PhD Dissertations - DO NOT EDIT
http://hdl.handle.net/10106/25177
2024-03-28T22:14:23ZTopics in Optimization for Sustainable Energy Planning
http://hdl.handle.net/10106/31799
Topics in Optimization for Sustainable Energy Planning
**Please note that the full text is embargoed until 8/1/2025** ABSTRACT: Due to the current trend of rising energy demand, finding alternate energy sources is vital. Many cities consider renewable energy as a component of a sustainable future. Typically, organic wastes are considered renewable energy sources. Wastes can be converted to proper energy forms using waste-to-energy technologies. On the other hand, more renewable energy sources in the power system may increase energy market stochasticity, alter system operation, and pose new problems for the current supply and demand equilibrium, which consequently requires new control methods. Hence, taking into account the issues mentioned above, in this research, we address organic waste conversion to renewable energy and demand response planning for supply-demand balance management. In this research, we present a mixed integer program with a quadratic cost term (MIQP) to optimize the location of anaerobic digestion facilities, the construction of new digesters, and the transportation of organic waste to these facilities. Moreover, this study introduces a bi-level demand response optimization problem using the concepts of approximate dynamic programming. The main objective of this problem is to reduce DR operational costs in the residential sector.
2023-08-24T00:00:00ZINCREASING THE SAFETY OF BICYCLISTS USING A CYCLIST BEHAVIOR QUESTIONNAIRE AND A SMARTPHONE BASED APPLICATION
http://hdl.handle.net/10106/31796
INCREASING THE SAFETY OF BICYCLISTS USING A CYCLIST BEHAVIOR QUESTIONNAIRE AND A SMARTPHONE BASED APPLICATION
**Please note that the full text is embargoed until 8/1/2025** ABSTRACT: Bicycling is beneficial for health, the environment, road users’ flexibility, and personal expenses. Compared to motor vehicles, they are an active mode of transport, cause minimum
pollution, are affordable, and can easily navigate through the increasing traffic all over the world. This increase in traffic, however, also increases the possibility of crashes with motor vehicles. Bicyclists, being more exposed to traffic than drivers, suffer fatal consequences from a crash. Therefore, a standard tool is required to understand bicyclist behavior on the road. This tool can provide insights into bicyclists’ behavior so that appropriate infrastructure or policy changes can be implemented. Furthermore, affordable technology can be utilized to assist bicyclists by alerting them of imminent danger ahead of time. The objectives of this research are to 1) develop and validate a Cyclist Behavior Questionnaire (CBQ) for the US population and 2) identify an effective warning system for a smartphone-based app to alert bicyclists. To accomplish the first objective, a CBQ was developed and administered online. A Principal Component Analysis (PCA) determined the 11-item 4 factorial structure of CBQ, which was later verified using a Confirmatory Factor Analysis (CFA). An innovative methodology was developed and implemented to validate self-reported responses of CBQ with bicyclists’ actual responses from a bike-simulator study. For the second objective, a focus group study with experts was conducted. Experts identified a list of potential warning signals including red/yellow flashing signals, and tone/speech audible signals. A bike-simulator experiment further investigated the efficacy of these signals under different environmental factors. The results were analyzed using cyclists’ response to the warnings, as well as their physiological and emotional reaction. Results identified a multimodal combination of red visual and tone audible warning to be the most efficient at alerting cyclists. The findings of these studies will improve the understanding of bicyclists’ behavior and their interaction with technologies while riding. Future research should focus on how the adoption of these technologies would affect bicycling skills and behavior (for example, situation awareness).
2023-08-23T00:00:00ZAPPLICATIONS OF PROBABILITY OF SUCCESS IN THE WELL DELIVERY PROCESS TO IMPROVE RISK, OPPORTUNITY, AND COST ASSURANCE
http://hdl.handle.net/10106/31757
APPLICATIONS OF PROBABILITY OF SUCCESS IN THE WELL DELIVERY PROCESS TO IMPROVE RISK, OPPORTUNITY, AND COST ASSURANCE
**Please note that the full text is embargoed until 08/01/2024** Oil and Gas (O&G) well drilling is risky and expensive. The cost of drilling is typically underestimated, and there is little understanding of the certainty or Probability of Success (POS) of achieving the well objectives within the estimated cost. This dissertation presents, for the first time, a publicly available POS Cost method/tool that enables O&G operators to estimate the cost of well drilling substantially more accurately and improve the POS of accomplishing the drilling for the estimated cost. This POS Cost method employs a comprehensive, expert-based assessment of risks and risk mitigations that are incorporated into a Monte-Carlo-based simulation of the well drilling process. Results for applying the POS Cost method/tool to a sample well drilling operation demonstrated that, without employing a risk assessment/risk mitigation approach, the POS Cost of the sample well was roughly 40%, but with the risk assessment/risk mitigation included the POS could be increased to over 80% for an associated cost of roughly 4% for the risk mitigation. Many O&G operators would gladly trade that additional cost for risk mitigation to obtain an 80% POS or greater for accomplishing the well for the estimated cost. This enhances the assurance for the Approval of Expenditures (AFE) and increases the likelihood of successfully achieving the objectives of the well while accounting for time efficiencies due to the incorporations of the mitigation strategies. The POS Cost Tool can also be used to create and evaluate best value options for problem-solving during the drilling process to improve POS for the remainder of the operation.
2023-07-06T00:00:00ZTowards Sustainable Additive Manufacturing: Assessment of Cost, Greenhouse Gas Emission, and Recyclability
http://hdl.handle.net/10106/31716
Towards Sustainable Additive Manufacturing: Assessment of Cost, Greenhouse Gas Emission, and Recyclability
Additive Manufacturing technologies fabricate 3D objects layer by layer following a predesigned CAD model. Owing to the unique layer-wise production method, additive manufacturing offers competitive advantages in comparison with traditional subtractive manufacturing, such as shortened production time, increased design freedom, improved manufacturing capability and complexity, and reduced manufacturing waste. Numerous research studies have been conducted to design, understand, and improve additive manufacturing technologies in order to facilitate the implementation in the supply chain. On the other hand, with the rapid growth of additive manufacturing, sustainability issues that exist on both process level and supply chain level have started to receive increasing public interest. The current literature on additive manufacturing sustainability is mostly focused on process energy consumption, emission, and cost evaluation, towards establishing the life cycle inventory for additive manufacturing and life cycle assessment. Research questions on how to evaluate the recyclability of additive manufacturing waste and how to evaluate the feasibility of additive manufacturing implementation in the supply chain in terms of cost and greenhouse gas emission have not yet been investigated. A comprehensive understanding of material recyclability and additive manufacturing supply chain performance is critical to evaluate the feasibility of large-scale implementation of additive manufacturing towards the circular economy.
To fill the knowledge gaps mentioned above, this Ph.D. dissertation aims to establish mathematical models to quantify the cost and greenhouse gas emission of additive manufacturing supply chains and improve performance through supply chain structure innovation and delivery route optimization. Case study results suggest that the overall supply chain cost can be reduced by up to 25.75% and the greenhouse gas emission can be reduced by up to 26.43% when using additive manufacturing in the supply chain. Results also indicate the potential to achieve same-day delivery with less than $20 per order and over 90% delivery rate in 3 hours by properly coordinating the visiting/fabricating sequence. In addition, this Ph.D. dissertation also aims to develop a framework to evaluate the recyclability of additive manufacturing thermoplastic wastes, embedded with quantified tools to investigate the process parameters and their impact on material recyclability in terms of fabrication quality, mechanical properties, and molecular weight distributions. Experimental results indicate that ultimate tensile strength degradation after each round of recycling varies from 27% to 50%, the surface roughness increases by 29.54% after three rounds of recycling, and molecular weight distribution of recycled material demonstrates an obvious shift in each round of recycling.
The results generated from this Ph.D. dissertation will help additive manufacturing designers, manufacturers, and users better understand the waste recycling process and design/optimization of additive manufacturing supply chain. The ultimate goal of this research is to facilitate the large-scale implementation of additive manufacturing while achieving circular economy and enabling sustainable additive manufacturing.
2023-08-11T00:00:00Z