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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Vydáno v:	Applied sciences Ročník 14; číslo 9; s. 3717
Hlavní autoři:	Wang, Junxian, Huang, Zhenwei, Xu, Ya, Xie, Dailiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.05.2024
Témata:	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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Abstract	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.
AbstractList	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. Application to the monitoring of gas–liquid two-phase flow links. 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 R[sup.2] value of 0.97, RMSE of 0.74 m[sup.3]/h, MAE of 0.52 m[sup.3]/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas. Featured ApplicationApplication to the monitoring of gas–liquid two-phase flow links.AbstractGas–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.
Audience	Academic
Author	Wang, Junxian Xie, Dailiang Huang, Zhenwei Xu, Ya
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CitedBy_id	crossref_primary_10_1016_j_flowmeasinst_2024_102794 crossref_primary_10_53759_7669_jmc202505157
Cites_doi	10.1088/1361-6501/ac0c48 10.1016/j.ijheatmasstransfer.2021.122439 10.1109/ACCESS.2023.3274784 10.1109/TII.2020.2978944 10.1016/j.dche.2022.100012 10.7717/peerj-cs.798 10.1016/j.ijheatmasstransfer.2020.120689 10.1109/TCYB.2021.3084860 10.1109/JSEN.2023.3235954 10.1016/j.ijmultiphaseflow.2023.104421 10.1016/j.cej.2020.126401 10.1088/0957-0233/20/11/115601 10.3390/s22030996 10.1016/j.expthermflusci.2023.110979 10.1016/j.cherd.2023.11.057 10.1109/JSEN.2022.3193981 10.3390/s23063152 10.1007/s11222-009-9153-8 10.1109/LSP.2014.2301037 10.1109/TIP.2014.2331140 10.3390/mi14020462 10.1109/WSC.2010.5679076 10.1016/j.ijmultiphaseflow.2021.103811 10.1109/TNNLS.2019.2905082 10.1016/j.eswa.2022.117704 10.3390/pr11010177 10.1016/S1003-6326(13)62487-5 10.1016/j.ins.2020.11.033 10.1016/j.specom.2007.12.003 10.1109/ICEST49890.2020.9232768 10.1016/j.ijmultiphaseflow.2022.104067 10.1109/JSEN.2021.3085995 10.1007/s12182-020-00493-3 10.1007/978-3-662-01657-2 10.1016/j.isprsjprs.2022.01.003
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References	Paolinelli (ref_1) 2021; 16 Gao (ref_8) 2021; 17 Ooi (ref_17) 2022; 185 ref_14 Hu (ref_7) 2020; 31 ref_10 ref_30 Zhao (ref_4) 2022; 22 Yang (ref_2) 2024; 201 Chen (ref_23) 2023; 148 Zhang (ref_35) 2021; 552 Gao (ref_12) 2021; 70 ref_37 Niu (ref_28) 2014; 23 Wajman (ref_21) 2024; 72 Lucas (ref_26) 1981; 81 Gao (ref_13) 2021; 18 Nie (ref_11) 2022; 152 Li (ref_5) 2023; 162 Li (ref_6) 2021; 32 Harris (ref_31) 1988; 1988 Dowe (ref_39) 2013; 7070 Rana (ref_33) 2022; 184 Yamagishi (ref_40) 2008; 50 Dong (ref_34) 2013; 23 ref_25 Ni (ref_36) 2009; 20 Nnabuife (ref_18) 2021; 403 ref_24 Nnabuife (ref_19) 2023; 53 OuYang (ref_16) 2022; 205 Zhong (ref_27) 2023; 11 Florez (ref_20) 2021; 7 ref_29 Tan (ref_3) 2021; 144 Batur (ref_32) 2010; 10 Zhang (ref_22) 2023; 23 Kuang (ref_15) 2022; 22 Gao (ref_9) 2021; 16 Assi (ref_38) 2014; 21 Fushiki (ref_41) 2011; 21
References_xml	– volume: 32 start-page: 105306 year: 2021 ident: ref_6 article-title: Gas–liquid intermittent flow rates measurement based on two-phase mass flow multiplier and neural network publication-title: Meas. Sci. Technol. doi: 10.1088/1361-6501/ac0c48 – ident: ref_30 – volume: 185 start-page: 122439 year: 2022 ident: ref_17 article-title: Brooks. Identification of flow regimes in boiling flows in a vertical annulus channel with machine learning techniques publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2021.122439 – volume: 11 start-page: 47179 year: 2023 ident: ref_27 article-title: An Improved Visual Odometer Based on Lucas-Kanade Optical Flow and ORB Feature publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3274784 – volume: 17 start-page: 6329 year: 2021 ident: ref_8 article-title: Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2020.2978944 – volume: 22 start-page: 100012 year: 2022 ident: ref_15 article-title: Gas-liquid flow regimes identification using non-intrusive Doppler ultrasonic sensor and convolutional recurrent neural networks in an s-shaped riser publication-title: Digit. Chem. Eng. doi: 10.1016/j.dche.2022.100012 – volume: 7 start-page: 798 year: 2021 ident: ref_20 article-title: Machine learning applications to predict two-phase flow patterns publication-title: PeerJ Comput. Sci. doi: 10.7717/peerj-cs.798 – volume: 16 start-page: 120689 year: 2021 ident: ref_1 article-title: Calculation of mass transfer coefficients for corrosion prediction in two-phase gas-liquid pipe flow publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2020.120689 – volume: 53 start-page: 3 year: 2023 ident: ref_19 article-title: Development of Gas–Liquid Flow Regimes Identification Using a Noninvasive Ultrasonic Sensor, Belt-Shape Features, and Convolutional Neural Network in an S-Shaped Riser publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2021.3084860 – volume: 23 start-page: 4022 year: 2023 ident: ref_22 article-title: Gas/Liquid Two-Phase Flow Pattern Identification Method Using Gramian Angular Field and Densely Connected Network publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2023.3235954 – volume: 162 start-page: 104421 year: 2023 ident: ref_5 article-title: Gas-liquid two-phase flow rates measurement using physics-guided deep learning publication-title: Int. J. Multiph. Flow doi: 10.1016/j.ijmultiphaseflow.2023.104421 – volume: 403 start-page: 126401 year: 2021 ident: ref_18 article-title: Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline riser using a Doppler ultrasonic sensor and deep neural networks publication-title: Chem. Eng. J. doi: 10.1016/j.cej.2020.126401 – volume: 20 start-page: 115601 year: 2009 ident: ref_36 article-title: Identification and measurement of gas mixture by using the support vector regression technique publication-title: Meas. Sci. Technol. doi: 10.1088/0957-0233/20/11/115601 – ident: ref_14 doi: 10.3390/s22030996 – volume: 148 start-page: 11097 year: 2023 ident: ref_23 article-title: Robust CNN-based flow pattern identification for horizontal gas-liquid pipe flow using flow-induced vibration publication-title: Exp. Therm. Fluid Sci. doi: 10.1016/j.expthermflusci.2023.110979 – volume: 201 start-page: 257 year: 2024 ident: ref_2 article-title: A review of gas-liquid separation technologies: Separation mechanism, application scope, research status, and development prospects publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2023.11.057 – volume: 22 start-page: 17234 year: 2022 ident: ref_4 article-title: The Gas-Liquid Flow Rate Measurement Based on Multisensors and Machine Learning publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2022.3193981 – ident: ref_29 doi: 10.3390/s23063152 – volume: 21 start-page: 137 year: 2011 ident: ref_41 article-title: Estimation of prediction error by using K-fold cross-validation publication-title: Stat. Comput. doi: 10.1007/s11222-009-9153-8 – volume: 21 start-page: 292 year: 2014 ident: ref_38 article-title: Modified Large Margin Nearest Neighbor Metric Learning for Regression publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2014.2301037 – volume: 23 start-page: 3535 year: 2014 ident: ref_28 article-title: Dynamically Removing False Features in Pyramidal Lucas-Kanade Registration publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2014.2331140 – ident: ref_10 doi: 10.3390/mi14020462 – volume: 1988 start-page: 147 year: 1988 ident: ref_31 article-title: A combined corner and edge detector publication-title: Proc. Alvey Vis. Conf. – volume: 10 start-page: 1160 year: 2010 ident: ref_32 article-title: Mean-variance based ranking and selection publication-title: Proc. 2010 Winter Simul. Conf. doi: 10.1109/WSC.2010.5679076 – volume: 144 start-page: 103811 year: 2021 ident: ref_3 article-title: Ultrasonic Doppler Technique for Application to Multiphase Flows: A Review publication-title: Int. J. Multiph. Flow doi: 10.1016/j.ijmultiphaseflow.2021.103811 – volume: 31 start-page: 475 year: 2020 ident: ref_7 article-title: Flow Adversarial Networks: Flowrate Prediction for Gas–Liquid Multiphase Flows Across Different Domains publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2019.2905082 – volume: 70 start-page: 1 year: 2021 ident: ref_12 article-title: A Deep Branch-Aggregation Network for Recognition of Gas–Liquid Two-Phase Flow Structure publication-title: IEEE Trans. Instrum. Meas. – volume: 205 start-page: 117704 year: 2022 ident: ref_16 article-title: A new deep neural network framework with multivariate time series for two-phase flow pattern identification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117704 – ident: ref_24 doi: 10.3390/pr11010177 – volume: 23 start-page: 472 year: 2013 ident: ref_34 article-title: Prediction of rockburst classification using Random Forest publication-title: Trans. Nonferr. Met. Soc. China doi: 10.1016/S1003-6326(13)62487-5 – volume: 552 start-page: 65 year: 2021 ident: ref_35 article-title: MBSVR: Multiple birth support vector regression publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.11.033 – volume: 50 start-page: 405 year: 2008 ident: ref_40 article-title: Phone duration modeling using gradient tree boosting publication-title: Speech Commun. doi: 10.1016/j.specom.2007.12.003 – volume: 81 start-page: 121 year: 1981 ident: ref_26 article-title: An iterative image registration technique with an application to stereo vision. Proc. DARPA Image Understand publication-title: Workshop – ident: ref_37 doi: 10.1109/ICEST49890.2020.9232768 – volume: 152 start-page: 104067 year: 2022 ident: ref_11 article-title: Image identification for two-phase flow patterns based on CNN algorithms publication-title: Int. J. Multiph. Flow doi: 10.1016/j.ijmultiphaseflow.2022.104067 – volume: 7070 start-page: 261 year: 2013 ident: ref_39 article-title: MMLD Inference of Multilayer Perceptrons publication-title: Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence – volume: 72 start-page: 148842 year: 2024 ident: ref_21 article-title: Machine learning for two-phase gas-liquid flow regime evaluation based on raw 3D ECT measurement data publication-title: Bull. Pol. Acad. Sci. – volume: 16 start-page: 18123 year: 2021 ident: ref_9 article-title: Stage-Wise Densely Connected Network for Parameter Measurement of Two-Phase Flows publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2021.3085995 – volume: 18 start-page: 259 year: 2021 ident: ref_13 article-title: A novel complex network-based deep learning method for characterizing gas–liquid two-phase flow publication-title: Pet. Sci. doi: 10.1007/s12182-020-00493-3 – ident: ref_25 doi: 10.1007/978-3-662-01657-2 – volume: 184 start-page: 189 year: 2022 ident: ref_33 article-title: Effect of feature standardization on reducing the requirements of field samples for individual tree species classification using ALS data publication-title: ISPRS J. Photo-Grammetry Remote Sens. doi: 10.1016/j.isprsjprs.2022.01.003
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Snippet	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... Application to the monitoring of gas–liquid two-phase flow links. Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these... Featured ApplicationApplication to the monitoring of gas–liquid two-phase flow links.AbstractGas–Liquid two-phase flows are a common flow in industrial...
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SubjectTerms	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
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Title	Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model
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