An improved ant colony optimization method and its application for the thermodynamic modeling of phase equilibrium

• Ant colony optimization is applied for phase stability, phase equilibrium and parameter estimation problems.
• Ant colony optimization is a reliable stochastic method for parameter estimation especially in error-in-variable formulation.
• Ant colony optimization is an alternative method for global optimization in phase equilibrium modeling.

Ant colony optimization (ACO) is a stochastic optimization method that emulates the indirect communication among the individuals of an ant colony. This multi-agent strategy has been used to solve optimization problems from engineering applications, especially for combinatorial problems. Although ACO seems to be effective for global optimization, only few ACO algorithms have been reported for solving engineering applications problems with continuous decision variables. In this study, a new continuous ACO algorithm, with feasible region selection, has been implemented and applied to perform thermodynamic calculations related to the modeling of phase equilibrium. In particular, the numerical capabilities of this ACO algorithm have been tested in the global optimization of thermodynamic functions from parameter estimation, phase equilibrium and phase stability problems. These thermodynamic calculations are relevant for chemical engineering process simulators. However, they are classed as challenging nonconvex optimization problems with continuous decision variables. The applicability and effectiveness of the proposed ACO algorithm, namely ACOFRS, have been studied using a challenging set of thermodynamic benchmark problems with both liquid–liquid and vapor–liquid equilibrium. Results show that the proposed ACOFRS is an alternative method for performing global optimization in phase equilibrium calculations of multicomponent systems. In particular, ACOFRS is more robust for solving VLE parameter estimation problems and it outperformed other stochastic optimization methods such as Particle Swarm Optimization, Differential Evolution and Genetic Algorithms.

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