Causal Model Framework for Nuclear Power Plant Licensing Process

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 paper 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. The U.S. Nuclear Regulatory Commission's combined license process is examined in this study as an opportunity to predict 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.