A HAZOP-based hazard identification model for urban gas accidents: Development and empirical validation

TL;DR

A new model for identifying gas hazards in urban areas uses HAZOP methods to improve safety and efficiency.

Contribution

A novel HAZOP-based hazard identification model for urban gas systems integrating human, machine, environment, and management factors.

Findings

The model identified 65 potential gas-related hazards in a restaurant case study.

It outperformed traditional methods by over eight times in hazard detection.

The model improves comprehensiveness, accuracy, and efficiency in hazard identification.

Abstract

Urban gas accidents pose significant threats to public safety and urban infrastructure, with traditional hazard identification methods often relying on manual inspections and experience-based judgments, leading to incomplete or inconsistent results. To address these issues, this study proposes a structured hazard identification model based on Hazard and Operability Analysis (HAZOP) deviation theory for urban gas systems. By integrating four key dimensions—human, machine, environment, and management—a comprehensive framework was developed to define system nodes, select relevant parameters, and apply guide words to identify potential hazards in a standardized manner. This approach allows for dynamic adjustment of influencing factors according to different application scenarios. The model was validated through a case study involving a restaurant, where it identified 65 potential gas-related hazards, over eight times more than traditional inspection approaches. Results demonstrate significant improvements in comprehensiveness, accuracy, and efficiency of hazard identification. This model provides a practical tool for urban gas safety management, supporting standardized hazard screening across various sectors including restaurants, residential buildings, schools, and commercial complexes. Furthermore, it lays the foundation for future integration of quantitative risk assessment and artificial intelligence-driven risk analysis, contributing to the digitalization and standardization of urban gas safety governance.

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