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.
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.
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.
The control of deposit precursors formation resulting from the oxidative degradation of alternative fuels relies strongly on the understanding of the underlying chemical pathways. Although C8–C16 n-alkanes are major constituents of commercial fuels and well-documented solvents, their respective reactivities and selectivities in autoxidation are poorly understood. This study experimentally investigates the influence of chain length, temperature (393–433 K), purity, and blending on n-alkanes autoxidation kinetics under concentrated oxygen conditions, using both Induction Period (IP) and speciation analysis. It also numerically constructs new detailed liquid-phase chemical mechanisms for n-C8–C14 obtained with an automated mechanism generator. Macroscopic reactivity descriptors such as IP, combined to microscopic ones, obtained from GC-MS analyses, are herein used to emphasize similarities and discrepancies in n-alkanes autoxidation processes. Experimental results highlight a nonlinear IP evolution with n-alkanes chain length, a linear IP variation for two component paraffinic blends, and similarities among oxidation product families. Experimental data from the present study and from the literature are used to evaluate n-C8–C14 mechanisms on IP and on monohydroperoxides (ROOH) concentrations. Under pure O2 conditions, mechanisms generally predict IPs within a factor of 3 for intermediate and high temperature and even lower when air is used instead of pure oxygen. In addition, the chain length impact is also well reproduced, with a reactivity increase from C8 to C12 and a plateau for higher chain length. Rate of Consumption (RoC) analyses of n-C8 and n-C12 mechanisms evidenced the main role of peroxy radicals in autoxidation through fuel consumption, and ROOH and polyhydroperoxides (R(OOH)2) formation.
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