摘要
建立了一个基于人工神经网络方法的定量结构-性质相关性(QSPR)研究模型,用于预测脂肪醇闪点。根据脂肪醇的分子结构特征,提出一组拓扑指数作为表征脂肪醇分子结构的分子描述符。同时引入具有高度非线性预测能力的误差反向传播人工神经网络方法,以分子结构描述符作为神经网络的输入参数,闪点作为输出,研究脂肪醇的闪点与分子结构之间的相关性。模拟结果表明,闪点预测值与实验值符合良好,优于传统基团贡献法所得结果。该方法不仅能够预测脂肪醇闪点与分子结构之间的定量关系,而且为工程上提供了一种预测有机物闪点的新的有效方法。
A quantitative structure--property relationship （QSPR） model based on artificial neural networks was established to predict the flash points of fatty alcohols, A set of topological indices was used as molecular structure descriptors to describe the molecular structure characteristics of fatty alcohols. Using the back--propagation artificial neural networks which have the satisfactory nonlinear prediction ability, the correlation between molecular structures and flash points of fatty alcohols was studied with molecular structure descriptors as input parameters and flash point as output one. The results show that the predicted flash points are in good agreement with the experimental data, which are superior to those of conventional group contribution methods. The method proposed can be used to predict not only the quantitative relation between flash points and molecular structures of fatty alcohols but also the flash points of organic compounds for engineering.
出处
《石油化工高等学校学报》
EI
CAS
2007年第1期85-89,共5页
Journal of Petrochemical Universities
基金
国家自然科学基金资助项目(No.29936110)
关键词
人工神经网络
闪点
分子结构描述符
脂肪醇
Artificial neural networks
Flash point
Molecular structure descriptor
Fatty alcohol
