Particle swarm optimization technique for the optimal design of plate-type distillation column

Abstract

The present study explores the application of non-traditional optimization technique, Particle swarm optimization (PSO), for the optimal design of plate-type distillation column from economic point of view. The optimization procedure involves the selection of the major plate geometric parameters such as hole diameters, ratio of downcomer area to tower area, weir height, fractional hole area in tray, tray spacing, tower diameter, etc. and minimization of total annual cost is considered as design target subjected to operational constraints like flooding, weeping entrainment, quality specifications, etc. The solution space of such type of problem is very complex due to the presence of various constraints and may contain multiple minima. In this chapter, recently proposed PSO method employing feasibility rule for handling constraints is applied for the optimal design of the plate-type distillation column. In contrast to the traditional penalty function method, the rule requires no additional parameters and can guide the swarm to the feasible region quickly. In addition, to avoid the premature convergence, simulated annealing (SA) is applied to the best solution of the swarm to help the algorithm escape from local optima. The presented PSO technique is simple in concept, few in parameters, and easy for implementations. Furthermore, the PSO algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. One case study is presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The PSO approach is able to reduce the total cost of distillation column as compared to cost obtained by commercial simulator.