基于紫外光谱的油气两相流含气率检测研究

doi:10.3964/j.issn.1000-0593(2025)02-0522-10

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Abstract The accurate and rapid measurement of the gas void fraction in the oil-gas two-phase flow of the engine lubrication system holds significant importance for ensuring the safe operation of industrial processes. The precise determination of the void fraction is particularly crucial for monitoring the operational status of the engine. In light of the limitations associated with traditional gas void fraction detection methods, a method is proposed that combines ultraviolet spectroscopy with a modeling algorithm for predicting the void fraction. Initially, within the range of 185 to 430 nm, 31 sets of absorption spectra data were collected at five different two-phase flow temperatures and three different two-phase flow velocities, encompassing 15 operational conditions. The spectra were obtained for gas void fractions ranging from 0.9% to 3%. A total of 1799 spectral wavelength variables were considered for analysis. The spectral-physicochemical value coexistence distance algorithm (SPXY) was employed to partition the spectral data set into 21 calibration and 10 test sets. Partial least squares (PLS) modeling was conducted for various gas void fraction conditions, resulting in a test set determination coefficient R²) range of 0.63 to 0.91. To address the significant variations in prediction performance across different operating conditions, various data preprocessing methods, including centering, autoscaling, Savitzky-Golay convolution smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, detrending, and orthogonal partial least squares (OPLS) orthogonal signal correction, were applied to optimize the model. The Detrend-center-PLS model exhibited the best predictive performance, with the R² increasing from 0.903 2 to 0.955 7. To address the issue of excessive spectral wavelength variables, three methods, Competitive Adaptive Reweighted Sampling (CARS), Monte Carlo Uninformative Variable Elimination (MCUVE), and Genetic Algorithm (GA), were employed for band dimension reduction. The reduced spectral data were then modeled using Multiple Linear Regression (MLR), Partial Least Squares, and Least Squares Support Vector Machine (LS-SVM). The optimal predictive model was determined to be the Detrend-center-CARS-PLS model, which exhibited an improved R² from 0.955 7 to 0.959 8 compared to the full-wavelength model. Due to the limited improvement in optimization, the intersection of the three-dimensionality-reduction methods was taken, and the modeling was re-conducted. The R² increased from 0.959 8 to 0.967 1, indicating a noticeable enhancement in optimization. Considering the influence of temperature and flow rate on the predictive model, a multi-condition gas void fraction prediction model was established using the same mprocess as the single-condition model- Autoscaling was identified as the optimal preprocessing method, and the R² for the autoscaling-PLS model was 0.948 8. The wavelength dimensionality reduction method reduced the 1799 wavelength variables to 400~500 demonstrating a significant dimensionality reduction. For different modeling methods, the autoscaling-MCUVE-LS-SVM model was identified as the best multi-condition predictive model, achieving an R² of 0.992 6. Finally, by comparing the single-condition predictive model with the multi-condition predictive model, it was found that the gas void fraction prediction performance of the multi-condition model was superior, improving overall predictive accuracy. The results indicate that using spectral analysis combined with modeling algorithms for predicting gas void fraction in oil-gas two-phase flow is feasible and provides an effective monitoring method for the safe operation of engines.

Key words：Ultraviolet spectrum; Gas void fraction; Two-phase flow; Engine

Received: 2024-01-14 Accepted: 2024-04-28

ZTFLH:

TK314

Corresponding Authors: ZHOU Yan E-mail: yan.zhou@mail.xjtu.edu.cn

Cite this article:

LI Min,YIN Xiong,LIU Xue-jing, et al. Study on Detection of Gas Void Fraction in Oil-Gas Two-Phase Flow Based on Ultraviolet Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 522-531.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)02-0522-10 OR https://www.gpxygpfx.com/EN/Y2025/V45/I02/522

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