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Parametric study of organic Rankine working fluids via Bayesian optimization of a preference learning ranking for a waste heat recovery system applied to a case study marine engine
| dc.contributor.author | Díaz Secades, Luis Alfonso | |
| dc.contributor.author | González Rodríguez, Rubén | |
| dc.contributor.author | Rivera Rellán, Noelia | |
| dc.contributor.author | Quevedo Pérez, José Ramón | |
| dc.contributor.author | Montañés Roces, Elena | |
| dc.date.accessioned | 2024-06-25T10:53:25Z | |
| dc.date.available | 2024-06-25T10:53:25Z | |
| dc.date.issued | 2024-08-15 | |
| dc.identifier.citation | Ocean Engineering, 306 (2024); doi:10.1016/j.oceaneng.2024.118124. | |
| dc.identifier.issn | 0029-8018 | |
| dc.identifier.uri | https://hdl.handle.net/10651/72988 | |
| dc.description.abstract | This work presents an analysis of environmentally friendly organic Rankine cycle (ORC) fluids and their application in a marine waste heat recovery system (WHRS) to reduce the utilization of fuel, thus reducing the emission of harmful pollutants. The proposed WHRS consists of four subsystems: steam Rankine, organic Rankine, Seebeck effect heat to electricity conversion and desalination. Among the 80 ORC working fluids analyzed, R1233zd(E), Novec 649 and SES36 exhibit the best overall performance in terms of power output, efficiency, safety and environmental impact. The results indicate that the implementation of innovative energy efficiency systems that comply with MEPC.1-Circ.896, such as this proposal, can reduce the IMO technical energy efficiency index (EEXI) up to 7.26 % and the operational carbon intensity indicator (CII) up to 14.24 %. | spa |
| dc.description.sponsorship | This research has been partially supported by the Spanish Ministry of Science and Innovation through the grant PID2019-110742RB-I00. | |
| dc.language.iso | eng | spa |
| dc.publisher | Elsevier | spa |
| dc.relation.ispartof | Ocean Engineering, Vol 306 | spa |
| dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | * |
| dc.rights | © 2024 The Authors. | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
| dc.subject | Waste heat recovery | spa |
| dc.subject | Marine diesel engine | spa |
| dc.subject | Organic Rankine fluids | spa |
| dc.subject | Preference learning | spa |
| dc.subject | Bayesian optimization | spa |
| dc.title | Parametric study of organic Rankine working fluids via Bayesian optimization of a preference learning ranking for a waste heat recovery system applied to a case study marine engine | spa |
| dc.type | journal article | spa |
| dc.identifier.doi | 10.1016/j.oceaneng.2024.118124 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110742RB-I00/ES/EXPLOTANDO EL CONOCIMIENTO DISPONIBLE PARA ADAPTAR Y APLICAR MODELOS APRENDIDOS A PARTIR DE DOMINIOS DIFERENTES/ | |
| dc.relation.publisherversion | https://doi.org/10.1016/j.oceaneng.2024.118124 | |
| dc.rights.accessRights | open access | spa |
| dc.type.hasVersion | VoR | spa |
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