An evolutionary, data-driven approach for mechanism optimization: theory and application to ammonia combustion

In this work, we propose a novel data-driven approach for detailed kinetic mechanisms optimization. The approach is founded on a curve matching-based objective function and includes a methodology for the optimisation of pressure-dependent reactions via logarithmic interpolation (PLOG format). In order to highlight the advantages of the new formulation of the objective function, a comparison with L1 and L2 norm is performed. The selection of impactful reactions is carried out by introducing a Cumulative Impact Function (CIF), while an Evolutionary Algorithm (EA) is adopted for the optimization. The capabilities of the proposed methodology were demonstrated using a database of ~635 experimental datapoints on ammonia combustion, covering standard targets like ignition delay times, speciation and laminar flame speed. The optimization was carried out starting from a recently published mechanism, describing ammonia pyrolysis and oxidation, largely developed using first-principles calculation of rate constants. After the selection of the 24 most impactful reactions, the related 101 normalized Arrhenius parameters were simultaneously varied, within their uncertainty bounds. Their uncertainty bounds were taken from the literature, when available, or estimated according to the level of theory adopted for the determination of the rate constant. Hence, we also provide guidelines to estimate uncertainty for reaction rate constants derived from first principles calculations using well consolidated computational protocols as a reference. The optimized mechanism was found to improve the nominal one, showing a satisfactory agreement over the entire range of operating conditions. Moreover, the use of ‘curve matching’ indices was found to outperform the adoption of L1 and L2 norms. The comparison between the nominal mechanism and the one optimized via curve matching allowed a clear identification of different critical reaction pathways for different experimental targets. From this perspective, the methodology proposed herein can find further application as a useful design-of-experiments tool for an accurate evaluation of crucial kinetic constants, thus driving further mechanism improvement.