dc.creator | Chicaiza Salazar, William David | es |
dc.creator | Ortiz Machado, Diogo | es |
dc.creator | Gallego Len, Antonio Javier | es |
dc.creator | Escaño González, Juan Manuel | es |
dc.creator | Bordons Alba, Carlos | es |
dc.creator | Andrade, Gustavo A. de | es |
dc.creator | Normey Rico, Julio Elías | es |
dc.date.accessioned | 2023-02-21T08:07:02Z | |
dc.date.available | 2023-02-21T08:07:02Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Chicaiza Salazar, W.D., Ortiz Machado, D., Gallego Len, A.J., Escaño González, J.M., Bordons Alba, C., Andrade, G. A. de y Normey Rico, J.E. (2022). Neuro-Fuzzy Digital Twin of a High Temperature Generator. En 11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022, IFAC-PapersOnLine, 55(9) (466-471), Virtual, Online: Elsevier. | |
dc.identifier.issn | 2405-8963 | es |
dc.identifier.uri | https://hdl.handle.net/11441/142815 | |
dc.description.abstract | Solar absorption plants are renewable energy systems with a special advantage: the cooling demand follows the solar energy source. The problem is that this plant presents solar intermittency, phenomenological complexity, and nonlinearities. That results in a challenge for control and energy management. In this context, this paper develops a Digital Twin of an absorption chiller High Temperature Generator (HTG) seeking accuracy and low computational efort for control and management purposes. A neuro-fuzzy technique is applied to describe HTG, internal Lithium-Bromide temperature, and water outlet temperature. Two Adaptative Neuro-Fuzzy Inference Systems (ANFIS) are trained considering real data of eight days of operation. Then, the obtained model is validated considering two days of real data. The validation shows a RMSE of 1.65e−2 for the internal normalized temperature, and 2.05e−2 for the outlet normalized temperature. Therefore, the obtained Digital Twin presents a good performance capturing the dynamics of the HTG with adaptive capabilities considering that each day can update the learning step. | es |
dc.format | application/pdf | es |
dc.format.extent | 6 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | 11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022, IFAC-PapersOnLine, 55(9) (2022), pp. 466-471. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Absorption Chiller | es |
dc.subject | ANFIS | es |
dc.subject | Fresnel Solar Collector | es |
dc.subject | High Pressure Generator | es |
dc.subject | Lithium-Bromide | es |
dc.subject | Solar Energy | es |
dc.title | Neuro-Fuzzy Digital Twin of a High Temperature Generator | es |
dc.type | info:eu-repo/semantics/conferenceObject | 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 de Sistemas y Automática | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2405896322004669 | es |
dc.identifier.doi | 10.1016/j.ifacol.2022.07.081 | es |
dc.contributor.group | Universidad de Sevilla. TEP-116: Automática y robótica industrial | es |
idus.validador.nota | This is an open access article under the CC BY-NC-ND license | es |
dc.publication.initialPage | 466 | es |
dc.publication.endPage | 471 | es |
dc.eventtitle | 11th IFAC Symposium on Control of Power and Energy Systems, CPES 2022, IFAC-PapersOnLine, 55(9) | es |
dc.eventinstitution | Virtual, Online | es |