Evaluation of integrated differential evolution and unified bare-bones particle swarm optimization for phase equilibrium and stability problems

Phase equilibrium calculations and phase stability analysis play a significant role in the simulation, design and optimization of separation processes in chemical engineering. These are very challenging problems due to the high non-linearity of thermodynamic models. Global optimization methods are required in order to solve these complex, non-convex optimization problems. Recently, stochastic global optimization algorithms were applied to solve these problems. However, these optimization methods have some parameters that need to be tuned in order to obtain good reliability and efficiency. In this study, we introduce three global optimization algorithms developed by our group for phase and chemical equilibrium calculations, namely, unified bare-bones particle swarm optimization (UBBPSO), integrated differential evolution (IDE) and IDE without tabu list and radius (IDE_N), which have fewer control parameters to be tuned. The performance of these three stochastic algorithms is tested and compared in order to identify their relative strengths for phase equilibrium and phase stability problems. The phase equilibrium problems include both without and with chemical reactions. Our results show that the effectiveness of the stochastic methods tested depends on the stopping criterion. Overall, IDE has achieved better performance for the phase equilibrium, chemical equilibrium and phase stability problems.

► Three optimization methods are tested for phase equilibrium and stability calculations.
► Algorithm parameters are self-adapted to enhance the performance and robustness.
► Integrated differential evolution (IDE) is the best among the three algorithms.
► IDE is more reliable than SA, GA and DETL for reactive phase equilibrium calculations.
► Stopping criterion can significantly reduce the computational time.