Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter
| Autor: | Osman Taylan, Abdulrahman Basahel, Ehsan Nazemi, Mohammad Amir Sattari |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
radiation-based flowmeter
Mean squared error Artificial neural network General Mathematics feature extraction time domain Detector Monte Carlo method Feature extraction Perceptron artificial intelligence Flow measurement two-phase flow Mean absolute percentage error Computer Science (miscellaneous) QA1-939 Engineering (miscellaneous) Algorithm Mathematics |
| Zdroj: | Mathematics, Vol 9, Iss 1227, p 1227 (2021) Mathematics Volume 9 Issue 11 |
| ISSN: | 2227-7390 |
| Popis: | The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|