• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.659-673
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• 2021

This study aims to investigate the impact of the High Pressure Selective Catalytic Reduction system (SCR-HP) on a large marine two-stroke engine performance parameters by employing thermodynamic modelling. A coupled model of the zero-dimensional type is extended to incorporate the modelling of the SCR-HP components and the Control Bypass Valve (CBV) block. This model is employed to simulate several scenarios representing the engine operation at both healthy and degraded conditions considering the compressor fouling and the SCR reactor clogging. The derived results are analysed to quantify the impact of the SCR-HP on the investigated engine performance. The SCR system pressure drop and the cylinder bypass valve flow cause an increase of the engine Specific Fuel Oil Consumption (SFOC) in the range 0.3-2.77 g/kWh. The thermal inertia of the SCR-HP is mainly attributed to the SCR reactor, which causes a delayed turbocharger response. These effects are more pronounced at low engine loads. This study supports the better understanding of the operating characteristics of marine two-stroke diesel engines equipped with the SCR-HP and quantification of the impact of the components degradation on the engine performance.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.