Using a Multi-Objective Genetic Algorithm to Discover Chemical Reaction Networks.

A Genetic Algorithm (GA) is used to design an automated system that utilizes concentration profiles of measured chemical species obtained from experiments in a batch reactor to construct chemical reaction network (CRN). Ordinary differential equation (ODE) models are constructed to represent the rate of production / consumption of the chemical species that participate in the network of reactions and through the ODEs, the CRN is identified. This is achieved through a two level optimization approach. The first level instantiates the network structure using a GA to evolve network stoichiometry while the second level optimizes the kinetic rate constants associated with each reaction in each candidate network. The algorithm is designed to cope with unmeasured data and the diversity of potential CRNs being discovered is enhanced through the use of the multi-objective optimization algorithm. The ability to discover a set of plausible CRNs is demonstrated using experimental data from a reaction between trimethyl orthoacetate and allyl alcohol. [ABSTRACT FROM AUTHOR]