Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.

Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids

In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network(ANN) with principle component analysis(PCA) based on back propagation training with28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.

Artificial synaptic and self-rectifying properties of crystalline(Na_(1-x)K_(x))NbO_(3)thin films grown on Sr_(2)Nb_(3)O_(10)nanosheet seed layers

Crystalline(Na_(1-x)K_(x))NbO_(3)(NKN)thin films were deposited on Sr_(2)Nb_(3)O_(10)/TiN/Si(S-TS)substrates at 370°C.Sr_(2)Nb_(3)O_(10)(SNO)nanosheets served as a template for the formation of crystalline NKN films at low temperatures.When the NKN film was deposited on one SNO monolayer,the NKN memristor exhibited normal bipolar switching characteristics,which could be attributed to the formation and destruction of oxygen vacancy filaments.Moreover,the NKN memristor with one SNO monolayer exhibited artificial synaptic properties.However,the NKN memristor deposited on two SNO monolayers exhibited self-rectifying bipolar switching properties,with the two SNO monolayers acting as tunneling barriers in the memristor.The conduction mechanism of the NKN memristor with two SNO monolayers in the highresistance state is attributed to Schottky emission,direct tunneling,and Fowler–Nordheim(FN)tunneling.The current conduction in this memristor in the low-resistance state was governed by direct tunneling and FN tunneling.Additionally,the NKN memristor with two SNO monolayers exhibited large ON/OFF and rectification ratios and artificial synaptic properties.Therefore,an NKN memristor with two SNO monolayers can be used for fabricating artificial synaptic devices with a cross-point array structure.