基于分布特征靶向降维的补充氢流量预测模型研究OA

Accurate prediction of supplementary hydrogen flow,a highly sensitive parameter,remains chal-lenging in the ultra-early warning of fire and explosion accidents in hydrocracking units due to its abrupt fluctua-tion characteristics.To address this issue and provide precise support for emergency warning decision-making,this study proposes a prediction method integrating the targeted dimensionality reduction of distribution charac-teristics and a hybrid neural networks.The distribution patterns and intrinsic correlations of supplementary hy-drogen flow time-series data were deeply analyzed to identify the evolutionary characteristics of the data during the ultra-early stages of an accident.Key distribution features including the mean,root mean square value,and rate of change were extracted to construct a highly representative input matrix.Subsequently,a CNN-LSTM-At-tention prediction model,which incorporated a Convolutional Neural Network(CNN),a Long Short-Term Mem-ory(LSTM)network,and an Attention mechanism,was developed to achieve the accurate prediction of these highly volatile time-series signals.The results indicated that the CNN-LSTM-Attention model based on distribu-tion features achieved a goodness-of-fit(R2)of 0.987 and a Root Mean Square Error(RMSE)of 0.028 2,out-performing other conventional models.This study can provide high-precision data support for the emergency warning of hydrocracking units in petrochemical enterprises.