The New ISO 10723 Advances and new concepts in the performance - - PowerPoint PPT Presentation

The New ISO 10723

Advances and new concepts in the performance evaluation and benchmarking

• f on‐line natural gas analysers.

Dr Paul Holland BD Director, EffecTech Group

Natural gas quality measurement

composition (content) of natural gas

• inert gases
• nitrogen, carbon dioxide, helium, (argon & hydrogen)
• hydrocarbons
• methane, ethane, propane, iso‐butane, n‐butane,

pentanes, hexanes + ......

properties (characteristics) of natural gas

• calorific value, Wobbe number, standard density (ISO 6976)
• compression factor, line density (ISO 12213)
• hydrocarbon dew point (ISO 23874)
• emission factors

Energy determination

Risks in energy metering

500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 4,500,000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Annual Value / € U(Energy) / %

Typical gas fired power station Power output : 500 MW Energy Price : €60 / MWh

Typical

Gas quality measurement instruments

Legal / commercial requirements

legislation

• customer protection (example in UK law)

Public Gas Transporters (PGTs) shall carry out performance evaluations of gas quality metering instruments in accordance with ISO 10723 following installation or maintenance. Provided that the results of the procedure show that the error on the calculated calorific value of transmission gas will not exceed 0.10 MJ.m‐3 for gas compositions allowed in the system, the PGT may then use that instrument for the determination of calorific values for the purposes of section 12 of the Gas Act 1986.”

• control of GHG emissions (example in EU directive)

commercial gas contracts

• sales gas agreements / contracts (end‐users)
• allocation agreements (upstream)

Revision of ISO 10723 : 1995

revision to existing standard required for

• inclusion of measurement uncertainties
• instrument precision, instrumental errors
• working calibration gas
• compliance with GUM
• more rigorous assessment of errors and uncertainties of

measurement of

• composition (gas content amount fraction)
• gas properties (calculated from composition)

revision by

• ISO/TC193/WG15 (with liaison from ISO/TC158)
• Drafting by G Squire (EffecTech, UK) and D Lander (NGG,

UK)

ISO/DIS 10723 : 2011 ‐ Scope

Determine E(x), E(P) and U(x), U(P) over a pre‐defined range of compositions for each specified component Determine a range of compositions for each specified component which satisfy pre‐ defined maximums in E(x), E(P) and U(x), U(P)

using a specified calibration gas composition and uncertainty calibration gas redesign composition and uncertainty

Instrument errors

1 2 3 4 5 6 7 8 9 10

response / (peak area) content / (% mol/mol)

y=Fass(x) xcal

Instrument errors

1 2 3 4 5 6 7 8 9 10

response / (peak area) content / (% mol/mol)

y=Fass(x) y=Ftrue(x) xcal

Instrument errors

1 2 3 4 5 6 7 8 9 10

response / (peak area) content / (% mol/mol)

y=Fass(x) y=Ftrue(x) xcal

Instrument errors

1 2 3 4 5 6 7 8 9 10

response / (peak area) content / (% mol/mol)

y=Fass(x) y=Ftrue(x) xcal

Challenge

measurement of TRUE (actual) response functions for the instrument for all components (i=1..q)

• calibration functions
• y = Fi,true(x)
• analysis functions
• x = Gi,true(y)

function types for F & G

• polynomials of order 1, 2 or 3
• yi = Fi,true(xi) = a0 + a1xi + a1xi

2 + a3xi 3

• xi= Gi,true(yi) = b0 + b1yi + b2yi

2 + b3yi 3

Design of reference gases

a series of reference gases is measured by the instrument being calibrated components included in reference gases

• depends on application

range of composition

• equal or greater than that expected to be measured by the

instrument (no extrapolation)

number of mixtures

• dependent upon expected order of F and G
• 3 (1st order), 5 (2nd order), 7 (3rd order)
• approximately equally spaced within the range

ISO 10723 ‐ Performance evaluations of on‐line analytical systems

• ISO 17025 accredited calibration

gases

• well established reference values &

uncertainties

• 7 ‐10 cylinders each containing 10,11
• r 12 components
• wide range natural gas compositions

Experimental design

replicate measurements reference gases

Batch‐wise calibration

 simplest / manual / most practical (p gas changes)  temporal drift has more significance

replicate measurements reference gases

Drift compensation calibration

 compensates for temporal drift (due to sample size effects)  automation required

Experimental design

Drift correction

Samples are injected at (or with reference to) ambient pressure.

response effective sample size ambient pressure

Batch‐wise calibration Drift compensation calibration

yijk = y’ijk . Pref / Pijk

measure ambient pressure at time of sample injection (Pijk)

Gas fired power station

Gas fired power station

Witnessed factory evaluation

LNG receiving terminal

Custody transfer border station

Drift compensation calibration (automated)

Offshore allocation / sales gas

Regression analysis

parameters F and G are calculated using GLS

• maximum liklihood functions relationships (MLFR)
• uncertainties in both variables (amount and response)
• procedure identical to that prescribed in ISO 6143

response functions validated for each component and in each domain F and G using ISO 6143

Calibration results

Errors

content / amount fraction & properties

• assumed
• true
• measured amount following calibration

(where functions coincide)

• normalise
• errors

) (

, i ass i i

y G x  ) (

, i true i i

x F y 

)) ( ( )) ( ( .

, , , , , , , * , cal i true i ass i true i true i ass i cal i meas i

x F G x F G x x 





* , * , , meas i meas i meas i

x x x

true i meas i meas i

x x x

, , ,

  

true meas meas

P P P   

Uncertainties in errors

contributions from

• calibration gas
• instrument precision

properties

• any property / characteristic calculated from composition

) (

,cal i

x u ) ( & ) (

, , meas i cal i

y u y u

) ,..., , , ,..., , (

2 1 2 1 m n

w w w x x x f P 

) ( ) ( ) (

2 1 2 2 1 2 2 i m i i i n i i c

w u w f x u x f P u

 

 

                     

produce a off‐line model of instrument

• errors as a function of amount fraction
• repeatability as a function of amount fraction
• uncertainties as a function of amount fraction

use Monte Carlo simulation

• generate 10,000 different gas compositions
• for each composition calculate
• errors in physical properties
• uncertainties in physical properties

Off‐line model

Errors and uncertainties on errors

• 0.15
• 0.10
• 0.05

0.00 0.05 0.10 0.15 78 80 82 84 86 88 90 92 94 96 98

E(CVSUP) / MJ.m-3 methane content / (% mol/mol)

Error distribution

• 0.15
• 0.10
• 0.05

0.00 0.05 0.10 0.15 78 80 82 84 86 88 90 92 94 96 98

E(CVSUP) / MJ.m-3 methane content / (% mol/mol)

Mean error (bias)

• 0.16
• 0.14
• 0.12
• 0.10
• 0.08
• 0.06
• 0.04
• 0.02

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 78 83 88 93 98

E(CVSUP) / MJ.m-3 methane content / (%mol/mol)

Maximum Permissible Bias (MPB)

mean error = bias ‐ B(P)

MPB P  

Uncertainty on mean error

• 0.16
• 0.14
• 0.12
• 0.10
• 0.08
• 0.06
• 0.04
• 0.02

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 78 83 88 93 98

E(CVSUP) / MJ.m-3 methane content / (%mol/mol)

Maximum Permissible Error (MPE) uncertainty on the mean error ≈ uncertainty on bias ‐ U(B(P))

  MPE

P U P

c

   

Errors and uncertainties ‐ summary

Example – design of calibration gas

Example – design of calibration gas

xC1,cal = 0.88 mean E(CV) = 0.001 ± 0.061 MJ.m‐3

Example – design of calibration gas

xC1,cal = 0.88 mean E(CV) = 0.001 ± 0.061 MJ.m‐3 xC1,cal = 0.81 mean E(CV) = 0.000 ± 0.028 MJ.m‐3

Analysis function correction ‐ superior CV

Instrument errors

1 2 3 4 5 6 7 8 9 10

response / (peak area) content / (% mol/mol)

y=Fass(x) y=Ftrue(x) xcal

Dove House Dove Fields Uttoxeter Staffordshire ST14 8HU United Kingdom tel : +44 (0)1889 569229 e‐mail : paul.holland@effectech.co.uk web‐site : www.effectech.co.uk

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