The explosive gases CO and C2H4, released mainly flammable gases during the process of coal self-ignition, are of the most important ingredients of the multi-component gases in goal areas, along with CH4. We have dete...The explosive gases CO and C2H4, released mainly flammable gases during the process of coal self-ignition, are of the most important ingredients of the multi-component gases in goal areas, along with CH4. We have determined some of the parame- ters of explosive properties of the one-component gases CH4, CO and C2H4 using an explosive trial device of polybasic explosive gas mixtures and emphasized particularly the effect on the limits of explosive concentration of the binary explosive mixed gases CH4＋CO, CH4＋C2H4, as a function of the amount of CO, C2H4 and inert flame resisting gases （N2, CO2）. The experimental results show that the effect of inert gases on the explosive limits of mixed gases, given the property of explosive gas, is obvious： the inert gases （N2, CO2） possess some inhibitory effects on the explosion of the multi-component explosive gas mixtures. The results will provide some experimental support to suppress the occurrence of the gas explosions in goaf areas and provide some directions for designing explosion-proof electric equipment and fire arresters.展开更多
为建立一种红外光谱指纹信息和挥发性组分信息融合鉴别模型,提高模型对大米产地的鉴别率。通过傅里叶红外光谱和气相色谱-质谱联用分析20份盘锦大米、19份射阳大米和15份五常大米样品中红外光谱吸光度和挥发性组分含量,利用方差分析筛...为建立一种红外光谱指纹信息和挥发性组分信息融合鉴别模型,提高模型对大米产地的鉴别率。通过傅里叶红外光谱和气相色谱-质谱联用分析20份盘锦大米、19份射阳大米和15份五常大米样品中红外光谱吸光度和挥发性组分含量,利用方差分析筛选出特征光谱和挥发性组分,结合偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)法建立融合这2种指纹信息的鉴别方法。结果表明,信息融合模型的大米产地鉴别准确率为97.4%,与单一光谱指纹信息模型(92.9%)和挥发性指纹信息模型(88.9%)相比,分别提高了4.5%和8.5%。因此,信息融合技术提高了该模型鉴别效果,采用PLS-DA法信息融合模型对大米产地进行鉴别是可行有效的。展开更多
基金The financial supports from the National Natural Science Foundation of China (No.50874088)the Changjiang Scholars and Innovative Research Team in University (No.IRT0856)
文摘The explosive gases CO and C2H4, released mainly flammable gases during the process of coal self-ignition, are of the most important ingredients of the multi-component gases in goal areas, along with CH4. We have determined some of the parame- ters of explosive properties of the one-component gases CH4, CO and C2H4 using an explosive trial device of polybasic explosive gas mixtures and emphasized particularly the effect on the limits of explosive concentration of the binary explosive mixed gases CH4＋CO, CH4＋C2H4, as a function of the amount of CO, C2H4 and inert flame resisting gases （N2, CO2）. The experimental results show that the effect of inert gases on the explosive limits of mixed gases, given the property of explosive gas, is obvious： the inert gases （N2, CO2） possess some inhibitory effects on the explosion of the multi-component explosive gas mixtures. The results will provide some experimental support to suppress the occurrence of the gas explosions in goaf areas and provide some directions for designing explosion-proof electric equipment and fire arresters.
文摘为建立一种红外光谱指纹信息和挥发性组分信息融合鉴别模型,提高模型对大米产地的鉴别率。通过傅里叶红外光谱和气相色谱-质谱联用分析20份盘锦大米、19份射阳大米和15份五常大米样品中红外光谱吸光度和挥发性组分含量,利用方差分析筛选出特征光谱和挥发性组分,结合偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)法建立融合这2种指纹信息的鉴别方法。结果表明,信息融合模型的大米产地鉴别准确率为97.4%,与单一光谱指纹信息模型(92.9%)和挥发性指纹信息模型(88.9%)相比,分别提高了4.5%和8.5%。因此,信息融合技术提高了该模型鉴别效果,采用PLS-DA法信息融合模型对大米产地进行鉴别是可行有效的。
