Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods

Abstract

In the present work, we report the development of models for the prediction of two fuel properties: flash points (FPs) and cetane numbers (CNs), using quantitative structure property relationship (QSPR) approaches. Compounds inside the scope of the QSPR models are those likely to be found in alternative jet and diesel fuels, i.e., hydrocarbons, alcohols, and esters. A database containing FPs and CNs for these types of molecules has been built using experimental data available in the literature. Various approaches have been used, ranging from those leading to linear models, such as genetic function approximation and partial least squares, to those leading to nonlinear models, such as feed-forward artificial neural networks, general regression neural networks, support vector machines, and graph machines. Except for the case of the graph machine method, for which the only inputs are the simplified molecular input line entry specification (SMILES) formulas, previously listed approaches working on molecular descriptors and functional group count descriptors were used to build specific models for FPs and CNs. For each property, the predictive models return slightly different responses for each molecular structure. Thus, final models labeled as "consensus models" were built by averaging the predicted values of selected individual models. Predicted results were compared with respect to experimental data and predictions of existing models in the literature. Models were used to predict FPs and CNs of molecules for which to the best of our knowledge there is no experimental data in the literature. Using information in the database, evolutions of properties when increasing the number of carbon atoms in families of compounds were studied.

Rational Formulation of Alternative Fuels using QSPR Methods: Application to Jet Fuels

“…The methodology used to develop models concerning the prediction of the targeted properties is fully documented in previous papers [20,21]. The main steps are described in Figure 2.…”

“…Indeed, in our previous work we have seen that, when averaging the predictions of different models, the robustness of the resulting consensus model is better than that of the individual models used to build it [20,21]. This is why, in this work, this methodology has been applied and all possible models (i.e., weighted combinations of individual model predictions) were ranked based on their performance on the validation set.…”

“…For each property, a final performance test of the generalization ability was carried out by looking at the performance of the best consensus model performed on the test set. Full details about the methodology and validation of models are given in our previous works [20,21].…”

“…Moreover, the FP of alcohols increases, at fixed number of Tendencies of the FP evolution for some hydrocarbons when increasing n, the number of carbon atoms along the principal chain in some hydrocarbons. The iso-number of carbon atom curves are represented in dashed gray (extracted from the work of Saldana et al) [20]. carbon atoms, from primary alcohol to secondary alcohol and tertiary alcohol.…”

“…Tendencies of the FP evolution for some alcohols with the parameter n. The parameter n denotes either the number of carbon atoms along the principal chain or the position of a specific functional group. The dotted line presents FP values for n-paraffins (extracted from the work of Saldana et al) [20]. Distribution of hydrocarbons in five SPK fuels using GC*GC [30].…”