Optimizing pressurized-water reactor equilibrium cycle using a novel loading pattern encoding and rule-based genetic crossover operators

This work presents an extended multibatch approach applied in a shuffling scheme optimization for the pressurized-water reactor equilibrium cycle using genetic algorithms (GAs). A new ruled-based GA crossover operator called inherited location and batch (ILB) was introduced to enhance offsprings reproduction efficiency specialized for the equilibrium cycle optimization problem. This approach was implemented within the Plant ReLoad Optimization (PRLO) framework and validated using a generic reactor model based on the AP1000 design, with core parameters calculated via the CASMO/SIMULATE software package. The ILB approach is then applied for both single- and multi-objective problems in maximizing cycle length and core average exposure while minimizing the average enrichment of the 57 fresh fuel assemblies per cycle. The optimal solutions are selected based on their dominance from all feasible solutions. This research identified three optimal solutions that satisfied safety constraints: the first minimizes feed enrichment costs with a cycle length of 338.8 days and core exposure of 25.39 MWd/MT, the second extends the cycle length to 361.2 days and core exposure of 26.84 MWd/MT using a 3.75 wt% average fuel enrichment, and the third balances both objectives with a cycle length of 349.6 days and core exposure of 25.82 MWd/MT with a slight enrichment increase compared to the first solution. Collectively, these findings underscore the efficiency and effectiveness of the proposed approach in achieving practical multiobjective optimal equilibrium cycle designs using GAs optimizer.