Authors:
Gorka Sorrosal Yarritu, Cruz Enrique Borges Hernández, Martin Holeňa, Ana María Macarulla Arenaza, Cristina Martín Andonegui, Ainhoa Alonso Vicario

Abstract:

This paper presents a study on dynamic optimization of the catalytic transformation of Bioethanol-To-Olefins process. The main objective is to maximize the total production of Olefins by calculating simultaneously the optimal control trajectories for the main operating variables of the process. Using Neural Networks trained with two different types of Evolutionary Algorithms, the optimal trajectories have been automatically achieved, defining both an adequate shape and their corresponding parameters. The results suggest that, comparing with constant setpoints, the maximum production is increased up to 37.31% when using Neural Networks. The optimization procedure has become totally automatic and therefore very useful for real implementation.



Proceedings: Proceeding of the Genetic and Evolutionary Computation Conference - GECCO 2016
Presented at:

GECCO 2016 (2016)


Year:

2016


Citations:
Citation
Gorka Sorrosal Yarritu, Cruz Enrique Borges Hernández, Martin Holeňa, Ana María Macarulla Arenaza, Cristina Martín Andonegui, Ainhoa Alonso Vicario. (2016) "Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks" In Proceeding of the Genetic and Evolutionary Computation Conference - GECCO 2016. DOI: 10.1145/2908961.2909056.