A dynamic ensemble selection approach to developing softcomputing models for two-phase flow metering

This paper presents a dynamic ensemble selection approach to developing least squares support vector regression (LSSVR) models for the flow metering of gas-liquid two-phase CO₂. Ensemble models based on flow pattern recognition and dynamic ensemble selection (FPR-DES) are established for the measurement of total mass flowrate and gas volume fraction of two-phase CO₂, respectively. The input variables of ensemble models are obtained from a Coriolis flowmeter and a differential-pressure transducer installed on a horizontal test section. Experimental tests were conducted with liquid CO₂ mass flowrate ranging from 200 kg/h to 3100 kg/h and gas volume fraction between 3.1% and 88.4%. Performance comparisons between the proposed FPR-DES based ensemble model, bagging based ensemble model and the single LSSVR model are undertaken with experimental data under stratified, bubbly and mist flow conditions. Experimental results suggest that the FPR-DES based ensemble model outperforms the other two models with maximum errors of ±1% and ±10% for the total CO₂ mass flowrate and gas volume fraction, respectively.