A gas-solid flow pattern identification algorithm based on cross-rod electrostatic sensor array

The accurate identification of gas-solid two-phase flow patterns is an important but challenging subject for pneumatic conveying. In this study, the sensitivity deficiencies of a single electrode were analysed via finite element analysis and a more sensitive cross-rod electrostatic sensor array structure was designed to measure the flow pattern signals. The experiment used Geldart D particles to verify the feasibility of the designed sensor array. Three types of feature vectors were extracted: the mean value, variance, and energy ratio. To identify the flow pattern accurately, the sine-cosine algorithm (SCA) is exploited to optimise the smoothing factor critical for a probabilistic neural network (PNN), namely SCA-PNN. The identification results show that the identification accuracy of the proposed algorithm outperforms the traditional PNN, the back propagation neural network (BPNN) and the support vector machine (SVM).

Automated summary

This study analysed the sensitivity deficiencies of a single electrode and designed a more sensitive electrostatic sensor array structure to accurately identify gas-solid two-phase flow patterns. The experimental results show that the proposed algorithm outperforms traditional methods in terms of identification accuracy.