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

Decision making under uncertainties for renewable energy and precision agriculture

In this dissertation, mathematical programming models and statistical analysis tools have been formulated and designed to study the strategic and optimal solutions to allocate the resources and manage the risk for the renewable energy and precision agriculture. The dissertation, which consists of four papers, lies at the interface of optimization, simulation, and statistical analysis, with a focus on decision making under uncertainty for biofuel process design, renewable energy supply chain management and precision agriculture. Bio-oil gasification which integrates fast pyrolysis and gasification processes is a relative new conversion technology and this integrated biofuel production pathway has been promoted to take advantage of economies of scale and logistic efficiency. The design of the supply chain networks, especially under uncertainties, is one of the most important decisions faced by the biofuel industry. In the first paper, we proposed a two-stage stochastic programming framework for the biofuel supply chain optimization problem considering uncertainties, including biomass supply availability, technology advancement, and biofuel market price. The results show that the stochastic factors have significant impacts on the decision on fast pyrolysis plant locations, especially when there is insufficient biomass. Also, farmers\u27 participation can have a significant impact on the profitability and robustness of this supply chain design. Another major challenge faced by the cellulosic biofuel industry is that investors are hesitant to take the risk to construct commercial scale production facilities. Techno- economic analysis (TEA) has been widely adopted to overcome this challenge. The optimal facility locations and capacities as well as the logistic flow decisions for biomass supply and biofuel distribution should be incorporated into techno-economic analysis as well. In the second paper, the author aims to provide a new method that integrated the supply chain design into the techno-economic analysis as well by evaluating the economic feasibility of an integrated pathway on biomass pyrolysis and bio-oil gasification. The results indicate that hybrid fast pyrolysis and bio-oil gasification pathway is more suitable for a decentralized supply chain structure while biomass gasification pathway is more suitable for a single centralized facility supply chain structure. Feeding millions of people throughout the world who face hunger every day is a formidable challenge. Precision agriculture has attracted increasing attention in the community of farmland management. Farmland management involves a sequence of planning and decision-making processes, including seed selection and irrigation schedule. In the third paper, a mixed integer programming optimization model is proposed to provide decision support on seed selection and irrigation water allocation for customized precision farmland management. The results show that significant increase of farmers’ annual profit can be achieved by carefully choosing irrigation schedule and type of seed. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture. The effect of limited water on corn grain yield is significant and management decisions are essential to optimize farmers’ profits, particularly under stochastic environment. The fourth paper takes uncertainties such as crop price, irrigation water availability and precipitation amount into consideration. A multi-stage stochastic programming is formulated to evaluate the effects of structure of decision making process on farmers’ income. The case study results indicate multi-stage stochastic programming is a promising way for farmland management under uncertainties and can increase farmers’ income significantly. In order to enhance the data utilization and results interpretation, statistical methods such as Monte-Carlo simulation considering parameter interactions, linear regression analysis, and moment matching method for scenario generation are also applied. The overarching goals of this dissertation is to quantify and manage the uncertainties along the modeling process and provide proper mechanisms that lead to optimal decisions. The outcomes of the research have the potential to accelerate the commercialization of second generation of biofuel and lead to sustainable utilization of water resources. The insights derived from the research contributed to the decision making process under uncertainties

A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments

Integrated supplier selection and order allocation is an important decision for both designing and operating supply chains. This decision is often influenced by the concerned stakeholders, suppliers, plant operators and customers in different tiers. As firms continue to seek competitive advantage through supply chain design and operations they aim to create optimized supply chains. This calls for on one hand consideration of multiple conflicting criteria and on the other hand consideration of uncertainties of demand and supply. Although there are studies on supplier selection using advanced mathematical models to cover a stochastic approach, multiple criteria decision making techniques and multiple stakeholder requirements separately, according to authors' knowledge there is no work that integrates these three aspects in a common framework. This paper proposes an integrated method for dealing with such problems using a combined Analytic Hierarchy Process-Quality Function Deployment (AHP-QFD) and chance constrained optimization algorithm approach that selects appropriate suppliers and allocates orders optimally between them. The effectiveness of the proposed decision support system has been demonstrated through application and validation in the bioenergy industry

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Global supply chains of high value low volume products

Imperial Users onl

Biorenewable value chain optimisation with multi-layered value chains and advanced analytics

A crucial element of the quest of curbing carbon dioxide emissions is deemed to rely on a biobased economy, which will rely on the development of economically and environmentally sustainable biorefining systems enabling a full exploitation of lignocellulosic biomass (and its macrocomponents such as cellulose, hemicellulose, and lignin) for the co-production of biofuels and bioderived platform chemicals. The thesis aims to develop comprehensive modelling frameworks to provide, through optimisation techniques, holistic decision-making regarding the strategic design and systematic planning of advanced biorefining supply chain networks. Therefore, the modelling of the entire value chain behaviour, involving both upstream and downstream aspects within a temporal and geographical context, is of great importance in this study. A deterministic, spatially explicit, multi-echelon and multi-period Mixed Integer Linear Programming prototype modelling framework is developed for the identification of profitably optimal strategic and operating decisions regarding a full supply chain system, integrated with a technology superstructure of multiple biomass feedstocks, bioproducts and processing portfolios. The potential dimensionality reduction of the resulting large-scale optimisation problem is explored by utilising a bilevel decomposition algorithm. The financial sustainability of such biobased supply chains is further analysed through two-stage stochastic optimisation and risk management models, incorporating biomass cultivation yield uncertainties and expected downside risk, respectively. Finally, greenhouse gas emission factors are added to the prototype modelling approach through a multi-objective optimisation scheme to steer decision-making on biorefining supply chain systems under both economic and environmental criteria, comparing two different solution procedures. The developed models are applied to a Hungarian case study of lignocellulosic biorefining production systems. An additional case study in a Southeastern Romanian region and Marseille, regarding a first-generation biorefining supply chain for the production of castor oil, is undertaken to further examine the compatibility and efficiency of the generic deterministic model.Open Acces