In the high-temperature and high-risk rare earth molten salt electrolysis process,in order to achieve intelligent identification of raw material content status during the rare earth molten salt electrolysis process,a classification model based on Gaussian mixed background modeling(GMM)and genetic algorithm optimized long short-term memory neural network(GA-LSTM)was proposed.The model accurately extracted the flame foreground and characteristics of the image through the GMM algorithm,R channel adaptive filtering and median filtering to quantify the intensity of the molten salt electrolysis reaction and then determine whether the rare earth molten salt electrolysis was in a state of excessive raw material content or normal content.Then the GA-LSTM neural network was used to establish the nonlinear mapping relationship between the flame characteristics of the molten salt surface and the raw material content state during the rare earth molten salt electrolysis process.The results show that the recognition accuracy of the model is as high as 99.79%,and it has good generalization,providing a certain reference value for realizing the automation of rare earth molten salt electrolysis process.
维普
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