Prediction of airflow classification effect of wet coal based on BP neural network

Wet and sticky raw coal normally results in great difficulties to the deep screening of coal preparation plants. The airflow classification technology can effectively overcome the problem of screen blending. The actual classification effect can be predicted by pre⁃establishing the mathematical model of the airflow classification process，and then the online control of the airflow classifier can be carried out，which can improve the grading effect of the airflow technolo⁃ gy. The paper established an artificial neural networks predictive model of airflow classification effect based on the py⁃ thon language，and selected the coal from Inner Mongolia Yitai Group as research sample. 50 groups of data under dif⁃ ferent classification conditions were obtained through experiments and 45 groups were randomly selected as training sets，5 groups as detection sets. Three factors of initial water content，vibration or no vibration，and grading time were selected as the input of the neural network，and the content of particles with size >6，3-6，<3 mm in the two coarse and fine⁃grain levels was taken as the output to find the best parameters of the neural network through cross⁃verifica⁃ tion. Two BP artificial neural network NN1 and NN2 were trained. The neural network NN1 included a hidden layer，the neuronal number of hidden layer was 6，and the selected activation function was tanh. The NN2 neural net⁃ work included two hidden layers，the neuronal numbers of hidden layers were 5 and 7，and the selected activation func⁃ tion was identity. The results show that the NN1 outperforms the second neural network as a whole，especially at the five levels of coarse 3-6 mm，<3 mm and fine⁃grained >6 mm，3-6 mm，and <3 mm，but deviates greatly for the coarse⁃grained >6 mm size. The overall prediction results of the NN2 are relatively average and small overall devi⁃ ation. The prediction results of coarse⁃grained >6 mm are similar to other predictions，obviously better than that of the first neural network and better in the overall prediction. The NN1 performs better overall predictions but the NN2 out⁃ performs NN1 in coarse grain set >6 mm prediction. Combining the two networks，the results of NN2 for coarse⁃grained >6 mm，and the results of NN1 for coarse⁃grained 3-6 mm，<3 mm and fine⁃grained >6 mm，3-6 mm，<3 mm as the final prediction result can improve the accuracy of the prediction，the model’s decision coefficient R2 is set to 0.917 8， which can better fit the input data.