Artificial neural network aided unstable combustion state prediction and dominant chemical kinetic analysis

Wang，Yueqiang1; Liang，Shengyao1; John Xu，Zhi Qin2; Zhang，Tianhan3; Ji，Lin1

2024-12-05

10.1016/j.ces.2024.120567

Chemical Engineering Science   影响因子和分区

0009-2509

An ANN model was designed to predict unstable states in MILD combustion systems out of six kinds of input factors. The effectiveness of the established ANN model was validated, demonstrating accurate predictions for the imbalanced classification problem in systems described by both GRI3.0 and POLIMI2003 mechanisms. The predictions in high-dimensional parameter spaces revealed that unstable states are more likely to occur under stoichiometric conditions or in the presence of a reactive bath gas, such as CO or HO. Additionally, these states could manifest in narrow parameter spaces, such as within a very confined mid-temperature range in a fuel-rich system with a low dilution level. Interestingly, the analysis of dominant reactions and feedback loops unveiled similarities in thermodynamic feedback mechanisms across a spectrum of parameter combinations. Meanwhile, feedback loops construct shortcut pathways on the level of oxidation extent and can facilitate the switching between high and low temperature chemistry.

Artificial neural network Kinetic analysis MILD combustion Thermodynamic feedback Unstable combustion

SCI

英语

通讯

CHEMISTRY

2-s2.0-85200977072

Scopus

1.Department of Chemistry,Capital Normal University,Beijing,100048,China
2.Institute of Natural Sciences,School of Mathematical Sciences,Shanghai Jiao Tong University,Shanghai,200240,China
3.Department of Mechanics and Aerospace Engineering,SUSTech,Shenzhen,518055,China

Wang，Yueqiang,Liang，Shengyao,John Xu，Zhi Qin,et al. Artificial neural network aided unstable combustion state prediction and dominant chemical kinetic analysis[J]. Chemical Engineering Science,2024,300.

Wang，Yueqiang,Liang，Shengyao,John Xu，Zhi Qin,Zhang，Tianhan,&Ji，Lin.(2024).Artificial neural network aided unstable combustion state prediction and dominant chemical kinetic analysis.Chemical Engineering Science,300.

Wang，Yueqiang,et al."Artificial neural network aided unstable combustion state prediction and dominant chemical kinetic analysis".Chemical Engineering Science 300(2024).