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.

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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.

We report the development of predictive models for two fuel specifications: melting points (T(m)) and net heat of combustion (Δ(c)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 Δ(c)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 Δ(c)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.

In this work, we propose a new methodology to determine association scheme and association parameters (energy and volume) of a SAFT-type EoS for hydrogen-bonding molecules. This paper focuses on 1-alkanol molecules, but the new methodology can also be applied for any other associating system. The idea is to use molecular simulation technique to determine independently monomer and free hydrogen fractions from which the association scheme can be deduced. The 3B scheme thus appeared to be the most appropriate for 1-alkanols. Once the association scheme is defined, the association strength can be back-calculated from molecular simulation results and used as an independent property for the equation of state parameters regression, in addition of the classical phase properties such as vapor pressure and liquid molar volume. A new set of parameters for 1-alkanol for the PPC-SAFT equation of state has been proposed following this methodology. Results are found in good agreement with experimental data for both phase properties and free hydrogen-bonding sites. Hence, this new methodology makes it possible to optimize parameters allowing an accurate reproduction of pure compounds data and yielding physically significant values for associating energy and associating volume.

Résumé -Caractérisation des asphaltènes et modélisation de la floculation -État de l'Art -Ce travail présente un état de l'art concernant la floculation des asphaltènes avec deux grands items : la caractérisation des asphaltènes dans différents milieux et la modélisation de la floculation. Cette revue commence par une caractérisation des asphaltènes proprement dite puis des caractérisations de ces derniers dans des solvants synthétiques ou des bruts. Nous insistons sur les techniques de détermination de masses molaires et de diffraction. L'incidence d'un ajout de bon/mauvais solvant est également discutée. La seconde partie de cette revue concerne la modélisation de la floculation. Elle examine les différents modèles de la littérature avant de présenter celui de Szewczyk et Béhar (1999) et son intégration dans un simulateur de réservoir. Les modèles disponibles requièrent encore des données expérimentales délicates à acquérir et un effort de recherche doit être réalisé pour améliorer cet état de fait. Abstract -Characterisation of Asphaltenes and Modelling of Flocculation -State of the Art -This document describes a state of the art concerning the flocculation of asphaltenes with two major sections: characterisation of asphaltenes in various environments and modelling of flocculation. This review starts with the characterisation of asphaltenes as such, then their characterisation in synthetic solvents or crudes. Emphasis is placed on molar mass determination and diffraction techniques. The impact of adding a good/poor solvent is also discussed. The second part of this review is dedicated to modelling.We first examine the various models in the literature before describing the Szewczyk and Béhar (1999) model and its integration in a reservoir simulator. The models available still require experimental data which is difficult to collect and further research is needed to improve this situation.Petroleum Industry Applications of Thermodynamics Applications de la thermodynamique dans l'industrie pétrolière