Application of Natural Language Processing in HAZOP Reports

This study applied NLP and machine learning to HAZOP analysis reports, using BERT-based word embeddings combined with classifiers to categorize consequence severity levels. The approach achieved high classification accuracy for consequence assessment in chemical safety text. For MOC in process safety, this is highly relevant because changes to process equipment, procedures, or operating conditions typically require HAZOP revalidation. Automating the classification of hazard consequences using NLP can significantly reduce the time and expert resources required for MOC-triggered HAZOP reviews, improve consistency of severity ratings across different reviewers, and help smaller facilities that lack dedicated safety experts perform quality change assessments.