基于VMD-LSTM的矿井粉尘浓度预测研究

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中图分类号：TD714.3 文献标志码：A

Abstract：To address the problem of insufficient accuracy in traditional prediction models caused by the nonlinear，non-stationary，and strongnoisecharacteristicsofundergroundcoal mine dustconcentrationdata，a hybrid mine dust concentration prediction method integrating Variational Mode Decomposition （VMD）and a Long Short-Term Memory Network （LSTM） was proposed.The raw dust concentration time series data were fed into VMD.Under the set conditions for the number of modes K and the constraint factor α ，VMD decomposed the raw data into K mode components with different frequency characteristics，with each component corresponding to amplitude information indiffrentfrequencybands.Thecomponent data were then fed intoLSTMand trained using a selective forgeting/input gate algorithm to output the component prediction results.The component predictionresults were superposed and reconstructed to produce the final prediction result.The dust concentration data froma working face in the Sandaogoucoal mine were used toanalyze the efects of the constraint factor a on the VMD decomposition performance and the number of modes K on the prediction performance. The analysis results showed that：when K=5 ，the samples were completely decomposed by VMD， and each mode component contained detailed frequency information，alowing for a clear and intuitiveanalysis of the overall signal's composition; when α=2000 ， the profiles of each mode component were complete and fully separated， whereas an excessively small a led to more redundant information in the independent components， and as the value of a （2号 increased，the bandwidth of the mode components continuously decreased while the resolution improved.The experimental results showed that： with K=5 and α=2000 ， the error between the VMD-LSTM's predicted results and the measured values was minimal，and its MAE，MSE，RMSE，and MRE were allsuperior to those of other models.The VMD-LSTM model exhibits strong generalization ability and robustness for predicting nonlinear， non-stationary， and high-noise dust concentrations under complex environmental conditions.

Key words： mine dust concentration prediction; variational mode decomposition; long short-term memory network;number of modes; constraint factor; VMD-LSTM

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我国是一个“少气、贫油、富煤”的国家，煤炭是我国主体能源材料和基础产业[1-3]。（剩余11955字）