Optimization of continuous distillation columns using stochastic optimization approaches

The genetic algorithms (GAs) and simultaneous perturbation stochastic appro ximation (SPSA), for the optimization of continuous distillation columns. B oth the simple and azeotropic systems are considered in the analysis. In pa rticular, for a specified degree of separation the problem of finding the o ptimal values of: (i) the number of stages, (ii) reflux ratio (entrainer qu antity in the case of azeotropic distillation), (iii) feed location(s), hav e been addressed. The GA-based optimization has several attractive features such as: (i) convergence to the global rather than to a local minimum, (ii ) the objective function need not satisfy smoothness, differentiability, an d continuity criteria, (iii) robustness of the algorithm. The other optimiz ation technique used in the study i.e., SPSA, is a rapid gradient-descent r elated method for multivariate optimization and is especially well-suited i n situations where direct computation of the objective function gradient is not feasible, or the objective function measurements could be noisy. The f easibility of utilizing the GA and SPSA techniques has been demonstrated by considering the separation of three binary and two azeotropic systems of i ndustrial relevance.