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A Hazard Index for Chemical Logistic Warehouses with Modified Flammability Rating by Machine Learning Methods
ACS Chemical Health & Safety Pub Date : 2020-04-10 , DOI: 10.1021/acs.chas.9b00026
Zhuoran Zhang 1 , Shuai Yuan 1 , Mengxi Yu 1 , M. Sam Mannan 1 , Qingsheng Wang 1
Affiliation  

With the rapid development of chemical process plants worldwide, the safe storage of hazardous chemicals continues to be an important topic. Chemical warehouse incidents related to fire and explosion are reported constantly. These incidents not only cause direct economic loss but also harm the local environment. Therefore, an accurate hazard identification method for a logistic warehouse is valuable since it can help people recognize the potential hazard in the chemical storage area. In this study, a storage hazard factor (SHF) was developed to evaluate and rank the inherent hazards of chemicals stored in logistic warehouses. In the calculation of SHF, the National Fire Protection Association (NFPA) hazard diamond was used to quantify the instability hazard; the U.S. Department of Energy’s Protective Action Criteria (PAC) values were utilized to modify chemical health hazard, and two machine learning based methods were used for the classification of flammability. The SHF and safety penalty factors (e.g., quantity, population density) were then utilized to create a hazard index of the facilities. This index can be used not only for developing a proper emergency response plan but also for effectively communicating risk with the residents who live near the facility. In addition, the index can be used by governments to allocate the resources better for first responders to make fire protection strategies, and by stakeholders to improve the risk management.

中文翻译:

通过机器学习方法修改易燃性等级的化学物流仓库的危险指数

随着全球化学加工厂的快速发展,危险化学品的安全存储仍然是一个重要的主题。不断报告与火灾和爆炸有关的化学仓库事件。这些事件不仅造成直接的经济损失,而且还损害当地环境。因此,用于物流仓库的准确的危害识别方法非常有价值,因为它可以帮助人们识别化学品存储区中的潜在危害。在这项研究中,开发了存储危害因子(SHF)来评估和排序物流仓库中存储的化学品的固有危害。在计算SHF时,使用了美国国家防火协会(NFPA)的危险钻石来量化不稳定的危险。美国 能源部的保护行动标准(PAC)值用于修改化学健康危害,并且使用两种基于机器学习的方法对可燃性进行分类。然后利用SHF和安全惩罚因子(例如数量,人口密度)创建设施的危害指数。该指数不仅可以用于制定适当的应急计划,还可以与居住在该设施附近的居民有效地交流风险。此外,政府可以使用该指数为急救人员更好地分配资源以制定消防策略,利益相关者可以使用该指数来改善风险管理。数量,人口密度)然后用于创建设施的危害指数。该指数不仅可以用于制定适当的应急计划,还可以与居住在该设施附近的居民有效地交流风险。此外,政府可以使用该指数为急救人员更好地分配资源以制定消防策略,利益相关者可以使用该指数来改善风险管理。数量,人口密度)然后用于创建设施的危害指数。该指数不仅可以用于制定适当的应急计划,还可以与居住在该设施附近的居民有效地交流风险。此外,政府可以使用该指数为急救人员更好地分配资源以制定消防策略,利益相关者可以使用该指数来改善风险管理。
更新日期:2020-04-10
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