Density prediction of liquid alkali metals and their mixtures using an artificial neural network method over the whole liquid range

作者：Sabzevari Shiva; Moosavi Majid^*

来源：Fluid Phase Equilibria, 2014, 361: 135-142.

DOI：10.1016/j.fluid.2013.10.044

• Alkali metal
• Alkali metal alloy
• Density
• Artificial neural network

In this study, the application of artificial neural network (ANN) method in predicting the density of alkali metals and their mixtures is investigated. A total number of 595 different data points of these compounds were used to train, validate and test the model. A typical three-layer feedforward backpropagation neural network has been trained by the Levenberg Marquardt algorithm. The tansig-tansig transfer functions with 15 neurons in the hidden layer makes the least error, so a network with (8-15-1) structure was used to design the ANN model. The average relative deviations for train, validation, and test sets are 0.1029, 0.1396, and 0.1002, respectively. A comparison between our results and those obtained from some previous works shows that this work, as an excellent alternative, can provide a simple procedure to predict the density of these compounds in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions.

• 出版日期2014-1-15