Vicente Serradell

Age-dependent reliability model considering effects of maintenance and working conditions

Abstract Nowadays, there is some doubt about building new nuclear power plants (NPPs). Instead, there is a growing interest in analyzing the possibility to extend current NPP operation, where life management programs play an important role. The evolution of the NPP safety depends on the evolution of the reliability of its safety components, which, in turn, is a function of their age along the NPP operational life. In this paper, a new age-dependent reliability model is presented, which includes parameters related to surveillance and maintenance effectiveness and working conditions of the equipment, both environmental and operational. This model may be used to support NPP life management and life extension programs, by improving or optimizing surveillance and maintenance tasks using risk and cost models based on such an age-dependent reliability model. The results of the sensitivity study in the example application show that the selection of the most appropriate maintenance strategy would directly depend on the previous parameters. Then, very important differences are expected to appear under certain circumstances, particularly, in comparison with other models that do not consider maintenance effectiveness and working conditions simultaneously.

Constrained optimization of test intervals using a steady-state genetic algorithm

Abstract There is a growing interest from both the regulatory authorities and the nuclear industry to stimulate the use of Probabilistic Risk Analysis (PRA) for risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, special attention is being paid on analyzing plant-specific changes to Test Intervals (TIs) within the Technical Specifications (TSs) of NPPs and it seems to be a consensus on the need of making these requirements more risk-effective and less costly. Resource versus risk-control effectiveness principles formally enters in optimization problems. This paper presents an approach for using the PRA models in conducting the constrained optimization of TIs based on a steady-state genetic algorithm (SSGA) where the cost or the burden is to be minimized while the risk or performance is constrained to be at a given level, or vice versa. The paper encompasses first with the problem formulation, where the objective function and constraints that apply in the constrained optimization of TIs based on risk and cost models at system level are derived. Next, the foundation of the optimizer is given, which is derived by customizing a SSGA in order to allow optimizing TIs under constraints. Also, a case study is performed using this approach, which shows the benefits of adopting both PRA models and genetic algorithms, in particular for the constrained optimization of TIs, although it is also expected a great benefit of using this approach to solve other engineering optimization problems. However, care must be taken in using genetic algorithms in constrained optimization problems as it is concluded in this paper.

Genetic algorithms in optimizing surveillance and maintenance of components

Abstract Nowadays the great ability of genetic algorithms (GA) to find solutions in complex optimization problems is known, where other methods used to give poor results. This has opened a wide range of application areas using GA and here we present a new approach aimed at the global and constrained optimization of surveillance and maintenance (S&M) of components based on risk and cost criteria. Also, a case study is performed using this approach which shows the benefits of the integration of S&M tasks of components based on optimized intervals. Moreover, this methodology is completely valid in solving other optimization problems with respect to risk and cost beyond the component level.

RAMS+C informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms

Abstract The role of technical specifications and maintenance (TSM) activities at nuclear power plants (NPP) aims to increase reliability, availability and maintainability (RAM) of Safety-Related Equipment, which, in turn, must yield to an improved level of plant safety. However, more resources (e.g. costs, task force, etc.) have to be assigned in above areas to achieve better scores in reliability, availability, maintainability and safety (RAMS). Current situation at NPP shows different programs implemented at the plant that aim to the improvement of particular TSM-related parameters where the decision-making process is based on the assessment of the impact of the change proposed on a subgroup of RAMS+C attributes. This paper briefly reviews the role of TSM and two main groups of improvement programs at NPP, which suggest the convenience of considering the approach proposed in this paper for the Integrated Multi-Criteria Decision-Making on changes to TSM-related parameters based on RAMS+C criteria as a whole, as it can be seem as a decision-making process more consistent with the role and synergic effects of TSM and the objectives and goals of current improvement programs at NPP. The case of application to the Emergency Diesel Generator system demonstrates the viability and significance of the proposed approach for the Multi-objective Optimization of TSM-related parameters using a Genetic Algorithm.

Alternatives and challenges in optimizing industrial safety using genetic algorithms

Safety (S) improvement of industrial installations leans on the optimal allocation of designs that use more reliable equipment and testing and maintenance activities to assure a high level of reliability, availability and maintainability (RAM) for their safety-related systems. However, this also requires assigning a certain amount of resources (C) that are usually limited. Therefore, the decision-maker in this context faces in general a multiple-objective optimization problem (MOP) based on RAMS+C criteria where the parameters of design, testing and maintenance act as decision variables. Solutions to the MOP can be obtained by solving the problem directly, or by transforming it into several single-objective problems. A general framework for such MOP based on RAMS+C criteria is proposed in this paper. Then, problem formulation and fundamentals of two major groups of resolution alternatives are presented. Next, both alternatives are implemented in this paper using genetic algorithms (GAs), named single-objective GA and multi-objective GA, respectively, which are then used in the case of application to solve the problem of testing and maintenance optimization based on unavailability and cost criteria. The results show the capabilities and limitations of both approaches. Based on them, future challenges are identified in this field and guidelines provided for further research.

Analysis of the influence of germanium dead layer on detector calibration simulation for environmental radioactive samples using the Monte Carlo method

Germanium crystals have a dead layer that causes a decrease in efficiency, since the layer is not useful for detection, but strongly attenuates photons. The thickness of this inactive layer is not well known due to the existence of a transition zone where photons are increasingly absorbed. Therefore, using data provided by manufacturers in the detector simulation model, some strong discrepancies appear between calculated and measured efficiencies. The Monte Carlo method is applied to simulate the calibration of a HP Ge detector in order to determine the total inactive germanium layer thickness and the active volume that are needed in order to obtain the minimum discrepancy between estimated and experimental efficiency. Calculations and measurements were performed for all of the radionuclides included in a standard calibration gamma cocktail solution. A Marinelli beaker was considered for this analysis, as it is one of the most commonly used sample container for environmental radioactivity measurements. Results indicated that a good agreement between calculated and measured efficiencies is obtained using a value for the inactive germanium layer thickness equal to approximately twice the value provided by the detector manufacturer. For all energy peaks included in the calibration, the best agreement with experimental efficiency was found using a combination of a small thickness of the inactive germanium layer and a small detection active volume.

Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

Abstract One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS&M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS&M-related parameters in a plant are associated with controlling risk or with satisfying requirements, and are candidate to be evaluated for their resource effectiveness in risk-informed applications. The resource versus risk-control effectiveness principles formally enter in optimization problems where the cost or the burden for the plant staff is to be minimized while the risk or the availability of the safety equipment is constrained to be at a given level, and vice versa. Optimization of TS&M has been found interesting from the very beginning. However, the resolution of such a kind of optimization problem has been limited to focus on only individual TS&M-related parameters (STI, AOT, PM frequency, etc.) and/or adopting an individual optimization criterion (availability, costs, plant risks, etc.). Nevertheless, a number of reasons exist (e.g. interaction, similar scope, etc.) that justify the growing interest in the last years to focus on the simultaneous and multi-criteria optimization of TS&M. In the simultaneous optimization of TS&M-related parameters based on risk (or unavailability) and cost, like in many other engineering optimization problems, one normally faces multi-modal and non-linear objective functions and a variety of both linear and non-linear constraints. Genetic algorithms (GAs) have proved their capability to solve these kinds of problems, although GAs are essentially unconstrained optimization techniques that require adaptation for the intended constrained optimization, where TS&M-related parameters act as the decision variables. This paper encompasses, in Section 2 , the problem formulation where the objective function is derived and constraints that apply in the simultaneous and multi-criteria optimization of TS&M activities based on risk and cost functions at system level. Fundamentals of a steady-state GA (SSGA) as an optimization method is given in Section 3 , which satisfies the above requirements, paying special attention to its use in constrained optimization problems. A simple case of application is provided in Section 4 , focussing on TS&M-related parameters optimization for a stand-by safety-related system, which demonstrates how the SSGA-based optimization approach works at the system level, providing practical and complete alternatives beyond only mathematical solutions to a particular parameter. Finally, Section 5 presents our conclusions.

The use of maintenance indicators to evaluate the effects of maintenance programs on NPP performance and safety

Abstract Nuclear Power Plants (NPPs) under commercial operation are under continuous demand to meet higher levels of performance and safety by NPP owners, regulatory authorities and the public in general. Maintenance plays an important role in achieving such a goal, therefore, many programs are being conducted in order to improve their effectiveness. A common link between these programs is the necessity to evaluate how maintenance affects NPP performance and safety. This paper presents the foundation of a methodology for a maintenance evaluation program based on maintenance indicators and how it is applied to monitoring the effectiveness of the maintenance at the Cofrentes NPP. The methodology is general and might be applied in other fields of industrial engineering, particularly in those activities which devote many resources to maintain plant equipment, and also in those with less but very critical maintenance.

Improving allowed outage time and surveillance test interval requirements: a study of their interactions using probabilistic methods

Technical Specifications (TS) define the limits and conditions for operating nuclear plants safely. We selected the Limiting Conditions for Operations (LCO) and Surveillance Requirements (SR), both within TS, as the main items to be evaluated using probabilistic methods. In particular, we focused on the Allowed Outage Time (AOT) and Surveillance Test Interval (STI) requirements in LCO and SR, respectively. Already, significant operating and design experience has accumulated revealing several problems which require modifications in some TS rules. Developments in Probabilistic Safety Assessment (PSA) allow the evaluation of effects due to such modifications in AOT and STI from a risk point of view. Thus, some changes have already been adopted in some plants. However, the combined effect of several changes in AOT and STI, i.e. through their interactions, is not addressed. This paper presents a methodology which encompasses, along with the definition of AOT and STI interactions, the quantification of interactions in terms of risk using PSA methods, an approach for evaluating simultaneous AOT and STI modifications, and an assessment of strategies for giving flexibility to plant operation through simultaneous changes on AOT and STI using trade-off-based risk criteria.

Comparing effectiveness and efficiency in technical specifications and maintenance optimization

Abstract Optimization of technical specification requirements and maintenance (TS&M) has been found interesting from the very beginning at Nuclear Power Plants (NPPs). However, the resolution of such a kind of optimization problem has been limited often to focus only on individual TS&M-related parameters (STI, AOT, PM frequency, etc.) and/or adopting an individual optimization criterion (availability, costs, plant risks, etc.). Nevertheless, a number of reasons exist (e.g. interaction, similar scope, etc.) that justify the interest to focus on the coordinated optimization of all of the relevant TS&M-related parameters based on multiple criteria. The purpose of this paper is on signifying benefits and improvement areas in performing the coordinated optimization of TS&M through reviewing the effectiveness and efficiency of common strategies for optimizing TS&M at system level. A case of application is provided for a stand-by safety-related system to demonstrate the basic procedure and to extract a number of conclusions and recommendations from the results achieved. Thus, it is concluded that the optimized values depend on the particular TS&M-related parameters being involved and the solutions with the largest benefit (minimum risk or minimum cost) are achieved when considering the simultaneous optimization of all of them, although increased computational resources are also required. Consequently, it is necessary to analyze not only the value reached but also the performance of the optimization procedure through effectiveness and efficiency measures which lead to recommendations on potential improvement areas.