摘要
为最优化后处理过程的实验条件、优化工艺、降低实验成本和时间,并提高后处理流程数学模拟的准确性,本文基于随机森林、支持向量回归和K近邻这3种经典的机器学习算法建立了30%TBP/煤油-硝酸体系中主要组分铀、钚、硝酸的分配比数学模型,并基于不同数据集进行了超参数优化和模型训练。通过对模型进行验证和测试,发现采用随机森林算法建立的分配比模型准确度最高,其对铀预测的平均绝对相对误差达7.73%,较传统方法提高了约7%。与传统建模方法相比,机器学习方法建立模型的准确度更高。
Spent fuel reprocessing is an important nuclear energy process which aimed at recovering resources and managing radioactive materials to control potential hazards.In this field,Purex technology is widely used for its high efficiency,scalability,and wide applicability.Purex technology,a liquid-liquid extraction technique to separate and purify uranium and plutonium from nuclear fuel,plays a key role in spent fuel reprocessing,enabling reprocessing and recycling of nuclear fuel,reducing the release of radioactive nuclear waste,and improving the efficiency of nuclear energy resources.Meanwhile,as an emerging technology,machine learning has attached wide attention and has been applied in the field of Purex,such as the selection of ligands and ionic liquids,the prediction of ligand properties,and so on.In this paper,machine learning is combined with distribution ratio prediction,which is defined as the distribution ratio of ionic liquids in different phases,which can reflect the extraction rate of ions,and plays an important role in Purex computer simulation,so the distribution ratio prediction model can help researchers to choose the optimal experimental conditions,optimize the process,and reduce the experimental cost and time.Since the traditional mathematical model of uranium distribution ratio leads to at least 15%prediction error,in this paper,three classical machine learning models(namely,random forest,support vector regression and K-nearest neighbor)were constructed to predict the distribution ratios of uranium,plutonium,and HNO3 in the 30%TBP/kerosene-HNO3 system.These models were trained based on different datasets,and their hyperparameters were optimized using algorithms such as grid search,Bayesian optimization,and K-fold cross-validation.The results show that random forest achieves the best results in distribution ratio prediction.The average absolute relative error(AARE)of uranium distribution ratio prediction reaches7.73%,which is about 7%higher than that of the traditional model.In addition,plutonium and HNO3distribution ratios are also predicted to verify the generalizability of the machine learning model,and the highest of 11.6%and 13.7%are achieved.The machine learning model prediction results show that the machine learning method proposed in this paper achieves better performance than the traditional distribution ratio mathematical model,effectively improves the accuracy of uranium distribution ratio prediction,and performs well in plutonium and HNO3 distribution ratio prediction.
作者
于婷
张音音
张睿志
金文蕾
罗应婷
朱升峰
何辉
叶国安
龚禾林
YU Ting;ZHANG Yinyin;ZHANG Ruizhi;JIN Wenlei;LUO Yingting;ZHU Shengfeng;HE Hui;YE Guoan;GONG Helin(China Institute of Atomic Energy,Beijing 102413,China;School of Mathematics,Sichuan University,Chengdu 610065,China;School of Mathematical Sciences,Esat China Normal University,Shanghai 200241,China;Paris Elite Institute of Technology,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《原子能科学技术》
北大核心
2025年第1期14-23,共10页
Atomic Energy Science and Technology
关键词
分配比数学模型
随机森林
支持向量回归
K近邻
distribution ratio mathematical model
random forest
support vector regression
K-nearest neighbor
作者简介
共同第一作者:于婷;共同第一作者:张音音;通信作者:龚禾林。
