(186j) A Procedure for Large Scale Process Optimization Based on Genetic Algorithm: Application to Dynamic Model of a Cyclic Alcohol Industrial Reactor | AIChE

(186j) A Procedure for Large Scale Process Optimization Based on Genetic Algorithm: Application to Dynamic Model of a Cyclic Alcohol Industrial Reactor


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 (GAs), as evolutionary procedure coupled with the concepts of evolutionary operation to be applied in deterministic mathematical models. As case study an industrial multiphase catalytic reactor was considered. The reactor is tubular in shape and is built-up with concentric tubes using the same concept of the auto-thermal reactors, with coolant fluid flow in the external annular. 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. Industrial data of temperature are available and they are used in the development and validation of the model. The studied example was related to production of a specific cyclical alcohol (CA), optimizing some important operational parameters. The interest of this work is to show that the Genetic Algorithms technique can be useful to CA production maximization, obtaining good results with operational improvements (reduction catalyst rate, reduction main reactant rate ? benzilic alcohol and undesired product rate ? cycloalcane). Some cares must be considered, mainly with the reactants emissions (main reactant- benzilic alcohol) that occur in high concentrations causing damages to the environment. This study observed the influence of some genetic parameters in the increase of the cyclic alcohol productivity. The parameters (genetic parameters) considered involved the population size, crossover types with variation of crossover rates. The used coding was the binary form. The results are quit good, showing high performance 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. The results point out that this technique is very promising to deal with large scale system with complex behavior due to non-linearity and variable interactions.