Simultaneous sensing of gas-liquid two-phase flow rate based on optical carrier microwave interferometry

Songlin Li, Ting Xue, Yan Wu, Zhuping Li and Bin Wu

Energy, 2025, vol. 331, issue C

Abstract: Gas-liquid two-phase flow is widespread in energy systems, and accurate measurement of its two-phase flow rate is vital to ensure process safety and improve efficiency. In this paper, a fiber-optic gas-liquid two-phase flow rate simultaneous sensing method based on optical carrier microwave interferometry (OCMI) is proposed. Since the gas-liquid two-phase flow has complex characteristics, the commonly used OCMI demodulation methods (including dip frequency tracking and phase demodulation methods) face challenges in measuring the two-phase flow rates simultaneously. Therefore, an artificial neural network (ANN) based demodulation method is developed. The impact of different inputs to the ANN model on the prediction results is investigated, where utilizing the amplitudes of the spatiotemporal reflection peaks as the input achieves the optimal performance. The experimental results of the 5-fold cross-validation demonstrate that the proposed method achieves gas flow rate prediction with a mean absolute error (MAE) of 0.94 ± 0.15 m3/h and a mean absolute percentage error (MAPE) of 3.91 ± 0.51 %, while the MAE and MAPE for predicting liquid flow rate are 0.13 ± 0.04 m3/h and 4.87 ± 0.61 %, respectively. In conclusion, the proposed fiber-optic gas-liquid two-phase flow rate sensing method based on OCMI and ANN provides new insights for the measurement and characteristic analysis of complex flows.

Keywords: Gas-liquid two-phase flow; Flow rate measurement; Optical carrier microwave interferometry; Machine learning; Fiber optic sensing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:331:y:2025:i:c:s0360544225027276

DOI: 10.1016/j.energy.2025.137085

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