Fuzzy Logic-Based Process Safety Management Framework for Risk Assessment in Liquefied Petroleum Gas Tanker Unloading

Process Safety Management (PSM) has long been the cornerstone of hazard prevention in the oil and gas sector. However, conventional PSM tools—such as deterministic checklists, risk matrices, and semi-quantitative methods including Hazard and Operability Study (HAZOP) and Layer of Protection Analysis (LOPA)—are limited by subjectivity, rigid decision thresholds, and weak integration with dynamic operating conditions. This paper proposes a fuzzy logic-based risk assessment framework to complement PSM in the context of Liquefied Petroleum Gas (LPG) tanker unloading operations at filling plants. A Mamdani fuzzy inference system is developed using three critical operational drivers: pressure differential, storage-sphere fill level, and vapor temperature. These inputs are integrated to generate a continuous Risk Index (0–100) mapped onto four operational decision bands (green, yellow, orange, red). Simulation results indicate that high risk emerges when two drivers simultaneously reach elevated states, while severe risk may be triggered by a single very high temperature condition, highlighting the dominant role of thermal escalation. Compared to traditional risk matrices, the proposed approach provides a continuous and dynamic risk index, avoiding artificial discontinuities at decision thresholds while maintaining transparency and clear operational decision bands. The proposed framework enhances PSM robustness and supports proactive operational decision-making during LPG tanker unloading.