A novel efficient model for gas compressibility factor based on GMDH network. (March 2020)
- Record Type:
- Journal Article
- Title:
- A novel efficient model for gas compressibility factor based on GMDH network. (March 2020)
- Main Title:
- A novel efficient model for gas compressibility factor based on GMDH network
- Authors:
- Lin, Luan
Li, Shiyang
Sun, Sihao
Yuan, Yaqi
Yang, Ming - Abstract:
- Abstract: With the development of the low-power flowmeters, it is urgent and crucial to calculate the gas compression factor (Z-factor) in real time. However, the traditional estimation methods for Z-factor such as AGA8-92DC, needed a long calculation time and were difficult to be applied to the low-power embedded-based gas flowmeters. The other empirical correlations also had large errors. To solve this problem, we proposed a novel model for quick calculation of gas Z-factor based on Group Method of Data Handling(GMDH) network. The accuracy and reliability of this model were verified under different T pr and P pr . Compared with other empirical correlations, our model has the lowest root mean sum of squares of the errors (RMSE) of 0.0066 and mean absolute percentage error (MAPE) of 0.5615%, which is only 1/13–2/5 of MAPE calculated by the other correlations. The results show that our model has higher accuracy. Moreover, our model avoids lots of complex exponential and logarithmic operations, so it is especially useful for real-time Z-factor calculation of the low-power flowmeters Highlights: An efficient compressibility factor model for flowmeters is built based on GMDH. Comprehensive comparisons are carried out between our model and other correlations. Our method enhances the accuracy of the compressibility factor estimation. Our model avoids lots of complex exponential and logarithmic operations. It is useful for real-time natural gas Z-factor calculation in low-powerAbstract: With the development of the low-power flowmeters, it is urgent and crucial to calculate the gas compression factor (Z-factor) in real time. However, the traditional estimation methods for Z-factor such as AGA8-92DC, needed a long calculation time and were difficult to be applied to the low-power embedded-based gas flowmeters. The other empirical correlations also had large errors. To solve this problem, we proposed a novel model for quick calculation of gas Z-factor based on Group Method of Data Handling(GMDH) network. The accuracy and reliability of this model were verified under different T pr and P pr . Compared with other empirical correlations, our model has the lowest root mean sum of squares of the errors (RMSE) of 0.0066 and mean absolute percentage error (MAPE) of 0.5615%, which is only 1/13–2/5 of MAPE calculated by the other correlations. The results show that our model has higher accuracy. Moreover, our model avoids lots of complex exponential and logarithmic operations, so it is especially useful for real-time Z-factor calculation of the low-power flowmeters Highlights: An efficient compressibility factor model for flowmeters is built based on GMDH. Comprehensive comparisons are carried out between our model and other correlations. Our method enhances the accuracy of the compressibility factor estimation. Our model avoids lots of complex exponential and logarithmic operations. It is useful for real-time natural gas Z-factor calculation in low-power flowmeters. … (more)
- Is Part Of:
- Flow measurement and instrumentation. Volume 71(2020)
- Journal:
- Flow measurement and instrumentation
- Issue:
- Volume 71(2020)
- Issue Display:
- Volume 71, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 71
- Issue:
- 2020
- Issue Sort Value:
- 2020-0071-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-03
- Subjects:
- Flowmeter -- Compressibility factor -- GMDH -- Low power
Fluid dynamic measurements -- Periodicals
Flow meters -- Periodicals
Fluides, Dynamique des -- Mesure -- Périodiques
Débitmètres -- Périodiques
681.2805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09555986 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.flowmeasinst.2019.101677 ↗
- Languages:
- English
- ISSNs:
- 0955-5986
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3958.300000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13449.xml