The project aimed at evaluating the technical and economic feasibility of using an ORC unit for recovering low-temperature waste heat sources (i.e. charge air, engine cooling system and lubricant oil system) on container vessels.
The project included numerical analyses as well as a demonstration of a 110 kW ORC unit utilizing the waste heat of the main engine cooling system on-board one of Maersk’s container vessels. The purpose of the project was to evaluate the retro-fitting potential of using ORC units, and through the test installation evaluate the matureness of this technology for maritime applications. The project focused only on the evaluation of the technology for retrofit solutions, but the learnings will also be relevant for new-building projects.
Use of low-temperature waste heat
This project provided Maersk with essential know-how and experience about the utilization of low-temperature waste heat on ships, enabling significant fuel savings and reductions of pollutant emissions for their vessels. Moreover, this project helps the Danish maritime industry to become a leading industry worldwide within green shipping. By including a supplier, a user/ship owner and a research organization, the project included all relevant participants, ensuring a successful outcome of the project and enable subsequent large-scale implementation of the ORC technology within the maritime industry.
Integration of test results with numerical models
Numerical thermodynamic models of the ORC pilot installation were derived. Steady-state models for design and part-load conditions as well as models for transient operation were developed. The tests of the pilot ORC unit were executed and operational performance data were measured on board. The test results were evaluated by comparing them to the results of the numerical models, forming the basis for the validation of the numerical models. Reasons for possible deviations between the performance data and results of the numerical models were identified, and, the numerical models were tuned, using intelligent optimization techniques, e.g. the genetic algorithm, to better match the operational data. Critical transient operational events, e.g. sudden load changes, were identified from the tests, and strategies for resolving these events were derived using the numerical models.