Multiple Linear Regressionamp;horbar;Artificial Neural Network Hybrid Model Predicting Heat of Vaporization of Pure Organic Compound at Normal Boiling Point

摘要

invention is hydrogen (H), carbon (C), nitrogen (N), oxygen (O) , sulfur (S) consists of elements, such as less than 5 kinds and provides a mathematical model for predicting the heat of vaporization of the normal boiling point of the pure organic compound consisting of not more than 25, the number of atoms other than hydrogen molecules at a high accuracy. The model is for a number of organic compounds is the experimental value of the heat of vaporization of the normal boiling point that satisfies the condition is known, any of a variety of molecules presenter (molecular descriptor) as independent variables, the heat of vaporization of the normal boiling point Multiple linear regression model to many as the dependent variable (multiple linear regression model) obtained after using a genetic algorithm (genetic algorithm) that one of the best, receives the value of the molecules presenters included with this model of the normal boiling point evaporation heat output of ANN (artificial neural network) to further improve the prediction performance was composed by multiple linear regression-ANN hybrid model (hybrid model) as QSPR (quantitative structure-property relationship) model and an example, the model included the If known, the specific value of the molecular presenter any molecule that way, gives to predict the heat of vaporization of the normal boiling point of the pure compound made by this molecule. As such, the present invention can maintain the cost and time savings by giving the experiment to provide a way to predict the value of the heat of vaporization of the boiling point in a normal reliable for the number of the organic compound of the experimental conditions is unknown, research in the relevant industry lays effects such as to facilitate the development activities. ;