Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

No abstract

Petrochemical enterprises store and process a large number of flammable, explosive, toxic and harmful chemicals, and their security protection systems are vulnerable. Once damaged by man‐made malicious will cause serious losses and adverse effects. Previous studies on the vulnerability of petrochemical enterprises have insufficient consideration of systematicity and uniqueness, and lack consideration of the correlation and game of vulnerability elements. In this study, from the perspective of system, the elements of the security accident system vulnerability are analyzed, and the vulnerability analysis model of security accident system is put forward by studying the interaction between the elements. Threat and exposure constitute the external vulnerability of security accident system, sensitivity and adaptability constitute the internal vulnerability of security accident system; and the threat plays a counter role with sensitivity and adaptability through exposure, which promotes the evolution of vulnerability; Moreover, the different vulnerability elements of security accident system evolve step by step, with the evolution of security accidents. Finally, an illustrative case proves the feasibility of the vulnerability analysis model proposed in this study. The results of this research enrich the theoretical research on vulnerability, provide a reference for the prevention and control of security accidents in petrochemical enterprises.

No abstract

The utilization of data-driven methods in chemical process modeling has been extensively acknowledged due to their effectiveness. However, with the increasing complexity and variability of chemical processes, predicting and warning of anomalous conditions have become challenging. Extracting valuable features and constructing relevant warning models are critical problems that require resolution. This research proposed a novel fused method that integrates K-means density-based spatial clustering of applications with noise (DBSCAN) clustering and bi-directional long short-term memory multilayer perceptron (Bi-LSTM-MLP) to enable early warning of abnormal conditions in chemical processes. The paper applied the proposed method to analyze the early warning using actual process data from Eastman Tennessee and the atmospheric pressure reduction unit as an example. In the TE model and example, the root mean square error (RMSE) of this method is 0.006855 and 0.052546, respectively, which is quite low when compared to other methods. The experimental results confirmed the effectiveness of our approach.