A hybrid optimization approach to interaction parameter identification in thermodynamic model problems

The interaction parameter identification problem in thermodynamic models is an important requirement and a common task in many areas of chemical engineering because these models form the basis for synthesis, design, optimization and control of process. For bad starting values the use gradient based result in local optimal solutions. To overcome this drawback, a global optimization approach, Simulated Annealing (SA) and genetic algorithm(GA), has been coupled with a Nelder-Mead Simplex(NMS) method. To improve the accuracy of the interaction parameter estimate. The experimental ternary coefficient model. In conclusion, the different obtained results of the prediction of liquid-liquid equilibrium are compared. These results were obtained to justify that the process of optimization recommended is very practical the interaction parameters of this ternary system.