Chemical process safety domain knowledge graph-enhanced LLM for efficient emergency response decision support

IF 1.9 4区 工程技术 Q3 ENGINEERING, CHEMICAL
Chen Zheng, Guohua Chen, Honghao Chen, Qiming Xu, Yimeng Zhao, Yuanfei Zhao, Yunfeng Yang
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引用次数: 0

Abstract

Chemical process safety accidents are characterized by their sudden onset, rapid evolution, and severe consequences. Developing effective emergency response decisions for such complex and dynamic incidents requires comprehensively considering various knowledge domains. Relying solely on expert experience and emergency plans often fails to meet the demands of effective emergency response. To enhance the efficiency of emergency response decision-making in chemical process accidents, this study proposes a method that leverages a chemical process safety knowledge graph (CPSKG) to enhance large language models (LLMs) for generating reliable emergency response decisions. The proposed method uses a seven-step approach to designing scenario and emergency response ontologies. By aligning with the characteristics of emergency domain knowledge texts and the ontology framework, natural language processing (NLP) and retrieval-augmented generation using graphs (Graph RAG) techniques are employed to construct a semantically rich CPSKG. The entities and relationships within the graph enhance the reasoning capabilities of LLMs, facilitating the generation of efficient and reliable emergency response decisions. A case study was conducted to validate the reliability of this approach. The results demonstrate that the LLM enhanced with the CPSKG outperforms other models in generating more effective emergency response decisions. As a key contribution, the proposed method improves the efficiency of knowledge sharing and emergency response in the chemical process safety domain while generating reliable and auxiliary emergency decisions.

化工过程安全领域知识图谱增强的LLM,用于高效的应急响应决策支持
化工过程安全事故具有突发、演变快、后果严重的特点。针对如此复杂和动态的事件制定有效的应急响应决策需要综合考虑各种知识领域。单纯依靠专家经验和应急预案往往不能满足有效应急的要求。为了提高化工过程事故应急响应决策的效率,本研究提出了一种利用化工过程安全知识图(CPSKG)增强大语言模型(llm)的方法,以生成可靠的应急响应决策。该方法采用七步方法来设计场景和应急响应本体。结合应急领域知识文本的特点和本体框架,采用自然语言处理(NLP)和图检索增强生成(Graph RAG)技术构建了语义丰富的应急领域知识文本。图中的实体和关系增强了llm的推理能力,有助于生成高效可靠的应急响应决策。通过案例研究验证了该方法的可靠性。结果表明,CPSKG增强的LLM在生成更有效的应急响应决策方面优于其他模型。该方法提高了化工过程安全领域的知识共享和应急响应效率,同时生成了可靠的辅助应急决策。
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来源期刊
Canadian Journal of Chemical Engineering
Canadian Journal of Chemical Engineering 工程技术-工程:化工
CiteScore
3.60
自引率
14.30%
发文量
448
审稿时长
3.2 months
期刊介绍: The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.
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