Machine learning-based prediction of operation conditions from plasma plume images of atmospheric-pressure plasma reactors

Abstract

A technique was proposed in this paper to monitor the key operating conditions of a plasma abatement system, which are the concentration of the carbon-containing process gas and the treatment flowrate, from a plasma plume image acquired using an inexpensive color camera. The technique is based on the observation that the shape and color of the plasma plume vary with the variations in the specific energy input and plasma gas composition. In addition, because these variations are marginal and it is challenging to identify an analytical relationship between these variations and the operating conditions, the prediction model is obtained in a data-driven manner. Specifically, the model was composed of a set of convolutional autoencoders (CAEs) and a dense neural network. Furthermore, it was trained only with images captured under normal operation so that (1) images captured under abnormal operations could be identified based on the reconstruction error of the trained CAEs and (2) predictions are made only on normal images. As a demonstration, methane was tested as a process gas, and oxygen was used as a reaction agent in a nitrogen-rich environment. The test results showed that the optimized model could predict the treatment flowrate and process gas concentration with 96% probability within ±3.08 slpm and ±300 ppm, respectively.