Data-driven safety analytics for dust explosion prevention

Abstract

This study introduces a two-phase data-driven multi-model framework to automate the holistic understanding of dust explosions prevention through critical safety drivers. The proposed framework integrates the Natural Language Processing (NLP), Self-Organizing Maps (SOM), and Bayesian Networks (BN) to identify and analyze critical safety drivers. The framework analyzes unstructured incident reports from the Chemical Safety and Hazard Investigation Board (CSB) and WorkSafeBC (WBC) databases using NLP, transforming textual data into actionable insights for proactive safety management utilizing SOM and BN models. Eight critical safety drivers—including process safety management, inherently safer design (ISD), ignition source control, and safeguard effectiveness—are identified and prioritized through sensitivity analysis. By embedding these insights into process operation, the methodology supports Safety 5.0, enabling predictive interventions and reducing the likelihood of catastrophic events.