Van der Waals Mixing Rules for Cubic Equations of State

Benmekki, E. H.; Kwak, Tae-Hoon; Mansoori, G. Ali

doi:10.1021/bk-1987-0329.ch009

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mansoori and coworkers made an interesting advance when they showed that the VDW mixing rules (VDWl) were not theoretically suited for use with other cubic EOS's and derived the appropriate rules for the RK and PR equations (Benmekki et al, 1987;Kwak and Mansoori, 1986). Park et al (1987) also showed how the new mixing rules (VDW2) conformed to conformal solution theory.…”

Section: Eos-lattice Gas and Scaling Formsmentioning

confidence: 99%

Phase equilibria for supercritical fluid process design

1989

View full text Add to dashboard Cite

Supercritical fluids (SCF's) are powerful solvents with many unique properties. They have great potential for many extraction processes, but reliable and versatile mathematical models of the phase equilibrium thermodynamics are needed for their use in process design and economic feasibility studies. This paper reviews existing experimental data and analytical methods and attempts to delineate their strengths and limitations. Also discussed are new data needs and possible new directions for a better fundamental understanding of the molecular processes in SCF solutions.

show abstract

Section: Eos-lattice Gas and Scaling Formsmentioning

confidence: 99%

Phase equilibria for supercritical fluid process design

1989

View full text Add to dashboard Cite

show abstract

“…The Peng-Robinson EOS gives the following explicit expression for pressure as a function of specific volume, temperature and mixture coefficients: The values of a mix and b mix are often calculated using the following mixture rules [104]:…”

Section: B1 Pressurementioning

confidence: 99%

Solving vapor-liquid flash problems using artificial neural networks

Poort

Ramdin

Kranendonk³

et al. 2019

Fluid Phase Equilibria

View full text Add to dashboard Cite

Vapor-liquid phase equilibrium (flash) calculations largely contribute to the total computation time of many process simulation models. As a result, process simulations, especially dynamic cases, are limited in the amount of detail that can be included due to time restrictions. In addition, under certain conditions flash calculations can fail to provide acceptable results. In this work, artificial neural networks were investigated as a potentially faster and more robust alternative to conventional flash calculation methods. Classification neural networks were used to determine the phase stability of a given mixture of fluids, while regression networks were used to make predictions of thermodynamic property values. In addition to conventional flash types such as the constant pressure, constant temperature (P T ), and constant pressure, constant entropy (P S) flash, neural networks are used to develop two concept flash types: a constant entropy, constant volume (SV ), and a constant enthalpy, constant volume (HV ) flash. All neural networks were trained on, and compared to, data generated using the P T -flash algorithm from the Thermodynamics for Engineering Applications (TEA) property calculator. Data was generated for mixtures of water and methanol over a wide range of pressures and temperatures. The artificial neural networks showed speed improvements over TEA of up to 35 times for phase classification and 15 times for property predictions. Overall phase classification accuracy scores of around 97% were achieved. Average property value prediction errors range between 0.5% and 7% when compared to the spread in magnitude of the test data, and R 2 scores were in the general order of 0.95 and higher, although classification and property prediction in the two-phase region showed markedly higher errors than properties in the pure liquid or vapor regions. Moreover, thermodynamic consistency and the stability of a system consisting of multiple neural network flash types both still require considerable improvement. Finally, this work shows that artificial neural networks can be used to create unconventional flash types such a the SV -and HV -flash.i

show abstract

“…The energetic interaction between components A and B was controlled by the implementation of a geometric mixing rule applied to a A and a B [18,28,29]:…”

Section: A Two Van Der Waals Fluidsmentioning

confidence: 99%

Lattice Boltzmann simulation of mixtures with multicomponent van der Waals equation of state

Ridl

Wagner

2018

Phys. Rev. E

View full text Add to dashboard Cite

We developed a general framework for simulating multicomponent and multiphase systems using the lattice Boltzmann framework. Despite the fact that there is no restriction on the number of components in principle, in this article we focus an application to two-component mixtures, but we also demonstrate that the algorighm works for larger numbers of components. To validate our algorithm we separately minimized this underlying free energy to generate theoretical phase diagrams for mixtures of fluids with a van der Waals-like free energy. All the theoretical phase diagrams are well recovered by our lattice Boltzmann method.

show abstract

Van der Waals Mixing Rules for Cubic Equations of State

Cited by 7 publications

References 0 publications

Phase equilibria for supercritical fluid process design

Phase equilibria for supercritical fluid process design

Solving vapor-liquid flash problems using artificial neural networks

Lattice Boltzmann simulation of mixtures with multicomponent van der Waals equation of state

Contact Info

Product

Resources

About