Using RMG Out-of-the-Box for Formic Acid Pyrolysis and Oxidation

The predictive modeling of formic acid (HOCHO), the simplest organic acid and a central intermediate in combustion chemistry, is of fundamental importance. Prior literature work [Energy Fuels 2022, 36, 23, 14,382–14,392] reported challenges in reproducing jet-stirred reactor (JSR) speciation with an automatically generated model, suggesting that hand-tuned mechanisms might be required. Here, we revisit this assessment. We demonstrate that an out-of-the-box, fully automated predictive model, built deliberately without quantum-chemical refinement or ad hoc fitting, reliably captures formic acid JSR oxidation speciation across 550–1150 K and an equivalence ratio range of 0.5–2.0 as well as laminar burning velocity observations. We further reveal that the model’s remaining discrepancies, which appear under pyrolysis conditions, stem from a core issue masked in prior work [Combust. Flame 2021, 223, 77–87]. Specifically, we show that previous apparent agreement relied on fitted pressure-independent rate coefficients that obscured the unresolved pressure-dependent branching ratio between decarboxylation (HOCHO ⇌ CO2 + H2) and dehydration (HOCHO ⇌ CO + H2O). This case underscores that genuine scientific advancement requires the transparent discussion of successes alongside remaining challenges rather than masking theoretical discrepancies through parameter fitting or using selective benchmarks. By sacrificing perfect agreement in favor of highlighting true challenges, we establish that predictive, automated chemical kinetic models are already within reach for small oxygenated fuels, and identify the accurate parameterization of pressure-dependent rate coefficients on the CH2O2 surface as the key remaining challenge for fully reliable formic acid modeling.