Burns, J. F.; Fairchild, W. R. (1954)
Benham, A. I.; Katz, D. L. (1957)
Price, A. R.; Kobayashi, R. (1959)
Cheung, H.; Wang, D. I. -J. (1964)
Cutler, A. J. B.; Morrison, J. A. (1965)
Mulholland, K. L. (1970)
Skripka, V. G.; Nikitina, I. E.; Zhdanovich, L. A.; Sirotin, A. G.;
Ben'yaminovich, O. A. (1970)
Wichterle, I.; Kobayashi, R. (1970)
Yesavage, V. F.; Katz, D. L.; Powers, J. E. (1970)
Wichterle, I.; Kobayashi, R. (1972)
Calado, J. C. G.; Garcia, G. A.; Staveley, L. A. K. (1974)
Rozhnov, M. S.; Kozya, V. G.; Zhdanov, V. I. (1988)
Nixdorf, J.; Oellrich, L. R. (1997)
Webster, Leigh A.; Kidnay, A. J. (2001)
Kandil, M. E.; Marsh, K. N.; Goodwin, A. R. H. (2005)
May, E. F.; Edwards, T. J.; Mann, A. G.; Edwards, C. (2003)
Methane n-Butane 138 13135 138 283
Rigas, T. J.; Mason, D. F.; Thodos, G. (1958)
Roberts, L. R.; Wang, R. H.; Azarnoosh, A.; McKetta, J. J. (1962)
Wang, R. H.; McKetta, J. J. (1964)
Sage, B. H.; Budenholzer, R. A.; Lacey, W. N. (1974)
Kahre, L. C. (1974)
Fenghour, A.; Trusler, J. P. M.; Wakeham, W. A. (1999)
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Gozalpour, F.; Danesh, A.; Todd, A. C.; Tohidi, B. (2005)
Ethane Propane 0.02 3456 127 300
Price, A. R. (1957)
Price, A. R.; Kobayashi, R. (1959)
Matschke, D. E.; Thodos, G. (1962)
Hirata, M.; Suda, S.; Hakuta, T.; Nagahama, K. (1969)
Djordjevich, L.; Budenholzer, R. A. (1970)
Kahre, L. C. (1973)
Miksovsky, J.; Wichterle, I. (1975)
Poll, H.; Huemer, H.; Moser, F. (1980)
Blanc, C. J.; Setler, J.-C. B. (1988)
Holcomb, C. D.; Magee, J. W.; Haynes, W. M. (1995)
Zhang, Y.; Gong, M.; Zhu, H.; Liu, J.; Wu, J. (2007)
Ethane n-Butane 144 5550 260 394
Benedict, M.; Webb, G. B.; Rubin, L. C. (1942)
Mehra, V. S.; Thodos, G. (1964)
Dingrani, J. G.; Thodos, G. (1978)
Lhotak, V.; Wichterle, I. (1981)
Uchytil, P.; Wichterle, I. (1983)
Kaminishi, G.-I.; Yokoyama, C.; Takahashi, S. (1986)
Clark, A. Q. ; Stead, K. (1988)
Propane n-Butane 26 3414 237 363
Nysewander, C. N.; Sage, B. H.; Lacey, W. N. (1940)
Grieves, R. B.; Thodos, G. (1963)
Hirata, M.; Suda, S.; Hakuta, T.; Nagahama, K. (1969)
Skripka, V. G.; Nikitina, I. E.; Zhdanovich, L. A.; Sirotin, A. G.;
Ben'yaminovich, O. A. (1970)
Beranek, P.; Wichterle, I. (1981)
Clark, A. Q. ; Stead, K. (1988)
Holcomb, C. D.; Magee, J. W.; Haynes, W. M. (1995)
Kayukawa, Y.; Fujii, K.; Higashi, Y. (2005)
Seong, G.; Yoo, K.-P.; Lim, J. S. (2008)
Table 11. Range in pressure and temperature for binary literature data
The literature data was implemented into the multi-component macro to analyse the impact on the
tuning. Within table 12 there is outlined the variation in Aij parameter, standard error from the fit and
Aij parameter for the default values in Hysys, and the tuned literature.
Hysys, default Tuned
Comp1 Comp2 aij S.E aij S.E fit
abs
S.E fit
rel aij S.E aij
S.E fit
abs
S.E fit
rel
C1 C2 0.0022 0.0003 0.0076 0.061 0.0043 0.0002 0.0073 0.059
C1 C3 0.0068 0.0005 0.015 0.095 0.0105 0.0004 0.015 0.094
C1 nC4 0.012 0.0013 0.031 0.179 0.0181 0.0012 0.030 0.186
C2 C3 0.0013 0.0002 0.017 0.098 -3.5E-04 5E-05 0.016 0.099
C2 nC4 0.0041 0.0025 0.028 0.092 -0.0041 0.0024 0.026 0.090
C3 nC4 0.00082 0.00057 0.015 0.065 0.0018 0.0006 0.015 0.065
iC4 nC4 1.3E-05 2.23E-
04 0.0146 0.050 0.0017 0.0002 0.012 0.041
Table 12. Aijs for default Hysys values and tuned with standard errors absolute and relative
4.3.2 Multi-component mixture
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An example of the tuning path for both the BIPs and the standard error fit, when tuned to the liquid
residuals, is given within figure 25 and 26.
The tuning path from the default values is included within the Appendix.
Figure 25. Representative BIP regresion pathway
Figure 26. Standard error against number iterations
Table 13 outline the new Aijs obtained, also the comparison with the literature binary values and
their standard error. The results are related only to the methane liquid phase. The deviation respect to
Hysys default values and an increase in the number of iterations in the code are illustrated within
figure 27.
Tuned literature Best fit, 1000 Iter
Comp1 Comp 2 aij S.E aij S.E fit abs S.E fit rel aij S.E aij S.E fit abs S.E fit rel
C1 C2 0.0043 0.0002 0.0073 0.059 -0.020 0.0194 0.0041 0.022
-1.00E-01
-5.00E-02
0.00E+00
5.00E-02
1.00E-01
0 200 400 600 800 1000 1200
Aij
Iterations
C1C2C3nC4 mixture,
Aij vs Number Iterations (from 250 Aij values)
C1C2 C1C3 C2C3 C1nC4 C2nC4 C3nC4
0.00405
0.0041
0.00415
0.0042
0 200 400 600 800 1000 1200
Sta
nd
ard
Err
or
Number Iterations
C1C2C3nC4 mixture, StdError vs Aij
All BIPs
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C1 C3 0.0105 0.0004 0.015 0.094 0.030 0.0504 0.0041 0.022
C1 nC4 0.0181 0.0012 0.030 0.186 0.047 0.0205 0.0041 0.022
C2 C3 -3.5E-04 5E-05 0.016 0.099 -0.056 0.0239 0.0041 0.022
C2 nC4 -0.0041 0.0024 0.026 0.090 0.057 0.0136 0.0041 0.022
C3 nC4 0.0018 0.0006 0.015 0.065 0.056 0.0103 0.0041 0.022
Table 13. Optimized parameters against tuned literature data
From figure 27 is significant the reduction in deviation with an increment in the number of iterations; as the
deviation reduces by 0.02 from to the default values.
Figure 27. Deviation in liquid phase methane using multicomponent code
An attempt to tune the BIPs to binary data and extend these predictions to the multi-component
mixture (C1C2C3nC4) was done to analyse the improvement in the model. The results are illustrated
within figure 28. As outlined in figure 28, use binary BIP parameters does not increase the ability of
prediction of the simulator.
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Xm
ea
s -
Xca
l
XC1
C1C2C3nC4 mixture, Comparison Iterations
Default BIPs 100 Iter from 150 150 Iter from Default
250 Iter. From default 500 Iter. From 250Aij 1000 Iter from 250Aij
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Figure 28. Deviation in liquid phase of methane when using tuned literature binary parameters
4.4 Scrub Colum results
To analyse the impact of those of optimized parameters in the scrub column a simulation was carried
out using the Aspen Tech HYSYS software. The conditions of the scrub column are a replication of
the case outlined within Page’s (Page, 2001) thesis which simulates an actual industry column
located at the North West Shelf (NWS) Gas Project onshore facility (Ryan B. , 2011). A 16 tray
distillation column includes an overhead condenser and reflux system, operating at about 4900 kPa
and within temperature range of -37ºC and 62 ºC. A screen print of the scrub column simulated is
outlined within figure 29.
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Xm
ea
s -
Xca
l
XC1
C1C2C3nC4 mixture, Using tuned literature Aijs
Default BIPs C1C2 Aij C1C3 Aij C2C3 Aij C3nC4 Aij
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Figure 29. Scrub column simulated using Aspen HYSYS
Within Table 14 there are the resultant conditions and mass flow from the simulation, when using the default
Aijs values and the optimized (1000 iterations).
Stream Default BIP Best fit BIP Difference
Cold Feed Vapour
Outlet
Liquid
Outlet
Vapour
Outlet
Liquid
Outlet
Vapour
Outlet
Liquid
Outlet Temperature C -16.4 -36.68 62.28 -36.7 62.39
Pressure kPa 4927 4812 4939 4812 4939
Mass Flow kg/h 467,000 443,400 23,600 447,848 19,146
Methane kg/h 364471.05 363,140.12 1,330.43 363,452.8
7
1,018.18 0.09% -23.47%
Ethane kg/h 54743.04 52,112.64 2,632.60 52,846.99 1,896.05 1.41% -27.98%
Propane kg/h 23381.25 19,215.79 4,168.60 20,552.67 2,828.58 6.96% -32.15%
n-Butane kg/h 24399.25 8,920.49 15,478.76 10,995.88 13,403.36 23.27% -13.41%
Table 14. Comparison in mass flow when using default Aijs values and optimized
The difference between stream has calculated according to equation 10;
%WXYY)Z)[) = \]@A^_`^a D@_>B?^aD@_>B?^a b ∗ 100 (10)
Analysing the differences between the mass flow using the best fit Aijs and the default values, there
is a significant decrease in the liquid outlet stream than the vapour outlet one. Table 14 also outlines
a significant reduction of 32.15% in liquid propane flow, another important reduction occurs for the
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liquid ethane mass flow with reduction of 27.98%. Is the significant importance that 23.27% of
butane in molar flow basis, as the liquefaction unit is the next section of the process to obtain LNG
and any heavy components will freeze in the downstream main cryogenic heat exchanger.
The findings above are significant as one of the main products from the LNG is the LPG, which
mostly consist on propane and butane mixture; according to the results from the best fit parameters
the reduction in flow of heavier components is considerable.
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CO#CLUSIO#S
Commercial simulation packages employ predictive models including PR and SRK EOSs, which are
commonly used for LNG simulation and design. These simulations require accurate thermodynamic
data at cryogenic conditions and high pressures. EOSs underlie in BIPs, which are extrapolated at
different conditions, therefore increasing the range of those BIPs would minimise the deviation of
EOSs predictions.
The experimental VLE data were taken for a binary and multi-component mixture, including
methane, ethane, propane and butane within temperature ranges between -70ºC and 25ºC, with
pressures from 1300 kPa up to 10900 kPa.
Available literature data for binary mixture of methane and n-butane were compared with
commercial cubic EOS softwares. Both experimental data, binary mixture and multi-component,
were compared against PR and SRK EOSs using HYSYS. The multi-component mixture was
compared against PR and SRK EOSs using HYSYS and GERG EOSs; PR EOS in HYSYS shows
better results for the prediction both liquid and vapour phase than the other EOSs.
The BIPs of the multi-component mixture were tuned using a VBA multi-component regression
macro to the obtained multi-component VLE experimental data using an absolute objective function
in the methane residuals. The same tuning method was completed using individual literature binary
VLE experimental data at the applicable conditions. Results demonstrated that tuning to multi-
component data was more proficient; as individual tuning of the BIPs and further extension to the
multi-component mixture provide similar values for the standard prediction error compared to the
default Hysys parameters.
The best fit Aij parameters from anchoring of model by using the VBA Multi-component mixture
were input into a scrub column simulation, proving a reduction in flow rate of liquid stream of 23%,
28%, 32% and 13% for methane, ethane, propane and butane respectively.
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The focus of future work of this project should include further multi-component VLE experimental
data at cryogenic temperatures and high pressures. Also analyse the best fit parameters into scrub
column simulation with industrial conditions with the aim to compare differences between the
model, the best fit parameters and real conditions.
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BIBLIOGRAPHY
• • Armines. (n.d.). Sample under extreme
conditions. Retrieved August 27, 2012, from
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techniques for equation of state binary
interaction parameters on their prediction of
binary VLE data. Computers and Chemical
Engineering , vol 20, no. 1, pp.79-97.
• Askarian, P. (2012, June). Measurements and
Modelling of Core Data for Cryogenic %atural
Gas and L%G mixtures for improved process
design, simulation and operation. Perth, WA,
Australia.
• Assael M., T. M. (1996). An Introduction to
their prediction of Thermophysical Properties
of Fluids. London: Imperial College Press.
• Australian Government, D. o. (n.d.).
Enhancing Australia's Economic Prosperity.
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Liquefied Natural Gas:
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oleum/australian_liquefied_natural_gas/Pages/
Home.aspx
• Butler. (2007). Fundamental Data and
Thermodynamic Modelling for Cryogenic L%G
Fluids to Improve Simulation, Design and
Operation. Perth: Honours, The University of
Western Australia.
• Department of Resources, E. a. (2010). Facts
Global Energy, Global L%G supply and
demand in 2010.
• Economides., W. M. (2009). The state of
natural gas. Journal of %atural Gas Science
and Engineering , Vol 1, pp1-13.
• Englezos P., K. N. (1989). Estimation of
binary interaction parameters for equations of
state subject to liquid phase stablility
requirements. Fluid Phase Equilibria , vol 53,
pp.81-88.
• IEA. (n.d.). L%G Trade Flows in 2009.
Retrieved May 18, 2012, from Business
Insider:
http://www.businessinsider.com/everything-
you-need-to-know-about-the-future-of-energy-
market-2010-6?op=1
• Kandil M., T. M. (2011). Vapor-Liquid
Equilibria Measurements of the
Methane+Pentane and Methane+Hexane
Systems at Temperatures from (173 to 330)K
and Pressures to 14 MPa. Journal of Chemical
and Engineering Data .
• Kunz O., K. R. (2007). The GERG-2004 Wide-
Range Equation of State of %atural Gases and
other mixtures. GERG Technical Monograph
15 .
• Laskowski L., K. M. (2008). Reliable
Thermodynamic Data for Improving L%G
Scrub Column Design. 8th Topical Conference
on %atural Gas Utilization. New Orleans,
Lousiana.
• May, E. (2009). Presentation slides LNG.
Perth, WA.
• May, E. (2012). Measurements and models of
vapour-liquid equilibrium in natural gas
processing; -Is the best we can do?,
CHEMECA 2012, Wellington, New Zealand,
25th September 2012.
• McCallum. (2010). Measurement and
Modelling of Vapour-Liquid Equilibria in
Cryogenic L%G fluids to improve process
design, simulation and Operation. Perth: The
University of Western Australia.
• Mohamed Kandil, E. M. (2010). Vapor-Liquid
Equilibria Measurements of Methane + 2-
Methylpropane (Isobutane) at temperatures
Masters in Oil and Gas Engineering, |Rudith Andrea Porras Cifuentes
Beneficiario COLFUTURO 2012
43
from (150 to 250)K and pressures to 9 MPa.
Journal of Chemical Data , 2725-2731.
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Masters in Oil and Gas Engineering, |Rudith Andrea Porras Cifuentes
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44
APPEDIX
Tuning results
Within figures 30 and 31, there effect on tuning Aijs parameters starting from the default BIPs
values. In figure 30, an analysis variating number of iterations is shown, while in figure 31 there is
illustrated the variation on Aij value with number of iterations.
Figure 30. Multi-component tuning starting from default BIP values
Figure 31. Aij parameters against number of iterations for tuning multi-component mixture starting from
default BIPs values
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Xm
ea
s -
Xca
l
XC1
C1C2C3nC4 mixture,
Varying number Iterations
Default BIPs 10 Iter 50 Iter. 70 Iter. 100 Iter. 150 Iter.
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0 20 40 60 80 100 120 140 160
Aij
Iterations
C1C2C3nC4 mixture,
Aij vs Number Iterations
C1C2 C1C3 C2C3 C1nC4 C2nC4 C3nC4
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A. Standard Operation Procedure; Assemble
VLE apparatus
1. Make sure all the connectors are on place and
all wires are restrained by tape to prevent
damage to the wires while assembling the
cans.
2. Connect all 4 electrical connectors at the top of
the apparatus. All temperature PRTs and
heaters except copper can bottom PRT and
heater (T10), copper can PRT heater (T4)
should be reading near room temperature.
Check connectors and wiring if anything is
abnormal. Refer to wiring diagram and
manual.
3. Check the fill valve as well as the stirrer
motors. Engage fill motor gear and have the
fill valve open.
4. Connect carrier gas and sample lines, fill cell
with test gas (methane) and test method on GC
to check for operating. If abnormal, refer to
manual on fixing Rolsi valve.
5. Make sure the gear is engaged before
reassembling
6. Disconnect connections and move the
apparatus to the lifting chain. Lift the
apparatus till there is enough space to insert
the Cu can.
7. Insert a plastic seal before placing the Cu can.
8. Place a support in the bottom of the can and
drop the can using the lifting chain. Make sure
the orientation of the PRT heater and sensors
connections match the position of the Cu can.
9. Bolt the copper can while it is resting on the
support. Make sure they are tight
10. Remove the support from the bottom of the
copper can and rest the apparatus on the stand.
11. Connect copper can PRTs and thermometers.
Connect the electrical connectors and test ALL
heaters and PRTs including program control.
Before heating, make sure the copper can PRT
and the copper can bottom PRT is close to
room temperature and has a deviation of no
more than 0.3K.
12. Disconnect electrical connection and move
back the apparatus to the lifting chain and
place the radiation shield can.
13. Lift the apparatus and place the SS can making
sure a plastic seal is in between.
14. Place a support on the bottom of the SS while
resting the apparatus on it.
15. Bolt the SS can while it is resting on the
support. Make sure they are tight
16. Lift the apparatus till it is able to go inside the
outer can, drop it slowly inside the outer can.
Once it is inside, remove lifting chain and
move close to the control system.
17. Connect fittings, electrical connections and
GC connections. Make sure fittings are tight to
avoid leaks in the system.
18. Connect He, N2 feed and vacuum pump
19. Connect digiquartz and ribbon.
20. Place plastic tubes for LN2 temperature sensor
and hose inside the outer can.
21. Connect the LN2 hose.
B. Standard Operation Procedure; Dissemble
VLE apparatus
1. Evacuate cell by opening valve V3.
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2. Disconnect all electrical connectors including
four in the lid of apparatus, the ribbon that
connects the switch valves and serial port
cable for the digiquartz. Make sure all heaters
are turned off or at 0 output (By having set
temperature lower than current temperatures,
except Liquid capillary which needs to be set
higher than current temperature).
3. Turn off LN2 pump. Remove hose, sensor out
of the can and the plastic tubes inside it.
4. Disconnect carrier gas and sample lines
between the apparatus and GC (a total of 4
1/16” fitting, requiring 2 ¼” spanners)
5. Disconnect the stainless steel and copper can
fittings attached to the lid of the apparatus
(requiring ¾” and 5/8” spanners).
6. Disconnect He and N2 feed. Also disconnect
vacuum pump.
7. Undo the bolts in the outer can.
8. Check all fittings off before moving it in
position with the lifting chains.
9. Lift the apparatus out of the outer can onto a
wooden bench. Make sure it is secure before
lifting. Be cautious when lifting and taking
down.
10. Place a support (wooden chair) in the bottom
of the can and drop the chain till the SS can
rest on the support.
11. Undo bolts for SS can, and remove the
support.
12. Undo and remove the SS and the radiation
shield cans.
13. Take out the connections between Cu can and
apparatus. Place the support down the Cu can
until it rest on it.
14. Undo the bolts from the Cu can. Remove the
support from the bottom of the Cu can, make
sure you are catching the Cu can to prevent it
from dropping.
15. Remove the Cu can.
C. Standard Operation Procedures related
with Filling and evacuating the cell
Filling the cell
1. Open the Ethane valve bottle
2. Make sure that the V4, V2 are closed. (Please
note that the V2 valve should be closed by
default)
3. Open V1 to fill manifold with high Pressure
Ethane
4. Close V1 valve and open V2 valve to
equilibrate
5. Close V2 and open V1 if repetition is required.
6. Close V1 and V4 valves and open V2 valve in
the end.
7. Close the Ethane bottle valve.
Evacuating the cell with vacuum
1. Ensure that the V2 valve is closed and the V1
valve is open.
2. Start the vacuum pump and open the valve to
fill manifolds with low pressure
3. Open V4 and monitor the pressure via
Labview and pressure gauge.
4. Once the desired pressure has been reached
monitor the pressure for signs of leakage.
Venting the cell
1. Ensure the V2 valve is closed and the V1
valve is open.
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2. Open V4 for 5 seconds, close it and wait for a
stable response.
3. Monitor the pressure on the Labview monitor.
Figure 32. Diagram valves for filling and evacuating the equilibrium cell
D. Standard Operation Procedure; Calibration
of pressure gauge with Methane
1. Evacuate the equilibrium cell and process
lines.
2. Test for leaks in the stainless steel (SS) and
the copper (Cu) cans by evacuating them.
Allow two hours to observe any increase in
pressure, which is indicative of a leak.
3. Fill the Cu can with Helium to provide a
uniform temperature inside the can. Leave the
SS can evacuated to act as thermal insulation.
4. Control the temperature of the cell to 25ºC or
30ºC and measure the vacuum point.
5. Once the temperature has reached the set
point - within; fill the cell with Ethane or
Methane until the desired pressure has been
reached. Ensure the V1 valve is closed before
changing the pressure.
6. Open the Methane valve bottle.
7. To fill the cell; ensure V3 and V4 are closed;
close V1 and monitor the pressure in the
Digiquartz, until it stabilises. Furthermore,
open V2 to fill the cell, while monitoring
Kulite and Digiquartz pressure readings for
consistency. Finally, close V1 and the
methane bottle valve.
8. Increase the pressure by increments of 25% of
the maximum pressure, allowing the pressure
to stabilise at every point. Measure and record
the Kulite pressure, Digiquartz pressure, cell
top temperature, cell bottom temperature, cell
body temperature and Kulite voltage
response. Once the stability has been reached,
import the resistance and temperature data
from Labview into Excel.
9. After reaching maximum pressure, decrease
the pressure by 30% of the maximum pressure
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to check for hysteresis until pressure is less
than 20% of the maximum pressure.
10. Start the LN2 pump to cool down the
experiment to the lowest point of the
experimental mixture using the same thermal
controls, then repeat step 5.
11. Repeat for at least one other temperature
between steps 5 and 7. At least three
temperature points are required for the
pressure calibration. The pressures within the
temperature range of the experimental
mixture must be less than that of the
calibration.
12. Once all the measurement points have been
collected then disconnect the Methane bottle.
E. Standard Procedure Operation for
Calibration of temperature with Ethane
1. Evacuate the equilibrium cell and process
lines.
2. Test for leaks in the Stainless Steel (SS) and
the copper (Cu) cans by evacuating them.
Allowing two hours to observe any increase
in pressure, which is indicative of a leak.
3. Fill the Cu can with Helium to provide a
uniform temperature inside the can. Leave the
SS can evacuated to act as thermal insulation.
4. Start the LN2 pump to cool down the cell
5. Once the temperature has reached a set point-
within; fill the cell with Ethane at the desired
pressure.
6. Open the Ethane bottle valve.
7. Set the pressure regulator to the desired
pressure.
8. To fill the cell; ensure V3 and V4 are closed;
close V1 and monitor the pressure in the
Digiquartz, until it stabilises. Furthermore,
open V2 to fill the cell, while monitoring
Kulite and Digiquartz pressure readings for
consistency.
9. Close the V1 valve and the ethane bottle
valve. Ensure the V1 valve is closed once the
cell has been filled. Disconnect the Ethane
bottle.
10. Wait until the temperature and pressure
stabilises at every point of the experimental
plan, then measure and records the Kulite
pressure, Digiquartz pressure, cell top
temperature, cell bottom temperature, cell
body temperature and Kulite voltage
response. Once the stability has been reached,
import the resistance and temperature data
from Labview into Excel.
11. Change the temperature to the desired point
by using thermal controls in the LN2 pump.
12. Repeat steps 10 to 12 until all the
measurement points have been taken,
duplicate each point. At least three
temperature points are required for
calibration. The temperatures within the range
of the experimental mixture must be less than
that of the calibration.
F. Standard Operation Procedures related
with L#2
Start L2 Pump
Once the sensor and the hose are connected to
the apparatus
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1. Press the red bottom On
2. Go to computer and set desired temperature
3. Default settings
4. Check the status on the computer, it should be
‘Pumping’
Change L2
1. Switch off the pump by pressing red Bottom
2. Remove the bolt that connects the pump and
the vessel.
3. Remove the pump carefully at the same time
that the empty vessel is placed in one side and
a full vessel come in
4. Take the new vessel and insert the pump in the
orifice. Be aware that some splashing could
occur.
5. Align the pump and the vessel’s orifice
6. Place back the connector
7. Switch on the pump
G. Standard Procedure for Preparation of
mixtures
1. Ensure all the valves are closed.
2. Connect the sample cylinder into the mixture
preparation system. Ensure that metallic balls
required for mixing are placed into the vessel
prior to mixture preparation. Connect the pure
gas source to the system.
3. Open the V4 valve to vent the system.
4. Check both the high and low pressure
regulators to ensure they are pressurized. Use
the appropriate pressure regulator according to
the “Gas from Bottles” pressure. If the loading
gas has high pressure (e.g. Methane) then use
the high pressure regulator. Conversely, if the
loading gas has low pressure (e.g. Propane)
use the low pressure regulator. Valves V1HP
and V3HP should be open and valves V1LP
and V3LP closed. Furthermore, the bypass V2
valve should be closed, when using gas from
high pressure source. Adjust the regulator to
the desired fill pressure.
5. Flush the whole system with loading gas at
least twice, except the cylinder. This is done to
minimise contamination of the mixture.
6. Evacuate to standard pressure lines and vessel,
then further pump into a vacuum (3.5x10-2
mbar is considered low enough pressure). Turn
on the vacuum pump and open the V7 and
V10 valves to vacuum the vessel. Monitor for
leaks in the system by observing pressures
changes once the minimum value is obtained.
7. Disconnect the vessel and weigh it to
determine the mass of the empty bottle.
Record the masses and uncertainties. Flush the
system, except the vessel, at least twice
between the loadings of each component into
the cylinder. After reconnection to the loading
system each component can be added to the
vessel. The addition of gases should be in
ascending order of vapour pressure or cylinder
pressure.
8. Open V5 to load each component into the
vessel at the regulated pressure in accordance
with experimental plan.
9. Disconnect and weight the vessel to determine
the actual mass of each component.
10. Repeat steps 6 to 8 until all the mixture
components are added into the vessel in
accordance with mixture specifications.
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Figure 33. Diagram of mixing apparatus
H. Wiring manual
ame PRT Description
PRT Liq Capillary T1 T1
The Pressure Resistance Transducer for the liquid
Capillary, the resistance in the pressure is converted
in terms of temperature for the liquid phase; the
lecture of it can be monitored by looking T1
PRT Vap Capillary T2 T2
The Pressure Resistance Transducer for the vapour
Capillary, the resistance in the pressure is converted
in terms of temperature for the vapour phase; the
lecture of it can be monitored by looking T2
Cell body T3 T3 Represents the temperature of the Cell body, it can be
monitored by looking T3
Cu Can Bottom PRT T4 T4
The Pressure Resistance Transducer for the bottom
cooper can, the lecture of it can be monitored by
looking T4
PRT Vap Rlsi T5 T5
The Pressure Resistance Transducer for the vapour
Rolsi valve, the lecture of it can be monitored by
looking T5
PRT Cell Top T6 T6 The Pressure Resistance Transducer for the top of the
Cell, the lecture of it can be monitored by looking T6
PRT Rolsi Control T7 T7
The Pressure Resistance Transducer for the Rolsi
Control, the lecture of it can be monitored by looking
T7
PRT SEC1 HTR Liq T8 T8
The Pressure Resistance Transducer for the section 1
Heater Liquid, the lecture of it can be monitored by
looking T8
PRT Cell Bottom T9 T9 The Pressure Resistance Transducer for the bottom of
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the cell, the lecture of it can be monitored by looking
T9
PRT Cu Can T10 T10 The Pressure Resistance Transducer for the cooper
can, the lecture of it can be monitored by looking T10
PRT SEC2 T11 T11 The Pressure Resistance Transducer for the Section 2,
the lecture of it can be monitored by T11
SEC3 PRT T12 T12
It read the Pressure Resistance Transducer for the
section 3 (Upper part of the apparatus), it can be
monitored by T12
SEC1 Sample HTR Represents the heater in the section 1 that allows
taking samples.
Fill motor The motor that open valves to fill the cell.
Stirrer Motor Represent the motor that stir the mixture to ensure
homogeneity.
Stirrer TC The temperature controller of the stir
Cell Htr + Switch The Heater that increases the heat in the cell and the
tool that allow start it of close it.
Lid Htr 2.5Ω The heater with 2.5 Ω that is on the lid of the cell
Rlsi htr 240 VAC The rolsi valve heater with 240 VAC
Rlsi Vap Trigger 24 VCD The rolsi valve for the vapour phase that operates as
24 VCD
Rlsi Liq Trigger 24 VCD The rolsi valve for the liquid phase that operates as 24
VCD
Earth Wire connected to the Earth to prevent electrical
shock.
Peltier Liq Represent the Peltier for the liquid phase
Peltier Vap Represent the Peltier for the vapour phase
SEC2 HTR 7.7Ω The heater in the section 2
Heater Cu Bottom Heater on the bottom of the cooper can control the
heat in the system.
Figure 34. Diagram for the VLE apparatus
Masters in Oil and Gas Engineering,
I. Rolsi Valve manual
Rapid On-Line Sampler Injector (ROLSI) is an
electromagnetic solenoid valve. The rolsi
valve allows reliable and representative
samples. The sampling amount can be finely
Figure
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Line Sampler Injector (ROLSI) is an
electromagnetic solenoid valve. The rolsi
valve allows reliable and representative
samples. The sampling amount can be finely
adjustable from 0.01 to 1 mg. The ROLSI
valve also ensures no dead volume, ease to use
and implement. (Armines)
Helium flows through the ROLSI valves,
pumped via the tow separate helium carrier
gas lines which lead to the
Figure 35. Rolsi Valve Diagram (Armines)
|Rudith Andrea Porras Cifuentes
UTURO 2012
53
adjustable from 0.01 to 1 mg. The ROLSI
valve also ensures no dead volume, ease to use
(Armines)
Helium flows through the ROLSI valves,
pumped via the tow separate helium carrier
gas lines which lead to the gas chromatograph
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APPE#DIX 2 ACTIO# PLA#S (SafetyHealthUWA)
1. Oxygen and Methane sensors – Laboratories 1.2 and 1.15
Blue lights indicate that low levels of methane have been detected in the air therefore follow the
procedure below;
• STOP what you are doing
• Turn off all gas sources
• DO NOT continue work until source of leak determined
Red lights and siren indicate that HIGH levels of methane have been detected in the air, therefore;
• EVACUATE LABORATORY
• Notify your direct supervisor
• DO NOT re-enter laboratory until deemed safe by an authorized person
In case of emergency, Call Uwa Security on 2222
2. Laboratory Emergency Response Procedures
Emergency and precautions Minor Major Medical Initiate first air
Report incident
Remain calm
Initiate lifesaving measures if required
Do not move person unless there is
danger of further harm
Keep person warm
Call emergency response
Fire
Small fires can be extinguished
without evacuation. Fire extinguishers
should only be used by trained
personal. Never enter a room that is
smoke filled.
Alert people in laboratory and activate
alarm.
Smother fire or use a correct fire
extinguisher.
Aim extinguisher at base of fire.
Always maintain accessible exit.
Avoid smoke or fumes.
Alert people in are to evacuate.
Activate nearest fire alarm or call
security number.
Close doors to confine fire.
Evacuate to safe area or exit building
through stairwell; do not use lift.
Have person knowledgeable of
incident and laboratory assist
emergency personnel.
Chemical spill Alert people in immediate area of
spill.
Wear PPE
Avoid breathing vapors from spill.
Confine spill to small area.
Use appropriate kit to neutralize and
absorb inorganic bases and acids.
Collect residue and dispose as
chemical waste.
Attend to injured or contaminated
persons and remove them from
exposure.
Alert people in the laboratory to
evacuate.
If spilled material is flammable, turn
off ignition and heat sources.
Call for assistance.
Close doors to affected area.
Table 15. Response Procedures (SafetyHealthUWA)
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APPEDIX 3 RESOURCES AD RISK ASSOCIATED WITH THE PROJECT
The collection of experimental Vapour Liquid Equilibrium Data (VLE) took place into the
Laboratory 1.2 in Physics Building. In the table below, there is a detailed list of equipments and
resources used.
Resources Description
Chemical Liquid Nitrogen, Helium gas, Ultrahigh purity methane, analytical grade
hydrocarbons such as Propane, Butane, pentane, hexane among others.
Equipment
Experimental work: Gas Chromatograph (GC) for hydrocarbons detection, Electronic
balances, computer connected to be able to control system, Cryogenic VLE
Apparatus consisted in a Equilibrium Cell (EC) resistant up to 30MPa, Platinum
resistance thermometers (PRT), sensors of temperature control (TC), pressure
transducers. Cryogenic Dewar equipped with an automatic liquid nitrogen pump,
also mixing system, HPLC pump and Vacuum Pump, soldering equipment, tools
such as screwdrivers, spanner tools, wiring in general.
Modelling tasks: Computer with Hysys licence and Excel MS Office softwares.
Softwares Hysys AspenTech, Excel MS Office, GERG, MS Office
Infrastructure Laboratory 1.2 in Physics Building, Computer with access to Hysys and Excel
Personnel
Supervisor
PhD student who knows the equipment and is able to operate it safely
Laboratory technicians
Services Electricity, ventilation system, internet connection, methane detectors, oxygen
detector
PPE Safety glasses, gloves cryogenic resistant, enclosure shoes
Table 16. Description of resources required for the project
Project Risks
In table 13 there is a simplified Job Safety Analysis (JSA) related with the project.
Hazard Description Consequence Likelihood Prevention
Chemical spills of
Corrosive
Chemicals,
Flammable and
non-flammable
gases
Leaks/Spills of
Helium gas,
propane, butane,
pentane, hexane
among others.
Irritation skin or
respiratory system,
asphyxiation, eye
damage
Medium Ventilation system, use of
gloves and safety glasses
when handling them. Keep
chemicals away from heat
sources and store in
chemical cabinet
Spills of N2 liquid Inadequate
handling/use of
Liquid Nitrogen
Frost burns on skin
or eyes, eye
damage,
asphyxiation,
suffocation
Medium-
High
Wear appropriate PPE;
gloves, closed shoes, safety
glasses, ventilation system,
C1 and O2 detectors
High contents of
Methane
Leaks of methane Asphyxiation,
Ignition
Medium Awareness of C1 and O2
detectors and signals,
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ventilation system. Keep C1
away from heat sources
Rupture High
Pressure Gas
Cylinders
Caused by
instability of
cylinder
Explosion, eye
damage, bruises
Low Keep cylinders positively
secured, store vertically
Fire Ignition of
flammable
chemicals
Damage of
equipment, burns,
asphyxiation
Medium Keep flammable
chemicals away from
flame sources (at least 3
m) and store them in
bottles inside chemical
cabinet. Electrical fire Faulty electrical
connexions, short
circuit
Fire Medium Ventilation system, do not
overload power points
Table 17. JSA for project