Strategic development in the petrochemical industry

Imperial Users onl

Multi-period whole system optimisation of an integrated carbon dioxide capture, transportation and storage supply chain

Carbon dioxide capture and storage (CCS) is an essential part of the portfolio of technologies to achieve climate mitigation targets. Cost efficient and large scale deployment of CCS necessitates that all three elements of the supply chain (capture, transportation and storage) are coordinated and planned in an optimum manner both spatially and across time. However, there is relatively little experience in combining CO2 capture, transport and storage into a fully integrated CCS system and the existing research and system planning tools are limited. In particular, earlier research has focused on one component of the chain or they are deterministic steady-state supply chain optimisation models. The very few multi-period models are unable to simultaneously make design and operational decisions for the three components of the chain. The major contribution of this thesis is the development for the first time of a multi-period spatially explicit least cost optimization model of an integrated CO2 capture, transportation and storage infrastructure under both a deterministic and a stochastic modelling framework. The model can be used to design an optimum CCS system and model its long term evolution subject to realistic constraints and uncertainties. The model and its different variations are validated through a number of case studies analysing the evolution of the CCS system in the UK. These case studies indicate that significant cost savings can be achieved through a multi-period and integrated system planning approach. Moreover, the stochastic formulation of the model allows analysing the impact of a number of uncertainties, such as carbon pricing or plant decommissioning schedule, on the evolution of the CSS system. In conclusion, the model and the results presented in this thesis can be used for system planning purposes as well as for policy analysis and commercial appraisal of individual elements of the CCS network.Open Acces

Strategic analysis and optimization of bioethanol supply chains

In modern times, the interest in renewable energy has been increasing considerably in response to the growing energy demand and to the simultaneous concern about global warming effects. The urgency of this issue is related to dissociation between the perspective of a steady growth in demand for fuel and its supply, which is projected to become ever more uncertain and expensive. The phenomenon of climate change is widely recognized as a consequence of the increased concentration of greenhouse gases (GHG) in the atmosphere caused by anthropogenic activity, and to which the transport sector is a significant contributor. Among biofuels, biomass-based ethanol has been in a leading position for substituting petroleum-based road-fuels. Even if its actual carbon footprint is still debated, it is generally acknowledged a reduction in net GHG emissions with respect to oil. The complexity of the context discussed previously, guides us to the transition towards a more sustainable transport system which requires the adoption of effective quantitative tools able to encompassing the problem to the whole production chain (supply chain), that may help defining a more comprehensive view of biofuels. In dealing with such problems involving high decisional level, the analytical modelling is recognized as the best optimization option, particularly in the initial phase of design of unknown infrastructures in order to cope with a comprehensive management of production systems taking into account all supply chain stages. Mixed Integer Linear Programming (MILP) in particular, emerges as one of the most suitable tools in determining the optimal solutions of complex supply chain design problems where multiple alternatives are to be taken into account. In this sense, the multi-objective MILP (moMILP) enables simultaneous consideration of conflicting criteria (i.e., financial, environmental) to assist the decisions of interested parties on biofuels industry at strategic and tactical levels. Moreover, this complex analysis is addressed effectively by incorporating the principles of Life Cycle Analysis (LCA) within supply chain analysis techniques aiming at a quantitative assessment of the environmental burdens of each supply chain stage. Accordingly, the main purpose of the research presented in this Thesis is to cover this gap of knowledge in the literature. In the context of the development and adoption of bioenergy systems, the overall objective of this work is to provide quantitative and deterministic tools to analyze and optimize the supply chain as whole, to thereby identify the most suitable and feasible strategies for the development of future road transport systems. In this sense, the research design for this Thesis begins with the development and analysis of a multi-period moMILP modelling framework for the design and the optimization of bioethanol supply chain where economics and environmental sustainability (GHG emissions reductions potential) for first generation ethanol is addressed, considering possibilities of several technologies integration (including biogas production). Then, the analysis is focused on the general interactions of market policies under the European Emission Trading System in order to enhance the bioethanol market development trends to boost sustainable production of bioethanol. Next, a comprehensive modelling analysis to predict commodity price evolution dynamics and to extend the price forecasts to other goods related to bioethanol production is addressed. An assessment of the impact on the supply chain design of the recent proposed by the European Commission to amend the existing Directive in terms of accountability technique for biofuels is analyzed and discussed. Besides, multi-criteria decision making tools to support strategic design and planning on biofuel supply chains including several Game Theory features are evaluated. Finally to close up, the main achievements of the Thesis are exposed as well as the main shortfalls and possible future research lines are outlined. Models capabilities in steering decisions on investments for bioenergy systems are evaluated in addressing real world case studies referring to the emerging bioethanol production in Northern Italy