Steam tables including superheated, saturated and compressed region were simultaneously modeled using the neural networks. Pressure and temperature were used as two inputs for superheated and compressed region. On the other hand Pressure and dryness fraction were two inputs for saturated region. The outputs were specific volume, specific enthalpy and specific entropy. The neural network model were compared with the linear interpolation model in terms of the percentage relative errors. The criterion of judgement was selected with the percentage relative error of 1%. In conclusion the neural networks showed better results than the interpolation method for all data of superheated and compressed region and specific volume of saturated region, but similar for specific enthalpy and entropy of saturated region.

Simultaneous modelling was carried out using the neural networks with three inputs including a distinguishing variable for the steam table. It covered whole steam tables including the compressed, saturated and superheated region of water. And relative errors of the thermodynamic properties such as specific volume, enthalpy, entropy were compared using the neural networks and the linear interpolation method. As a result of the analysis, The neural networks has proven to be powerful in modeling the steam table because it has slightly better results than the interpolation method.

The state variables of saturated and superheated region in the steam table were simultaneously modeled using the neural networks. And the results were compared with quadratic spline interpolation and Lagrange interpolation. Two input data without distinguishing parameter were used in the neural networks. For comparison, quadratic spline interpolation method for superheated region and Lagrange interpolation method for saturated region were applied. The overall results revealed that the neural networks were greatly superior to quadratic interpolation method or Lagrange interpolation method.

The steam table in saturated and superheated region was modeled simultaneously using the neural networks. A variable was introduced to distinguish between the saturation and the superheat. The relative errors were compared with the quadratic spline interpolation method. The relative errors by the neural networks were superior to those by the quadratic spline interpolation method over almost all ranges of temperatures and properties. The overall errors in the saturated region were better than those in the superheated region. From the analysis, it was confirmed that the neural networks could be a very powerful tool for simultaneous modeling of superheated and saturated steam table

The thermodynamic state variables in superheated region of steam table are not wholy obtained by measurements. This means that steam table contains a little error. In this study small error was artificially added to superheated variables and modeled using neural networks. The results were compared with the analysis using quadratic spline interpolation method. By and large the relative errors of variables by neural networks were sufficiently small and similar to or less than those by quadratic spline interpolation method. It was concluded that neural networks could be one good way of modeling for superheated steam table.