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.