Abstract
After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed.
| Original language | English |
|---|---|
| Pages (from-to) | 30-43 |
| Number of pages | 14 |
| Journal | Flow Measurement and Instrumentation |
| Volume | 60 |
| DOIs | |
| State | Published - Apr 2018 |
| Externally published | Yes |
Keywords
- Computational intelligence
- Data-driven model
- Machine learning
- Multiphase flow measurement
- Sensor fusion
- Soft computing
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