Prediction of Coal Loading Rate for Thin Coal Seam Coal Winning Machine under Downward Mining Conditions Based on Improved DBO-BPNN
WANG Pengfei
GUO Dai
Abstract：In order to improve the coal loading performance of thin coal seam coal winning machines under downward mining conditions,a coal loading rate prediction model based on improved DBO-BPNN was proposed to address the difficulties in quantifying the relationship between coal seam inclination angle and loading rate,motion parameters,and low prediction accuracy of ordinary BP neural networks.It is based on BP neural network and uses an improved DBO optimization algorithm to optimize its initial weights and thresholds,improving its prediction accuracy and stability.In order to verify the predictive effect of the prediction model,the EDEM discrete element simulation software was used to establish a simulation model for the loading of thin coal seam coal winning machines under downward mining conditions,collect data for model training,and compare it with BP neural network models optimized by other algorithms.The results showed that the BP neural network optimized by the improved DBO algorithm had a significant advantage in predicting the accuracy of the loading rate of thin coal seam coal winning machines under downward mining conditions.The average prediction error is only 2.0525％,which is an effective method for predicting the loading rate and can provide some assistance for parameter optimization of thin coal seam coal winning machines under downward mining conditions.
Keywords：EDEMBP neural networkDBO optimization algorithmcoal loading rate
Publication Date：2024-08-12
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 69-74,80 )