Vessel's trim optimization using IoT data and machine learning models

Abstract

The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure. © 2022 IEEE.