Title: Reducing ships' fuel consumption and emissions by learning from data
Subject: Maritime science
Faculty: Faculty of technology
Date: Thursday 13 December 2018 at 10.00 am
Place: Room B135, building Kocken, Kalmar
External reviewer: Professor Erik Dahlquist, Mälardalen University, Sweden
Examining committee: Professor Andrew Martin, KTH Royal Institute of Technology, Sweden; Professor Eva Thorin, Mälardalen University, Sweden; Professor Sven B Andersson, Chalmers University of Technology, Sweden
Chairperson: Professor Kjell Larsson, Kalmar Maritime Academy, Linnaeus University
Supervisor: Associate professor Marcus Thern, Faculty of Engineering LTH, Sweden
Examiner: Professor Carl Hult, Kalmar Maritime Academy, Linnaeus University
Spikning: Wednesday 21 November 2018 in Kalmar, at 1.00 pm at the University library and at 2.00 pm at Kalmar Maritime Academy
In the context of reducing both greenhouse gases and hazardous emissions, the shipping sector faces a major challenge as it is currently responsible for 11 % of the transport sector's anthropogenic greenhouse gas emissions. Even as emissions reductions are needed, the demand for the transport sector rises exponentially every year. This thesis aims to investigate the potential to use ships' existing internal energy systems more efficiently.
The thesis focusses on making existing ships in real operating conditions more efficient based logged machinery data. This dissertation presents results that can make ship more energy efficient by utilising waste heat recovery and machine learning tools. A significant part of this thesis is based on data from a cruise ship in the Baltic Sea, and an extensive analysis of the ship's internal energy system was made from over a year's worth of data. The analysis included an exergy analysis, which also considers the usability of each energy flow.
In three studies, the feasibility of using the waste heat from the engines was investigated, and the results indicate that significant measures can be undertaken with organic Rankine cycle devices. The organic Rankine cycle was simulated with data from the ship operations and optimised for off-design conditions, both regarding system design and organic fluid selection. The analysis demonstrates that there are considerable differences between the real operation of a ship and what it was initially designed for. In addition, a large two-stroke marine diesel was integrated into a simulation with an organic Rankine cycle, resulting in an energy efficiency improvement of 5 %.
This thesis also presents new methods of employing machine learning to predict energy consumption. Machine learning algorithms are readily available and free to use, and by using only a small subset of data points from the engines and existing fuel flow metres, the fuel consumption could be predicted with good accuracy. These results demonstrate a potential to improve operational efficiency without installing additional fuel metres. The thesis presents results concerning how data from ships can be used to further analyse and improve their efficiency, by using both add-on technologies for waste heat recovery and machine learning applications.