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A New Method for Predicting the Net Heat of Combustion of Organic Compounds

机译:一种预测有机化合物燃烧净热的新方法

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A quantitative structure-property relationship (QSPR) model for prediction of standard net heat of combustion (△H_c~°) was developed based on the ant colony optimization (ACO) method coupled with the partial least square (PLS) for variable selection. For developing this model, a diverse set of 1650 organic compounds were used, and 1481 molecular descriptors were calculated for every compound. Four molecular descriptors were screened out as the parameters of the model, which was finally constructed using multi-linear regression (MLR) method. The squared correlation coefficient R~2 of the model was 0.995 for the training set of 1322 compounds. For the test set of 328 compounds, the corresponding R~2 was 0.996. The results of this study showed that an accurate prediction model for △H_c~° could be obtained by using the ant colony optimization method. Moreover, this study can provide a new way for predicting the △H_c~° of organic compounds for engineering based on only their molecular structures.
机译:基于与用于可变选择的局部最小二乘(PLS)耦合的蚁群优化(ACO)方法,开发了用于预测标准净燃烧的标准净热(△H_c〜°)的定量结构 - 性质关系(QSPR)模型。为了开发该模型,使用多样化的1650种有机化合物,对每种化合物计算1481个分子描述夹。筛选出四种分子描述符作为模型的参数,最终使用多线性回归(MLR)方法构建。用于训练组1322化合物的模型的平方相关系数R〜2为0.995。对于328个化合物的试验组,相应的R〜2为0.996。该研究的结果表明,通过使用蚁群优化方法,可以获得△H_c〜°的精确预测模型。此外,该研究可以提供一种新的方式,用于基于其分子结构预测△H_c〜°的工程化合物的△H_c〜°。

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