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Conference paper

Prediction of the annual performance of marine organic Rankine cycle power systems

In Proceedings of Ecos 2018: 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems — 2018
From

Department of Mechanical Engineering, Technical University of Denmark1

Thermal Energy, Department of Mechanical Engineering, Technical University of Denmark2

Chalmers University of Technology3

The increasing awareness about the environmental impact of shipping and the increasingly stricter regulations introduced by the International Maritime Organization are driving the development of solutions to reduce the pollutant emissions from ships. While some previous studies focused on the implementation of a specific technology, others considered a wider perspective and investigated the feasibility of the integration of various technologies on board vessels.

Among the screened technologies, organic Rankine cycle (ORC) power systems represent a viable solution to utilize the waste heat contained in the main engine exhaust gases to produce additional power for on board use. The installation of ORC power systems on board ships could result in a reduction of the CO2 emissions by 5 – 10 %.

Although a number of methods to derive the optimal design of ORC units in marine applications have been proposed, these methods are complex, computationally expensive and require specialist knowledge to be included as part of a general optimization procedure to define the optimal set of technologies to be implemented on board a vessel.

This study presents a novel method to predict the performance of ORC units installed on board vessels, based upon the characteristics of the main engine exhaust gases and the ship sailing profile. The method is not computationally intensive, and is therefore suitable to be used in the context of large optimization problems, such as holistic optimization and evaluation of a ship performance given the operational profile, weather and route.

The model predicted the annual energy production of two case studies with an accuracy within 4 %

Language: English
Year: 2018
Proceedings: 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2018)International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
Types: Conference paper
ORCIDs: Montagud, M. E. and Haglind, F.

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