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Novel reduced-order framework combining proper orthogonal decomposition and multi-parallel gaussian process regression: Multi-physics prediction of ethylene cracking furnaces

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CHEMICAL ENGINEERING SCIENCE 2025年 305卷

作者： Hu, Guihua Chen, Mimi Long, Jian East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Engn Res Ctr Proc Syst Engn Minist Educ Shanghai 200237 Peoples R China

Accurate multi-physics field prediction is important in the design and optimization for ethylene cracking furnaces. However, traditional computational fluid dynamics (CFD) simulations are time-consuming and traditional surrogate models lack information about the physical field. The proposed novel reduced-order model (ROM) framework integrates proper orthogonal decomposition (POD) and multiple-parallel gaussian process regression (mGPR) to predict the multi-physics of an industrial ethylene cracking furnace while significantly reducing computational time and resource requirements. CFD simulations are first conducted to obtain multi-physics data, which are then compared to industrial values. A dataset covering various operating conditions is generated through pairwise experimental design methods. POD is employed to extract modes and coefficients of the physical fields, and mGPR is used to model the nonlinear relationship between the POD coefficients and operating parameters. The results show that the relative error of the outer wall temperature of reactor tube between the POD simulation results and the industrial values is 4.13%. The proposed ROM achieves a global error on the order of 10-3, with minimal truncation degrees of 5, 3, and 6 for flue gas temperature, pressure, and mass fraction of H2O, respectively. The mGPR model outperforms GPR model, demonstrating lower mean squared errors (MSE) (609.667 versus 3718.822) and higher R2 (0.9907 versus 0.9433). In comparison to CFD, the ROM improves computational efficiency by a factor of at least 900 and reduces storage space by approximately 96.3%. The proposed ROM provides reliable technical support for the design and optimization of ethylene cracking furnaces.

关键词： Ethylene cracking furnaces Computational fluid dynamics Reduced-order model Proper orthogonal decomposition multiple-parallel gaussian process regression

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