final project report - colfuturo 2012

Masters in Oil and Gas Engineering, |Rudith Andrea Porras Cifuentes

Beneficiario COLFUTURO 2012

1

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



2

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.



3

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



6

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



7

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.



9

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)



2

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.



11

Figure 3. Liquefied facility process schematic and the location of scrub column (Laskowski L., 2008)



12

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



14

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



15

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



16

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)



17

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.



19

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



20

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



21

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



22

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



23

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



24

-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



25

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



26

-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



27

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


-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


-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




28

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



29

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



30

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


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



31

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


-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




32

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


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


Isotherm experiment C1C2C3nC4 Without point 8778 kPa

PR SRK GERG



33

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)



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)



34

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)



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



35

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



36

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



37

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



38

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^_`â D@_>B?âD@_>B?â 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



39

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.



40

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.



41

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.



42

BIBLIOGRAPHY

• • Armines. (n.d.). Sample under extreme

conditions. Retrieved August 27, 2012, from

http://www.rolsi.com/Poster.pdf

• Ashour I., A. G. (1996). Effect of computation

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:

http://www.ret.gov.au/resources/upstream_petr

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



43

from (150 to 250)K and pressures to 9 MPa.

Journal of Chemical Data , 2725-2731.

• Paunaovic R.S., J. S. (1981). Rapid

computation of binary interaction coefficients

of an equation of state for vapour-liquid

equilibrium calculations: application to the

Redlich-Kwong-Soave equation of state. Fluid

Phase Equilibria , vol 6, pp. 141-148.

• Peneloux A., R. E. (1982). A consistent

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L%G mixtures for improved process design,

simulation and operation. Perth: Final Year

Project Thesis, The University of Western

Australia.

• Ryan, B. (2011). Measurements and modelling

of core data for cryogenic natural gas and

L%G mixtures for improved process design,

simulation and operation. Perth: University of

Western Australia Thesis.

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• Shibata S.K., S. S. (1989). Critical evaluation

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Industrial and Engineering Chemistry , Vol

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cryogenic L%G fluids to improve process

design, simulation and operation. Perth: The

University of Western Australia.

• U.S. Energy Information Administration.

(n.d.). Retrieved May 3, 2011, from

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• Valderrama J O. (2003). The State of Cubic

Equations of State. Ind. Engineering Chem ,

1603-1618.

• Wong D., S. S. (1992) A theoretically correct

mixing rule for Cubic Equations of State.

AIChem , 671-680.



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



45



46

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.



47

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.



48

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



49

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



50

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.



51

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



52

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

Masters in Oil and Gas Engineering, |Rudith Andrea Porras Cif

Beneficiario COLFU

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



54

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)



55

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,



56

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

final project report - colfuturo 2012

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