Measurement of gas volume fraction in gas-liquid two-phase flow using arrayed fiber-optic probes combined with the PSO-BP-AdaBoost algorithm

•Proposed a strategy for gas fraction measurement using arrayed fiber-optic probes and neural networks.•Optimizes the structure of arrayed fiber-optic probes to enhance gas phase detection performance.•Constructs a gas phase volume fraction prediction model via the PSO-BP-AdaBoost algorithm.•PSO-BP-...

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Bibliographic Details
Published in:	Optical fiber technology Vol. 93; p. 104264
Main Authors:	Xing, Wenju, Gao, Hong, Qiao, Xueguang
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.09.2025
Subjects:	Adaptive boosting algorithm Arrayed fiber-optic probes Backpropagation neural network Gas volume fraction Particle swarm optimization algorithm Adaptive boosting algorithm Backpropagation neural network Gas volume fraction Arrayed fiber-optic probes Particle swarm optimization algorithm
ISSN:	1068-5200
Online Access:	Get full text
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Summary:	•Proposed a strategy for gas fraction measurement using arrayed fiber-optic probes and neural networks.•Optimizes the structure of arrayed fiber-optic probes to enhance gas phase detection performance.•Constructs a gas phase volume fraction prediction model via the PSO-BP-AdaBoost algorithm.•PSO-BP-AdaBoost outperforms SVM, BP, and PSO-BP in accuracy, with a max relative error of 0.14%. For the measurement of gas volume fraction in natural gas wells, a strategy based on the fusion of arrayed fiber-optic probes (AFOP) and artificial intelligence algorithms is proposed to enhance the precision and efficiency of gas volume fraction monitoring. As a key front-end component for obtaining gas phase information, AFOP determines the optimal structure by analyzing its performance metrics in bubble capture and its interference with fluid flow. A back-end gas volume fraction prediction model was constructed using a machine learning algorithm. The model first uses a particle swarm optimization (PSO) algorithm to enhance the backpropagation (BP) neural network as a weak predictor and then integrates multiple weak predictors through the adaptive boosting (AdaBoost) algorithm to create a strong predictor. The experimental results show that compared with the support vector machine (SVM), BP neural network, and PSO-BP neural network, the PSO-BP-AdaBoost algorithm has advantages in prediction precision, with a maximum relative error of only 0.14 %, providing a more effective solution for research and application in related fields.
ISSN:	1068-5200
DOI:	10.1016/j.yofte.2025.104264