MOX燃料ペレットの機械学習焼結密度予測モデル

Machine learning sintering density prediction model for MOX fuel pellet

土持 亮太; 加藤 正人   ; 中嶋 竜矢; 廣岡 瞬 ; 渡部 雅  ; 中道 晋哉  ; 村上 龍敏 ; 石井 克典 

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

MOX燃料の製造は、異なる特性を有する複数の原料粉末を用いて機会混合法により行われるが、工程が多く、MOX特有の性質により独特の困難さがある。本研究では、これまでの製造データから、原料粉末の種類、製造条件及び焼結密度の関係を機械学習させることによって、MOX燃料の機械学習焼結密度予測モデルを導出した。

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.