Abstract:



Abstract： Aiming at the difficulty of sensing the tube temperature in cracking furnace under high temperature closed ethylene cracking environment， a method of surface temperature field reconstruction of cracking furnace tube based on fusion mechanism and Long Short-Term Memory（LSTM） is proposed. Firstly， the mechanism model of ethylene cracking reaction is constructed based on fluent， a computational fluid dynamics simulation platform， which is used to describe the mathematical relationship between cracking reaction and furnace tube temperature. Then， the mechanism model is numerically corrected and the process parameters are solved using the industrial field data. Major process parameters with strong applicability are determined based on Pearson correlation coefficient. Based on this， a convolutional block attention module （CBAM） is designed to extract the characteristics of the main process parameters reflecting the relationship between the cracking reaction and the temperature of the furnace tube. Finally， a bidirectional multistep prediction model （GA-BMLSTM） is designed based on genetic algorithm and long and short memory neural network to predict the temperature distribution of furnace tubes. Experimental results show that this method has high accuracy and applicability to the reconstruction of temperature field of furnace tube.
Key words： ethylene cracking furnace； temperature field reconstruction； computational fluid dynamics； attention mechanism； genetic algorithm