Natural gas properties and flow computation

Abstract

The detailed procedure for the calculation of thermodynamic properties based on formulations explicit in Helmholtz energy (Lemmon & Starling, 2003) and on AGA-8 detail characterization equation (Starling & Savidge, 1992) is given in (ISO-207651-1, 2005). Here we elaborate an alternative procedure for the calculation of properties of a natural gas that was originally published in the Journal Flow Measurement and Instrumentation (Maric, 2005 & 2007). The procedure is derived using fundamental thermodynamic equations (Olander, 2007), DIPPR AIChE (DIPPR® Project 801, 2005) generic ideal heat capacity equations, and AGA-8 (Starling & Savidge, 1992) extended virial-type equations of state. The procedure specifies the calculation of specific heat capacities at a constant pressure cp and at a constant volume cv, the JT coefficient μJT, and the isentropic exponent κ of a natural gas. The effect of a JT expansion on the accuracy of natural gas flow rate measurements will be pointed out. The possibilities of using the computational intelligence methods - Artificial Neural Networks - ANNs (Ferrari & Stengel, 2005, Wilamowski et al., 2008) and machine learning tools - Group Method of Data Handling - GMDH (Ivakhnenko, 1971, Nikolaev & Iba, 2003) for meta-modeling the effects of natural gas properties in flow rate measurements (Maric & Ivek, 2010) will be illustrated. The practical examples of ANN and GMDH surrogate models for the compensation of natural gas flow rate measurement error caused by the thermodynamic effects, with the corresponding accuracies and execution times will be given. The models are particularly suitable for implementation in low computing power embedded systems.