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Comparative Performance of Machine Learning and Deep Learning Algorithms in Predicting Gas-Liquid Flow Regimes

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... Recent advancements in artificial intelligence, using traditional machine learning algorithms and deep learning architectures, address complex classification problems [16,17]. In the petrochemical industry, the development of artificial intelligence has facilitated the adoption of techniques for identifying the hydrodynamic characteristics of two-phase liquid-gas flow using experimental data to identify flow hydrodynamic characteristics automatically [18]. ...
... In Figure 16, the search was conducted from 2019 to 2024, indicating an ongoing research topic. Analyzing the publication trend, it was possible to identify that 2022 saw the highest number of articles published (18) regarding artificial intelligence techniques for the hydrodynamic characterization of liquid-gas two-phase flows. Figure 15. ...
... In Figure 16, the search was conducted from 2019 to 2024, indicating an ongoing research topic. Analyzing the publication trend, it was possible to identify that 2022 saw the highest number of articles published (18) regarding artificial intelligence techniques for the hydrodynamic characterization of liquid-gas two-phase flows. Figure 17 illustrates the relevance of sources measured by the number of published articles. ...
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Accurately and instantly estimating the hydrodynamic characteristics in two-phase liquid–gas flow is crucial for industries like oil, gas, and other multiphase flow sectors to reduce costs and emissions, boost efficiency, and enhance operational safety. This type of flow involves constant slippage between gas and liquid phases caused by a deformable interface, resulting in changes in gas volumetric fraction and the creation of structures known as flow patterns. Empirical and numerical methods used for prediction often result in significant inaccuracies during scale-up processes. Different methodologies based on artificial intelligence (AI) are currently being applied to predict hydrodynamic characteristics in two-phase liquid–gas flow, which was corroborated with the bibliometric analysis where AI techniques were found to have been applied in flow pattern recognition, volumetric fraction determination for each fluid, and pressure gradient estimation. The results revealed that a total of 178 keywords in 70 articles, 29 of which reached the threshold (machine learning, flow pattern, two-phase flow, artificial intelligence, and neural networks as the high predominance), were published mainly in Flow Measurement and Instrumentation. This journal has the highest number of published articles related to the studied topic, with nine articles. The most relevant author is Efteknari-Zadeh, E, from the Institute of Optics and Quantum Electronics.
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