基于炼油化工过程复杂,设备众多,某一设备的监测变量发生扰动可能会传播至其相邻设备引发出一系列故障链.现有方法多是针对某一设备进行监测与诊断,以期降低事故后果,而忽视了对过程风险传播路径的预测以防止事故的发生.因此,提出一种基于传递熵与核极限学习机的炼油化工过程风险传播路径分析方法,该方法针对某一工艺扰动,分析其在风险发展过程中的扰动传播过程,基于传递熵分析法建立炼油化工过程风险传播推绎模型;并提出一种基于KELM的风险传播搜索方法,预测风险传播路径;将该方法应用于分馏塔冲塔过程.研究结果表明:该方法可辨识出未来一段时间内风险的可能传播路径,以便操作人员及时采取预防措施,保证过程安全及产品质量.%The refining petrochemical process is complex and has a large amount of equipments, so the disturbance in the monitored variables of a certain equipment may propagate to its adjacent equipments and cause a series of fault chain. The existing methods are mostly monitoring and diagnosing a certain equipment to reduce the accident consequence, but neglect the prediction on the risk propagation paths of the process to prevent the occurrence of accidents. An analysis method on the risk propagation paths of the refining petrochemical process based on the transfer entropy and the kernel extreme learning ma-chine (KELM) was put forward. Aiming at the disturbance of a certain technology, the disturbance propagation process in the risk development process was analyzed,and the reasoning model of risk propagation in the refining petrochemical process was established based on the analysis method of transfer entropy. A searching method of risk propagation based on KELM was proposed to predict the risk propagation paths. The method was applied in the flooding process of the fractionator. The results showed that the proposed method can identify the probable propagation paths of risk during a period of time in the future,so that the operators can adopt the prevention measures in time to ensure the process safety and the product quality.
展开▼
