Abstract
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.
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Acknowledgments
The authors are grateful to FCT for financial support through national funds FCT/MCTES UIDB/00690/2020 to CIMO and UIDB/05757/2020. M. Carocho also thanks FCT through the individual scientific employment program-contract (CEECIND/00831/2018).
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Lima, L.A., Pereira, A.I., Vaz, C.B., Ferreira, O., Carocho, M., Barros, L. (2021). Dynamic Response Surface Method Combined with Genetic Algorithm to Optimize Extraction Process Problem. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_1
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