Use of artificial neural networks for calculating derived thermodynamic quantities from volumetric property data

Thermodynamic and transport property data on environmentally acceptable refrigerant fluids are of the utmost interest for the refrigeration industry and, in particular, for designing and optimising refrigeration equipment: heat exchangers and compressors. Up to now, the simultaneous representation of vapour-liquid equilibria (VLE) and pressure-volume-temperature (PVT) data is not satisfactory enough with respect to experimental accuracies. New models are then highly required. Therefore, an effort has been made to develop an alternative to a classical equation of state. This work deals with the potential application of artificial neural networks to represent PVT data within their experimental uncertainty. The second aim of the work is to obtain, by numerical derivatives, other properties such as enthalpies, entropies, heat capacities, expansion coefficients, speed of sounds, etc. Tests presented here were performed on data corresponding to six refrigerants from 240 to 340 K at pressures up to 20 MPa. (C) 2003 Elsevier Science B.V. All rights reserved.