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In this study, a multilayer perceptron (MLP) network is proposed to predict the thermal conductivity (λ) of an electrolyte solution at atmospheric pressure, over a wide range of temperatures (T) and concentrations (x) based on the molecular weight (M) and number of electrons (n) of the solute. The accuracy of the proposed artificial neural network (ANN) was evaluated through performing a regression analysis on the predicted and experimental values of various aqueous solutions, some of which were not used in the network training. The comparison of the developed MLP network to other correlations recommended in the literature indicates that the proposed neural network outperforms other alternative methods, with respect to accuracy as well as extrapolation capabilities. Besides, others’ conductivity correlations are usually suggested for a specific electrolyte solution and a limited range of temperatures and concentrations, while such limitations do not exist for the proposed MLP network.

Prediction of gas thermal conductivity is crucial in the heat transfer process. In this article, we develop a novel method to estimate conductivities of binary gaseous mixtures at atmospheric pressure. The method is a neural network scheme consisting of two consecutive multilayer perceptrons (MLPs). The first MLP estimates pure component conductivities as a function of critical temperature, critical pressure, molecular weight, and temperature. The conductivities calculated in the first MLP as well as molecular weights of both compounds and mole fraction of the light components are fed to the second MLP to predict the thermal conductivity of the mixture. The proposed model was trained and tested through a large set of experimental data over wide ranges of temperatures, compositions, and substances. Comparing the test and training results indicates that the accuracy of the neural model is remarkably better than other alternative methods proposed in the literature. Conventional conductivity correlations requ...

Steam distillation could improve the oil recovery efficiency during the steam injection enhanced oil recovery process. Because of its immense effects on oil recovery, it is important to investigate the main parameters of steam distillation as well as the effects of oil and reservoir properties during this thermal process. In this work, the simulation of batch steam distillation is performed on 18 sets of crude oil found in the literature. The developed model is highly compatible with respect to the input oil properties that can also characterize the oil with minimum entry. The calculated distillates were compared to the experimental data, and the results show an average relative error of 13.74% for 15 sets of crude oil data, each calculated at 20 different points. According to this study, the superheat conditions of steam and the amount of light oil fractions have the greatest effect on the distillation yields, while the steam saturation conditions have less considerable effects. It was also found that th...