Modelling the Vapor-Liquid Equilibrium of Polymer Solutions Using a Cubic Equation of State

Gomes, Jefferson L. M. A.; Henderson, Nélio; Rocha, Marisa Cristina Guimarães

doi:10.1002/1521-3919(20011101)10:9<816::aid-mats816>3.0.co;2-m

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…The SA is a stochastic method for obtaining good solutions to difficult optimization problems which has been received much attention over the last few years [6,10,11], whose convergence does not depend on initial guesses and gives preference to global optima. The cooling schedules employed here (inherent part of SA, and fundamental for a good performance of the algorithm) are presented in Section 3.…”

Section: Simulated Annealing Algorithm (Sa)mentioning

confidence: 99%

Novel approach for the calculation of critical points in binary mixtures using global optimization

Freitas

Platt

Henderson

2004

Fluid Phase Equilibria

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Section: Simulated Annealing Algorithm (Sa)mentioning

confidence: 99%

Novel approach for the calculation of critical points in binary mixtures using global optimization

Freitas

Platt

Henderson

2004

Fluid Phase Equilibria

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“…The nonlinearity and multimodality of the problem described in eq have required the use of global optimization methods. Thus, many deterministic − or stochastic − algorithms have been used to obtain global minimizers of TPD ( x ).…”

Section: Introductionmentioning

confidence: 99%

Phase Stability Analysis using a Polarization Technique and the Randomness of a Stochastic Method in an Unconstrained Optimization Framework

Henderson

Sartori

Sacco

2014

Ind. Eng. Chem. Res.

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This article deals with the phase stability analysis of multicomponent systems via the determination of all the stationary points of the so-called tangent plane distance function, which is known as an important but difficult problem of the thermodynamics of phase equilibrium, where high nonlinearities are inherent aspects of the different thermodynamic models commonly used in the description of this distance function. To analyze phase stability described from different models, we combine three basic procedures here that were selected in order to attack points that we deem of relevant difficulty. To this end, we encapsulate in a single iterative algorithm the following steps: (i) polarization of a merit function associated with first-order stationary conditions of the phase stability problem, in order to avoid (or at least minimize) the repetition of stationary points previously calculated; (ii) a stochastic optimization method, which (at each iteration of the encapsulation algorithm) minimizes m times the same merit function polarized with the predetermined stationary points, using m different seeds for a reliable random number generator (where m is a given integer), in order to calculate new stationary points; (iii) a change of variables designed to allow the numerical method to work in the unconstrained optimization framework, providing the location of stationary points near the boundary of the original feasible set. Using the NRTL and UNIQUAC models for liquid–liquid equilibria at low pressures and the Soave–Redlich–Kwong and Peng–Robinson cubic equations of state for vapor–liquid equilibria at high pressures, we analyze 9 multicomponent systems studied in the literature, equipped with different feed compositions, totaling 32 tested mixtures, whose component numbers ranged between 3 and 12. We show 14 new stationary points obtained here that were not detected by methods previously used by other authors.

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Prediction of critical points: A new methodology using global optimization

2004

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Modelling the Vapor-Liquid Equilibrium of Polymer Solutions Using a Cubic Equation of State

Cited by 6 publications

References 31 publications

Novel approach for the calculation of critical points in binary mixtures using global optimization

Novel approach for the calculation of critical points in binary mixtures using global optimization

Phase Stability Analysis using a Polarization Technique and the Randomness of a Stochastic Method in an Unconstrained Optimization Framework

Prediction of critical points: A new methodology using global optimization

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