A multiobjective genetic algorithm approach to the optimization of the technical specifications of a nuclear safety system

Systems, structures, and components of Nuclear Power Plants are subject to Technical Specifications (TSs) that establish operational limitations and maintenance and test requirements with the objective of keeping the risk associated to the plant within the limits imposed by the regulatory agencies. Recently, in an effort to improve the competitiveness of nuclear energy in a deregulated market, modifications to maintenance policies and TSs are being considered within a risk-informed viewpoint, which judges the effectiveness of a TS, e.g. a particular maintenance policy, with respect to its implications on the safety and economics of the system operation. In this regard, a recent policy statement of the US Nuclear Regulatory Commission declares appropriate the use of Probabilistic Risk Assessment models to evaluate the effects on the system of a particular TS. These models rely on a set of parameters at the component level (failure rates, repair rates, frequencies of failure on demand, human error rates, inspection durations, and others) whose values are typically affected by uncertainties. Thus, the estimate of the system performance parameters corresponding to a given TS value must be supported by some measure of the associated uncertainty. In this paper we propose an approach, based on the effective coupling of genetic algorithms and Monte Carlo simulation, for the multiobjective optimization of the TSs of nuclear safety systems. The method transparently and explicitly accounts for the uncertainties in the model parameters by attempting to minimize both the expected value of the system unavailability and its associated variance. The costs of the alternative TSs solutions are included as constraints in the optimization. An application to the Reactor Protection Instrumentation System of a Pressurized Water Reactor is demonstrated.