Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks
| Autor: | Gorka Sorrosal, Ana Maria Macarulla, Cruz E. Borges, Cristina Martin, Ainhoa Alonso-Vicario, Martin Holena |
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| Rok vydání: | 2016 |
| Předmět: |
Catalytic transformation
Mathematical optimization Artificial neural network Computer science Control (management) Evolutionary algorithm Process (computing) 02 engineering and technology 021001 nanoscience & nanotechnology Optimal control 020401 chemical engineering Production (economics) 0204 chemical engineering 0210 nano-technology Constant (mathematics) |
| Zdroj: | GECCO (Companion) |
| DOI: | 10.1145/2908961.2909056 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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