Nuclear Maintenance Scheduling Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The maintenance operations in Pressurized Water Reactor (PWR) power plants, including unloading, permutation, and reloading of fuel assemblies, are complex and often planned without precise scheduling, leading to lengthy reactor shutdowns and high costs due to the numerous constraints and uncertainties in identifying the best schedule among numerous possible combinations.
Innovation Solution
A method is introduced that involves planning and optimizing the movement of fuel assemblies and clusters using computer means, where each assembly is assigned a destination location, and cluster transfers are identified and optimized in a chronological order, with parameters modified iteratively to reduce the estimated time of transfers, allowing for adaptable planning and real-time optimization to minimize shutdown duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If precise planning and optimization of maintenance operations is implemented, then the reactor shutdown duration is reduced, but the complexity of the planning process increases
Solution Approach 1:
The patent applies preliminary action by performing comprehensive planning and optimization of maintenance operations before the reactor shutdown begins. The system identifies all assembly movements, cluster transfers, and robot operations in advance, creating a detailed chronological schedule that accounts for all constraints. This pre-planning approach allows the complex optimization to be completed while the reactor is still operational, so that during the actual shutdown, only the pre-determined operations need to be executed, thereby reducing the shutdown duration without adding complexity during the critical maintenance period.
2Reliability
If the number of iterations for optimization is increased, then the quality of the maintenance schedule improves, but the planning time increases
Solution Approach 1:
The patent applies dynamics by making the number of optimization iterations adaptive rather than fixed. The system dynamically adjusts the optimization process based on available time resources and constraint complexity. If time is abundant, more iterations can be performed to achieve higher optimization quality. If time is constrained, the system reduces iterations to meet deadlines. This dynamic approach allows the same planning system to deliver high-quality schedules when resources permit while still providing acceptable schedules under time pressure, resolving the contradiction between optimization quality and planning time.
3Adaptability or versatility
If constraints such as assembly identification are known late, then the flexibility to adapt to actual conditions is improved, but the time available for planning is reduced
Solution Approach 1:
The patent applies preliminary action by creating a flexible planning framework that can accommodate late-known constraints. The system pre-establishes the planning structure, robot configurations, and operational sequences, but maintains the ability to incorporate assembly identification and other constraints that become known closer to the shutdown time. The optimization algorithm is designed to efficiently integrate newly known constraints without requiring complete replanning, thus maintaining adaptability while minimizing the time penalty for incorporating late information.
Data Source
Figure 1
Figure 2
Figure 3~5
AI summary
A method for carrying out maintenance in a nuclear power station, comprising: - scheduling movements of assembly elements, by attributing, to each assembly, a destination location, by identifying all of the transfers of clusters to be carried out and by establishing a chronological order in which to carry out said transfers; - optimising the schedule with IT means by estimating a time required to carry out the transfers of clusters, by modifying certain at least of the parameters of the schedule, by estimating a time required to carry out the transfers of clusters with the parameters thus modified, by retaining the parameters thus modified if the calculated time estimation is lower than that estimated beforehand and by reiterating subsequently the preceding operations at least once; and - moving the assembly elements depending on the result of the optimisation.