IDENTIFICATION OF GAS-SOLID TWO-PHASE FLOW REGIMES USING HILBERT-HUANG TRANSFORM AND NEURAL-NETWORK TECHNIQUES

This work presents a new methodology for flow regime identification in a gas-solid two-phase flow system. The approach of identification employs the artificial neural network (ANN) technique, considering the applications with electrostatic sensor as a measuring device and Hilbert-Huang transformation (HHT) as the post-processing method. The electrostatic fluctuation signals detected from an electrostatic sensor are processed using HHT to gain the Hilbert marginal spectrums. Then four characteristic parameters of the marginal spectra are extracted as the input of BP neural network for flow regime identification. They are subband energy (SE), first-order difference of subband energy (DSE), subband energy cepstrum coefficients (SECC), and first-order difference of the subband energy cepstrum coefficients (DSECC). The results show that the characteristic parameters of the Hilbert marginal spectrum of the electrostatic signal can identify the three flow regimes of gas-solid two-phase flow in a horizontal pipe, especially the DSECC.