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Wednesday, November 25, 2020

Maritime Logistics Professional

November 9, 2017

AI to Estimate Vessel Speed and Fuel Consumption

 Mitsui O.S.K. Lines (MOL) announced that it teamed up with Fujitsu Laboratories Ltd., and Tokyo University of Marine Science and Technology to verify the accuracy of technology to estimate vessel performance at sea by applying Fujitsu's artificial intelligence (AI) technology, "FUJITSU Human Centric AI Zinrai."

This project is a part of MOL's initiative to assess the effectiveness of AI technology, and aims to reduce fuel consumption and vessels' environmental impact by verifying the accuracy of the technology, using Fujitsu's AI Technology to estimate vessel performance at sea.

MOL provided actual voyage data collected from MOL fleet in operation to Fujitsu Laboratories, which, along with Tokyo University of Marine Science and Technology, verified the data by using their jointly developed machine learning method.
Verification methods: Learned the correlation of each item of operation data using Fujitsu's unique AI technology and high-dimensional statistics analysis technology, and established the technology that estimates vessel performance.
Estimated the ship speed from the data other than the speed and verified the comparison between that estimated value and actual operation data, in case to assess allowance of speed. This same assessment was also applied to fuel consumption.
Results of verification: Could estimate vessel speed with about a 1.4% allowance, and fuel consumption with an allowance of about 0.8%, based on the above-mentioned method.
 This project, as a part of the "ISHIN NEXT - MOL SMART SHIP PROJECT –", assesses the effectiveness of AI technology and aims to enhance operating safety and reduce environmental impact through reduction of fuel consumption.
MOL aims to become the corporate group that customers think of first when they choose a logistics business partner, by drawing upon AI and ICT technologies to enhance its services.
AI technologyartificial intelligencemachine learning