Abstract
Ship speed optimization is a primary and direct method for controlling carbon emissions. This study uses simulations based on shipboard measurements from a 28,000 DWT bulk carrier collected between 2015 and 2016. Model predictive control (MPC) with nonlinear receding horizon optimization is employed to optimize the original voyage speeds while ensuring trajectory tracking. Local weighted linear regression is used to establish the relationship between fuel consumption and speed based on the measured data. Additionally, speed constraints corresponding to each Carbon Intensity Indicator (CII) emission level are identified and incorporated into the navigation controller. The results show that ship speed optimization effectively accounts for CII emissions while maintaining adherence to the original trajectory. However, under the carbon reduction constraint, the distance sailed by the ships will be reduced.
Published Version
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