Abstract
The random forest algorithm was used to analyze the degree of influence of four variables, namely, voltage, current, main gas flow rate, and powder feed, on the temperature of the plasma jet zone of the DC arc graphite purification. The BP algorithm was then used to build a network prediction model. After the training of the network, the validity of the model was verified by comparing the measured temperature values with the predicted results. From the results, it can be seen that the relative error of the prediction results is between -0.3 %and +0.3%, which proves that the built BP neural network temperature model has a satisfactory prediction effect. In addition, this paper also derives specific mathematical expressions based on the threshold and weight values of this temperature prediction model, which can be used to fit the temperature variation curve in the plasma jet region.
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