Metallic radionuclides in irradiated graphite waste are extremely hazardous to the biosphere. In consideration of its simple process, the chlorination roasting could be a promising option for the decontamination of metallic radionuclides from irradiated graphite waste. A novel numerical model for the removal of metallic radionuclides in irradiated graphite via a bubbling fluidized bed reactor was built up to evaluate the decontamination performance of chlorination roasting in large-scale treatment. The gas-solid flow was coupled with the reaction kinetics determined by experimental results and quantum chemistry calculation. A bubbling fluidized bed reactor with treatment capacity of 36–360 kg h−1 was designed, which was in terms of the effects of temperature, carrier gas velocity, HCl content and feed rate on the decontamination efficiency (DE). Temperature and HCl content had significant effects on DE. DE increased from 12 % to 100 % with rising temperature from 1173 to 1573 K. DE grew from 62 % to 99 % with increasing HCl content from 10 % to 20 %. Machine learning model was applied to directly determine the decontamination performance of the fluidized bed reactor, which provided reliable assessment in the chlorination roasting process. This study would be beneficial to large-scale treatment of metallic radionuclides in irradiated graphite via a fluidized bed reactor.
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