Micron-sized particle discrimination and classification with deep learning and electrical sensing zone technique

Abstract

This paper used deep learning algorithms based on the electrical sensing zone (ESZ) method to realize real-time online monitoring and discrimination of particle sorts and their size distribution in liquid–solid systems. Numerical simulations reveal that the characteristics of the temporal pulse signals generated by particles flowing through the ESZ provide the clue to discriminate particle sorts. Based on the above-mentioned results, a deep learning algorithm was used to successfully discriminate and classify micron-sized particles based on the differences in particle pulse signals. This study was conducted to train and validate the residual network (ResNet) model in the deep learning algorithm using four synthetic mixed datasets of different particle pulse signals in a similar size range and to evaluate the performance of the model. Furthermore, the trained ResNet model was applied to the in-house online micron-sized particle analyzer to monitor and discriminate four sets of different particle pulses in a similar size range at the micron level, and the accuracy of the results was above 89%. The results were compared with the measured particle pulse distribution to confirm the feasibility and accuracy of the method. A new method for real-time online monitoring and discrimination of micron-sized particles in industrial production was provided.