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

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.