dc.creator | Pardo, Eduardo G. | es |
dc.creator | Blanco-Linares, Jaime | es |
dc.creator | Velázquez Alonso, David | es |
dc.creator | Serradilla García, Francisco | es |
dc.date.accessioned | 2022-03-01T14:36:37Z | |
dc.date.available | 2022-03-01T14:36:37Z | |
dc.date.issued | 2020-11 | |
dc.identifier.citation | Pardo, E.G., Blanco-Linares, J., Velázquez Alonso, D. y Serradilla García, F. (2020). Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks. Electronics, 9 (11), 1923. | |
dc.identifier.issn | EISSN 2079-9292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/130290 | |
dc.description.abstract | The objective of this research is to improve the hydrogen production and total
profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to
optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs).
Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different
sets of inputs depending on the physical processes simulated. The model is then optimized as a
black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that
the proposed ANN model presents a high correlation between the real output and the predicted
one. Additionally, the performance of the proposed optimization techniques has been validated by
the engineers of the plant, who reported a significant increase in the benefit that was obtained after
optimization. Furthermore, this approach has been favorably compared with the results that were
provided by a general black-box solver. All methods were tested over real data that were provided
by the factory. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades PGC2018-095322-B-C22 | es |
dc.description.sponsorship | Comunidad de Madrid P2018/TCS-4566 | es |
dc.description.sponsorship | Unión Europea P2018/TCS-4566 | es |
dc.format | application/pdf | es |
dc.format.extent | 20 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Electronics, 9 (11), 1923. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial neural networks | es |
dc.subject | Genetic algorithm | es |
dc.subject | Memetic algorithm | es |
dc.subject | Black-box optimization | es |
dc.subject | Steam reforming plant | es |
dc.title | Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Energética | es |
dc.relation.projectID | PGC2018-095322-B-C22 | es |
dc.relation.projectID | P2018/TCS-4566 | es |
dc.relation.publisherversion | https://doi.org/10.3390/electronics9111923 | es |
dc.identifier.doi | 10.3390/electronics9111923 | es |
dc.journaltitle | Electronics | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 11 | es |
dc.publication.initialPage | 1923 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Comunidad Autónoma de Madrid | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |