Refined Oil Loading and Unloading Process Risk Assessment using Stochastic Colored Petri Nets Integrated with Risk Factors

Abstract

This paper presents a novel risk assessment method using Stochastic Colored Petri Nets (SCPN) specifically designed for the loading and unloading process of refined oil. The method incorporates a comprehensive analysis of risk factors by employing event trees and fault trees. Based on the real logistics operation process of an enterprise, four key risk factors and their corresponding evolution processes are identified, including equipment quality, improper operations, wrong instructions, and illegal operations. Subsequently, an SCPN model is constructed to integrate these risk factors and evaluate the system's performance using isomorphic Markov chain analysis. The overall risk assessment of the system is determined based on a risk function, which captures the system's risk level considering the influence of the identified risk factors. The results reveal that personnel engaging in illegal operation behaviors pose a high-risk factor, demanding preventive measures and increased attention. This research provides valuable insights for risk management in the refined oil loading and unloading process, emphasizing the significance of addressing risk factors and enhancing safety measures.