Parameter estimation for cubic equations of state models subject to sufficient criteria for thermodynamic stability

• Published in: Chemical engineering science Vol. 192; pp. 981 - 992
• Main Authors: Glass, Moll, Djelassi, Hatim, Mitsos, Alexander
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 31.12.2018
• Subjects: Bilevel program; Cubic equation of state; Data regression; False liquid-liquid split; Tangent criterion; Cubic equation of state; PR; LLE; BLP; LICQ; UCST; TPD; SRK; False liquid-liquid split; LBD; LLP; NLP; VL(L)E; LHS; UBD; NRTL; Data regression; CEOS; IC; Tangent criterion; LBP; RHS; EC; PC-SAFT; UNIFAC; MINLP; TB; KKT; RK; SIP; Bilevel program
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2018.08.033

[Display omitted] •Regression of CEOS models s.t. isopotential only leads to wrong parameter values.•Wrong parameter values cause erroneous phase equilibria in process simulation.•We propose bilevel program for regression that leads to correct parameter values.•Bilevel program can handle any number of real roots to CEOS, and discriminates them.•Illustrative isothermal VLE of C5H12/H2S using PR and SRK is considered. A formulation for parameter estimation in cubic equations of state (CEOS) models for phase equilibrium thermodynamics is proposed. This formulation guarantees for the regressed parameters that the predicted mole fractions correspond to stable equilibria, when standard methods fail and demonstrably entail erroneous process simulation results. The present formulation overcomes these deficiencies, which is predicated on a bilevel structure extending Mitsos et al. (2009a). That is, an upper-level (parameter fitting) problem is minimized, subject to multiple lower-level problems, which encode thermodynamic stability. The CEOS constitutes an equality constraint on the lower level, which adds to the difficulty of the bilevel program. For the VLE of C5H12/H2S, it is demonstrated that the method permits an acceptable fit with physically sensible CEOS root values. Thus, the regressed parameter values may be applied to, e.g., process simulation.