易燃液体
可燃极限
爆炸物
无量纲量
甲烷
灵敏度(控制系统)
燃烧
材料科学
化学
热力学
机械
物理
电子工程
工程类
有机化学
作者
Wentao Ji,Li Wang,Jingjing Yang,Jia He,Xiaoping Wen,Yan Wang
出处
期刊:Fuel
[Elsevier]
日期:2021-11-19
卷期号:310: 122138-122138
被引量:20
标识
DOI:10.1016/j.fuel.2021.122138
摘要
In order to study variations of the lower explosion limit associated with hybrid mixtures of flammable gas and dust, four hybrid mixtures of methane/lycopodium, methane/polyethylene, ethylene/lycopodium, and ethylene/polyethylene with various concentration ratios were constructed. The lower explosion limits of these mixtures were measured in a 20 L spherical explosion vessel. According to the experimental results, it was obtained that the lower explosion limits of these hybrid mixtures were decreased by increasing the flammable gas concentration. The two-dimensional coordinate system was divided into explosion area and non-explosion area using the lower explosion limit curve. For different hybrid mixtures, the proportions of the explosion area were different in the two-dimensional coordinate system. The larger the proportion, the greater the explosion sensitivity. Based on this proportionality relationship, a dimensionless parameter called sensitivity index was defined as the ratio of the explosion area to the non-explosion area. The greater the sensitivity index, the greater the explosive sensitivity of the hybrid mixture. According to the relationship between the sensitivity indices of different hybrid mixtures, the influencing factors for the sensitivity index were established, including combustion heat of the flammable gas and dust, chemical reactivity of the flammable gas, as well as dimensionless parameters of dust decomposition characteristics. Considering these influencing factors, a calculation method for the sensitivity index was established using maximum explosion pressure as well as explosion indices of the flammable gas and dust. Through this comprehensive study, a new predictive approach for determining variations of the lower explosive limit of hybrid mixtures with the newly proposed sensitivity index was established. The predictive performance of this newly developed approach was verified using data from other hybrid mixtures.
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