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.

An extension of the PPC-SAFT equation of state to treat strong electrolyte aqueous solutions is presented. It is capable of describing the behavior of such systems up to 473 K with a good precision and without requiring temperature-dependent model parameters. Long-range Coulombic interactions are taken into account using the mean spherical approximation (MSA) for a primitive model of the electrolyte solution, and the effect of solvation is described using short-range ion–water interactions mediated through association sites. Pairing between anions and cations is also treated through site–site interactions. A Born term is added to describe the change of dielectric constant resulting from solvation. A single ion-specific, temperature-independent model parameters are used, for 20 alkali-halide aqueous solutions. The Pauling ionic diameters are used for all terms (hard sphere, MSA, and Born). The dispersion energy of the ions is considered negligible. In the resulting ePPC-SAFT model, only the water–ion association energy is considered as an adjustable parameter. The results show coherent energy density behavior with respect to ionic size. The approach allows the calculation of the mean ionic activity coefficient, density, or vapor pressure of the aqueous solutions over a wide range of temperatures and molalities (298–573 K and 0–6 m). Moreover, salting out of carbon dioxide and methane in saline water can also be predicted accurately. A discussion of the changes in ion hydration at different salinity is also presented taking advantage of the model proposed that explicitly includes ion–water site–site interactions.

In the present work, temperature dependent models for the prediction of densities and dynamic viscosities of pure compounds within the range of possible alternative fuel mixture components are presented. The proposed models have been derived using machine learning methods including Artificial Neural Networks and Support Vector Machines. Experimental data used to train and validate the models was extracted from the DIPPR database. A comparison between models using an ample range of molecular descriptors and models using only functional group count descriptors as inputs was performed, and consensus models were created by testing different combinations of the individual models. The resulting consensus models’ predictions were in agreement with the available experimental data. Comparisons were also made between predictions of our models and correlations validated by the DIPPR staff. Our models were used to predict densities and dynamic viscosities of compounds for which no experimental data exists. Our models were also used to estimate other properties such as kinematic viscosities, critical temperatures, and critical pressures for compounds in the database. Finally, predictions were used to study the main trends of density and viscosity at the aforementioned temperatures as a function of the number of carbon atoms for chemical families of interest.

No abstract

In this work, a set of computationally efficient, yet accurate, methods to predict flash points of fuel mixtures based solely on their chemical structures and mole fractions was developed. Two approaches were tested using data obtained from the existing literature: (1) machine learning directly applied to mixture flash point data (the mixture QSPR approach) using additive descriptors and (2) machine learning applied to pure compound properties (the QSPR approach) in combination with Le Chatelier rule based calculations. It was found that the second method performs better than the first with the available databank and for the target application. We proposed a novel equation, and we evaluated the performance of the resulting, fully predictive, Le Chatelier rule based approach on new experimental data of surrogate jet and diesel fuels, yielding excellent results. We predicted the variation in flash point of diesel−gasoline blends with increasing proportions of gasoline.