A study was performed of the problem of increasing the measurement precision of the flow rate of fluids and gases by differential-pressure flow meters. Modeling of the discharge coefficient of an orifice plate of differential-pressure flow meters by machine learning methods was examined. It was shown that existing models of the discharge coefficient of an orifice plate are complex, and the values of the coefficients must be made more precise in the process of flow meter use. It is proposed to use, for the discharge coefficient of an orifice plate, a model based on a support vector machine. The structure and training process of the model are described, and the training parameters are specified. The modeling results obtained in training and testing the model are given, and its effectiveness was confirmed. A comparative analysis was performed of the proposed model based on a support vector machine and the existing model in the form of the Reader- Harris–Gallagher empirical equation. It is shown that the proposed model of the discharge coefficient is not inferior to the precision and effectiveness of the working model, and this makes it possible to improve systems for measuring the flow rate of fluids and gases. The results of the study will be useful for operations in the fields of production, transport, and storage of natural gas.
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Translated from Izmeritel’naya Tekhnika, No. 4, pp. 37–42, April, 2022.
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Dayev, Z.A., Shopanova, G.E. Modelling of the Discharge Coefficient of Differential Pressure Flowmeters by the Support Vector Machine. Meas Tech 65, 266–272 (2022). https://doi.org/10.1007/s11018-022-02078-5
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DOI: https://doi.org/10.1007/s11018-022-02078-5