Phase separation flow measurement of plug flow based on ensemble learning and information fusion
Plug flow is a typical flow pattern in gas-liquid two-phase flow,and accurate measurement of plug flow is conducive to real-time monitoring and optimizing process of production process,ensuring safe and economical operation of the system.Based on the improved Venturi tube with long throat diameter,an intelligent multi-sensor system for horizontal gas-liquid flow is designed,which integrates near infrared(NIR)and acoustic emission(AE)technology.AE sensor and NIR sensor were used to detect the gas-liquid phase interaction and disturbance information,and empirical mode decomposition(EMD)was used to extract the characteristic variables of gas volume fraction.The integrated learning algorithm was used for feature-level fusion.The mean absolute percentage error(MAPE)of the fused plug flow volume gas content prediction model was 4.11％,and the deviation of 92.45％of the predicted results was within±10％.On the basis of Collins model,a mass flow prediction model of plug flow based on gradient lifting decision tree(GBDT)is proposed.The MAPE value of GBDT is 0.96％,and the deviation of all prediction results is within±20％.This study provides a new method for measuring the parameters of gas-liquid two-phase plug flow,which provides a research basis for sensing mechanism and measurement of multiphase flow.
万方数据
维普
