In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to correlate and predict the thermal conductivity from the triple point temperature up to 0.98 times critical temperature (T c) for 23 refrigerants and 11 n-alkanes. It requires the temperature (T) as well as the molecular mass (M), acentric factor (ω), critical temperature, and critical pressure (P c) as input variables. The optimal ANN model is obtained by a trial-and-error procedure and consists of the input layer and the output layer together with one hidden layer with seven neurons. This ANN model can not only correlate the thermal conductivity but also accurately predict the thermal conductivity of refrigerants and n-alkanes. The correlation coefficients (R) in the training and testing phases are 0.9994 and 0.9993, respectively. Furthermore, the average absolute deviation (AAD) values are less than 1% for 14 out of 34 fluids, less than 2% for 28 fluids, and less than 4.5% for all the considered fluids.

A kind of highly transparent Also3 ceramics is fabricated in a way of reducing t i e scattering origins such as the boundary scattering and t i e facies-differenae scattering. Tbe ceramics is a bopeful substitute o f tbe optoelectronic detector windows made of c r y s t d n eA l 2 0 3 witb several advantages such as lower cost, improvement of detecting accuracy and increase of the damage tbreshold etc.

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