Browsing by Author "Yaw, Sean"

Now showing 1 - 3 of 3

Designing multi-phased CO2 capture and storage infrastructure deployments
(Elsevier BV, 2022-08) Jones, Erick C.; Yaw, Sean; Bennett, Jeffrey A.; Ogland-Hand, Jonathan D.; Strahan, Cooper; Middleton, Richard S.
CO2 capture and storage (CCS) is a climate change mitigation strategy aimed at reducing the amount of CO vented into the atmosphere by capturing CO emissions from industrial sources, transporting the CO via a dedicated pipeline network, and injecting it into geologic reservoirs. Designing CCS infrastructure is a complex problem requiring concurrent optimization of source selection, reservoir selection, and pipeline routing decisions. Current CCS infrastructure design methods assume that project parameters including costs, capacities, and availability, remain constant throughout the project’s lifespan. In this research, we introduce a novel, multi-phased, CCS infrastructure design model that allows for analysis of more complex scenarios that allow for variations in project parameters across distinct phases. We demonstrate the efficacy of our approach with theoretical analysis and an evaluation using real CCS infrastructure data.
Scalable Algorithms for Designing CO2 Capture and Storage Infrastructure
(Springer Science and Business Media LLC, 2022) Whitman, Caleb; Yaw, Sean; Hoover, Brendan; Middleton, Richard
CO2 capture and storage (CCS) is a climate change mitigation strategy that aims to reduce the amount of CO2 vented into the atmosphere from industrial processes. Designing cost-effective CCS infrastructure is critical in meeting CO2 emission reduction targets and is a computationally challenging problem. We formalize the computational problem of designing cost-effective CCS infrastructure and detail the fundamental intractability of designing CCS infrastructure as problem instances grow in size. We explore the problem’s relationship to the ecosystem of network design problems, and introduce three novel algorithms for its solution. We evaluate our proposed algorithms against existing exact approaches for CCS infrastructure design and find that they all run in dramatically less time than the exact approaches and generate solutions that are very close to optimal. Decreasing the time it takes to determine CCS infrastructure designs will support national-level scenario analysis, undertaking risk and sensitivity assessments, and understanding the impact of government policies (e.g., tax credits for CCS).
Scheduling for optimized network resource utilization #smartgrid #cloud
(Montana State University - Bozeman, College of Engineering, 2017) Yaw, Sean; Chairperson, Graduate Committee: Brendan Mumey
The performance of distributed applications is heavily dependent on the interplay between the applications and the underlying network. Disparity between the requirements of the applications and the capabilities of the network leads to degraded application performance, which in turn results in a drop in application usage or revenue. For example, many real-time interactive applications require lower latency than the public Internet provides, resulting in a poor experience for application users. At other times though, applications fail to effectively utilize all network capabilities. For example, conventional electrical appliances are currently unable to leverage the increased communication capabilities provided by the future smart power grid to decrease costs or modify consumption. Scheduling is an optimization technique to temporally and spatially allocate resources in such a way as to achieve some desired parameter optimization, such as minimized cost. In this dissertation, I study the use of scheduling techniques to counteract application performance degradation present due to the disparity between application requirements and network capabilities. I explore this disparity in both the smart grid and cloud networks, and propose novel algorithms that rely on numerous algorithmic techniques to realize application performance increases.