Data-driven regression models for voyage cost optimisation based on the operating conditions of the SA Agulhas II

• Main Author: Durandt, Petrus Gerhardus
• Other Authors: Bekker, Annie, Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
• Format: Thesis
• Language: English
• Published: Stellenbosch : Stellenbosch University 2020
• Subjects: Machine Learning; Decision support systems; Ship speed optimisation; Digital twin technology; UCTD; Gough; Antarc*; Antarctica
• Online Access: http://hdl.handle.net/10019.1/109321

Thesis (MEng)--Stellenbosch University, 2020. ENGLISH ABSTRACT: The maritime industry is a cornerstone in the modern globalised economy. Efficient operation of ocean-going vessels is of great importance from both financial and environmental perspectives. Carbon emissions from maritime activities are projected to increase significantly in the coming decades. Short term strategies to address the carbon footprint issue calls for research around topics such as efficiency optimisation of ocean-going vessels.Emerging digital twin platforms are allowing asset owners and operators to manage the vast information networks that monitor asset performance. Dig-ital twins provide a way to plan, monitor and simulate various operating environments to find optimum configurations. Machine learning methods are harnessed to provide an innovative solution to modelling of data-driven problems which could be very useful in the prediction of asset responses for various operational scenarios. Speed and route optimisation with the use of data-driven models are prerequisites in the attempt to provide decision support capacity to gain tactical foresight for maritime operations. The SA Agulhas II (SAAII) is a polar supply and research vessel owned and operated by the South African Department of Environment, Forestry and Fisheries (DEFF). This vessel is of particular importance due to the large quantity and variety of data, for both open water and ice navigation, that are recorded during annual voyages to Antarctica, Marion and Gough Islands. Data is comprised of physical measurements from on-board sensors and diligent observations of ocean and ice conditions. Reconciliation and synchronisation of observed and machine data from the ship’s central measurement unit (CMU)was successful and paved the way towards effective data-driven modelling.Two different machine learning models, support vector regression (SVR) and artificial neural networks (ANN), were trained to predict the powering performance of the SAAII for open water and ice ...