Optimization of CCUS supply chains in the UK: A strategic role for emissions reduction

The UK is the second largest emitter of carbon dioxide in Europe. It aims to take urgent actions to achieve the 2030 target for CO_{2} emissions reduction imposed by EU environmental policies. Three different carbon capture utilization and storage (CCUS) supply chains are developed giving economic indicators for CO_{2} utilization routes not implying carbon dioxide hydrogenation (i.e. with high TRL). The study presents an innovative proposal to reduce CO_{2} impact in the UK, a country rich in coal, which requires reduction of carbon dioxide emissions from flue gases as the easiest and best performing solution. Bunter Sandstone, Scottish offshore and Ormskirk Sandstone are the storage sites considered, while several attractive potential utilization options are considered. Through minimization of total costs, the CCUS supply chain with Bunter Sandstone as storage site results in the most economically profitable solution due to the highest value of net present value (€ 0.554 trillion) and lowest value of pay back period (2.85 years). Only carbon tax is considered. The total cost is € 1.04 billion/year. Across the supply chain, 6.4 Mton/year of carbon dioxide emissions are avoided, to be either stored or used for calcium carbonate production. Future work should consider uncertainty, dynamics of market demand and social aspects

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

Optimization of Supply Chain Management and Facility Location Selection for a Biorefinery

If renewable energy and biofuels are to attain success in the market place, each step of their production and the system as a whole must be optimized to increase material and energy efficiency, reduce production cost and create a competitive alternative to fossil fuels. Systems optimization techniques may be applied to product selection, process design and integration, feedstock procurement and supply chain management to improve performance. This work addresses two problems facing a biorefinery: technology selection and feedstock scheduling in the face of varying feedstock supply and cost. Also addressed is the optimization of a biorefinery supply chain with respect to distributed processing of biomass to bio-products via preprocessing hubs versus centralized processing and facility location selection. Two formulations are proposed that present a systematic approach to address each problem. Case studies are included to demonstrate model capabilities for both formulations. The scheduling model results display model sensitivity to feedstock price and transport distance penalized through carbon dioxide emissions. The distributed model shows that hubs may be used to extend the operating radius of a biorefinery and thereby increase profits

A Review on Quantitative Models for Sustainable Food Logistics Management

The last two decades food logistics systems have seen the transition from a focus on traditional supply chain management to food supply chain management, and successively, to sustainable food supply chain management. The main aim of this study is to identify key logistical aims in these three phases and analyse currently available quantitative models to point out modelling challenges in sustainable food logistics management (SFLM). A literature review on quantitative studies is conducted and also qualitative studies are consulted to understand the key logistical aims more clearly and to identify relevant system scope issues. Results show that research on SFLM has been progressively developing according to the needs of the food industry. However, the intrinsic characteristics of food products and processes have not yet been handled properly in the identified studies. The majority of the works reviewed have not contemplated on sustainability problems, apart from a few recent studies. Therefore, the study concludes that new and advanced quantitative models are needed that take specific SFLM requirements from practice into consideration to support business decisions and capture food supply chain dynamics

Switching transport modes to meet voluntary carbon emission targets

The transport sector is the second largest carbon emissions contributor in Europe and its emissions continue to increase. Many shippers are committing themselves to reducing transport emissions voluntarily, possibly in anticipation of increasing transport prices. In this paper we study a shipper that has outsourced transport and has decided to cap its carbon emissions from outbound logistics for a group of products. Setting an emission constraint for a group of products allows taking advantage of reducing emissions substantially where it is less costly and less where it is more costly. We focus on reducing emissions by switching transport modes within an existing network, since this has a large impact on emissions. In addition, the company sets the sales prices for the products, which in uences demand. We develop a solution procedure that uses Lagrange relaxation. Conditions on total logistics cost and unit emissions are derived determine which transport mode is selected for a product. It is observed that a diminishing rate of return applies in reducing emissions by switching transport modes. In a case study we apply our method to a producer of bulk liquids and find that emissions can be reduced by 10% at only a 0.7% increase in total logistics cost. Keywords: carbon emissions, green supply chains, sustainability, transport mode selection, Lagrange relaxation, pricing

Energy and Carbon Dioxide Impacts from Lean Logistics and Retailing Systems: A Discrete-event Simulation Approach for the Consumer Goods Industry

abstract: Consumer goods supply chains have gradually incorporated lean manufacturing principles to identify and reduce non-value-added activities. Companies implementing lean practices have experienced improvements in cost, quality, and demand responsiveness. However certain elements of these practices, especially those related to transportation and distribution may have detrimental impact on the environment. This study asks: What impact do current best practices in lean logistics and retailing have on environmental performance? The research hypothesis of this dissertation establishes that lean distribution of durable and consumable goods can result in an increased amount of carbon dioxide emissions, leading to climate change and natural resource depletion impacts, while lean retailing operations can reduce carbon emissions. Distribution and retailing phases of the life cycle are characterized in a two-echelon supply chain discrete-event simulation modeled after current operations from leading organizations based in the U.S. Southwest. By conducting an overview of critical sustainability issues and their relationship with consumer products, it is possible to address the environmental implications of lean logistics and retailing operations. Provided the waste reduction nature from lean manufacturing, four lean best practices are examined in detail in order to formulate specific research propositions. These propositions are integrated into an experimental design linking annual carbon dioxide equivalent emissions to: (1) shipment frequency between supply chain partners, (2) proximity between decoupling point of products and final customers, (3) inventory turns at the warehousing level, and (4) degree of supplier integration. All propositions are tested through the use of the simulation model. Results confirmed the four research propositions. Furthermore, they suggest synergy between product shipment frequency among supply chain partners and product management due to lean retailing practices. In addition, the study confirms prior research speculations about the potential carbon intensity from transportation operations subject to lean principles.Dissertation/ThesisPh.D. Sustainability 201

Lean and green in the transport and logistics sector – a case study of simultaneous deployment

© 2016 Informa UK Limited, trading as Taylor & Francis Group. The transport and logistics sector is of vital importance for the stimulation of trade and hence the economic development of nations. However, over the last few years, this sector has taken central stage in the green agenda due to the negative environmental effects derived from its operations. Several disciplines including operations research and sub-areas of supply chain management such as green supply chains, green logistics and reverse logistics have tried to address this problem. However, despite the work undertaken through these disciplines, theoretical or empirical research into the sequential or simultaneous deployment of the lean and green paradigms, particularly, in the road transport and logistics sector is limited. This paper presents a case study where both paradigms have been combined to improve the transport operations of a world leader logistics organisation in the metropolitan area of Monterrey, Mexico. To do this, a systematic methodology and a novel tool called Sustainable Transportation Value Stream Map (STVSM) were proposed. The results obtained from the case study indicate that the concurrent deployment of the green and lean paradigms through such methodology and the STVSM tool is an effective approach to improve both operational efficiency and environmental performance of road transport operations. The paper can be used as a guiding reference for transport and logistics organisations to undertake improvement projects similar to the one presented in this paper. Additionally, this research also intends to stimulate scholarly research into the application of lean and green paradigms in the transport and logistics sector to expand the limited research pursued in this area

A Multiperiod Supply Chain Network Design Considering Carbon Emissions

This paper introduces a mixed integer linear programming formulation for modeling and solving a multiperiod one-stage supply chain distribution network design problem. The model is aimed to minimize two objectives, the total supply chain cost and the greenhouse gas emissions generated mainly by transportation and warehousing operations. The demand forecast is known for the planning horizon and shortage of demand is allowed at a penalty cost. This scenario must satisfy a minimum service level. Two carbon emission regulatory policies are investigated, the tax or carbon credit and the carbon emission cap. Computational experiments are performed to analyze the trade-offs between the total cost of the supply chain, the carbon emission quantity, and both carbon emission regulatory policies. Results demonstrate that for a certain range the carbon credit price incentivizes the reduction of carbon emissions to the environment. On the other hand, modifying the carbon emission cap inside a certain range could lead to significant reductions of carbon emission while not significantly compromising the total cost of the supply chain