137 research outputs found
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General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling
A general and efficient technique is developed to predict the thermal conductivity of well-characterized hydrocarbon mixtures, rocket propellant (RP) fuels, and jet fuels up to high temperatures and high pressures (HTHP). The technique is based upon entropy scaling using the group contribution method coupled with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state. The mixture number averaged molecular weight and hydrogen to carbon ratio are used to define a single pseudo-component to represent the compounds in a well-characterized hydrocarbon mixture or fuel. With these two input parameters, thermal conductivity predictions are less accurate when the mixture contains significant amounts of iso-alkanes, but the predictions improve when a single thermal conductivity data point at a reference condition is used to fit one model parameter. For eleven binary mixtures and three ternary mixtures at conditions from 288 to 360 K and up to 4,500 bar, thermal conductivities are predicted with mean absolute percent deviations (MAPDs) of 16.0 and 3.0% using the two-parameter and three-parameter models, respectively. Thermal conductivities are predicted for three RP fuels and three jet fuels at conditions from 293 to 598 K and up to 700 bar with MAPDs of 14.3 and 2.0% using the two-parameter and three-parameter models, respectively
Prediction of surface tension of ionic liquid based on imidazolium using artificial neural network
Nowadays, with the progresses in technology to solve problems where there is no exact mathematical relationship between input and output, neural networks are efficiently proposed and used. In the shadow of its unique features, in this study, two multilayer perceptron neural networks including feedforward artificial neural network (FFANN) and cascade artificial neural network (CANN) were proposed to predict the surface tension of imidazolium-based ionic liquids. To verify the validity of the proposed models, 1251 experimental data points were collected from various previously published literature including the surface tension of 40 ionic liquids in a wide range of temperatures (from 263.61 to 533.2 K). The results showed that the proposed CANN consists of three inputs including molecular weights of anionic and cationic part of ionic liquid and temperature with a hidden layer containing 8 neurons with a hyperbolic tangent activation function and trained with Levenberg–Marquardt algorithm has the best correlative capability for surface tension of ionic liquids. In addition, error analysis of test data set with an average absolute relative deviation percent of 1.07 indicates the appropriate performance of the nonlinear CANN model in the linking between network inputs and surface tensions. Also, comparing the accuracy of the proposed model with existing models, including the corresponding states principle, Parachor, the group method of data handling (GMDH) and the model based on least-squared supported vector machine (LSSVM) indicate the superiority of the proposed model
The role of CO2 and ion type in the dynamic interfacial tension of acidic crude oil/carbonated brine
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