Evolutionary Optimization of the Reduced Gas-Phase Isoprene Oxidation Mechanism

Atmospheric chemistry is highly complex, and significant reductions in the size of the chemical mechanism are required to simulate the atmosphere. One of the bottlenecks in creating reduced models is identifying optimal numerical parameters. This process has been difficult to automate, and often relies on manual testing. In this work, we present the application of particle swarm optimization (PSO) toward optimizing the stoichiometric coefficients and rate constants of a reduced isoprene atmospheric oxidation mechanism. Using PSO, we are able to achieve up to 28.8% improvement in our error metric when compared to a manually tuned reduced mechanism, leading to a significantly optimized final mechanism. This work demonstrates PSO as a promising and thus far underutilized tool for atmospheric chemical mechanism development.