Predicted Flash Points Research Articles

Improved Prediction of Hydrocarbon Flash Points from Boiling Point Data

Flash points (TFP) of hydrocarbons are calculated from their flash point numbers, NFP, with the relationshipTFP(K)=23.369NFP2/3+20.010NFP1/3+31.901In turn, the NFP values can be predicted from experimental boiling point numbers (YBP) and molecular structure with the equationNFP=0.987YBP+0.176D+0.687T+0.712B−0.176where D is the number of olefinic double bonds in the structure, T is the number of triple bonds, and B is the number of aromatic rings. For a data set consisting of 300 diverse hydrocarbons, the average absolute deviation between the literature and predicted flash points was 2.9 K.

Calculating Flash Point Numbers from Molecular Structure: An Improved Method for Predicting the Flash Points of Acyclic Alkanes

We report a novel method for calculating flash points of acyclic alkanes from flash point numbers, NFP, which can be calculated from experimental or calculated boiling point numbers (YBP) with the equationNFP=1.020YBP−1.083Flash points (FP) are then determined from the relationshipFP(K)=23.369NFP2/3+20.010NFP1/3+31.901For a data set of 102 linear and branched alkanes, the correlation of literature and predicted flash points has R2 = 0.985 and an average absolute deviation of 3.38 K. NFP values can also be estimated directly from molecular structure to produce an even closer correspondence of literature and predicted FP values. Furthermore, NFP values provide a new method to evaluate the reliability of literature flash point data.

Evaporation characteristics of multi-component liquid

Evaporation rate of multi-component liquid such as motor gasoline is expected to change greatly with progress of evaporation because its composition changes. Therefore it is difficult to predict accurately amount of generated combustible vapor in the case that a multi-component liquid is spilt on a floor. Then, we assumed an evaporation model that composition of the vapor obeys the Raoult's law which considers the activity coefficient. Using the model, we calculated the vapor composition, the vapor pressure and the evaporation rate of the liquid. Then, comparison was done between the calculated values and the measured values obtained in this study. “Weight loss fraction” was used as a parameter signifying the progress of evaporation. The variations of the evaporation property were accurately predicted by the model. We derived a prediction model of the amount of generated combustible vapor from the relation between weight loss fraction and evaporation rate. It was found that the amount of generated vapor was expressed as a logarithmic function of time in case of a five-component liquid. Furthermore, we showed that the predicted flash point of a liquid using the composition obtained from the model was in good agreement with an experimental result.

Changes in evaporation rate and vapor pressure of gasoline with progress of evaporation

The evaporation properties of motor gasoline are expected to change markedly with the progress of evaporation because gasoline is a multi-component fuel. The aim of this paper was to develop a prediction model of the amount of vapor generated from gasoline spill. The risks associated with gasoline spills can be accurately evaluated by the models. Degraded samples of regular gasoline and high-octane gasoline were prepared by leaving them under conditions of no wind at 20 °C, and their vapor pressures and flash points were measured. The evaporation rate was measured by determining weight loss using an electronic balance, and the relation between the weight loss fraction and the evaporation rate was investigated. “Weight loss fraction” was used as a parameter signifying the progress of evaporation and expressed the changes in vapor pressure and evaporation rate as a function of the weight loss fraction. The vapor pressure and the evaporation rate could be shown by exponential functions of the weight loss fraction, and a prediction model of the amount of gasoline vapor was obtained. Furthermore, the prediction model of the flash point of degraded gasoline was derived from the relational expression of temperature and vapor pressure, and the predicted flash points were compared with the measured values.

Quantitative Structure–Property Relationship Studies for Predicting Flash Points of Organic Compounds using Support Vector Machines

AbstractA Quantitative Structure–Property Relationship (QSPR) model was developed to predict the flash points of organic compounds. The widely used group contribution method was employed, and a new collection of 57 functional groups were selected as the molecular descriptors. The new chemometrics method of Support Vector Machine (SVM) was employed for fitting the possible quantitative relationship that existed between these functional groups and flash points. A total of 1282 organic compounds of various chemical families were used and randomly divided into a training set (1026) and an external prediction set (256).The optimum parameters of the SVM were obtained by employing the leave‐one‐out cross‐validation method. Simulated with the final optimum SVM, the results show that most of the predicted flash point values are in good agreement with the experimental data, with the average absolute error being 6.894 K, and the root mean square error being 11.367 for the whole dataset, which are lower than those obtained by previous works. Moreover, by employing the convenient group contribution method as well as the large modeling dataset, the presented model is also expected to be simple to apply and with a wide applicability range.

Prediction of the flash points of alkanes by group bond contribution method using artificial neural networks

A group bond contribution model using artificial neural networks, which had the high ability of nonlinear of prediction, was established to predict the flash points of alkanes. This model contained not only the information of group property but also connectivity in molecules. A set of 16 group bonds were used as input parameters of neural networks to study the correlation of molecular structures with flash points of 44 alkanes. The results showed that the predicted flash points were in good agreement with the experimental data that the absolute mean absolute error was 6.9 K and the absolute mean relative error was 2.29%, which were superior to those of traditional group contribution methods. The method can be used not only to reveal the quantitative correlation between flash points and molecular structures of alkanes but also to predict the flash points of organic compounds for chemical engineering.

Quantitative structure–property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network

Models of relationships between structure and flash point of 92 alkanes were constructed by means of artificial neural network (ANN) using group bond contribution method. Group bonds were used as molecular structure descriptors which contained information of both group property and group connectivity in molecules, and the back-propagation (BP) neural network was employed for fitting the possible nonlinear relationship existed between the structure and property. The dataset of 92 alkanes was randomly divided into a training set (62), a validation set (15) and a testing set (15). The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [9-5-1], the results showed that the predicted flash points were in good agreement with the experimental data, with the average absolute deviation being 4.8 K, and the root mean square error (RMS) being 6.86, which were shown to be more accurate than those of the multilinear regression method. The model proposed can be used not only to reveal the quantitative relation between flash points and molecular structures of alkanes, but also to predict the flash points of alkanes for chemical engineering.

Estimation of flash points and molecular masses of alkanes from their IR spectra

The use of transformed infrared spectra as descriptors of a molecular structure makes it possible to predict flash points and relative molecular masses of alkanes. The prediction does not require additional information on the structure of molecules.

Note: Correlation of flash points

AbstractA general correlation has been obtained for prediction of closed‐cup flash points of organic compounds from their normal boiling points and specific gravity. Predicted flash points are within an average deviation of 8.301 of the reported values.