Temperature Prediction Model of Rotary Kiln Firing Zone Based on Improved BP Neural Network

The temperature of the burning zone in the acquisition process is not stable and has an important impact on the quality of pellet. In order to improve the burning zone temperature stability, zone of combustion temperature prediction model is proposed based on the improved BP neural network. According to the field data characteristics, using cluster analysis method for data processing in order to reduce the prediction of interference, the results show that the improved algorithm of the model can overcome the standard BP network algorithm parameter optimization problems, and have better forecasting effect which has important significance to improve the rotary kiln burning zone temperature control precision.