Abstract
The acoustic emission (AE) method is used in certain industries for the measurement of pneumatic conveying. Instead of the non-intrusive sensors, the comparison of two different intrusive probes in pneumatic conveying is presented in this work, and the AE signals generated by the flow for different particle flow rates and particle sizes were studied. Comparing the distribution of root mean square (RMS) values indicates that the AE signal acquired by a wire mesh probe was more reliable than that from a T-type probe. Limited intrinsic mode functions (IMFs) were extracted from the raw signals by the ensemble empirical mode decomposition (EEMD) algorithm. The characteristics of these signals were analyzed in both the time and frequency domains, and the energies of different IMFs were used to predict the particle mass flow rates, demonstrating a relative error under 10% achieved by the proposed monitoring system. Additionally, the mean squared error contribution fraction, instead of the energy fraction, can predict the particle size.
Highlights
Pneumatic conveying is widespread in the power, pharmaceutical, metallurgy, food, and various other industrial processes [1]
Determining pneumatic conveying parameters, including particle size, particle mass flow rate, flow velocity, and humidity, is complicated, and with current technology, it is challenging to establish an accurate model for these conveying characteristics
One can note that all of the relative deviations are under 10%. In view of these additional data points, it is clear that the energies for IMF3 and IMF4 have the best linear relationship with particle mass flow rates for different particle sizes, which is useful in characterizing flow rate change
Summary
Pneumatic conveying is widespread in the power, pharmaceutical, metallurgy, food, and various other industrial processes [1]. Alessandro et al [10] applied the approach to research the relationship between AE signal and particle size distribution for limited operating conditions To this end, they developed a three-step data processing procedure, using wavelet packet decomposition to extract useful features and multivariate data analysis to decrease feature numbers used as the input of the neural network. Wei [16] developed a regression model between AE energy and particle mass flow rate by using the wavelet packet decomposition and partial least square method. The ensemble empirical mode decomposition method was used to decompose the signal, and the effective signal is extracted to establish a specifying relationship between AE signal and mass flow rate, and the relationship of the signal and particle size is built as well
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