Date of Award
Doctor of Philosophy (PhD)
Computer Engineering and Sciences
Luis Daniel Otero
Aldo Fabregas Ariza
Interests in clean energy revived the nuclear power industry. For the first time in decades, innovative technologies and plant designs are being considered by regulatory agencies. This dissertation explores a Bayesian Network and AHP approach to causal modeling of the Combined License review process for new nuclear power plants (NPP). Historically lengthy and expensive, NPP licensing is critical to ensuring safe operation of the plants. With this comes a high standard for applicants to reach that can result in multiple revision cycles and long review times. New plant designs and fluctuating public support lead to a complex and dynamic series of codependent factors. By developing a modeling framework for the NPP licensing process, causal relationships are visually communicated to support data driven decisions and risk assessment. As the socioeconomic and regulatory environments change for nuclear power stakeholders, there is a critical need to predict delays in the license review process resulting from changes in causal factors. The U.S. Nuclear Regulatory Commission’s combined license process is examined in this dissertation as an opportunity to understand the causal relationships between factors impacting delays based on project criteria and aid informed decision making. A Bayesian Network combined with Analytic Hierarchy Process inputs is presented to show causality relationships between licensing process steps, plant parameters, and time delays.
Kiser, Lauren Kimberly, "Causal Modeling Framework for Nuclear Power Plant Licensing Process" (2023). Theses and Dissertations. 1261.