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 neur...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.展开更多
From the measurement of liquid flow field on a large plate by a hot-film anemometer, three different regions on the large plate were presented, including a liquid circulation area near the inlet down-comer, a region w...From the measurement of liquid flow field on a large plate by a hot-film anemometer, three different regions on the large plate were presented, including a liquid circulation area near the inlet down-comer, a region with very slow moving or stagnant liquid on the side of the tray and an active flow region at the center of the plate. According to the contribution of the three regions, the tray efficiency for large plates was proposed. The prediction plate efficiency by the present model are compared with the experimental data in the literature and those calculated by other models. It is shown that the present model is more accurate for prediction of efficiency of large plates, and the calculation is simpler.展开更多
文摘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.
基金Supported by the National Natural Science Foundation of China(No. 20176037)
文摘From the measurement of liquid flow field on a large plate by a hot-film anemometer, three different regions on the large plate were presented, including a liquid circulation area near the inlet down-comer, a region with very slow moving or stagnant liquid on the side of the tray and an active flow region at the center of the plate. According to the contribution of the three regions, the tray efficiency for large plates was proposed. The prediction plate efficiency by the present model are compared with the experimental data in the literature and those calculated by other models. It is shown that the present model is more accurate for prediction of efficiency of large plates, and the calculation is simpler.
