Identification of gas-liquid two-phase flow patterns in dust scrubber based on wavelet energy entropy and recurrence analysis characteristics

摘要

For a wet dust scrubber, dust collection efficiency is tightly connected with the gas-liquid two-phase flow pattern. Using the characteristic parameters selected by current flow pattern identification methods, regions of significant coincidence exist among different patterns, thereby leading to a decline in the identification efficiency. In this study, a new method for processing the wavelet decomposition signal of the dust collector pressure was proposed to obtain the characteristic parameters that distinguish the flow pattern. Firstly, detailed information regarding the different frequency bands of the pressure signal was extracted via wavelet analysis. Then, in combination with the information entropy theory, wavelet energy entropy (WEE) was proposed to evaluate the uniformity of energy distribution in different frequency bands. The results show that WEE is sensitive to the change in gas-liquid two-phase flow patterns, and the corresponding distinguishing efficiency of flow patterns is 92.5%. There was only a small amount of crossover between the shear liquid curtain and entrainment air bubble pattern. For this, using the recursive analysis method (RAM), the characteristic recurrence plots (RP) and recurrence quantification analysis (RQA) of the original pressure signals and the wavelet decomposition signal with different frequency bands were obtained. Results show that the RP characteristics can intuitively reflect the gasliquid flow state of different flow patterns. Although RQA characteristics are not sensitive to the change in low-level gas/liquid resonance flow pattern in the dust scrubber, it exhibits strong distinction to the evolution of other flow patterns. It effectively compensates for the crossover at the flow pattern distinguish between the shear liquid curtain and entrainment of air bubble using the parameters of the wavelet energy entropy. It is the highlight of this article that the combination of WEE and recurrence characteristics can effectively address the problems of high coincidence of flow pattern features in the dust scrubber. (C) 2020 Elsevier Ltd. All rights reserved.