Gas-Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms
- Submitting institution
-
The University of Kent
- Unit of assessment
- 12 - Engineering
- Output identifier
- 15637
- Type
- D - Journal article
- DOI
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10.1109/TIM.2016.2634630
- Title of journal
- IEEE Transactions on Instrumentation and Measurement
- Article number
- -
- First page
- 852
- Volume
- 66
- Issue
- 5
- ISSN
- 0018-9456
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
https://kar.kent.ac.uk/58588/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first publication on the application of SVM and Genetic Programming algorithms to gas-liquid two-phase flow metering. The technique developed outperforms the earlier method that incorporates neural networks developed by University of Oxford. This research was funded by the EPSRC via the UK CCSRC (UKCCRSC-2-216) and financially funded by industrial partner Krohne UK Ltd. The output has underpinned the successful award of a KTP grant and subsequently led to the development of a new product range by Krohne UK Ltd for the measurement of gas-liquid two-phase flows in the oil & gas, marine engineering and power generation industries.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
