Optimization of the Monte Carlo Simulation to Improve the Calibration Uncertainty of Volume Meters for Hydrocarbons Custody Transfer
Since its publication by ISO in 1993, the GUM document has become the model to follow for the estimation of measurement uncertainty in the industry. However, the document itself recognizes its limitations in justifying the level of coverage. The calibration of volumetric meters for the custody trans...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/24/11472 |
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| Summary: | Since its publication by ISO in 1993, the GUM document has become the model to follow for the estimation of measurement uncertainty in the industry. However, the document itself recognizes its limitations in justifying the level of coverage. The calibration of volumetric meters for the custody transfer of liquid products can be affected by these limitations. The objective of this work is to calculate the calibration uncertainty of such meters by applying the Monte Carlo method of Supplement 1 of the GUM document (GUM-S1). In this way, the use of the classic GUM method is avoided. Therefore, the adaptive implementation of the Monte Carlo method (AMCM) has been applied to optimize the calibration uncertainty of a positive displacement meter against a standard tank in an ISO 17025 accredited volume laboratory. These meters are used for custody transfer in liquid hydrocarbon logistics where any reduction in the uncertainty estimation may have an important economic impact. Several innovations are proposed when applying AMCM; regulation of AMCM convergence by applying the Student’s t-distribution, validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM, and characterization of the measurand’s Probability Density Function (<i>PDF</i>) which results in a Flatten–Gaussian. This work proves that the GUM method does not assign the correct <i>PDF</i>. The uncertainty estimation is reduced by 7.1% compared to the GUM method. |
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| ISSN: | 2076-3417 |