A joint hydrogen and syngas chemical kinetic model optimized by particle swarm optimization

In this work, we propose a novel data-driven framework for detailed kinetic mechanisms optimization applying the heuristic algorithm, namely canonic Particle Swarm Optimization (PSO). The PSO is more effective and robust in coping with uncertainties and incomplete information than deterministic and probabilistic optimization algorithms and is more suitable for machine learning applications. In the proposed framework, to avoid trapping in a local minimum, 1000 local optimums have been obtained and statistically handled to select the final feasible model parameter set with reduced uncertainty intervals and parameter correlations. The developed framework was successfully used for the optimization of the joint H₂ and syngas oxidation chemical kinetic model. The data set collected for the model optimization includes 41 reactions and 16 species, and 3000 experimental data targets supplied with uncertainty boundaries measured in shock tubes, jet stirred reactors, plug flow reactors, and premixed laminar flames under wide ranges of temperature, pressure, equivalence ratio, and H₂/CO ratios. The initially estimated uncertainties of the reaction rate constants for 15 key reactions were significantly constrained. The reaction rate constants for the H₂ oxidation sub-model were re-optimized and their uncertainties were further reduced.