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Dynamic response surface method combined with genetic algorithm to optimize extraction process problem
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Orientador(es)
Resumo(s)
This study aims to find and develop an appropriate optimization
approach to reduce the time and labor employed throughout a
given chemical process and could be decisive for quality management. In
this context, this work presents a comparative study of two optimization
approaches using real experimental data from the chemical engineering
area, reported in a previous study [4]. The first approach is based on the
traditional response surface method and the second approach combines
the response surface method with genetic algorithm and data mining.
The main objective is to optimize the surface function based on three
variables using hybrid genetic algorithms combined with cluster analysis
to reduce the number of experiments and to find the closest value to
the optimum within the established restrictions. The proposed strategy
has proven to be promising since the optimal value was achieved without
going through derivability unlike conventional methods, and fewer
experiments were required to find the optimal solution in comparison to
the previous work using the traditional response surface method.
Descrição
Palavras-chave
Optimization Genetic algorithm Cluster analysis
Contexto Educativo
Citação
Lima, Laires A.; Pereira, Ana I.; Vaz, Clara B.; Ferreira, Olga; Carocho, Márcio; Barros, Lillian (2021). Dynamic response surface method combined with genetic algorithm to optimize extraction process problem. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 3-14. ISBN 978-3-030-91884-2
Editora
Springer Nature