Physics-informed data-driven Bayesian network for the risk analysis of hydrogen refueling stations DOI
Jinduo Xing,

Jiaqi Qian,

Rui Peng

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 110, P. 371 - 385

Published: March 1, 2024

Language: Английский

Prevention and control strategy of coal mine water inrush accident based on case-driven and Bow-Tie-Bayesian model DOI

Xin Tong,

Xuezhao Zheng, Yongfei Jin

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135312 - 135312

Published: Feb. 1, 2025

Language: Английский

Citations

0
Chemical process safety domain knowledge graph‐enhanced LLM for efficient emergency response decision support DOI
Chen Zheng, Guohua Chen, Honghao Chen

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

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

Language: Английский

Citations

0
STheBaN - System-Theoretic Bayesian approach for the evaluation of inspections workability in hydrogen operations DOI
Antonio Javier Nakhal Akel, Alessandro Campari, Nicola Paltrinieri

et al.

Journal of Loss Prevention in the Process Industries, Journal Year: 2025, Volume and Issue: unknown, P. 105687 - 105687

Published: May 1, 2025

Language: Английский

Citations

0
Physics-informed data-driven Bayesian network for the risk analysis of hydrogen refueling stations DOI
Jinduo Xing,

Jiaqi Qian,

Rui Peng

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 110, P. 371 - 385

Published: March 1, 2024

Language: Английский

Citations

2