Prediction of Flash Points for Fuel Mixtures Using Machine Learning and a Novel Equation

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

Simulations of Interfacial Tension of Liquid–Liquid Ternary Mixtures Using Optimized Parametrization for Coarse-Grained Models

In this work, liquid–liquid systems are studied by means of coarse-grained Monte Carlo simulations (CG-MC) and Dissipative Particle Dynamics (DPD). A methodology is proposed to reproduce liquid–liquid equilibrium (LLE) and to provide variation of interfacial tension (IFT), as a function of the solute concentration. A key step is the parametrization method based on the use of the Flory–Huggins parameter between DPD beads to calculate solute/solvent interactions. Parameters are determined using a set of experimental compositional data of LLE, following four different approaches. These approaches are evaluated, and the results obtained are compared to analyze advantages/disadvantages of each one. These methodologies have been compared through their application on six systems: water/benzene/1,4-dioxane,water/chloroform/acetone, water/benzene/acetic acid, water/benzene/2-propanol, water/hexane/acetone, and water/hexane/2-propanol. CG-MC simulations in the Gibbs (NVT) ensemble have been used to check the validity of parametrization approaches for LLE reproduction. Then, CG-MC simulations in the osmotic (μ_soluteN_solventP_<i>zz</i>T) ensemble were carried out considering the two liquid phases with an explicit interface. This step allows one to work at the same bulk concentrations as the experimental data by imposing the precise bulk phase compositions and predicting the interface composition. Finally, DPD simulations were used to predict IFT values for different solute concentrations. Our results on variation of IFT with solute concentration in bulk phases are in good agreement with experimental data, but some deviations can be observed for systems containing hexane molecules

Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods

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

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

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

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

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

Damage severity descriptions used for visual evaluation of microprojections.

<p>All images were categorized into four groups according to the degree of surface damage. (1) extensive – the cell surface was destroyed and cytoskeleton was exposed; (2) moderate – all microprojections were damaged and appeared flat or normal; (3) minimal – microprojections were slightly damaged; and (4) normal – microprojections showed no damage.</p

Figure 5

<p>A. Measurement of epithelial surface microprojection density and height. Objective measurement of the density (Dn) and height (H) of vocal fold epithelial surface microprojections in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions after 30, 60, and 120 minutes of phonation. Measurement bar represents 1 µm. B. Objective examination of epithelial surface microprojection density using TEM. The number of microprojections per 10 µm field in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions after 30, 60, and 120 minutes. * Denotes a significant difference between groups (p<0.0167). C. Objective examination of epithelial surface microprojection height using TEM. Average height (µm) of microprojections in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions after 30, 60, and 120 minutes. * Denotes a significant difference between groups (p<0.0167).</p

Figure 4

<p>A. SEM representative images for control, modal intensity, and raised intensity phonation. Representative 2×2 µm SEM images for the control, modal intensity, and raised intensity phonation conditions after 30, 60, and 120 minutes of phonation. Measurement bar represents 0.5 µm. B. Visual examination of epithelial surface microprojections. Classification of images by damage severity using routine visual examination of epithelial surface microprojections. Four categories of damage severity: extensive (black fill), moderate (dark gray fill), minimal (light gray fill), and normal (white fill) in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions after 30, 60, and 120 minutes of phonation. C. Objective examination of epithelial surface microprojection density using SEM. The percentage of vocal fold surface covered by microprojections in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions after 30, 60, and 120 minutes of phonation. * Denotes a significant difference between groups (p<0.01).</p

Figure 6

<p>A. Measurement of depth of the remaining viable cell surface. Objective measurement of the depth (Dp) of the remaining viable cell surface layer in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions after 30, 60, and 120 minutes of phonation. Measurement bar represents 4 µm. B. Objective examination of depth of the remaining viable cell surface using TEM. Average depth (µm) of the remaining viable cell surface in the control (C), modal intensity phonation (M), and raised intensity phonation (R) conditions for 30, 60, and 120 minutes. * Denotes a significant difference between groups (p<0.0167).</p

SEM image of a rabbit vocal fold.

<p>Scanning electron microscopy image of a rabbit vocal fold. Large box represents the central portion of the middle one-third region of the vocal fold. Small box represents a 2×2 µm image used for SEM analysis.</p