Application of high-pressure water jets and U-Net based deep learning for the efficient decontamination of radioactive tanks

An automated decontamination and monitoring system was developed by integrating a high-pressure rotating nozzle with a U-Net-based deep learning segmentation model. A high-pressure water jet (400 bar, 28 L/min) was applied inside radioactive tanks, and decontamination efficiency was evaluated by measuring residual Co concentrations using inductively coupled plasma–optical emission spectroscopy (ICP–OES). After four decontamination cycles, the overall residual contamination was reduced to 4.23% ± 8.10%. Specifically, Groups 4 and 5 achieved complete decontamination, while Group 1 retained 19.3%, highlighting the influence of nozzle positioning. The ICP–OES results confirmed that areas appearing visually decontaminated still contained 400–800 mg/kg of Co, thereby supporting the feasibility of image-based assessments. To enable real-time decontamination monitoring, deep learning models were trained to segment contaminated and decontaminated regions. Among the tested models, DeepLabv3 achieved the highest Dice coefficient (0.6988) and F0.5 score (0.7178), offering a balance between segmentation accuracy and false-positive reduction. The proposed system eliminated the need for manual contamination verification with smear paper, thereby reducing radiation exposure and enhancing decontamination efficiency. This study demonstrates that high-pressure jet decontamination, combined with real-time deep learning-based monitoring, offers a safer and more effective approach to radioactive waste management during nuclear decommissioning.