<p>This paper presents the statistical analysis to reduce carbon emission from tugboat operations by utilizing a proposed machine learning operational scoring system. The time-series statistical analysis is performed on the data collected from a tugboat to investigate the correlation between the environmental data with the fuel consumption to achieve fuel efficiency. The relevant parameters that influence the fuel consumption of the tugboat, such as engine RPM, fuel consumption, vessel route, vessel speed and wind metrics are collected from sensors installed onboard the ship to monitor and to gauge the vessel’s performance. By using the artificial intelligent approaches, this paper attempts to explain the correlation between these collected parameters and to understand the adverse effect of uncollected parameters such as the wave condition and water current have on the fuel efficiency of the boat. The knowledge discovery on the correlation between these parameters enables the classification of the tugboat into different operational categories by using a proposed activity cost system. The scores are produced by a trained machine learning model developed in the project. Based on the environmental conditions, the big data analytics and machine learning system developed in this project shall suggest the optimal ship operation, i.e., ship speed and route,&nbsp;that produces the most efficient ship condition in terms of fuel efficiency.</p>

Efficient Harbor Craft Monitoring System: Time-Series Data Analytics and Machine Learning Tools to Achieve Fuel Efficiency by Operational Scoring System

Paper Proceedings: https://asmedigitalcollection.asme.org/OMAE/OMAE2021/volume/85116

OMAE 2021

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