Zhiwei Qin

Spent fuel reprocessing is of great significance to the nuclear fuel cycle and the sustainable development of nuclear energy. At the same time, nuclear security radiation incidents in the spent fuel reprocessing plant are also related to national personal and property safety, which play a pivotal role. In this paper, the spent fuel reprocessing plant is divided into four plant areas: the main process area, the three-waste area, the auxiliary equipment area, and the pre-plant area, which are further subdivided into 12 evaluation units. The expert scoring method is used to score and evaluate the possibility of eight basic nuclear accident types in each area, namely radioactive dispersal device, computer nuclear security, destruction of nuclear facilities, transportation nuclear security, internal threat, potential threat, illegal transfer, and theft. According to the professional titles, length of service, education and other qualifications of experts, different weights are assigned to the experts. The scoring results are applied to the Fault Tree Analysis (FTA) of nuclear security events as the probability of basic events, so as to obtain the risk of each basic event. At the same time, the fuzzy comprehensive evaluation method and probability–mathematical statistics method are used to evaluate each evaluation unit to determine the risk of each evaluation unit and the plant area. There results show that the main process area has the highest risk degree, while the pre-plant area has the lowest risk degree, and there is a 1.5-fold relationship. This research provides theoretical and technical support for the safety management and operation of spent fuel reprocessing plants. The analysis results of this paper can be used as a reference for the proportion of nuclear security protection improvements in each plant area, so as to achieve an efficient safety protection effect. The research method in this paper can be also applicable to other similar places by providing as input the corresponding probability of occurrence to obtain the index of its risk degree, so as to reasonably allocate funds and manpower and reduce risks.

In this paper, we present a calculation method for the radiation response eigenvalue based on a monolithic active pixel sensor. By comparing the statistical eigenvalues of different regions of a pixel array in bright and dark environments, the linear relationship between the statistical eigenvalues obtained by different algorithms and the radiation dose rate was studied. Additionally, a dose rate characterization method based on the analysis of the eigenvalues of the MAPS response signal was proposed. The experimental results show that in the dark background environment, the eigenvalues had a good linear response in the region of any gray value in the range of 10–30. In the color images, due to the difference in the background gray values in adjacent color regions, the radiation response signal in dark regions was confused with the image information in bright regions, resulting in the loss of response signal and affecting the analysis results of the radiation response signal. For the low dose rate radiation field, as the radiation response signal was too weak and there was background dark noise, it was necessary to accumulate frame images to obtain a sufficient response signal. For the intense radiation field, the number of response events in a single image was very high, and only two consecutive frames of image data needed to be accumulated to meet the statistical requirements. The binarization method had a good characterization effect for the radiation at a low dose rate, and the binarization processing and the total gray value statistics of the response data at a high dose rate could better characterize the radiation dose rate. The calibration experiment results show that the binarization processing method can meet the requirements of using a MAPS for wide-range detection.

Herein, we report the γ-ray ionizing radiation response of a commercial monolithic active-pixel sensor (MAPS) camera under strong-dose-rate irradiation with an online detection and monitoring system for strong radiation conditions. We present the first results of the distribution of three types of MAPS camera and establish a linear relationship between the average response signal and radiation dose rate in the strong-dose-rate range. There is an obvious response signal in the video frames when the camera module parameters are set to automatic, but the linear response is very poor. However, the fixed image parameters are not good at adapting to the changes of the environment and affect the quality of the video frames. A dual module online radiation detection and monitoring probe was made to carry out effective video monitoring and radiation detection at the same time. The measurement results show that the dose rate detection error is less than 5% with a dose rate in the range of 60 to 425 Gy/h, and the visible light image does not have obvious distortion, deformation, or color shift due to the interference of the radiation response event and radiation damage. Hence, the system test results show that it can be used for online detection and monitoring in a strong radiation environment.