Hazardous Chemical Accident Prevention Based on K-Means Clustering Analysis of Incident Information

Hazardous chemicals are inflammable, explosive, and/or toxic and are prone to accidental leakage, fire, and explosion during production, storage, and transportation. It is time-consuming 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.