Comparative study on methane concentration prediction models in coal mines based on intelligent algorithms
Gas advanced prediction is crucial for the safety production of coal mines.However,there is still no consensus on which model is more suitable for underground drilling gas prediction.Based on this,this paper introduces the common prediction models,and through the comparison of the prediction performance of the grey prediction model,the LSTM model,and the BP neural network model using the actual measured data from a driving face in the Caojiatan mining area.The experimental results show that all three models have good prediction accuracy under the actual underground mining conditions,but the LSTM model has the best prediction performance,with an ideal prediction error of less than 0.15%.The research results of this paper can realize high-precision detection of gas concentration during drilling,and provide necessary data support for the dynamic control of gas drainage hole trajectories in under-ground coal mines.
gas concentration predictionconcentration predictionintelligent algorithmcoal mine safety