BWR fuel cycle optimization using neural networks

In nuclear fuel management activities for BWRs, four combinatorial optimization problems are solved: fuel lattice design, axial fuel bundle design, fuel reload design and control rod patterns design. Traditionally, these problems have been solved in separated ways due to their complexity and the required computational resources. In the specialized literature there are some attempts to solve fuel reloads and control rod patterns design or fuel lattice and axial fuel bundle design in a coupled way. In this paper, the system OCONN to solve all of these problems in a coupled way is shown. This system is based on an artificial recurrent neural network to find the best combination of partial solutions to each problem, in order to maximize a global objective function. The new system works with a fuel lattices’ stock, a fuel reloads’ stock and a control rod patterns’ stock, previously obtained with different heuristic techniques. The system was tested to design an equilibrium cycle with a cycle length of 18 months. Results show that the new system is able to find good combinations. Cycle length is reached and safety parameters are fulfilled.

► OCONN a new system to optimize all nuclear fuel management steps in a coupled way.
► OCON is based on an artificial recurrent neural network to find the best combination of partial solutions to each fuel management step.
► OCONN works with a fuel lattices’ stock, a fuel reloads’ stock and a control rod patterns’ stock, previously obtained with different heuristic techniques.
► Results show OCONN is able to find good combinations according the global objective function.