Prediction of Henry’s Law Constant of Organic Compounds in Water from a New Group-Contribution-Based Model

Abstract

In this work, a new model is presented for estimation of Henry’s law constant of pure compounds in water at 25 °C (H). This model is based on a combination between a group contribution method and neural networks. The needed parameters of the model are the occurrences of a new collection of 107 functional groups. On the basis of these 107 functional groups, a feed forward neural network is presented to estimate the H of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation error, and root-mean-square error of the model over a diverse set of 1940 pure compounds used are, respectively, 0.9981, 2.84%, 2.4, and 0.1 (all the values obtained using log H based data). Therefore, the model is a comprehensive and an accurate model and can be used to predict the H of a wide range of chemical families of pure compounds in water better than previously presented models.

Prediction of blood:air and fat:air partition coefficients of volatile organic compounds for the interpretation of data in breath gas analysis

“…The training dataset of organic compounds with measured Henry’s constant values was assembled from four different sources: Compilation of measured Henry’s law constants provided by Sander [42] (214 compounds).PHYSPROP database (411 compounds) containing data stemming from an article by Katriztky et al (1998) [43]. Compilation of Henry’s law constants published by Dearden et al (2007) containing directly and indirectly measured Henry’s constant values [44] (940 compounds).Yaws’ Handbook of Thermodynamic and Physical Properties of Chemical Compounds [45] (1940 compounds). All values of Henry’s constant were converted to atm × m 3 × mol −1 . This unit corresponds to the Henry’s constant expressed as the ratio between gas partial pressure and its water concentration.…”

An accurate model for prediction of autoignition temperature of pure compounds

“…Using the Neural Network toolbox of the MATLAB software (Mathworks ® Inc.), a three layer Feed Forward Artificial Neural Network (FFANN) is developed for the problem. The capabilities of this kind of ANNs have been demonstrated in the previous works [13][14][15][28][29][30][31][32][33][34][35][36][37][38][39][40][41].…”

“…The simplest method for this purpose is assumption of existence of a multi-linear relationship between these groups and the desired property (here is the AIT of pure compounds) [13][14]. This technique is a similar method used in the most of classical group contribution methods.…”

Predictive Modeling of Henry’s Law Constant in Chemical Structures Using LSSVM and ANFIS Algorithms

“…The fates of different organic materials in environment extensively depend on various processes especially transfer of chemical materials between aqueous and air phases [1]. For the compounds in water, the Henry's law constant is known as one of the utmost important process's parameter [2].…”