Pressure prediction for air cyclone centrifugal classifier based on CNN-LSTM enhanced by attention mechanism

In this study, deep learning (DL) is employed to analyze the entire flow field and predict the pressure in air cyclone centrifugal classifiers. To this end, a novel neural network model is proposed, which combines convolutional neural network (CNN), long short-term memory (LSTM) network, and attention mechanism. This study predicts the cases of air cyclone centrifugal classifiers with and without screening cages. Compared to a support vector machine (SVM) and a backpropagation neural network (BPNN) model, the RMSE (root mean square error) of the proposed model with screening cage is reduced by 22.6% and 56.0%, respectively; the RMSE of the proposed model without screening cage is reduced by 50.1% and 42.8%, respectively. This demonstrates the effectiveness and versatility of the CNN-LSTM-Attention model in predicting the pressure of air cyclone centrifugal classifiers. In addition, this study explored the effect of different parameters on air classifier by macroscopic and microscopic screening efficiency. The results show that the air classifier can achieve the best screening effect when the rotational speed is 45 Hz, the feeding speed is 0.3 kg/s and the inclination angle is - 4 degrees. This study is expected to provide new ideas for pressure prediction and flow field simulation in air classifiers.