Abstract:

To mitigate the risks of leakage, fires and explosions in petrochemical equipment, focusing on a typical catalytic cracking unit, a novel early warning method for detecting abnormal states using probability distribution functions was introduced. Spline fitting principles were used to uncover the trends in operating parameters such as pressure, temperature and flow rate over time, and to extract characteristic parameters such as deviation rate and deviation amount. By employing the Weibull distribution, the failure probability distribution function of the equipment was determined. The extracted characteristic parameters were integrated with the failure function to construct a probabilistic distribution mathematical model incorporating these features. Based on this model, a comprehensive early warning process was developed, facilitating real-time risk assessment and anomaly detection during the catalytic cracking process. The findings demonstrate that this method can effectively predict anomalies under conditions of oscillation, step changes, and gradual trends in operating parameters. Compared to traditional instrument systems, this early warning method advances the warning time by 87 to 621 seconds, addressing the limitation of limited response time following single-threshold alarms in the conventional systems. Furthermore, a comparison of various data processing methods reveals that the early warning model based on spline fitting exhibits superior performance.

Key words: probability distribution function, petrochemical equipment, abnormal states, early warning, operating parameters