Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (9): 79-86.doi: 10.16180/j.cnki.issn1007-7820.2022.09.012

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Establishment of a Predictive Model of the Process Parameters of Secondary Moisturizing Based on BP Neural Network

ZHOU Yongchang,HUANG Yayu   

  1. Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China
  • Received:2021-03-19 Online:2022-09-15 Published:2022-09-15
  • Supported by:
    Yunnan Provincial Major Science and Technology Special Plan Projects(202002AD080001);Digitization Research and Application Demonstration of Yunnan Characteristic Industry


In this study, the influence of the process parameter setting of the hot-air leaf moisturizer on the quality index of the exit leaf during the secondary leaf conditioning of threshing and redrying is studied, and the corresponding prediction model is established. A BP neural network prediction model is established based on the characteristics of the secondary leaf conditioning process data. The current popular neural network writing framework TensorFlow's high-level API interface is called to construct the neural network structure. The activation function, optimizer, number of hidden layer neurons and other key parameters are gradually adjusted in the neural network structure to make the prediction result of the test set reach the best state. By inputting the parameters of the front steam nozzle pressure, front-end water flow rate, hot air temperature, return air temperature, feed blade temperature, and feed blade moisture combination, the two key tobacco leaf evaluation indicators, namely, outlet leaf moisture and temperature, are predicted. According to the mean square error, root mean square error, and average absolute error of the prediction results, it is concluded that when the number of neurons in the hidden layer is 7, the activation function selects ReLU, and the optimizer selects RMSprop, the effect is the best.

Key words: BP neural network, secondary leaf conditioning, TensorFlow, activation function, optimizer, exit leaf temperature, exit leaf moisture, mean square error

CLC Number: 

  • TP399