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

Abstract:

In this work the kinetic modelling of the transformation of Bioethanol-To-Olefins (BTO) process over a HZSM-5 catalyst treated with alkali using Artificial Neural Networks (ANN) is presented. The main goal has been to obtain a BTO process neuronal model with the desired accuracy that allows the simplification and reduction of the computational cost with respect to a mechanistic knowledge model. To check the goodness of ANN base model structures, during the study a comparison with other alternative modelling techniques such as Support Vector Machines was performed. Following a parameters optimization procedure and testing different training methods, the optimal ANN structure results to be a Feed-Forward 3-5-1 network with the Bayesian Regularization training method. Using a set of experimental data obtained in a laboratory scale fixed bed reactor, we have obtained a similar fit to the knowledge model but with the advantage of being up to 43 times faster. These results are important for moving forward real time automatic control strategies in the biorefinery context.



Published in: Applied Soft Computing
 Impact factor: 2.857 (2017)
ISSN:

1568-4946


Year:

2017


Citations:
Citation
Gorka Sorrosal Yarritu, Cruz Enrique Borges Hernández, Cristina Martín Andonegui, Ana María Macarulla Arenaza, Ainhoa Alonso Vicario. (2017) "Artificial Neural Network Modelling of the Bioethanol-To-Olefins Process on a HZSM-5 Catalyst Treated with Alkali" In Applied Soft Computing. DOI: 10.1016/j.asoc.2017.05.006.