Impact Factor:4.0

DOI number:10.1016/j.ces.2024.120567

Journal:Chemical Engineering Science

Key Words:Artificial neural network; Unstable combustion; MILD combustion; Kinetic analysis; Thermodynamic feedback

Abstract: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.

Co-author:Zhi-Qin John Xu,Shengyao Liang

First Author:Yueqiang Wang

Indexed by:Journal paper

Correspondence Author:Tianhan Zhang,Lin Ji

First-Level Discipline:Power Engineering and Engineering Thermophysics

Document Type:J

Volume:300

Page Number:120567

Translation or Not:no

Date of Publication:2024-07-31

Included Journals:SCI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S0009250924008674

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