Abstract
Though a global optimization procedure using a randomized algorithm and a commercial process simulator is relatively easy to implement for complex design problems (i.e., intensified design processes), a dominant problem is their heavy computation load. As the process simulation is repeatedly executed to calculate the objective function, it is inevitable to spend long computation time to derive the optimal solution. Also, the randomized algorithms consider the treatment of all variables as continuous. Thus, the reduction of the number of iterations is crucial for such optimization procedures that include integer variables. In this work, an estimation procedure of the objective function having integer design variables is proposed. In the proposed procedure, the values of the objective function at the nodes of hyper-triangle that includes the suggested next search point are used to estimate the objective function, at the same time normalization of the design optimization variables is recommended. The procedure was implemented on the simulated annealing stochastic algorithm with a trivial case of a binary mixture in order to know the optimal solution and compare the traditional optimizations procedures and the proposed one. The proposed procedure show improvement not only for reducing the number iterations, but also for an increase of accuracy of finding the optimal solution.
Highlights
With the new tendencies of the intensified processes, many designs had been proposed
It is necessary to improve the efficiency of these algorithms, because of their use in solving optimization problems have been increased in order to find new intensified process
It can be noticed how the performance of the optimization improves with the combination of the options, and exist and improvement of the efficacy to find the optimal solution
Summary
With the new tendencies of the intensified processes, many designs had been proposed. The advantages and disadvantages have been widely discussed Both approaches have tackled the global optimization problems for intensified processes (Floudas and Gounaris, 2009; Ricardez-Sandoval et al, 2009; Yuan et al, 2012; Segovia-Hernández et al, 2015). Randomization approaches are increasing their popularity due to their relatively easy implementation in combination with software frameworks (Cabrera-Ruiz et al, 2012; Vazquez-Castillo et al, 2009; Gitizadeh, et al, 2013; Santaella et al, 2014). As both approaches show advantages in the disadvantages with respect to each other, hybrid optimization has been suggested as a way to complement each other (Grossmann and Westerberg, 2000). It is necessary to improve the efficiency of these algorithms, because of their use in solving optimization problems have been increased in order to find new intensified process
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