A Knowledge-Driven Model to Assess Inherent Safety in Process Infrastructure

Process safety has drawn increasing attention in recent years and been investigated from different perspectives, such as quantitative risk analysis, consequence modeling, regulations. However, rare attempts have made to focus on inherent design assessment, despite being the most cost-effective tactic its vital role sustainable development safe operation of process infrastructure. Accordingly, present research proposed a knowledge-driven model assess infrastructure under uncertainty. We first developed holistic taxonomy contributing factors into considering chemical, reaction, process, equipment, human factors, organizational concerns associated with plants. Then, we used subject matter experts, content validity ratio (CVR), index (CVI) validate data collection tools. then employed fuzzy inference system Extent Analysis (EA) method for knowledge acquisition tested steam methane-reforming plant that produces hydrogen renewable energy. The findings revealed indicators improve studied effectively support decision-making assign proper countermeasures.