Abstract 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 gas-liquid 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.

中文翻译：

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基于小波能量熵和回归分析特征的除尘器气液两相流型识别

摘要 对于湿式除尘器，除尘效率与气液两相流型密切相关。使用当前流动模式识别方法选择的特征参数，不同模式之间存在显着重合的区域，从而导致识别效率下降。本研究提出了一种处理除尘器压力小波分解信号的新方法，以获得区分流型的特征参数。首先，通过小波分析提取关于压力信号不同频带的详细信息。然后，结合信息熵理论，提出了小波能量熵（WEE）来评价不同频段能量分布的均匀性。结果表明，WEE对气液两相流型变化敏感，相应的流型判别效率为92.5%。剪切液幕和夹带气泡模式之间只有少量交叉。为此，利用递归分析方法（RAM），得到了原始压力信号和不同频段小波分解信号的特征递归图（RP）和递归量化分析（RQA）。结果表明，RP特性可以直观地反映不同流型的气液流动状态。尽管 RQA 特性对除尘器中低水平气/液共振流型的变化不敏感，但它与其他流型的演变表现出很强的区别。它使用小波能量熵的参数有效地补偿了在流型区分剪切液幕和气泡夹带处的交叉。将WEE与循环特性相结合，可以有效解决除尘器中流型特征重合度高的问题，是本文的亮点。