Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical...

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Veröffentlicht in:Applied sciences Jg. 14; H. 9; S. 3717
Hauptverfasser: Wang, Junxian, Huang, Zhenwei, Xu, Ya, Xie, Dailiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.05.2024
Schlagworte:
Accuracy
Algorithms
Classification
Deep learning
Fourier transforms
Gases
gas–liquid two-phase flow
Lucas–Kanade optical flow detection
Machine learning
Measurement
Methods
Natural gas
Neural networks
Nuclear energy
Pattern recognition
Sensors
Ultrasonic imaging
ISSN:2076-3417, 2076-3417
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Zusammenfassung:Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app14093717