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

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.

Key words: large language model, spent fuel reprocessing, solvent extraction, mathematical modeling, computer simulation