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Sensibility Analysis of Genetic Algorithm Operators in the Productivity of a Large Scale Dynamic Process

The objective is the development of an optimization methodology, using genetic algorithms, as evolutionary technical coupled with the concepts of evolutionary operation to be applied in a deterministic mathematical model. The process considered is a multiphase catalytic reactor, where hydrogenations reactions take place. A series of parallel and consecutive reactions may happen, so that the reactor has to be operated in a suitable way to achieve high conversion as well as high selectivity. The reactor is constituted of a series of tubes, which are immersed in a boiler. In fact, they consist of concentrical tubes. The reactants flow through the tubular as well as through the external annular region, while the thermal fluid flows inside the other regions. The study was related to a specific Cyclic Alcohol (CA) production, optimizing some important operational parameters. The mathematical equations of the deterministic model are based on conservation principles (mass, energy and momentum) for the reactants and for the coolant fluid and validated with real operational data and developed for the dynamic regime. The study was made analyzing the genetics operator sensibility and their influence on the specific cyclic alcohol productivity. In order to do this, some genetic operators and parameters had been studied and analyzed as: two crossover types (one-point and uniform) and the variation of crossover rates, the presence or not mutation operator types, the variation mutation rates and the consequences in the increase of the production (improvement of the reactor performance). The results showed an increase in the CA productivity (considerable increase CA production) with changes in the operational parameters analyzed and showing that this optimization procedure is very robust and efficient.