WHAT WE HAVE LEARNT FROM GC CONDITIONAL BASED MONITORING › cz_doc › download › ...have_Learnt... · WHAT WE HAVE LEARNT FROM GC CONDITIONAL BASED MONITORING Anwar Sutan, i-Vigilant

South East Asia Flow Measurement Conference

3 – 4 March 2015

Technical Paper

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WHAT WE HAVE LEARNT FROM GC CONDITIONAL BASED

MONITORING

Anwar Sutan, i-Vigilant Technologies

1 Introduction

Online gas chromatograph (GC) is frequently used within fiscal and custody transfer

measurement systems. The accuracy of the analysis from the GC is of the utmost

importance since the resultant analysis is frequently used as the core parameter in

economic transactions.

The modern GC is an extremely repeatable device. However, sources of measurement

error do not always arise from the GC itself. More frequently errors appear due to non-

representative sample provided by the sampling system. Ensuring accuracy and

repeatability of the system requires that suitable monitoring and maintenance is in place.

From GC point of view, even though it may be repeatable, it does not preclude the system

from the possibility of systematic errors. These may include, among others, poor

calibration gas handling resulting in non-homogeneous reference mixture; unsuitable

selection of reference mixture resulting in biases due to linearity or the assumed response

functions; peak drifts; valves leakage etc. One case study shows that in a situation where

systematic error remains undetected, it resulted in an on-going error in the calorific value

of up to 1.4%. Seemingly insignificant on surface, in monetary terms this 1.4% is equated

to a value of more than £300,000 per month.

Assuming reasonable steps have been taken to eliminate systematic errors in GC, it is

essential that sampling and conditioning systems are accordingly verified. Often GC itself

measures correctly, however if the sampling and conditioning system fails to provide

representative gas sample, there would consequently occur a measurement error. Another

case study by the author reveals a situation where heavy end drop out in a system caused

a systematic error with value equated to more than £200,000 per month.

Once it is proved that GC is healthy and sampling system provides representative sample,

then GC measurement uncertainty can be calculated. This paper presents several case

studies that have been performed on various GC. The studies look at the GC current

maintenance regime, then it proposes new ideas for GC maintenance using a conditional

based monitoring tool which monitors all GC calibration parameters to ensure GC

instantaneous correctness. Further analysis of the data allows determination of the

uncertainty of the GC.

The studies performed looked into the comparison between spot sampling and GC reading.

Spot sampling at line pressure can help detect heavy end drop out within the pressure

letdown system. Knowing the long term history of gas composition in the pipeline helps to

identify the stability of the gas and determine the most appropriate gas mixtures to be


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used in calibration. Where the gas composition in a pipeline is stable, independent GC

multilevel calibration may not be essential.

Overall, this paper presents a novel philosophy for determining initial health of the GC,

health of the sample let down system and for the continued monitoring and assessment

of both GC and sample conditioning system performance. The goal being to provide on-

line estimates of the repeatability of each component thereby allowing uncertainty

calculations to be performed for calorific value and density (or any other parameter that

can be calculated from gas composition). The philosophy will be demonstrated based on

real data obtained from several case studies. The paper will illustrate how an ‘expert

system’ may be used to provide uncertainty of the GC as well as provide early fault

indication. Therefore, the system would provide assurance that GC is operating within

specified limits and meeting contractual obligations.

2 Conventional GC Maintenance Regime

2.1 Sample let down system

GC is a device that is designed to measure gas composition at near atmospheric pressure;

therefore it is necessary to bring the pressure down from the pipeline pressure to around

2 Barg where the GC operates as illustrated in Figure 1.

Figure 1. Sample pressure let down system illustration

Dropping gas pressure will also drop the temperature of the gas (known as the Joule-

Thomson effect); this brings a risk for the gas to fall into dual phase as the gas

temperature drops below its dew point temperature within phase envelope as shown in

Figure 2 below.

Note: Joule-Thomson effect is the change in temperature that accompanies expansion of

a gas without production of work or transfer of heat [6]. Every 1 bar pressure drop the

temperature will drop as well by approximately 0.5 deg C.


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Figure 2. Phase envelope with pressure drop simulation

On an analyser system, it is important to provide representative sample. This means the

gas that is sampled from the pipeline has the same composition as the gas that is injected

to the analyser. If the gas during the pressure drop enters a dual phase region, some of

the gas will change phase into liquid and will be knocked out by filters. In this case the

gas injected to the GC will not be representative of the gas in the pipeline.

This is a challenge that is normally faced by the cold region such as the North Sea.

Therefore the design of the sample let down system always considers the cricondentherm

temperature of the gas to ensure that the gas will never enter the dual phase region within

the phase envelope. The simplified design of sample let down system is illustrated in Figure

3 below.

Figure 3. Typical sample let down system design


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As shown in Figure 3, every piece of sample line must have either heat tracing or heated

elements to ensure that the temperature will be high enough for the gas to remain on the

gaseous phase. It is normal to have two stages of pressure drop to ensure that

temperature remains higher than dew point temperature as illustrated in Figure 4.

Figure 4. Illustration of pressure drop with two stages of pressure drop

ISO 10715:2001 [2] standard specify that to ensure gaseous stage, temperature within

sample system needs to be maintained 10 deg C above dew point temperature.

This means that this issue is not isolated for the cold area. Poor design of sample let down

system can also bring similar issues in warm areas such as South-East Asia. A case been

encountered where only single stage pressure let down is implemented and icing in the

regulator has happened. In this case most likely the gas within the sample system would

have gone into dual phase and a non-representative sample injected to the GC.

The issue of heavy end drop out is important with large financial implication for the

operator. A case study was done in the UK where more than 50% of drop out in the heavy

end occurred (i.e. 0.1% C7+ was measured by GC while spot sampling shows in average

around 0.25% C7+ should have been measured). This contribute to 0.75% of a mis-

measurement. Dependent on the volume of the gas flowing this can worth more than

£100,000.00 /month of error.

Therefore sample let down system design should not be taken lightly, analysis of the gas

by comparing spot sampling result against GC result needs to be taken, and when heavy

end drop out is detected necessary actions need to be taken.

2.2 Calibration Gas and Calibration Process

Calibration gas on gas chromatography is very important. The accuracy of GC

measurement is dependent on the accuracy of the calibration gas. The uncertainty of GC


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measurement, among other things, is affected by how close the calibration gas represents

the pipeline gas and the uncertainty of the calibration gas. Wrong selection of calibration

gas and faults in calibration process have proven to be sources of significant error in GC

measurement.

Issues in calibration gas or calibration process will not be detected by having redundant

GC as both GC will experience the same issue and have the same systematic error that

can go undetected. The following are some of the issues that have been encountered with

calibration gas or calibration process in the GC.

1. High uncertainty of calibration gas

In a measurement system, accuracy is at the utmost importance. Calibration gas that is

made with high uncertainty will result in high uncertainty GC measurement result. This

may lead to less confidence in GC measurement. To ensure the most accurate

measurement, calibration gas needs to be made with the most accurate method aiming at

the lowest uncertainty possible.

As an example, as illustrated in Table 1 below, the calibration gas that is made with higher

accuracy only contribute to overall CV uncertainty of 0.03% while calibration gas that is

made with uncertainty of ±2% on all components contribute to 0.15% of CV uncertainty.

This lead to low confidence of GC measurement, furthermore the use of poor calibration

gas may bring the overall system uncertainty out with the allowable metering system

uncertainty budget.

Description High accuracy uncertainty 2% uncertainty

Methane 0.0352 % 2 %

Nitrogen 0.3259 % 2 %

CO2 0.2506 % 2 %

Ethane 0.2801 % 2 %

Propane 0.3327 % 2 %

i-Butane 0.3205 % 2 %

n-Butane 0.3742 % 2 %

Neo-Pentane 1.0000 % 2 %

i-Pentane 0.6653 % 2 %

n-Pentane 0.5513 % 2 %

Hexane 2.0854 % 2 %

Heptane 2.6785 % 2 %

CV 0.0276 % 0.1511 %

Table 1. Calibration gas accuracy effect towards CV calculation uncertainty

2. Calibration gas is as not as per the calibration gas certificate

On cold climate, this often happens on the heavy end components. Because the calibration

gas has been stored below its dew point temperature, the gas composition became non-

homogeneous. Some of the heavy end components condense and sit at the bottom of the

bottle. The calibration gas certificate is always used as the basis of the calibration gas

bottle concentration. However, when the gas is not homogeneous, it leads to an error. The

most common cases is illustrated in Figure 5 below.


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Figure 5. Non homogenous calibration gas

From the Figure 5, it can be seen that hexane and heptane response factor is lower than

expected. This is due to the gas being stored in the cold lower than its dew point

temperature. This issue if goes undetected can lead to quite significant mis-measurement.

However, this can be avoided by looking at the response factor trend as well as the

correlation chart. A healthy GC will have the property of RF trending and correlation as

shown in Figure 6.

Figure 6. RF trend and correlation chart of healthy GC

A healthy GC will have an ascending response factor trend and high correlation between

the Response Factor and Molecular Weight. The case where the heavy end sits at the

bottom of the bottle can be overcome by re-heating the gas to a homogenous state and

running the GC using calibration gas for several cycles. Test performed has shown that


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the detected gas return into its normal homogeneous state after 10 runs as shown in

Figure 7.

Figure 7. Runs of calibration gas prior to returning to homogenous state

Arguably after this process the composition within the bottle has been altered, however it

can also be argued that normally the heavy end has low concentration with low sensitivity

towards CV and density measurement and therefore the calibration gas will still be fit for

purpose. Also the acceptability of calibration gas can be determined by the gas thermal

conductivity property. Gas that flows through thermal conductivity detectors will follow

consistent characteristic as shown in Figure 6 earlier. As long as the RF trend is ascending

in the order of Methane – Nitrogen – CO2 – Ethane – Propane – iButane – nButane –

neoPentane – iPentane – nPentane – Hexane – Heptane, and the correlation between RF

and MW is high, it can be concluded that calibration gas is healthy as well as the valve and

other time events in the GC is healthy.

This principle can also be used to determine if a calibration gas is as per what stated in its

calibration certificate. There are some occasions where there have been errors in the

making of calibration gas which result in the bottle composition not as per the bottle’s

certificate. This can be determined by checking the calibration result.

The principal can also be used to identify human error in entering calibration data from

the certificate into the GC as shown in the case study below.

Case Study 1

The GC in this case study had been offline for a while, and it was time to bring it back

online again. A new calibration gas was installed and a calibration was done. Instead of

checking the trend and the result of the calibration, a forced calibration was performed

and GC was assumed to run correctly as it did not produce any alarm. However, because

it was a new calibration gas, the composition of the new calibration gas and the

composition of the old calibration gas that still existed in the GC were not the same. The

initial calibration RF trend on this GC is as the following:


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Figure 8. RF trend due to wrong component concentration entered in GC data table

Following this, component data table within the GC was changed to match the calibration

gas certificate and further calibration was performed. The result was a better response

factor, with several minor issues as shown in Figure 9.

Figure 9. RF trend after component data table in GC was adjusted

From Figure 9, the trend shows that N2 level was too high and hexane level was too low.

The N2 levels can be high after changing the calibration gas as the sample line can fill with

air. To rectify this issue, the calibration sample line was purged with calibration gas. After

clearing the air, the RF trend was significantly better as shown in Figure 10.

Figure 10. RF trend after calibration sample line has been purged

The blue line above shows the trend prior purging, and the red line shows the trend after

purging. The N2 is now at its expected level; however hexane RF was still lower than


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expected. Inspection of the correlation between MW and RF is then used to help determine

the cause of the low Hexane RF as shown in Figure 11.

Figure 11. MW-RF Log chart of calibration data

It was determined from the plot that based on the poor correlation of C1-C2-C6; the

problem was caused by some of the heavy component (hexane) leaving column 1 to flow

through column 2 instead of all of it being back-flushed due to incorrect valve timing. The

result was not all hexane component being detected by the GC and the RF reading low.

Adjustment was performed to the valve timing to prevent C6+ from entering column 2.

This resulted in the change of the response factor chart as depicted in Figure 12.

Figure 12. RF trend and MW-RF log chart of healthy GC

The charts now clearly indicate that the problem with the GC has been rectified and the

trends are all as expected. The error introduced by this problem may not be apparent at

the individual component level, however analysis of the resultant calculated calorific values

of the two calibrations clearly shows the difference to be significant. The potential

difference in the final output result is given in Table 2.


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Description Wrong RF Correct RF Difference

CV 45.07 44.44 1.41%

Volume 100 100

Volume 2,831,684 2,831,684

Energy (MJ) 127,624 125,840 1,784

Energy (KWh) 35,451,330 34,958,311 493,019

Value per KWh £0.02459 £0.02092

Value /day £871,748 £859,625 £12,123

Value /month £26,152,446 £25,788,746 £363,700

Table 2. Unhealthy vs healthy result comparison, Value per KWh data is taken from

Quarterly Energy Prices, December 2013

2.3 Repeatability Test

Repeatability test has been used a lot as part of GC preventive maintenance by many

operators. The tolerance used for repeatability test normally taken from ASTM

D1945:2001 [1], or GPA 2261:1995 [7] as shown in table 3 and 4 respectively.

Mole % Lookup Tolerance (absolute mole %)

0 to 0.1 mole% 0.01%

0.1 to 1 mole% 0.04%

1 to 5 mole% 0.07%

5 to 10 mole% 0.08%

Over 10 mole% 0.1%

Table 3. ASTM D1945:2001 section 10.1.1

Component Mole % Range Tolerance (percent relative)

Nitrogen 1 to 7.7 2

CO2 0.14 to 7.9 3

Methane 71.6 to 86.4 0.2

Ethane 4.9 to 9.7 1

Propane 2.3 to 4.3 1

Isobutene 0.26 to 1 2

n-Butane 0.6 to 1.9 2

Isopentane 0.12 to 0.45 3

n-Pentane 0.14 to 0.42 3

C6+ 0.1 to 0.35 10

Table 4. GPA 2261:1995 section 9

Although it has been used a lot, testing has been conducted to see the GC calculated CV

uncertainty if tolerance of ASTM D1945:2001 has been used. Dependent on the

composition, the CV uncertainty varies between 0.28% - 0.31%. This value is not

representative of the capability of modern GC where most of the modern GC is capable of

having CV uncertainty of below 0.12%.

Figure 13 below shows an example of 5 runs repeatability test performed on methane. It

can be seen from the illustration that the GC repeatability is far better than the set

tolerance.


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Figure 13. Five run repeatability test on methane

Furthermore, when a 40 hour repeatability test was done on methane, similar results were

seen: as demonstrated in Figure 14.

Figure 14. Forty hours repeatability test on methane

The result is also consistent to every other component of the gas, for example figure 15

shows the result for Propane.


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Figure 15. Forty hours repeatability test on propane

Based on these results, questions can be raised if this repeatability is really worth doing if

continuous monitoring is being performed. The cases where the repeatability test has

failed occur due to the following reasons:

1. Repeatability test was performed on un-normalised composition

2. GC calibration has already failed due to excessive response factor shifts

On another note, repeatability test can also lead to wrong impression. Passing repeatability

test does not mean that GC is accurate. It only means that the GC will provide answer

within a certain tolerance. The accuracy of the GC is not determined by repeatability test.

For example, GC can provide repeatedly wrong result when component data table is not

the same as actual value of the gas composition.

2.4 Control Chart

In many pipeline agreements, control charts have been stated as a requirement for

operators. The control chart is a plot of response factors and retention time of GC. The

issue with the control chart is that there are no standards to follow on what the control

limit to be used. Therefore, many versions of control limits in control charts have been

found. Some use 3 standard deviations of first 25 data, some other operators use 3

standard deviations of all data. However, none of these versions can determine how well

the GC has been working.

In fact control chart in the GC cannot be used in isolation because every component in the

GC is correlated. This means an error that occurs in one component will result in error of

every other components. This can be illustrated by the following example.


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Consider two control charts from GC A and GC B that have the same standard deviation

of three percent. Current practice says that both of this GC is performing equally good as

shown in Figure 16.

Figure 16. Control chart of methane RF on two different GC

The difference between these two GC is that on GC A, all components have the same exact

synchronous change of its response factors, while GC B has a completely random change

of its response factors. Figure 17 shows RF chart of two components (Methane and

Nitrogen) in the same chart.


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Figure 17. Methane and Nitrogen RF deviation trend

From Figure 17, it is visible that GC A chart is better than GC B chart. However, when GC

B chart is shown individually, it will only shows to individual components that both have

similar standard deviation as shown in Figure 18.

Figure 18. Methane and Nitrogen chart of GC B shown separately


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Looking at more components, it becomes very clear that GC A performs better than GC B

as shown in figure 19.

Figure 19. RF chart of 4 components for GC A and GC B

For the case of GC A, it will yield to 0% uncertainty regardless if the deviation is 3% or

10% or even 100% as when all the gas varies in the same exact way, it will always provide

the same normalised result. However, for the case of GC B where every gas response

factor behaves in random way, although each gas has similar standard deviation, GC B

has significantly higher uncertainty in comparison to GC A. The uncertainty comparison

between GC A and GC B is shown in Figure 20.

Figure 20. GC A vs GC B uncertainty


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This 3% standard deviation example is only meant to illustrate that GC A and GC B can

show the same deviation, but have completely different uncertainty outcomes based on

random vs synchronous variations in data. In fact this example cannot happen in an

actual GC because the measurement data gas to have a random spread, whereas GC A

data is derived from simulated absolute numbers that move synchronously. This

example shows that looking at individual component control charts alone cannot be used

to confirm the health of a GC. It is only when every gas component is analysed and

plotted together and there individual and collective uncertainty values calculated will one

get a proper representation of actual GC performance

Figure 21 shows a control chart for methane of a healthy GC. From the Figure 21 below,

it looks like the methane has gone out with the 2 std deviation limit and therefore based

on the current practice, GC may have been performing out with its acceptable limit.

Figure 21. Methane control chart of a healthy GC

However, when the full RF chart is drawn in the same chart as shown in Figure 22, it can

be seen that GC has been performing well. From the chart it can be seen that all

components varies in similar general trend. Also from the chart, it is visible that hexane

and heptane response factor has more variation in comparison to the rest of the

components.

Figure 22. Healthy GC RF chart


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When the chart is interpreted in terms of uncertainty value of each gas, it can be seen

that hexane and heptane has higher uncertainty value in comparison to the rest of the

component. It can also be seen the uncertainty toward CV measurement is very low, much

lower than the normally assumed CV uncertainty of between 0.2% - 0.3%.

The monitoring of uncertainty trend can be used to monitor longer term GC performance

including early failure detection. Alarm limit can be placed towards the uncertainty value

and at any point in time GC performance can then quantified in terms of its uncertainty

value as shown in Figure 23.

Figure 23. Uncertainty trend of GC derived from RF data

2.5 Composition Comparison

In many facilities, redundant GC is implemented as a fail save feature of measurement.

However, the redundant GC is also often used as a validation tool of each GC

measurement. However, in many places the component comparison is performed on un-

normalised basis. And because of this, many times the GC failed to perform within its limit

due to the wrong procedure of comparison.

Consider another GC A and GC B measures the same gas, and both of the GC are

configured to measure an un-normalised composition. The measurement result is shown

in Table 5.


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Description GC A GC B Difference Tolerance Status

Methane 86.7944 83.3907 3.4037 0.1 Fail

Nitrogen 5.6327 5.4118 0.2209 0.08 Fail

CO2 2.0359 1.9560 0.0798 0.07 Fail

Ethane 4.3703 4.1989 0.1714 0.07 Fail

Propane 1.8412 1.7690 0.0722 0.07 Fail

i-Butane 0.2548 0.2448 0.0100 0.04 Pass

n-Butane 0.3546 0.3407 0.0139 0.04 Pass

Neopentane 0.1018 0.0978 0.0040 0.01 Pass

i-Pentane 0.3068 0.2948 0.0120 0.04 Pass

n-Pentane 0.2041 0.1961 0.0080 0.04 Pass

Hexane 0.1033 0.0993 0.0041 0.04 Pass

Total 102 98

Table 5. Un-normalised result comparison between GC A and GC B

As shown on Table 5, the result has failed. However when the result is normalised, both

GC shows the same exact result as shown in Table 6.

Description GC A GC B Difference Tolerance Status

Methane 85.09254 85.09254 0.0000 0.1 Pass

Nitrogen 5.522274 5.522274 0.0000 0.08 Pass

CO2 1.995952 1.995952 0.0000 0.07 Pass

Ethane 4.284621 4.284621 0.0000 0.07 Pass

Propane 1.805088 1.805088 0.0000 0.07 Pass

i-Butane 0.249819 0.249819 0.0000 0.04 Pass

n-Butane 0.347691 0.347691 0.0000 0.04 Pass

Neopentane 0.099842 0.099842 0.0000 0.01 Pass

i-Pentane 0.300822 0.300822 0.0000 0.04 Pass

n-Pentane 0.200065 0.200065 0.0000 0.04 Pass

Hexane 0.10129 0.10129 0.0000 0.04 Pass

Total 100 100

Table 6. Normalised result comparison between GC A and GC B

This example shows that when performing comparison between two or more GC, the

comparison has to be made in normalised value.

2.6 ISO 10723 Multilevel Calibration

ISO 10723:2002 standard [3] was developed to quantify GC repeatability and linearity. It

also provides a method to correct for the non-linearity of thermal conductivity detector in

case GC needs to measure large variation of gas. This case may happen if the GC measures

gases that come from different sources, such as different wells, export composition that

may differ from import composition, etc.

In the recent years it has gained popularity in Europe as part of the Commission of the

European Communities decision [4] that enforce all GC which is used to measure gas

emission to be validated using ISO 10723:1995 [8] as a performance benchmark on yearly

basis. Although the method is very good to determine and correct for non-linearity in GC

detectors, recent study shows that it does not require to be performed on a yearly basis.


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Also, the latest Commission Regulation (EU) No 601/2012 [5] has omitted the requirement

for ISO 10723 and only requires routine validation of the GC with no specific preference

of the validation method.

The following are few case studies from the current practices of ISO 10723 performance

test.

1. Two tracking GC where one failed ISO 10723 performance test

This case study shows where GC A and GC B have been tracking each other very well.

However when ISO 10723 performance evaluation test was done on both of the GC’s, GC

A failed the test while GC B passed. After investigation it was found that the test failed in

the composition area where the GC has not been operating as can be illustrated in Figure

24.

Figure 24. ISO 10723 test range on methane

From Figure 24, it is illustrated that throughout the year the range of methane going

through the pipeline is between 82 – 86%. ISO 10723:2002 [3] specifies that the GC to

be tested with 7 different gases with the range as shown in Figure 25.

Figure 25. Test gas mixture range for ISO 10723 performance test


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Following the formula, for the gas range illustrated in Figure 24, the methane gas

concentration range should lie between 81 to 87 mole%. However, the test gas range on

this case was between 78 to 98 mole%. Most of the tested range will never be measured

by the GC as historically GC only has range between 82 – 86 mole%.

Having a broader range may appear to be a good thing to do, however looking at the failed

result of the non-operating region, it would be fairer to perform the test over the actual

gas range being measured since the non-linearity picked up on GC A had not affected its

accuracy of measurement.

2. GC is proven to be linear for its operating range

In some cases, ISO 10723 is performed regardless if there is actual need for it. One can

argue that if the GC composition always fall within the linear range of the detector, then

ISO 10723 is not necessary.

The difference between multilevel calibration constant implemented mole % and single

point mole % is that the multilevel calibration corrects for the non-linearity of detector

using a polynomial curve. Single point calibration mole % assumes that detector is fully

linear. The calculation of single point calibration mole % is shown in Equation 1.

𝑥𝑖 =𝑃𝑒𝑎𝑘 𝐴𝑟𝑒𝑎

𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝐹𝑎𝑐𝑡𝑜𝑟

Where: 𝑥𝑖 = mole % of component i

Equation 1. Single point mole % calculation

Multilevel calibration mole % calculation is shown in equation 2.

𝑥𝑖 = 𝑏0 + 𝑏1𝑦𝑖 + 𝑏2𝑦𝑖2 + 𝑏3𝑦𝑖

3

Where: 𝑥𝑖 = mole % of component i

𝑏𝑛 (n = 1, 2, 3, 4) parameters of analysis function

𝑦𝑖 = peak area of component i

Equation 2. Multilevel calibration mole% calculation

Figure 26 and Figure 27 shows a comparison between mole% of methane produced by

multi-level calibration constant (MLC) implemented vs single point calibration constant

implemented.


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Figure 26. Methane MLC implemented mole % vs single point implemented mole %

Figure 27. Difference and tolerance between MLC and single point implemented mole%


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From Figure 26, it can be seen that the difference is negligible and well within the tolerance

specified by ISO 10723:2002 [3] as shown in Figure 27.

When the gas always falls within the linear range of the GC such as the example above,

the ISO 10723 performance test should not be required. Therefore the method is more

suitable for laboratory GC due to the wider range of composition it would be exposed to

that to an online metering GC that measures stable composition with little variations.

3 Conditional Based Monitoring

In recent years, method for GC conditional based monitoring has been developed that

allows constant monitoring of GC parameters. This method has proven to provide

confidence of the GC throughout the year by simply monitoring a few parameters of the

GC detailed as follows:

3.1 Response factor trend and correlation chart

Response factor trend and correlation chart are two very valuable tool to determine that

GC is healthy in terms of all its time events settings, and that the calibration gas

concentration is as per stated in its calibration certificate. The RF trend and correlation

chart is shown in Figure 28.

Figure 28. Response factor trend and correlation chart

The RF trend needs to be ascending. When a composition on the right hand side is lower

than the left hand side, it is an indication that something is not right with the GC. For


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instance Figure 29 shows that the gas is not purged enough due to high level of Nitrogen;

also there is valve timing issue due to low level of hexane response factor.

Figure 29. Response Factor of un-healthy GC

When such issue exist in the GC, correlation chart will also show a bad result. Figure 30

shows correlation chart associated with the same data as shown in Figure 29.

Figure 30. Correlation chart of un-healthy GC

The method can then be used to ensure that the GC is healthy at any point of time prior

to monitoring the longer term health of the GC. Because of the repeatable nature of a GC,

it operates in a repeatable manner with consistent a systematic error. Therefore it is

important to ensure that both GC is measuring accurately and calibration gas

concentration is as per the calibration gas certificate.

3.2 GC uncertainty monitoring

As described in section 2.4, the uncertainty of GC parameter is one of the ways to quantify

GC performance derived from response factor control charts. The monitoring of the GC

response factor not only will ensure the health of the GC throughout the year, but also will


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provide with early indication in case of deterioration of GC performance as it will be

revealed by increment of the GC uncertainty value.

Healthy GC response factor trend is shown in Figure 31.

Figure 31. 1 year RF trend of healthy GC

Visually from the trend, many stories can be told. From Figure 31 it can be seen that there

was a shutdown between August 2013 and October 2013. There was also change in

calibration gas in April 2015 and some parameter changes were done on GC in June 2014.

Also it can be seen visually that all response factor trends up and down in harmonious

way. Uncertainty trend is one way to quantify these parameters. Uncertainty trend of the

GC associated with Figure 31 is shown in Figure 32.

Figure 32. Uncertainty of CV associated with Figure 31


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From here uncertainty limits can be set and alarm flagged at any time the uncertainty

value exceed the limit. Figure 32 shows an example where uncertainty limit is set to

0.12%. This limit is very useful to catch issues with GC before the issue becomes

significant.

Figure 33. Uncertainty with set limit of 0.12%

3.3 Calibration data update monitoring

To get the best result, automatic calibration on the GC should be active. GC can be

automatically calibrated every day or every two days. When calibration data is not updated

in a GC, it is an indication that calibration has failed. The monitoring of this calibration

updated on online conditional based monitoring system shows that one of the following

has occurred:

Communication failure between GC and CBM system

Calibration failure has occurred

Both occasions can easily be checked. The three parameters (R2, Uncertainty, and

calibration data update) when checked continuously will provide continuous confidence on

the GC measurement. A dashboard system can also be provided to show these three

parameters for daily check as shown in Figure 34.

Figure 34. Dashboard to of GC health status


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It has also been learned that with the increased GC confidence, the GC that is historically

blamed for many issues with the process has now become the source of confidence of

measurement which can be used to validate other systems such as pressure let down

system that feed the gas to the GC.

3.4 Pressure let down system validation

With the increase of the confidence in GC measurement, when the GC result is compared

against spot sampling, it will provide the following information:

If the sample collected is representative

If sample let down system provides the GC with representative sample

It is possible to see if the sample collected is representative of what the pipeline

composition is by looking at Nitrogen value as well as the methane value. These two

components are very light and have very low dew point temperature, therefore it is

expected that these two components will be comparable to the GC component. Figure 35

shows a case where three samples have been taken.

Figure 35. Nitrogen GC result vs spot sample

The first sample shows that there is air contamination during the spot sampling process.

It shows high level of Nitrogen in comparison to the GC reading. The other two sampling

show better correlation between spot sample result and GC result. Both of these results

fall within acceptable reproducibility limits as stated in ASTM D1945 standard. A similar

result is shown in methane component as shown in Figure 36.

Figure 36. Methane GC result vs spot sample


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When all the components are checked, heavy end drop out can also be detected as shown

in Figure 37.

Figure 37. Heavy end detection

Undetected heavy end drop out has proven to be a big issue. From case studies, errors of

more than £100,000.00 /month have been detected using this method.

3.5 Auditable comment

One of the most useful features of the conditional based monitoring tool is the auditable

comment. Any events that happen in the GC can have an associated comment. This makes

analysis of historical data easy for anybody that has interest in the particular GC

maintenance. A comment system can be made where every comments is linked to a

particular events such as shown in Figure 38.

Figure 38. CBM comment feature


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Each event that has been done in the GC can then be traced and analysed from cause and

effect perspective.

3.6 Conditional based monitoring overall impact

In effect the overall impact of conditional based monitoring tools is to virtually eliminate

the need of conventional maintenance of the GC. The maintenance requirement that can

be handled by conditional based monitoring is summarised in Table 7.

Description Remarks

Daily health check / validation Correlation and uncertainty feature

Early failure detection Uncertainty feature

Valve timing check Correlation feature

Calibration gas correctness check Correlation feature

Pressure let down validation Trending comparison between GC and spot sample

GC uncertainty check On daily basis

Confidence in GC On daily basis

Table 7. GC conditional based monitoring functions

4 Conclusion

This paper has shown the conventional GC maintenance methods that have been used in

the industry and also a new novel method that may increase the confidence in the GC

operation while eliminating the need for conventional maintenance methods (i.e.

continuous performance monitoring). The conclusion that can be taken is that the

conventional maintenance method although is widely used by the industry, it adds little

value towards overall GC confidence. GC still fails with the need of specialist knowledge to

rectify the situation, but not having enough detailed background information to analyse

the actual cause of the failure.

The idea of conditional based monitoring provides operators with full confidence of the GC.

Although some errors in GC’ may still require specialist knowledge to rectify, the source

of the issue can be traced by looking at complete historical events that’s recorded in a

database.

The implementation of the conditional based monitoring is easy without the need for

change of any equipment currently being used.


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5 References

[1] ASTM D1945. Standard test method for analysis of natural gas by gas

chromatography. ASTM, 2001.

[2] BS EN ISO 10715. Natural gas - sampling guidelines. BS EN ISO, 2001.

[3] BS EN ISO 10723. Natural gas - performance evaluation for on-line analytical

systems. BS EN ISO, 2002.

[4] Commission Decision 2007 589 EC. Commission Decision. Establishing guidelines

for the monitoring and reporting of greenhouse gas emissions pursuant to Directive

2003/87/EC of the European Parliament and of the Council. Official Journal of the European

Union, May 2007.

[5] COMMISSION REGULATION (EU) No 601/2012. The monitoring and reporting of

greenhouse gas emissions pursuant to Directive 2003/87/EC of the European Parliament

and of the Council. Official Journal of the European Union, 2012.

[6] Encyclopaedia Britannica. Joule-Thomson effect. britannica.com. Accessed January

13, 2015. http://www.britannica.com/EBchecked/topic/306635/Joule-Thomson-effect.,

2015.

[7] GPA 2261. Analysis for Natural Gas and Similar Gaseous Mixtures by Gas

Chromatography. GPA, 1995.

[8] ISO 10723. Natural gas - performance evaluation for on-line analytical systems.

ISO, 1995.

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WHAT WE HAVE LEARNT FROM GC CONDITIONAL BASED MONITORING › cz_doc › download › ...have_Learnt... · WHAT WE HAVE LEARNT FROM GC CONDITIONAL BASED MONITORING Anwar Sutan, i-Vigilant