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Thermodynamic calculations using a simulated annealing optimization algorithm

In Chemical Engineering, several thermodynamic calculations can be formulated as optimization problems with or without restrictions. As indicated by Henderson et al. [1] the formulation of thermodynamic calculations for optimization problems offers some advantages: a) the use of a robust optimization method, b) the possibility of using a direct optimization method which requires only calculations of the objective function and c) the use of an iterative procedure whose convergence is almost independent on the initial guesses. Some examples of these calculations are phase stability analysis, phase equilibrium problems, parameter estimation in thermodynamic models, calculation of critical points, among others. These problems are non-linear, multivariable and the objective function used as optimization criterion is non-convex with several local optimums. By consequence, its solving with local optimization methods is not reliable because they generally converge to local optimums. During the last years, the development and application of global optimization strategies have increased in many areas of Chemical Engineering. Global optimization methods can be classified as deterministic and stochastic [2]. The first class offers a guarantee to find the global optimum of the objective function [21, 32, 24]. However, these strategies often require high computational time (generally more time than stochastic methods) and in some cases the problem reformulation is needed. In the other hand, stochastic optimization methods are robust numerical tools that present a reasonable computational effort in the optimization of multivariable functions; they are applicable to ill-structure or unknown structure problems and can be used with all thermodynamic models [3]. Thermodynamic Calculations Using a Simulated Annealing Optimization Algorithm