Hence, the enthalpy (hf) of saturated liquid and the entropy (sf) of saturated liquid have values of approximately zero at 32F.

The values of enthalpy and entropy given in these tables are measured relative to the properties of saturated liquid at 32F. Figure A-2 shows a portion of a typical saturated steam temperature table and a portion of a typical saturated steam pressure table. Normally, the saturated steam tables are divided into two parts: temperature tables, which list the properties according to saturation temperature (Tsat) and pressure tables, which list them according to saturation pressure (Psat). The saturated steam tables give the energy transfer properties of saturated water and saturated steam for temperatures from 32 to 705.47F (the critical temperature) and for the corresponding pressure from 0.08849 to 3208.2 psi. Some tables use v for n (specific volume) because there is little possibility of confusing it with velocity. The following notation is used in steam tables. Both sets of tables are tabulations of pressure (P), temperature (T), specific volume (n), specific enthalpy (h), and specific entropy (s). Portions of the tables are shown in Figure A-2. Promising results obtained with this technique are highlighted in the present paper, demonstrating the reliability of using ANNs in lieu of polynomials algorithms and look-up tables.Steam tables consist of two sets of tables of the energy transfer properties of water and steam saturated steam tables and superheated steam tables. This new approach avoids the limitations of the traditional methods and can be advantageously implemented in heat and mass balance programs to speed up calculations. ANNs enable the production of user-friendly software, which furthermore increases the computational speed while sustaining good accuracy. This paper aims to show the potential of ANNs for generating the water/steam tables.

On the other hand, there is a group of useful tools, called Artificial Neural Networks (ANNs), that have been successfully applied for pattern recognition and function approximation tasks in, for instance, the areas of medicine, engineering, and economics. The traditional methods for implementing these tables in software imply either the use of polynomial algorithms, which demand long iteration times, or look-up tables, which require a large memory capacity. The industrial formulations for the thermodynamic properties of water/steam, which are approximations of the scientific one, are intended to be used in applications where computational speed is of importance, such as in power plant modelling and control.

Steam tables online