Data-Driven Hull-Fouling Cleaning Schedule Optimization to Reduce Carbon Footprint of Vessels

TL;DR

This paper presents a data-driven optimization method for scheduling hull and propeller cleaning to minimize fuel consumption and carbon emissions, balancing environmental impact with economic costs for shipping vessels.

Contribution

It introduces a novel dynamic programming approach for vessel cleaning schedule optimization using real-world sensor data, addressing environmental and economic trade-offs.

Findings

Fuel consumption reduced by up to 5% over four years

Optimized cleaning schedules balance environmental and economic factors

Method validated on data from ten real-world vessels

Abstract

In response to climate change, the International Maritime Organization has introduced regulatory frameworks to reduce greenhouse gas emissions from international shipping. Compliance with these regulations is increasingly expected from individual shipping companies, compelling vessel operators to lower the CO2 emissions of their fleets while maintaining economic viability. An important step towards achieving this is performing regular hull and propeller cleaning; however, this entails significant costs. As a result, assessing whether ship performance has declined sufficiently to warrant cleaning from an environmental and economic standpoint is a critical task to ensure both long-term viability and regulatory compliance. In this paper, we address this challenge by proposing a novel data-driven dynamic programming approach to optimize vessel cleaning schedules by balancing both environmental and economic considerations. In numerical experiments, we demonstrate the usefulness of our proposed methodology based on real-world sensor data from ten tramp trading vessels. The results confirm that over a four-year period, fuel consumption can be reduced by up to 5%, even when accounting for the costs of one or two additional cleaning events.