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.

Sulfur hexafluoride (SF 6) gas leakage in populous urban areas, once occurring, can cause death from suffocation if its concentration largely exceeds 1000ppm and oxygen concentration is low than 19 vol-%. Leakage that cannot be detected and responded to with prompt and effective measures can even lead to large death tolls. Presently, few systematic technical approaches to monitoring, early-warning, consequence prediction, and emergency response of SF 6 leakage have been reported. In this paper, a method for constructing the early-warning and emergency response system for SF 6 leakage in substations is proposed. Firstly, the concentration distribution of leaked SF 6 gas at different leakage points within the substation space is analyzed using CFD simulation to determine the coordinates of sensitive areas where the exceeding of the threshold value of SF 6 concentrations is first detected and thus to ascertain sensor monitoring points. By altering leakage locations and leakage orifice diameters within the substation space, the data concerning the coupling relationship between leakage time, leakage orifice diameter, and concentration are obtained, and a prediction model of diffusion concentration of SF 6 leakage in substations is established through regression. Based on the prediction model, an emergency response system for SF 6 leakage in substations is constructed; additionally, in combination with safety management data of substations, the files required for emergency responses to SF 6 leakage can be identified immediately after occurrence, which provide a guidance for on-field personnel to take emergency responses and safety prevention measures. In this paper, a case study of a substation leakage event is presented to describe the method to construct an early-warning and emergency response system for leakage in substations, as well as the application of the method. The results of this research can provide a theoretical basis for early-warning and emergency response to SF 6 leakage, thereby improving the inherent safety levels of substations.

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Ventilator is a kind of critical medical equipment with the highest clinical risk, and it plays an essential role in intensive care and maintaining patient lives. Identifying and eliminating ventilator false alarms is one of the most critical issues in the clinical treatment process. A considerable number of false-positive alarms may happen because of inaccurate parameter alarm threshold setting and inappropriate alarm rule application. This study proposes a method for identifying and reducing the false alarms of the ventilator based on clinical data analysis. It firstly establishes a real-time monitoring system for the ventilator. A wireless network module can be used to transmit data, including parameter data and alarm data, to the server. Then, the data changing range for one parameter can be calculated and determined. The change range of one parameter can be divided into 10 sub-ranges. The frequency of each parameter monitoring value presented in each sub-range can be calculated. The parameter alarm thresholds can be set according to the frequencies and the value distribution in different sub-ranges. The alarm times for one or more parameters in a specified period can be acquired. The clinical data can be utilized to verify the alarm thresholds. The method has been applied to identify and eliminate false alarms for ventilators in a hospital. The application effect shows that this method can help set the parameter alarm thresholds and identify and eliminate most false alarms.