Abstract：A multi-domain feature processing scheme is proposed for the accurate identification of gas-liquid two-phase flow. The electrical resistance tomography (ERT) system is used to obtain the flow data of vertical rising pipeline. From the perspective of measurement data and cross-sectional conductivity distribution image, the time-domain features are extracted after dimensionality reduction of high-dimensional measurement data, and the spatial features of the reconstructed image are extracted by linear back projection (LBP) algorithm. Further, Walsh-Hadamard transform is performed on the image to extract column rate domain features. The uniform manifold approximation and projection (UMAP) algorithm is used to reduce the dimension of the quantized multi-domain features, and finally a support vector machine (SVM) is built to realize flow pattern recognition. The results show that the classification accuracy of bubble flow, bubble-slug transition flow, slug flow and severe slug flow are 98.1%,96.3%,95.2% and 94.8%, respectively.
Tenenbaum J B. A global geometric framework for nonlinear dimensionality reduction ［J］. Science, 2000, 290(5500): 2319-2323.
Wang X X, Wang B, Chen Y Z. Two phase flow pattern recognition based on reconstructed image of electrical capacitance tomography ［J］. Acta Metrologica Sinica, 2020, 41 (8): 942-946.
Fang L D, Wang P P, Wang S, et al. Study on slug flow mechanism of gas-liquid two-phase flow in long throat venturi ［J］. Acta Metrologica Sinica, 2020, 41 (1): 48-54.
［4］
Mahvash A, Ross A. Application of CHMMs to two-phase flow pattern identification ［J］. Engineering Applications of Artificial Intelligence, 2008, 21(8): 1144-1152.
［7］
Zhang L, Wang Z, Wu S, Gas-liquid flow behavior analysis based on phase-amplitude coupling and ERT ［J］. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 4502410.
Zhang L F, Zhu Y F. Two phase flow pattern identification based on MO-PLP-ELM and electrical capacitance tomography ［J］. Acta Metrologica Sinica, 2021, 42 (3): 334-338.
［9］
Arif M Z, Lehtikangas O, Seppnen A, et al. Joint reconstruction of conductivity and velocity in two-phase flows using electromagnetic flow tomography and electrical tomography: A simulation study ［J］. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1010017.
［10］
Li F, Tan C, Dong F. Electrical resistance tomography image reconstruction with densely connected convolutional neural network ［J］. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 4500811.
［11］
Somtochukwu G N, Karl E S, Liyun L, et al. Identification of gas-liquid flow regimes using a non-intrusive Doppler ultrasonic sensor and virtual flow regime maps ［J］. Flow Measurement and Instrumentation, 2019, 68: 101568.
［13］
Sun B, Chang H, Zhou Y L. Flow regime recognition and dynamic characteristics analysis of air-water flow in horizontal channel under nonlinear oscillation based on multi-scale entropy ［J］. Entropy, 2019, 21(7): 667.
［16］
Robert H, Marcin Z, Maciej K, et al. Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods ［J］. Flow Measurement and Instrumentation, 2018, 60: 17-23.
［17］
Mandal B, Gangopadhyay A K. A note on generalization of bent boolean functions ［J］. Advances in Mathematics of Communications, 2021, 15(2): 329-346.
［19］
Gao J, Lin P, Yang Y, et al. Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning ［J］. Neural Comput & Applic, 2010, 19: 1217-1226.
Salgado C M, Brando L E, Pereira C M, et al. Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks ［J］. Progress in Nuclear Energy, 2014, 76: 17-23.
［5］
Tan C, Dong X, Dong F. Continuous wave ultrasonic doppler modeling for oil-gas-water three-phase flow velocity measurement［J］. IEEE Sensors Journal, 2018, 18: 3703-3713.
［6］
Yang Q, Jin N, Deng Y, et al. Water holdup measurement of gas-liquid flows using distributed differential pressure sensors ［J］. IEEE Sensors Journal, 2021, 21(2): 2149-2158.
［12］
Shubhankar C, Prasanta K D, Characterisation and classification of gas-liquid two-phase flow using conductivity probe and multiple optical sensors［J］. International Journal of Multiphase Flow, 2020,124: 103193.
［14］
Gao Z K, Liu M X, Dang W D, et al. Multilayer limited penetrable visibility graph for characterizing the gas-liquid flow behavior ［J］. Chemical Engineering Journal, 2021, 407: 127229.
［15］
Gao Z, Yang Y, Zhai L, et al. A four-sector conductance method for measuring and characterizing low-velocity oil-water two-phase flows ［J］. IEEE Transactions on Instrumentation and Measurement, 2016, 65:1690-1697.
［22］
Roweis S T. Nonlinear dimensionality reduction by locally linear embedding ［J］. Science, 2000, 290(5500): 2323-2326.
［24］
Zheng J, Peng L. A deep learning compensated back projection for image reconstruction of electrical capacitance tomography ［J］. IEEE Sensors Journal, 2020, 20: 4879-4890.
［25］
Taitel Y, Bornea D, Dukler A E, Modelling flow pattern transitions for steady upward gas-liquid flow in vertical tubes ［J］. AIChE Journal, 1980, 26:345-354.
［20］
Liu X, Sun A, Li D, et al. Sensitive feature extraction of telemetry vibration signal based on referenced manifold spatial fusion learning ［J］. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 7281-7294.
［26］
Hsu C W, Lin C J. A Comparison of methods for multiclass support vector machines ［J］. IEEE Transactions on neural networks, 2022, 13(2): 415-425.
Zhang L F, Xiao K, Hua H C. Identification of Two-phase Flow Pattern Based on 1D-CNN-AdaBoost and Electrical Resistance Tomography［J］. Acta Metrologica Sinica, 2022, 43(12): 1622-1626.
2023, Vol. 44 
