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Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.

The recovery of high temperature thermal energy released by propulsion engines in order to cover thermal loads is commonplace in contemporary ships. However, the medium-and low-temperature thermal energy is only partially exploited or not exploited at all. In the present work, an organic Rankine cycle system driving an electric generator is considered, in addition to the exhaust gas boiler, in order to recover available heat and produce electrical energy. The specifications of the system are determined by an optimization procedure taking economic criteria into consideration, apart from the technical criteria usually used in this kind of studies. More specifically, with the net present value as the objective function and by application of optimization algorithms, the optimal synthesis, design and operation of the organic Rankine cycle system are determined. For the particular vessel considered, the installation of the organic Rankine cycle is technically feasible and economically profitable, with a dynamic payback period of 4 years. The solution of the optimization problem is supplemented with a sensitivity analysis with respect to important parameters.

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