CHENG L, LI Z J, HE Z Y,et al.Prediction method of heading face wind temperature based on MIV-PSO-BPNN[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(6):11-17.

Received:2023/07/15

Revised:2023/09/08

Online:2024-09-24

Prediction method of heading face wind temperature based on MIV-PSO-BPNN

CHENG Lei1,2, LI Zhengjian2, HE Zhiyong3, SHI Haorong1, WANG Xin1

1.College of Safety Science and Engineering，Henan Polytechnic University，Jiaozuo  454000，Henan，China；2.Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization，Jiaozuo  454000，Henan，China；3.Henan Academy of Sciences，Zhengzhou  450046，Henan，China

Abstract: Objectives To prevent and control thermal damage in mines and solve the problem of predicting wind temperature in mining excavation faces,  Methods a PSO-BPNN prediction model optimized by MIV algorithm was proposed. The MIV algorithm was used to determine the input variables for the model, followed by BP network modeling. The particle swarm optimization algorithm combined with the BP neural network was then employed to predict the airflow temperature of the excavation working face. The predicted results were compared with those from the BPNN model, PSO-BPNN model, and SVR model.  Results The results showed that the relative error range of the MIV-PSO-BPNN prediction model was-0.47% to 1.81%, which was superior to the PSO-BPNN, BPNN, and SVR prediction models with ranges of -3.96% to 1.93%, -5.54% to 2.98%, and -2.16% to 2.95%, respectively. The prediction error was between -0.1 ℃ and 0.5 ℃, indicating that the predicted values and tested values were basically consistent; Compared to the BPNN, PSO-BPNN, and SVR prediction models, the MIV-PSO-BPNN model’s average absolute error decreased by 65%, 54%, and 50%, respectively， and the mean square error had decreased by 88%, 78%, and 69%, respectively. This demonstrated  that the prediction effect of the MIV-PSO-BPNN model was superior to the other three models.  Conclusions The proposed model was suitable for predicting the air temperature of mining excavation working faces.

Key words:BP neural network;MIV algorithm;particle swarm optimization algorithm;airflow temperature prediction;algorithm optimization