Research on fusion prediction model of wind speed, gas and dust concentration under wind flow control in fully-mechanized heading face

Abstract

In view of the problem that the traditional ventilation total amount control management mode of the fully mechanized heading face cannot carry out wind flow control according to the actual demand, which causes gas and dust accumulation and pollution hidden danger, the multi-source data fusion neural network prediction model of wind speed, gas and dust concentration under wind flow control is studied. The gas-solid coupling model of gas and dust under wind flow control is established by using the Euler-Lagrange method and tested and verified. The distribution of gas and dust particles in the integrated excavation roadway is simulated and analyzed, and numerous sample data of gas and dust concentration in wind speed under different wind flow control schemes are obtained. Multilayer perceptron neural network technology is used to establish the prediction model structure, and parameters such as wind flow regulation, which have a great impact on gas and dust concentration, are selected as the input layer, and the output layer is determined according to the hidden danger location of gas and dust in wind speed. The sample data is preprocessed, and the differential evolution algorithm is introduced to search the node number and learning rate of the best hidden layer. TensorFlow framework is used to build a multi-source data fusion neural network prediction model. Taking the fully mechanized heading face of a mine in northern Shaanxi as the research object, different wind flow control schemes are predicted and verified by underground measurement. The results show that the maximum relative error of the model is 9.7%, which has high accuracy. The optimal control scheme is selected under the conditions of the shortest distance of 5 m from the outlet and the farthest distance of 10 m from the working face. Compared with before the control, the wind speed meets the standard requirements, the gas concentration in the dead corner of the working face is reduced by 34% and 35%, the average dust concentration at the pedestrian side of the return air side is reduced by 40% and 41%, and the dust concentration at the driver’s side is reduced by 38% and 36%, respectively. The study can provide reference for wind flow control.

Keywords

• fully-mechanized heading face
• wind flow control
• wind speed
• gas and dust concentration
• multi-source data fusion
• neural network prediction
• differential evolution algorithm