In view of the frequent occurrence of major fires and explosions in the production, transportation, and storage of aromatic nitro compounds, the reactive thermal hazards of these compounds have attracted widespread attention in the chemical industry. Thermal hazard assessment is of great significance for the standardized storage of reactive chemicals. Based on adiabatic calorimetry and quantitative structureproperty relationship (QSPR) prediction, a thermal hazard assessment and classification method dominated by the molecular structure of nitro compounds were proposed. While obtaining thermodynamic parameters, stable configuration and molecular descriptors, the prediction models were constructed by multiple linear regression (MLR) and artificial neural network (ANN), respectively, including selfaccelerating decomposition temperature (SADT), maximum power density (MPD), and the apparent activation energy (E a ). On this basis, SADT, H 50 , and E a were selected as the possible characterization parameters of thermal risk of nitro compounds, while MPD and ΔT ad were selected as the characterization parameters of consequence severity. When combined with weight and reference TNT, this method comprehensively evaluated the thermal hazards of nitro compounds and divided them into four types of thermal hazard levels. By comparing the method with the previous classification standards, it is found that the evaluation method is expected to provide theoretical support for the safe standardized storage of nitro compounds in chemical enterprises.

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.

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.

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