Determination of the Boyle temperature of pure gases using artificial neural networks

The Boyle temperature represents the temperature at which a real gas behaves more like an ideal gas. It is an useful thermodynamic parameter but it is difficult to measure experimentally. Thus, in this work we provide the Boyle temperatures of 16 pure gases, namely: argon, carbon dioxide, carbon monoxide, deuterium, helium-3, helium-4, hydrogen, krypton, methane, neon, nitric oxide, nitrogen, oxygen, tetrafluoromethane, tritium and xenon. The Boyle temperatures of the gases were determined by setting to zero their second virial coefficient (B) functions. Starting from 1663 available experimental data of B, an artificial neural network (ANN) was trained to correlate the second virial coefficient of the gases to two parameters: the reduced temperature and the critical molar volume. In order to obtain results as accurate as possible, different ANN configurations were tested, with variable numbers of neurons/hidden layers and up to 10000 iterations. It was found that the best configuration consists of 2 hidden layers with 24 neurons each. The ANN root-mean-square error was compared to those of other correlations available in literature and resulted to be very low, being equal to 2.74 cm3/mol. The temperatures determined by setting the ANN mapping of B to zero are comparable to those obtained from accurate curve fittings and, by virtue of their low deviations, can be adopted for calculation and as reference for experimental measures.