Hazardous chemicals are inflammable, explosive, and/or toxic and are prone to accidental leakage, fire, and explosion during production, storage, and transportation. It is timeconsuming and laborious to study the properties of hazardous chemicals individually for systematic accident prevention because of the wide variety of hazardous chemicals and conditions resulting in accidents. Moreover, accidents have numerous causes, and the relationships among the causative factors are complex. It is a problem that is difficult to accurately identify the effects of correlations among accident factors and determine the laws governing accident occurrence. In this paper, we propose a generic method of hazardous chemical accident prevention based on K-means clustering analysis of incident information to illustrate how to solve the problems. A database of hazardous chemical incidents was constructed, and a K-means clustering algorithm was adopted to classify the incidents. The numbers of occurrences and frequencies of the words in the textual descriptions of the consequences, processes, and causes of hazardous chemical incidents were counted and calculated using a class-based method. For words with a high frequency, risk scenarios were constructed, checklist items of newly revealed dangers were developed, and a system for systematic risk assessment and accident prevention was established. Finally, the information on hazardous material transportation incidents in the Pipeline and Hazardous Materials Safety Administration database of the U.S. Department of Transportation from 2009 to 2018 had been taken as an example to illustrate the method application. The results demonstrate that the proposed method of hazardous chemical accident prevention can be used to improve accident classification. The classification results make it possible to determine the optimal sequence of key targets on which to focus and the requirements for accident prevention and formulate preventive measures. Thus, they provide a technical basis for accident prevention.

The anaerobic reactor is one of the most critical reaction devices for biogas engineering, wherein is usually a large amount of flammable gas methane. Fire and explosion accidents will be easily triggered if the gas leaks, threatening the surrounding buildings, equipment, personnel, and so forth. Avoiding the significant accidents caused by CH 4 leakage has become a critical issue in the design and condition monitoring for an anaerobic reactor. This article presents a model construction method for leakage early-warning. Incident database and hazard and operability analysis (HAZOP) can be utilized to identify the leakage risks, and computational fluid dynamics (CFD) simulation and consequence quantity calculation can be used to determine the consequence influence ranges. The calculation results can be employed to establish prediction models for abnormal situations. Process safety management (PSM) data and risk analysis results can be combined with the possible abnormal situations to assist operators in adopting the right solutions. An early-warning system has been developed to illustrate the industrial application of the model. It can be concluded that collecting multi-parameter values according to the real-time changes in the actual production process, continuous monitoring and earlywarning of leakage risk, and so forth, will contribute to accident avoiding and emergency response in reactor operations.

Most transformer substations in power supply facilities rely on sulfur hexafluoride electrical equipment. A sulfur hexafluoride gas leak can cause serious health concerns if effective measures are not adopted in time. Therefore, in this study, a sulfur hexafluoride gas leakage monitoring, early-warning, and emergency disposal model was established. First, taking the main transformer chamber of an underground transformer substation as the research object, a 3D-model was built, and a numerical simulation was performed. Second, the simulation results were utilized to determine the dispersion and concentration distribution of the sulfur hexafluoride gas, identify concentration-sensitive areas, and arrange sensors based on the simulation results, to ensure early-warning in case of leaks. Then, a sulfur hexafluoride gas leakage monitoring and early-warning model was built based on the data collected using sensors at the monitoring points; thereafter, a construction method was developed for a sulfur hexafluoride gas leakage emergency disposal model, which can be referenced to establish a leakage gas recycling system. This paper also provides some recommendations regarding the determination of the optimal conditions for this emergency recycling device, which can be utilized to maintain the concentration of sulfur hexafluoride gas below a specified value and to construct a recycling time prediction model. The results of the study can provide a theoretical basis for sulfur hexafluoride gas leakage early-warning and emergency disposal, which will contribute to the prevention of suffocation-related accidents.

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