Shipping industryâs significant economic and environmental impact along with the enforcement of stringent environmental regulations led to growing interest in enhancing shipping operations sustainability. In recent years, various technologies and alternative fuels were introduced to address the ship energy systems contribution to energy consumption and air pollution during the ship lifetime. The abundance of the available technologies and potential combinations renders ship energy systems selection process very challenging during the early design phase. The novelty of this research lies in the decision support method developed to optimise the ship energy systems synthesis at the early stage design with respect to environmental and economic objectives, as well as considerations of the ship lifetime operating requirements.Mathematical models of established and emerging technologies were developed to estimate the ship energy systems performance. The integrated ship energy systems synthesis was formulated as a multi-objective combinatorial optimisation problem, with the objectives of life cycle cost and exhaust gas emissions minimisation, whilst considering the environmental regulations as constraints. NSGA-II was employed to solve the ship energy systems synthesis problem.The developed method was evaluated with two case studies, an Aframax oil tanker and a cruise ship. The visualisation of the optimal configurations on the Pareto front allows decision makers understanding and managing trade-offs between the environmental and economic objectives. The comparison of the optimal solutions estimated carbon emissions with the EEDI values indicated that the index does not capture the real carbon impact of the configurations. An uncertainty analysis assessed the robustness of the solutions. The sensitivity analysis of the two case studies indicated that changes in the fuel prices and emerging technologies have different implications on the two ships. The optimal configurations for different operating profiles were identified and insights were gained on the most promising future configurations under derived carbon pricing scenarios.
|Date of Award||27 Aug 2019|
- University Of Strathclyde
|Sponsors||University of Strathclyde|
|Supervisor||Athanasios Rentizelas (Supervisor) & Gerasimos Theotokatos (Supervisor)|