石油化工装置动态风险监测模型的构建与应用研究OA

To address the issues of delayed dynamic risk monitoring,the disconnection between dynamic and static risks,and the frequent underestimation of critical signals in petrochemical units,this study developed a dynamic risk monitoring model based on multi-source data fusion.Grounded in accident causation theory,the model determined indicator weights by categorizing safety barrier types,thereby achieving a comprehensive char-acterization of risk status through the integration of dynamic and static indicators.Specifically,dynamic indica-tor-encompassing process parameter alarms,the operational status of the Safety Instrumented System(SIS),and equipment condition monitoring data-were calculated at 5-minute intervals.Notably,Gas Detection System(GDS)and fire alarms were designated as"one-vote escalation"criteria,meaning that their activation instantly elevates the model′s output to the highest risk level.Concurrently,static indicators were updated on a 24-hour cycle to adjust the dynamic risk values using correction coefficients.The proposed model was validated in a des-ulfurization unit over a 417-day period,processing more than 120 000 sets of dynamic data and 417 sets of static data.The results indicated that,compared to traditional manual inspection methods,the detection of hidden hazards increased by 37%,while the frequency of unplanned shutdowns decreased from three to one during the validation period.Consequently,these findings provide support for the application of the proposed model in risk early warning systems for petrochemical facilities.