RESUMEN
Syngas, composed of hydrogen and carbon monoxide, serves as an alternative fuel for hydrogen energy and a key raw material for chemical synthesis. However, due to its flammable nature, syngas poses risks of forming explosive mixtures in the event of a leak. This study explores potential accident scenarios in coal chemical environments involving syngas reaction vessels. Experimental investigations focus on the overpressure and propagation dynamics of jet flames resulting from syngas leakage, with CO volume fractions ranging from 50 to 80% and release pressures between 2 and 5 MPa. Results reveal that maximum flame overpressure occurs within a CO volume fraction range of 55-65%, with no consistent relationship observed between overpressure and CO fraction at fixed release pressures. During our experiments, the maximum recorded overpressure of 28.4 kPa was reached during vented explosions. Additionally, ignition outcomes categorize into three types based on flame propagation speed: combustion/flare, resembling normal deflagration; and high-velocity deflagration, characterized by rapid propagation and potential for steady jet fire formation. While shockwave-like features may be observed, these do not indicate true detonation. These findings offer insights for the safe handling and storage of syngas.
RESUMEN
Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.
RESUMEN
BACKGROUND: With the rapid development of China's chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars' attention. METHODS: Based on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises. RESULTS: The results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees' unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc. CONCLUSION: Compared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.