This work proposes an innovative framework to automate HAZOP analysis by integrating Generative AI and knowledge graphs2, addressing the limitations of traditional methods. Domain knowledge is structured into equipment-specific knowledge graphs, which encapsulate process parameters, deviations (e.g., high temperature, reverse flow), causes, consequences, and safeguards3. These graphs serve as modular, reusable building blocks, enabling semantic reasoning across interconnected data sources. For example, a reactor’s knowledge graph might link its design specifications (e.g., maximum pressure rating) to historical incident reports involving similar equipment. Generative AI, such as Large Language Models (LLMs), automates the extraction and analysis of process-specific data from unstructured texts, including P&ID annotations, operating procedures, and material safety data. By leveraging LLMs, the system identifies critical process-specific information, such as material properties and reactivity hazards and accident patterns4, enabling the generation of accurate hazard scenarios. This integration facilitates a holistic view of risks, significantly enhancing early-stage hazard identification. For instance, the system can predict potential failures in a equipment by cross-referencing material compatibility data with operating conditions extracted from SOPs.
The methodology combines multi-modal inputs essential for practical HAZOP analysis. SDS provide material-specific hazards (e.g., corrosivity, flammability), while accident databases offer insights into historical failures. Process descriptions and P&IDs are parsed using computer vision algorithms to digitize equipment layouts and pipeline networks, which are then mapped to knowledge graphs. Initially, equipment-specific HAZOP models are developed by encoding conceivable deviations (e.g., no flow, high pressure) and their corresponding causes (e.g., valve malfunction, sensor failure), consequences (e.g., over pressurization, thermal runaway), and safeguards (e.g., pressure relief systems, emergency shutdown protocols). These models are dynamically combined to generate process-specific HAZOP analyses.
The framework is illustrated through a case study of a simple batch process involving a reactor, heat exchanger, and storage tank. The automated HAZOP tool analyzes the P&ID and process description, utilizing equipment-specific knowledge graphs to identify hazards. Generative AI supplements this analysis by extracting material properties (flammability, toxicity and corrosiveness etc.) from SDSs and linking them to relevant accidents. The inference mechanism evaluates consequences specific to the process and proposes safeguards relevant to the particular hazards and scenario. The proposed framework can identify the critical deviations and reduces the manual effort compared to traditional HAZOP. The outcome of this work is a scalable, intelligent system that enhances the speed, reliability, and comprehensiveness of hazard analysis. By automating HAZOP, the framework reduces time and resource demands while improving consistency and scalability. The integration of structured knowledge graphs with Generative AI’s unstructured data processing capabilities reduces the time take to conduct HAZOP study while ensuring the accuracy and completeness.
Keywords: Process Safety, HAZOP, Generative AI, Knowledge Graphs, Hazard Identification, Risk Assessment, Unstructured Data, Safety Analysis
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