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
Palabra(s) clave:
Waste heat recovery
Marine diesel engine
Organic Rankine fluids
Preference learning
Bayesian optimization
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Editorial:
Elsevier
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Resumen:
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 %.
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 %.
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This research has been partially supported by the Spanish Ministry of Science and Innovation through the grant PID2019-110742RB-I00.
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