Modeling of changes in the nuclide composition of VVER reactor fuel using artificial neural network

Abstract

The paper seeks to give a computer-based model, using an artificially intelligent technique. This is the adaptive neuro-fuzzy inference system (ANFIS), to predict the concentration changes of certain nuclides in the uranium fuel used for VVER reactors. It uses low-enriched uranium dioxide as a fuel in its solid state. The reactivity in the core is controlled by the control rods. Nuclide concentration changes in the reactor fuel, if not monitored, may cause the unsafe operation of the reactor. Hence, the need for this study. The nuclides considered in this study are, U-235, U-236, U-237, U-238, Pu-239, Pu-240, Pu-241, Am-242 and Am-243. The initial computational technique was performed using MATLAB Simulink. The simulation data for all the concentrations of the nuclides were obtained. Then the proposed ANFIS model was performed and tested using data from the Simulink. Results from the simulink and ANFIS were compared and the results were in good agreement. Again, the results were compared to the Calculating Actinide Inventory (CAIN) code from the IAEA-TECHDOC-1535 published in 2007 and both showed a good agreement. An RMSE of about 0.98% and 1.25% were obtained for training and testing data respectively. The developed model will allow technologists to quickly perform calculations for the reactor, which is essential for safety systems. It could be concluded that the ANFIS model can effectively be used to predict the concentration of each nuclide in the uranium fuel because it is effective, precise with lesser error, and does not consume time.