Treatment And Optimization Of Iodine Contamination By Modified Materials

The action plan for achieving carbon peak before 2030 clearly calls for "active,safe and orderly development of nuclear power",which indicates that China’s nuclear power development will usher in a new period of rapid growth.However,the development of nuclear power has been faced with the problems of proper disposal of radioactive spent fuel and safety assessment of reactor pressure vessel.Radioactive iodine(129I)is a fission product of 239Pu and 235U,and it is volatile during nuclear fuel reprocessing.In solution,iodine is mainly in the form of iodide and iodate.We use modified materials to enhance the adsorption of iodine,and density functional theory(DFT)is used to simulate the reaction mechanism of iodine anion with adsorbent.Meanwhile,machine learning(ML)models are constructed to predict the distribution coefficients(Kd)of radionuclides on buffer materials and rocks and the irradiation embrittlement behavior of reactor pressure vessel.Specific research details and results are as follows:(1)Bentonite and diatomite were modified with bismuth oxide and zirconia respectively.The adsorption properties of the two modified materials for iodine anion were studied by single factor method.The experimental results show that for bismuth oxide modified bentonite,it achieves rapid and efficient adsorption of iodine by forming stable BiOI and BiOIO3 with I-and IO3-,respectively.DFT calculation shows that in the adsorption process of I-,the p orbital of I,the s and p orbitals of Bi are both presented in the hybridization.For IO3-,the adsorption site of O(IO3-)plays a dominant role.For zirconia-modified diatomite,it also performs a good adsorption capacity for IO3-.When pH is greater than 6,the adsorption effect drops sharply and the adsorption capacity is close to zero.This may be because in acidic environments,the protonation of the adsorbent produces more positive charge.The adsorption was consistent with Freundlich isotherm and pseudo-first-order kinetic model.XPS characterization showed that some iodate was reduced to iodide during adsorption.(2)The prediction ability for the adsorption of radionuclides on buffers and rocks were studied by machine learning(ML)methods.Tree-based algorithms(especially AdaBoost,RF and XGBoost)are relatively stable with different input combinations for the adsorption of Cs and Sr.The four input variables,BET,pH,t and Na,have important effects on the adsorption of Cs and Sr by clay minerals.In order to promote the adsorption of radioactive Cs and Sr in the underground repository,the specific surface area of buffer material should be increased and the pH should be kept in an alkaline environment.(3)A reliable irradiation embrittlement model of reactor pressure vessel(RPV)is critical to the safe operation and life extension of nuclear power plants(NPP).In this study,6 machine learning(ML)methods were adopted to accurately predict the irradiation embrittlement behavior(ΔRTNDT)of RPV with comprehensive input factors involving irradiation conditions and metal composition.The GBDT model has the best prediction performance.The calculated result by GBDT model is close to those of JEAC-4201 and ASTM E900-15.Cu content is the most sensitive factor for ΔRTNDT,followed by neutron injection amount.This work demonstrates a successful application of ML in promoting NPP safety and RPV materials design.