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

IF 2.6 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Optical Fiber Technology Pub Date : 2025-05-12 DOI:10.1016/j.yofte.2025.104264

Wenju Xing , Hong Gao , Xueguang Qiao

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引用次数: 0

Abstract

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.

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利用阵列光纤探头结合PSO-BP-AdaBoost算法测量气液两相流中的气体体积分数

针对天然气井中气体体积分数的测量，提出了一种基于阵列光纤探头（AFOP）与人工智能算法融合的方法，以提高天然气井中气体体积分数监测的精度和效率。AFOP作为获取气相信息的关键前端组件，通过分析其气泡捕获性能指标及其对流体流动的干扰来确定最优结构。利用机器学习算法构建后端气体体积分数预测模型。该模型首先使用粒子群优化（PSO）算法增强BP神经网络作为弱预测器，然后通过自适应增强（AdaBoost）算法整合多个弱预测器创建强预测器。实验结果表明，与支持向量机（SVM）、BP神经网络和PSO-BP神经网络相比，PSO-BP- adaboost算法在预测精度上具有优势，最大相对误差仅为0.14%，为相关领域的研究和应用提供了更有效的解决方案。

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来源期刊

Optical Fiber Technology 工程技术-电信学

CiteScore

4.80

自引率

11.10%

发文量

327

审稿时长

63 days

期刊介绍： Innovations in optical fiber technology are revolutionizing world communications. Newly developed fiber amplifiers allow for direct transmission of high-speed signals over transcontinental distances without the need for electronic regeneration. Optical fibers find new applications in data processing. The impact of fiber materials, devices, and systems on communications in the coming decades will create an abundance of primary literature and the need for up-to-date reviews. Optical Fiber Technology: Materials, Devices, and Systems is a new cutting-edge journal designed to fill a need in this rapidly evolving field for speedy publication of regular length papers. Both theoretical and experimental papers on fiber materials, devices, and system performance evaluation and measurements are eligible, with emphasis on practical applications.