Cláudio M.N.A. Pereira

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

Abstract This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation. Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.

Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem

Abstract This work extends the research related to genetic algorithms (GA) in core design optimization problems, which basic investigations were presented in previous work. Here we explore the use of the Island Genetic Algorithm (IGA), a coarse-grained parallel GA model, comparing its performance to that obtained by the application of a traditional non-parallel GA. The optimization problem consists on adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Our IGA implementation runs as a distributed application on a conventional local area network (LAN), avoiding the use of expensive parallel computers or architectures. After exhaustive experiments, taking more than 1500 h in 550 MHz personal computers, we have observed that the IGA provided gains not only in terms of computational time, but also in the optimization outcome. Besides, we have also realized that, for such kind of problem, which fitness evaluation is itself time consuming, the time overhead in the IGA, due to the communication in LANs, is practically imperceptible, leading to the conclusion that the use of expensive parallel computers or architecture can be avoided.

The fuzzy clearing approach for a niching genetic algorithm applied to a nuclear reactor core design optimization problem

Abstract This article extends previous efforts on genetic algorithms (GAs) applied to a core design optimization problem. We introduce the application of a new Niching Genetic Algorithm (NGA) to this problem and compare its performance to these previous works. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a three-enrichment zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. After exhaustive experiments we observed that our new niching method performs better than the conventional GA due to a greater exploration of the search space.

A niching genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization

This article extends previous efforts on genetic algorithms (GAs) applied to a nuclear power plant (NPP) auxiliary feedwater system (AFWS) surveillance tests policy optimization. We introduce the application of a niching genetic algorithm (NGA) to this problem and compare its performance to previous results. The NGA maintains a populational diversity during the search process, thus promoting a greater exploration of the search space. The optimization problem consists in maximizing the system’s average availability for a given period of time, considering realistic features such as: (i) aging effects on standby components during the tests; (ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; (iii) components have distinct test parameters (outage time, aging factors, etc.) and (iv) tests are not necessarily periodic. We find that the NGA performs better than the conventional GA and the island GA due to a greater exploration of the search space.

Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints

Abstract In order to maximize systems average availability during a given period of time, it has recently been developed a non-periodic surveillance test optimization methodology based on genetic algorithms (GA). The fact of allowing non-periodic tests turns the solution space much more flexible and schedules can be better adjusted, providing gains in the overall system average availability, when compared to those obtained by an optimized periodic test scheme. This approach, however, turns the optimization problem more complex. Hence, the use of a powerful optimization technique, such as GA, is required. Considering that some particular features of certain systems can turn it advisable to introduce other specific constraints in the optimization problem, this work investigates the application of seasonal constraints for the set of the Emergency Diesel Generation of a typical four-loop pressurized water reactor in order to planning and optimizing its surveillance test policy. In this analysis, the growth of the blackout accident probability during summer, due to electrical power demand increases, was considered. Here, the used model penalizes surveillance test interventions when the blackout probability is higher. Results demonstrate the ability of the method in adapting the surveillance test policy to seasonal constraints. The knowledge acquired by the GA during the searching process has lead to test schedules that drastically minimize test interventions at periods of high blackout probability. It is compensated by more frequent redistributed tests through the periods of low blackout probability in order to improve on the overall average availability at the system level.

An application of genetic algorithms to surveillance test optimization of a PWR auxiliary feedwater system

Nuclear power plant systems are comprised of both on‐line and standby components. Standby components differ from on‐line ones, as they might be unavailable due to unrevealed failures. The usual procedure employed to reveal failures before real demands is to submit the component to surveillance tests. Surveillance test policies might deal with two conflicting scenarios: the test frequency must be sufficiently high in order to reveal failures before demands, but, on the other hand, it must be low enough due to its influence on the component unavailability.

Genetic algorithms applied to turbine extraction optimization of a pressurized-water reactor

In this work, we propose the use of a genetic algorithm (GA) for the determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines of a typical pressurized-water reactor (PWR) secondary side, in order to increase cycle efficiency. Here, we show some preliminary results obtained in a case study in which the PEPSE® system was used as simulation tool.

PWR's xenon oscillation control through a fuzzy expert system automatically designed by means of genetic programming

This work proposes the use of genetic programming (GP) for automatic design of a fuzzy expert system aimed to provide the control of axial xenon oscillations in pressurized water reactors (PWRs). The control methodology is based on three axial offsets of xenon (AOx), iodine (AOi) and neutron flux (AOf), effectively used in former work. Simulations were made using a two-point xenon oscillation model, which employs the non-linear xenon and iodine balance equations and the one group, one-dimensional neutron diffusion equation, with non-linear power reactivity feedback, also proposed in the literature. Results have demonstrated the ability of the GP in finding a good fuzzy strategy, which can effectively control the axial xenon oscillations.

Preventive Maintenance Policy Optimization of a Nuclear Reactor High Pressure Injection System Using a Reliability-Cost Model

Previous works have demonstrated that Genetic Algorithms (GA) and Probabilistic Safety Analysis (PSA) can be applied to maintenance policy optimization problems. Here, the flexible scheduling approach proposed in former works has been enhanced. Now, operational costs are considered in the objective function, resulting in a Reliability-Cost model for evaluating industrial systems performance. The GA searches for optimum preventive maintenance policies, which considers factors, such as: probability of needing a repair, costs of related to the repair, outage times, etc. A typical Pressurized Water Reactor High Pressure Injection System has been chosen as a case study. Results ratify the good performance of the method, allowing specific analysis on the weighting factors related to costs and reliability.