Decarbonising the transport sector is a top priority worldwide. The difficult-to-decarbonise transport applications (including mainly shipping, road freight and aviation) emit more than 50% CO2 of the entire transport sector. Among efforts on developing low-emission fuels, liquid synthetic fuels that can massively reduce pollutant emissions are drawing increasing attention, as they can be integrated into the current transportation system using existing infrastructure and combusted in existing engines (such as diesel engines for optimal fuel economy) with minor adjustments as drop-in fuels. Liquid synthetic fuels such as oxymethylene ethers (OMEx, which possess liquid properties similar to diesel when x=3-5) can be produced from a range of waste feedstocks and biomass, thereby avoiding new fossil carbon from entering the supply chain. OMEx can also be produced as an electrofuel (or e-fuel), thereby used as a sustainable energy carrier. However, due to the lack of complete knowledge of the physicochemical properties associated with the fuel composition variability, i.e. variation in the oligomer length (the x value of OMEx) and the composition variation of OMEx-diesel blends in real engine environment, there are challenges in utilising OMEx in practical engines, mainly in engine and its operation adjustments for optimal performance and minimal pollutant emissions. To address the technical issues of OMEx utilisation, accurate information on physicochemical properties and pollutant emissions of the synthetic fuels over the engine operational ranges is mandatory, but this is not readily available. This project is intended to obtain a thorough understanding on liquid synthetic fuel utilisation. The project will address the fundamental challenges in utilising renewable synthetic fuels, in particular OMEx and the associated OMEx-diesel fuel blends. The study will follow a combined modelling / simulation - experimentation approach, predicting the physicochemical properties including emission characteristics of the alternative fuels using molecular dynamics simulations, tailor-made experimentation for first-hand information on fuel utilisation, and establishing a database / mapping to guide the synthetic fuel utilisation in real engines over a wide range of conditions using machine learning.

Cooperative game theory is a branch of game theory that offers a conceptually simple and intuitive mathematical framework to model collaborative settings involving multiple decision makers (players). Solutions of cooperative games offer different ways to share the profit or cost among the players in a way that ensures the fairness and stability of the collaboration, while considering the possibility that any subgroup of players has the option to form their own coalition. The focus of this project is on the most generic class of cooperative games - the integer maximisation games. These games arise in settings where the players in each coalition need to solve an integer maximisation problem to achieve the best interests of their coalition. This proposed research addresses a fundamental question of how to distribute payoff under a new paradigm with the presence of uncertainty and in the context of reasonably large games. Often, formulating a real-life application as a cooperative game, where relevant, is not a difficult task. The part that discourages the use of cooperative game theory is the difficulty in undertaking numerical computation of the solutions due to their combinatorial structures. This is particularly true in integer maximisation games where the set of inputs of the problem, i.e., the value that each coalition can create, involves solving an exponentially large number of integer linear programs. The first part of the proposed research provides efficient algorithms for payoff allocation in reasonably large integer maximisation games. In addition, an open-source software package for computing these solutions and showcase real-world applications is made available. This promises to extend the impact to wide groups of practitioners and academics who want to apply cooperative game theory to profit-/cost-sharing applications. The proposed project also aims to study cooperative games with uncertain payoffs. While uncertainty is a natural part of most decision-making problems, the issue has been largely ignored in the literature of cooperative game theory and there is currently no rigorous framework for handling these. We propose a new framework where fundamental concepts such as stability and fairness are redefined in the face of uncertainty.

Cooperative game theory is a branch of game theory that offers a conceptually simple and intuitive mathematical framework to model collaborative settings involving multiple decision makers (players). Solutions of cooperative games offer different ways to share the profit or cost among the players in a way that ensures the fairness and stability of the collaboration, while considering the possibility that any subgroup of players has the option to form their own coalition. The focus of this project is on the most generic class of cooperative games - the integer maximisation games. These games arise in settings where the players in each coalition need to solve an integer maximisation problem to achieve the best interests of their coalition. This proposed research addresses a fundamental question of how to distribute payoff under a new paradigm with the presence of uncertainty and in the context of reasonably large games. Often, formulating a real-life application as a cooperative game, where relevant, is not a difficult task. The part that discourages the use of cooperative game theory is the difficulty in undertaking numerical computation of the solutions due to their combinatorial structures. This is particularly true in integer maximisation games where the set of inputs of the problem, i.e., the value that each coalition can create, involves solving an exponentially large number of integer linear programs. The first part of the proposed research provides efficient algorithms for payoff allocation in reasonably large integer maximisation games. In addition, an open-source software package for computing these solutions and showcase real-world applications is made available. This promises to extend the impact to wide groups of practitioners and academics who want to apply cooperative game theory to profit-/cost-sharing applications. The proposed project also aims to study cooperative games with uncertain payoffs. While uncertainty is a natural part of most decision-making problems, the issue has been largely ignored in the literature of cooperative game theory and there is currently no rigorous framework for handling these. We propose a new framework where fundamental concepts such as stability and fairness are redefined in the face of uncertainty.