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Project Matador joins EIS pilot program; NRC seeks public input
The Nuclear Regulatory Commission has released a notice of intent to conduct a scoping process and prepare an environmental impact statement to evaluate Fermi America’s plan to construct and operate four AP1000 reactors at its Project Matador Advanced Energy and Intelligence Campus in Texas.
While that announcement may seem routine, the process envisioned is not. As part of the company’s combined license (COL) application with the NRC, it has agreed to participate in an accelerated environmental review pilot program under the National Environmental Policy Act (NEPA). Under this pilot, the applicant(s) develop a draft EIS under NRC supervision.
Ion Munteanu, Tunc Aldemir
Nuclear Technology | Volume 144 | Number 1 | October 2003 | Pages 49-62
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT03-A3428
Articles are hosted by Taylor and Francis Online.
While techniques have been developed to tackle different tasks in accident management, there have been very few attempts to develop an on-line operator assistance tool for accident management and none that can be found in the literature that uses probabilistic arguments, which are important in today's licensing climate. The state/parameter estimation capability of the dynamic system doctor (DSD) approach is combined with the dynamic event-tree generation capability of the integrated safety assessment (ISA) methodology to address this issue. The DSD uses the cell-to-cell mapping technique for system representation that models the system evolution in terms of probability of transitions in time between sets of user-defined parameter/state variable magnitude intervals (cells) within a user-specified time interval (e.g., data sampling interval). The cell-to-cell transition probabilities are obtained from the given system model. The ISA follows the system dynamics in tree form and braches every time a setpoint for system/operator intervention is exceeded. The combined approach (a) can automatically account for uncertainties in the monitored system state, inputs, and modeling uncertainties through the appropriate choice of the cells, as well as providing a probabilistic measure to rank the likelihood of possible system states in view of these uncertainties; (b) allows flexibility in system representation; (c) yields the lower and upper bounds on the estimated values of state variables/parameters as well as their expected values; and (d) leads to fewer branchings in the dynamic event-tree generation. Using a simple but realistic pressurizer model, the potential use of the DSD-ISA methodology for on-line probabilistic accident management is illustrated.