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Chemcial Industry and Engineering 2025, Vol. 42 Issue (1) :130-137    DOI: 10.13353/j.issn.1004.9533.20220317
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Hydrodynamic calculation of structured packing column based on neural network and CFD
LI Xing1, ZENG Aiwu1,2
1. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China;
2. State Key Laboratory of Chemical Engineering, Tianjin 300354, China

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Abstract A multi-scale calculation method combining computational fluid dynamics (CFD) and back propagation neural network (BPNN) was proposed to calculate the hydrodynamic behavior of structured packing column. According to the actual size of the representative elementary units, a small-scale 3D CFD model was established, and the flow distribution of single gas phase and gas-liquid two-phase fluid in the packing column was studied, which made up for the defects in the pressure drop of the column wall unit and the interlayer conversion unit. A node network model was established to calculate the macroscopic information such as the fluid distribution of the whole column. Two neural network models were trained with the data set collected by CFD calculation, and the dry pressure drop and liquid holdup were calculated respectively with the node flow as input neuron. Compared with the relevant experimental data, the average relative deviation of the prediction model for dry pressure drop was 8.63% and the maximum relative deviation was 14.2%. The average relative deviation and maximum relative deviation of the prediction model for liquid holdup were 9.63% and 13.97%, respectively. The trained artificial neural network model was proved to be prospective to determinate dry pressure drops and liquid holdup of structured packing columns.
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LI Xing
ZENG Aiwu
Keywords： structured packing columns   computational fluid dynamic   back propagation neural network   dry pressure drops   liquid holdup     
Received 2022-04-11;
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LI Xing, ZENG Aiwu.Hydrodynamic calculation of structured packing column based on neural network and CFD[J]  Chemcial Industry and Engineering, 2025,V42(1): 130-137
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