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A new group contribution-based method for estimation of flash point temperature of alkanes

ISSN号：1009-2501
期刊名称：《中国临床药理学与治疗学》
时间：0
分类：TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] TU753[建筑科学—建筑技术科学]
作者机构：[1]School of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China, [2]School of Chemistry and Biological Engineering,Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation, Changsha University of Science and Technology, Changsha 410004, China
相关基金：Projects（21376031,21075011）supported by the National Natural Science Foundation of China; Project（2012GK3058）supported by the Foundation of Hunan Provincial Science and Technology Department,China; Project supported by the Postdoctoral Science Foundation of Central South University,China; Project（2014CL01）supported by the Foundation of Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation,China; Project supported by the Innovation Experiment Program for University Students of Changsha University of Science and Technology,China

作者：戴益民[1], 刘辉[2], 陈晓青[1], 刘又年[1], 李浔[1], 朱志平[2], 张跃飞[2], 曹忠[2]

关键词：人工神经网络模型, 基础, 烷烃, 平均绝对偏差, 多元线性回归, 温度, 估算, 模型表示, flash point alkane group contribution artificial neural network（ANN） quantitative structure-property relationship（QSPR）

中文摘要：

Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression(MLR)and artificial neural network(ANN). This simple linear model shows a low average relative deviation(AARD) of 2.8% for a data set including 50(40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.

英文摘要：

Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression（MLR）and artificial neural network（ANN）. This simple linear model shows a low average relative deviation（AARD） of 2.8% for a data set including 50（40 for training set and 10 for validation set） flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.

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