The spent fuel safety information delivered from the consignor to the disposal facility operator directly affects the operation and safety of the disposal facility. Therefore, the operator of a disposal facility must perform data quality management to increase data reliability, and anomaly detection is a representative method among quality control methods. We propose a quality control method to detect anomalies using XGBoost, known for its excellent performance, prevention of overfitting, and fast training speed. First, we select significant variables such as release burnup, enrichment, and amount U from the spent fuel safety information and train models for each variable using only normal data. A model trained using only normal data generates a small error for a normal pattern and a large error for an abnormal pattern. Then, when the data error exceeds a set threshold, the data is determined as an anomaly. In this paper, we implement the XGBoost models using virtual spent fuel information and optimize the hyperparameter of XGBoost using a simulated annealing method for high accuracy. The optimized XGBoost models show high accuracy in a normal input and provide a stable prediction value even in an abnormal input. In addition, we perform anomaly detection by including defect input in the data to validate the presented method. The proposed method shows the result of effectively classifying normal values and anomalies.

미임계 시설은 정상 또는 사고상태에서 핵임계안전성이 확보되어야 한다. 이를 위해선 계산된 임계도가 바이어스와 불확실도로 결정된 미임계상한치(USL)를 초과하지 않는다는 것을 검증하는 절차가 반드시 필요하다. 하지만 핵임계안전성 검증 방법론은 여러 가지가 존재하며, 방법론이 달라지면 USL도 달라지므로 가장 적절한 한가지의 방법론으로 평가하는 것이 중요하다. 본 연구에서는 핵임계안전성 검증 방법론이 기술된 두 개의 문서를 비교 분석하여 한 가지 방법론으로 정립하였고, SCALE6.1 코드를 이용한 용기 설계에서의 미임계상한치 결정에 적용하였다.