AI-Enabled HAZOP: Automating Hazard Scenario Development and Risk Classification

This paper presents an innovative application of artificial intelligence (AI) and machine learning (ML) to significantly enhance Hazard and Operability (HAZOP) studies. Our data-driven methodology directly addresses critical aspects of HAZOP analysis:

First, we leverage Natural Language Processing (NLP) with semantic similarity analysis and generative models to produce highly relevant, facility- and equipment-specific potential consequences for identified causes from historical data.

Second, we explore various NLP and vectorization techniques for predicting key risk attributes. This includes using binary classification to determine consequence categories (e.g., People, Environment, Asset, Reputation), and employing multi-label classification for predicting severity from consequence descriptions and likelihood from cause statements. Model performance is rigorously evaluated using confusion matrices and accuracy metrics to select optimal classifiers.

This comprehensive AI/ML integration aims to aid traditional HAZOP practices by streamlining processes, accelerating analysis sessions, and fostering greater consistency in risk assessments within process plants.