影响因子：4.0
DOI码：10.1016/j.ces.2024.120567
发表刊物：Chemical Engineering Science
关键字：Artificial neural network; Unstable combustion; MILD combustion; Kinetic analysis; Thermodynamic feedback
摘要：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 CO2 or H2O. 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.
合写作者：Zhi-Qin John Xu,Shengyao Liang
第一作者：Yueqiang Wang
论文类型：期刊论文
通讯作者：Tianhan Zhang,Lin Ji
一级学科：动力工程及工程热物理
文献类型：期刊
卷号：300
页面范围：120567
是否译文：否
发表时间：2024-07-31
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0009250924008674