Highly accurate heat release rate marker detection in NH3–CH4 cofiring through machine learning and domain knowledge-based selection integration

Abstract

Ammonia emerges as a promising substitute for traditional fuels, offering a potential reduction in fossil fuel consumption and the associated emissions. Given its weak reactivity, an effective strategy to harness the potential of ammonia involves cofiring it with methane, to establish a self-sustaining combustion process. The understanding of heat release rate (HRR) in the cofiring process of ammonia and methane is crucial for burner design. This study introduces novel HRR indicators through a machine learning-based approach, focusing on species significantly contributing to HRR. Kinetic analysis is carried out utilizing a one-dimensional freely propagating flame model, applying a mechanism including 59 species and 356 reactions. Domain knowledgebased feature selection narrows down the search space, enhancing computational efficiency during HRR indicator identification. A random forest model is then employed to identify radical/radical combinations and their respective reaction orders, representing HRR optimally based on mean decrease impurity. The proposed HRR markers are [CH4]0 & sdot;76[OH]1.08, [HCO]0 & sdot;65[NO]0.24, [CH3]0.65[O]0.63, [NH2][O]0.59, and [NH3]0 & sdot;93[OH]0.96. The root mean square error for each marker was extracted and compared with literature data, demonstrating higher reliability of the proposed indicators over those previously suggested. Additionally, the paper concludes with a discussion of the feasibility of measuring these markers from an experimental perspective.