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

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Chinese Journal of Chemical Engineering 2017年第5期25卷 547-554页

作者： Mostafa Lashkarbolooki Ali Zeinolabedini Hezave mahdi bayat School of Chemical Engineering Babol University of Technology Islamic Azad University Dashtestan Branch Department of Chemical Engineering Faculty of EngineeringUniversity of Bojnord

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 *** 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.

关键词： Thermal conductivity Artificial neural network Critical properties Hydrocarbons Aromatics

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Germanium separation and purification by leaching and precipitation

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Journal of Central South University 2016年第9期23卷 2214-2222页

作者： Saeid bayat Sajjad Aghazadeh Mohammad Noaparast mahdi Gharabaghi Behrooz Taheri School of Mining Engineering College of Engineering University of Tehran

In this research work, extraction and purification of germanium from zinc leach residues(ZLR) were investigated. The results of ICP, XRF, and atomic adsorption spectroscopy(AAS) tests show that contents of germanium, iron, lead, and zinc within the leaching residue were 105×10^(-6), 3.53%, 10.35%, and 8.8%, respectively. XRD results indicate that the main minerals were in different forms of sulfates(CaSO_4·2H_2O, PbSO_4 and ZnSO_4·6H__2O), silicate(SiO_2), and oxide(Fe_2O_3). Dissolution of leaching filter cake was carried out using 5 parameters and each in 4 levels(acid concentration, temperature, time, liquid-to-solid ratio, and stirring speed) by Taguchi method(L_(16)), and then optimization of the effective parameters by response surface method. Under optimum conditions, zinc and germanium dissolution efficiencies were 88.71% and 8%, respectively. Leaching tests with sulfuric acid(added di-ammonium oxalate monohydrate) and hydrochloric acid(HCl) on the residues obtained from previous-stage sulfuric acid dissolution, yielded germanium and iron recoveries of 83%, 88%, 40%, and 90%, respectively. Thus, leaching experiment with sulfuric acid(added di-ammonium oxalate monohydrate) was superior to that with hydrochloric acid due to high and low extraction amounts of germanium and iron, respectively. Precipitation experiments revealed that germanium purification with tannic acid presented a better result compared to sodium hydroxide and ammonia. Under optimum conditions, contents of germanium and iron in the solution after precipitation were 0.1505% and 14.7% with precipitation yields of 91% and 52%, respectively.

关键词： experimental design di-ammonium oxalate monohydrate germanium leaching, tannic acid

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