Machine learning sintering density prediction model for MOX fuel pellet

Tsuchimochi, Ryota; Kato, Masato   ; Nakajima, Tatsuya; Hirooka, Shun ; Watanabe, Masashi  ; Nakamichi, Shinya  ; Murakami, Tatsutoshi ; Ishii, Katsunori 

Uranium and Plutonium mixed oxide (MOX) pellets used as fast reactor fuels have been produced from several raw materials by mechanical blending method. It is essential to control the pellet density which is one of the important fuel specifications, but it is difficult to understand relationships among many parameters in the production. Database for MOX production was prepared from production results of JOYO and MONJU. The input data of eighteen types were chosen from production process and made a data set. A machine learning model for predicting the sintered density of MOX pellets was derived by gradient boosting regressor.