On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods

Abstract

We report the development of predictive models for two fuel specifications: melting points (T m) and net heat of combustion (Δc H). Compounds inside the scope of these models are those likely to be found in alternative fuels, i.e. hydrocarbons, alcohols and esters. Experimental T m and Δc H values for these types of molecules have been gathered to generate a unique database. Various quantitative structure–property relationship (QSPR) approaches have been used to build models, ranging from methods leading to multi-linear models such as genetic function approximation (GFA), or partial least squares (PLS) to those leading to non-linear models such as feed-forward artificial neural networks (FFANN), general regression neural networks (GRNN), support vector machines (SVM), or graph machines. Except for the case of the graph machines method for which the only inputs are SMILES formulae, previously listed approaches working on molecular descriptors and functional group count descriptors were used to develop specific models for T m and Δc H. For each property, the predictive models return slightly different responses for each molecular structure. Therefore, models labelled as ‘consensus models’ were built by averaging values computed with selected individual models. Predicted results were then compared with experimental data and with predictions of models in the literature.