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Data-driven solid phase fraction estimation for liquid-solid two-phase flow using multifrequency ultrasound attenuation
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DOI：10.1088/1361-6501/ae2647.png)
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Abstract
En 中文
Accurate measurement of the solid phase fraction in liquid–solid two-phase flow remains a significant challenge in industrial applications. Traditional methods are often constrained by the prerequisite for precise physical parameters and the inherent complexities of hydrodynamic modeling, which collectively hinder accurate online measurement. To address this, a novel data-driven framework is proposed whose core innovation lies in the unique combination of the empirical wavelet transform (EWT) and the light gradient boosting machine (LightGBM) for ultrasonic solid phase fraction estimation. This synergistic approach overcomes traditional limitations by combining EWT’s adaptive feature extraction for complex signals with LightGBM’s efficient and robust prediction, achieving rapid and reliable online measurement. The proposed method adaptively extracts features from ultrasonic signals via energy partitioning using EWT. Then, a regression model is constructed using LightGBM to enable rapid prediction of the solid phase fraction, which provides an efficient, precise, and robust solution for real-time online measurement in liquid–solid two-phase system. To validate the proposed methodology, circulating flow experiments were conducted using polystyrene particles and water. Various feature extraction methods and regression models were tested and compared, using the combined McClements and Bouguer–Lambert–Beer–Law model (MCBL model) as a performance benchmark. Results demonstrate that EWT-based energy partitioning (EWT-EP) yields superior performance in feature extraction, while LightGBM achieves higher prediction accuracy and robustness. For instance, at a single ultrasonic frequency of 7.5 MHz, the EWT-EP combined with the LightGBM model achieved a coefficient of determination (R2) of 0.975, representing a substantial 71.78% improvement over the MCBL model. In addition, the implementation of a multifrequency fusion strategy enhanced overall model performance by providing richer feature representations, result in improving both prediction accuracy and generalization capability, with R2 increasing to 0.978.
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