(226c) Sensibility Analysis of Genetic Algorithm Operators in the Productivity of a Large Scale Dynamic Process | AIChE

(226c) Sensibility Analysis of Genetic Algorithm Operators in the Productivity of a Large Scale Dynamic Process


Victorino, I. R. S. - Presenter, State University of Campinas (UNICAMP)
Morais, E. R. - Presenter, State University of Campinas (UNICAMP)
Freitas Jr, B. B. - Presenter, State University of Campinas (UNICAMP)
Maciel Filho, R. - Presenter, University of Campinas, UNICAMP

In the last decades, with the increase of the competitiveness of the world market (reduction of costs, prices increase of the productivity and efficiency of the productive processes) there was a great interest in to improve and to optimize the processes of chemical industries. Several optimization classic techniques have been used with this intention, but many of those techniques are not efficient, mainly when the problem is complex, and typically a high number of variables of the processes, non-linearity models that supply many possible solutions, and constraints that have to be considered lead the problem to be of difficult solution. As alternative a class of algorithms, denominated of Genetic Algorithms (an evolutionary algorithms category) present good potential to be used as a tool for complex and large scale systems. Genetic Algorithms (GAs) are general-purpose search techniques for resolution of complex problems. They are based on the genetics and natural evolution principles of the species. The GAs work through repeated modifications in an artificial structures population denominated of individuals (chromosomes or set of solutions) applying the selection, crossover, and mutation operators. The evaluation of optimization happens with an objective function (fitness) that determines the performance of the genetic process. The fitness could be understood as the capacity of the individuals to survive in a natural environment. This work has as objective the development of an optimization methodology, using genetic algorithms, as evolutionary technical coupled with the concepts of evolutionary operation to be applied in deterministic mathematical models. 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 regimen. The study was made analyzing the genetic operators sensibility and influence in 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. This study observed the influence of some genetic operators and parameters in the 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.


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