Learning HAZOP expert system by case-based reasoning and ontology

Presents an AI-based HAZOP expert system that integrates case-based reasoning with an ontology to learn from prior analyses and guide hazard scenario generation. In PSM, MOC frequently requires revalidation or update of hazard analyses when process conditions, equipment, materials, or control strategies change. Reusing prior HAZOP knowledge and providing structured, ontology-driven reasoning can reduce effort and increase consistency in MOC-driven PHA updates, helping teams focus attention on what is truly new or changed and on whether safeguards remain adequate.