化工学报2025，Vol.76Issue(12)：6497-6507,11.DOI:10.11949/0438-1157.20250802
基于大语言模型的乏燃料后处理脉冲柱萃取过程预测
Evaluating large language models for prediction of pulsed column extraction process for spent fuel reprocessing
摘要
Abstract
To evaluate the potential of large language models(LLM)in assisting the modeling and prediction of the pulsed column extraction process for spent fuel reprocessing,this study designed five case studies of progressively increasing complexity to assess four mainstream LLM.Model outputs were obtained through structured prompt engineering and quantitatively scored across multiple dimensions via a double-blind evaluation by three domain experts.The results indicate that while all LLM demonstrated excellent performance in instruction adherence,their capabilities declined significantly when addressing diagnostic problems involving complex physicochemical coupling or ambiguous information,lacking the analytical depth to diagnose critical engineering issues precisely.The study concludes that the most suitable role for current LLM is as"intelligent research assistants"to domain experts,jointly forming a highly efficient"human-computer collaborative"research paradigm,rather than acting as independent decision-makers.This paradigm can reduce days of modeling preparation to half an hour.However,all model outputs must undergo rigorous expert review and revision to avoid potential risks such as factual illusions.关键词
大语言模型/乏燃料后处理/溶剂萃取/数学模拟/计算机模拟Key words
large language model/spent fuel reprocessing/solvent extraction/mathematical modeling/computer simulation分类
能源科技引用本文复制引用
于婷,刘英琦,王恒飞,朱涛,龚禾林,卢宗慧,信远征,何辉,叶国安..基于大语言模型的乏燃料后处理脉冲柱萃取过程预测[J].化工学报,2025,76(12):6497-6507,11.基金项目
中国原子能科学研究院青年英才培育基金项目(25799) （25799）
国防科工局稳定支持科研项目(24862) （24862）
