Prediction of standard heat of combustion using two-step regression

Abstract

Heat of combustion is a thermochemical property that is used for assessing the heating value of solid and liquid fuels as well as the calorific value of food and supplements. It is also used to identify fire hazards of hazardous materials. Heat of combustion has many applications across diverse areas including in jet fuel and propellant formulations, the disposal of combustible waste, the study of foods and supplements for humans and animals, as well as in ecological studies. This study proposes a simple and predictive model for predicting standard heat of combustion. This model was developed using a group contribution approach. The group contribution method represents chemicals according to 220 first-order and 130 second-order groups. The first-order groups are simple groups that describe a wide variety of chemicals, whereas the second-order groups describe polyfunctional compounds and are used to differentiate between isomers. In this study, 680 experimental data points comprising the standard heat of combustion for pure chemicals were collected from open literature. This data set represents various types of groups. The group contributions were regressed using linear regression in MATLAB, yielding an R2 value of 0.9993 with SD, AAE, and ARE values of 71.9892, 53.1008, and 4.6162. The proposed model was found to be predictive and capable of predicting the heat of combustion of various chemicals, which are not only limited to hydrocarbons but also include chemicals that contain groups of alcohol, ester, ether, amine, amide, aromatic, halogen, and sulfur.