Optimisation

Because of the lost of intermediates species, the reduced mechanisms obtained after the DRGEP and QSS simplifications do not predict well the trajectories of the detailed scheme. An optimisation step is then needed.

The optimisation algorithm performed in ORCh is the genetic algorithm which apply the principles of natural selection and evolution, in order to look for the optimal solution of a problem initialised by a population of potential solutions.

In this case, we want to optimise the parameters of the Arrhenius law, describing the reaction rates. For example, the forward Arrhenius law for the reaction rate of the jth reaction reads :

${\dot {Q}}_{j}=A_{j}T^{\beta _{j}}exp(-{\frac {E_{aj}}{RT}})[A]^{n_{Aj}}[B]^{n_{Bj}}$

with $A_{j}$ the pre-exponential factor, $E_{aj}$ the activation energy, $\beta _{j}$ the temperature exponent, $[A]$ and $[B]$ the concentrations of the reactants involved and $n_{Aj}$ and $n_{Bj}$ the exponents of concentrations.

Given te high values of $A_{j}$, we prefer to work with $a_{j}=log(A_{j})$.

The parameters values to be optimised are then put in the form of a triplet ($a_{j}$,$\beta _{j}$,$E_{aj}$) considered as a chromosome. A population will be composed of a set of chromosomes which will evolve and mutate until converging towards the best solution, i.e. the best chromosome.