Optimization of continuous distillation columns using stochastic optimization approaches

Abstract

The present work describes the use of two stochastic optimization formalisms, namely, genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA), for the optimization of continuous distillation columns. Both the simple and azeotropic systems are considered in the analysis. In particular, for a specified degree of separation the problem of finding the optimal values of: (i) the number of stages, (ii) reflux ratio (entrainer quantity in the case of azeotropic distillation), (iii) feed location(s), have 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, and continuity criteria, (iii) robustness of the algorithm. The other optimization technique used in the study i.e., SPSA, is a rapid gradient-descent related method for multivariate optimization and is especially well-suited in situations where direct computation of the objective function gradient is not feasible, or the objective function measurements could be noisy. The feasibility of utilizing the GA and SPSA techniques has been demonstrated by considering the separation of three binary and two azeotropic systems of industrial relevance.