Data-driven regression models for voyage cost optimisation based on the operating conditions of the SA Agulhas II
Date
2020-12
Authors
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Journal ISSN
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Publisher
Stellenbosch : Stellenbosch University
Abstract
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 navigation while subjected to various atmospheric and ocean conditions. Output power is directly relatable to fuel consumption and was successfully estimated from trained models. Anon-linear relationship between power and speed is observed and provides an opportunity to optimise ship operations in terms of cost or time.Speed optimisation illustrates the financial cost-benefit impact of operating at higher speeds and power levels. A pilot exercise is defined to assess the applicability of data-driven models in a route selection context. A dynamic optimisation technique is successfully implemented to account for the stochastic, time-series characteristics of weather conditions over a voyage path. Data-driven modelling and optimisation offer breakthrough opportunities to ensure the modernisation and sustainability of the SAAII in the context of a South African presence within Antarctic and Southern Ocean research.
AFRIKAANSE OPSOMMING: Raadpleeg teks vir opsomming
AFRIKAANSE OPSOMMING: Raadpleeg teks vir opsomming
Description
Thesis (MEng)--Stellenbosch University, 2020.
Keywords
Machine Learning, Decision support systems, Ship speed optimisation, Digital twin technology, UCTD