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Heat-integrated crude oil distillation systems are energy and capital intensive, and have a very complex structure with strong interactions between the individual units. Retrofit of these systems is of major interest to petroleum refiners. Retrofit objectives are various and preferably achieved with minimum capital expenditure, while equipment constraints are met. Traditional approaches to retrofit design of crude oil distillation systems identify promising modifications based on experience or pinch analysis. Later, sequential approaches to retrofit design were developed, in which distillation and heat recovery units are modified individually. Recent approaches considered simultaneously the distillation column and heat integration targets, rather than the existing heat recovery system. That shortcut models for retrofit design of distillation columns are not available is an additional limitation of established methodologies. In this thesis, a new approach is presented for retrofit design of heat-integrated crude oil distillation systems. Shortcut models are developed for distillation retrofit, including reboiled and steam-stripped columns. These models are based on the Underwood equation and are appropriate for retrofit design of simple columns and various complex column arrangements. Models are also proposed for exchanger network retrofit, addition of new columns to the existing distillation unit, modifying column internals, enhancing heat transfer in exchanger tubes and for evaluating CO2 emissions in existing crude oil distillation units. The retrofit design methodology is optimisation-based, and considers the existing distillation process simultaneously with the details of the associated heat recovery system. Existing equipment limitations, such as the hydraulic capacity of the distillation column, exchanger network pressure drop and bottlenecked exchangers, are accounted for. The approach considers various structural modifications and design options resulting in significant benefits. Examples of these are the installation of preflash and prefractionator units to the existing column configuration, replacement of column internals with packing, enhancement of exchanger heat transfer and integration of a gas turbine with an existing furnace. The optimisation framework comprises column and exchanger network retrofit models, cost models and suitable objective functions. The approach optimises all operating conditions of the existing distillation process and any new columns to minimise or maximise a specified objective function, while satisfying existing constraints. The objective function is flexible and varies according to retrofit objectives. Several objectives are taken into account, such as reducing energy consumption and overall cost, increasing capacity, improving profit and reducing CO2 emissions. The approach allows these objectives to be met by considering several design alternatives. The new retrofit approach is applied to different industrial cases of crude oil distillation units, for energy and total cost savings, throughput enhancement, product yield changes, profit increase and emissions reduction. Typical results conclude that retrofit goals can be achieved with substantial savings in energy and total cost, and improved profit with minimal capital investment.

In today’s global and competitive markets selling products at competitive prices, coordination of supply chain configuration, and environmental and ecological consciousness and responsibility become important issues for all companies around the world. The price of products is affected by costs, one of which is inventory cost. Inventory does not give any added value to products but must be kept in order to fulfill the customer demand in time. Therefore, this cost must be kept at the minimum level. In order to reduce the amount of inventory across a supply chain, coordination of decisions among all players in the chain is necessary. Coordination is needed not only for a two-level supply chain involving a manufacturer and its customers, but also for a complex supply chain of multiple tiers involving many players. With increasing attention being placed to environmental and ecological consciousness and responsibility, companies are keen to have a reverse supply chain where used products are collected and usable components remanufactured and reused in production to minimize negative impacts on the environment, adding further complexity to decision making across a supply chain. To deal with the above issues, this thesis proposes and develops the mathematical models and solution methods for coordinating the production inventory system in a complex manufacturing supply chain involving reverse logistics and multiple products. The supply chain consists of tier-2 suppliers for raw materials, tier-1 suppliers for parts, a manufacturer who manufactures and assembles parts into finished products, distributors, retailers and a third party who collects the used products and returns usable parts to the system. The models consider a limited contract period among all players, capacity constraints in transportation units and stochastic demand. The solution methods for solving the models are proposed based on decentralized, semi-centralized and centralized decision making processes. Numerical examples are used by adopting data from the literature to demonstrate, test, analyse and discuss the models. The results show that centralised decision making process is the best way to coordinate all players in the supply chain which minimise total cost of the supply chain as a whole. The results also show that the selection of the length of limited horizon/ contract period will be one of the main factors which will determine the type of coordination (decentralised, centralised or semi-centralised) among all players in the supply chain. We also found that the models developed can be viewed as generalised models for multi-level supply chain by examining the models using systems of different tiers from the literature. We conclude that the models are insensitive to changes of input parameters since percentage changes of the supply chain’s total cost are less than percentage changes of input parameters for the scenarios studied.

The American Institute for Manufacturing Integrated Photonics (AIM Photonics) is focused on developing an end- to- end integrated photonics ecosystem in the U.S., including domestic foundry access, integrated design tools, automated packaging, assembly and test, and workforce development. This paper describes how the institute has been structured to achieve these goals, with an emphasis on advancing the integrated photonics ecosystem. Additionally, it briefly highlights several of the technological development targets that have been identified to provide enabling advances in the manufacture and application of integrated photonics.

My overarching modeling goal for my dissertation is to maximize generalization â some function of data and knowledge â from one sample, with its observations drawn independently from the distribution D, to another sample drawn from D, while also obtaining interpretable insights from the models. The processes of collecting relevant data and generating features from the raw data impart substantive knowledge into predictive models (and the model representation and optimization algorithms applied to those features contain methodological knowledge). I combine this knowledge with the data to train predictive models to deliver generalizability, and then investigate the implications of those models with simulations systematically exploring parameter spaces. The exploration of parameter space provides insights about the relationships between key variables. Chapter 2 describes a method to generate descriptive models of strategic decision-making. I use an efficient representation of repeated game strategies with state matrices and a genetic algorithm-based estimation process to learn these models from data. This combination of representation and optimization is effective for modeling decision-making with experimental game data and observational international relations data. Chapter 3 demonstrates that models can deliver high levels of generalizability with accurate out-of-sample predictions and interpretable scores of variable importance that can guide future behavioral research. I combine behavioral-game-theory-inspired feature design with data to train predictive models to deliver generalizability, and then investigate interactive implications of those models with optimization and sensitivity analyses. Chapter 4 presents a computational model as a test-bed for designing climate prediction markets. I simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and the most plausible hypothesis advanced by prominent skeptics. Then I conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect tradersâ beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the âtrueâ climate model.