The Z-factor
Z(p, T) correlations for fluid mixtures provide fast computational procedures using the fluid mixture properties rather than multi-component procedures of Equation of State.
These correlations are usually modelled through the pseudo-reduced fluid properties
(T_{pr}, P_{pr}):
T_{pr} = T/T_{pc} | Pseudo-reduced temperature | T_{pc} | Pseudo-critical temperature |
P_{pr} = P/P_{pc} | Pseudo-reduced pressure | P_{pc} | Pseudo-critical pressure |
Charts
Implicit Correlations
These correlations are quite accurate and work in a wide range of pressures and temperatures but computationally expensive and may have problems with convergence when approaching the critical temperature.
Explicit Correlations
The explicit correlations do not have convergence issues, generate smooth derivatives for compressibility calculations and provide fast computing.
| Artificial Neural Network correlations |
| Ahmed (2017) |
| Kareem (2016) |
| Brill & Beggs (1973) |
| Standing-Katz (1942) |
| Sanjari and Nemati’s Correlation (2012) |
| Azizi, Behbahani and Isazadeh’s Correlation (2010) |
| Heidaryan, Moghdasi and Rahimi’s Correlation (2010) |
See also
Natural Science / Physics /Thermodynamics / Equation of State / Z-factor
[ Pseudo-Critical Point Correlations @model ]