Prediction Method of Coal Dust Explosion Flame Propagation Characteristics Based on Principal Component Analysis and BP Neural Network

• Main Authors: Cai, Z. (Author), Jia, R. (Author), Liu, T. (Author), Tian, W. (Author), Wang, N. (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2022
• Subjects: Backpropagation; BP neural networks; Characteristic three; Coal; Coal dust; Coal dust explosions; Errors; Flame propagation; Forecasting; Maximum speed; Neural networks; Prediction errors; Prediction methods; Principal component analysis; Principal Components; Principal-component analysis; Propagation characteristics
• Online Access: View Fulltext in Publisher
• ISBN: 1024123X (ISSN)
• DOI: 10.1155/2022/5078134

To study the flame propagation characteristics of coal dust explosion, principal component analysis and BP neural network are used to predict the farthest distance and the maximum speed of flame propagation. Among the eight influencing factors of flame propagation characteristics, three principal components are extracted and named "the factor of volatility,""the factor of intermediate diameter,"and "the factor of environmental temperature."By using BP neural network, it is found that the minimum prediction error of the farthest distance of flame propagation is 2.4%, and the minimum prediction error of the maximum speed of flame propagation is 0.4%, which also proves the necessity of principal component analysis by comparing the prediction errors. The research results provide a theoretical method for predicting the flame propagation characteristics of coal dust explosion. © 2022 Tianqi Liu et al.