Computation of Critical Points of Mixtures Using Particle Swarm Optimization with Low-Discrepancy Sequences

Abstract

The prediction of critical points of thermodynamic systems is an important tool for modeling many high-pressure processes of theoretical and practical interest. In this article, the calculation of critical points of multicomponent mixtures is treated as a global minimization problem of a modified merit function associated with the criticality conditions obtained from the Gibbs tangent plane criterion, designed to discriminate the scale of the problem. The methodology used to solve the optimization problem is based on two versions of the Particle Swarm Optimization (PSO), equipped with Lowdiscrepancy sequences to prevent the sensitivity of the swarm with respect to the location of the initial population. To avoid a rapid decrease of the weight inertia, and to prevent stagnations near undesirable local minimizers, we present a modification of the PSO method which uses different search cycles with the same inertia weight. This new version developed here is a fast and robust algorithm for solving the critical-point problem, via global optimization.

Reliable mixture critical point computation using polynomial homotopy continuation

“…Our results are presented in Table . The two critical points identified vary significantly ( ) from those reported by Henderson . The difference is characteristic of the use of slightly different pure component and binary interaction parameters.…”

“…We investigated the eight‐component mixture initially studied by Nagarajan et al and examined on two different occasions by Henderson using stochastic global optimization . Both the SRK and PR EOS were utilized in identifying the critical points, with pure component properties and non‐zero binary interaction parameters for the C 7 – C 16 and pseudofluid taken from Saber and Shaw .…”

“…However, it is clear that the high and low critical points have corresponding values from the PR EOS. This mixture highlights one of the advantages of NPHC: Henderson reports the low temperature critical point as being a strong attractor, appearing in approximately 90% of their results. NPHC is entirely oblivious to basins of attraction of solution, and will always find all solutions.…”

“…Henderson et al used the Gibbs tangent plane criterion to formulate the calculation as an optimization problem, employing simulated annealing to locate global minima. Other stochastic methods applied include differential evolution and particle swarm optimization . More recently, a comprehensive study comparing various nature‐inspired metaheuristic algorithms has been published .…”

Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

“…The outcome depicted that because of the considered algorithm, the union turns out to be quicker and the last arrangement is more exact than any other time. The work in [44] considered the determination of the basic purposes of multicomponent combinations as a global minimization issue of improved legitimacy work, which is intended to separate the scope of the issue. The method is applied to determine the global improvement issue.…”