Optimization of Pressure Drop in a Spouted Bed via Genetic Algorithm

Abstract

Dropping of pressure drop (PD) across the spouted bed could reduce the dissipated pumping energy and improve stability and uniformity of solid particles. The selected decision variables are; gas velocity, solid's density and solid's diameter. Steady-state measurements were carried out in the 60^° conical shape spout-air bed. Concentration of solid particles (glass and steel beads) at various elevations of the bed under different flow patterns were measured  by using sophisticated optical probes. Optimization technique helps the decision makers to select the best set of operating conditions. Stochastic genetic algorithm has found suitable for the non-linear hybrid spouted bed. Optimum results would provide the design and operation of the bed. It has been found that the low-density glass beads of high -particle diameter at low gas' velocity, could obtain minimum PD. Particle's density is the effective variable on PD. Velocity of gas and diameter of solid particle have found the sensitive decision variables with PD changing. The sensitivity of the variables could be increased at unlimited upper bounds.

Keywords: Genetic algorithm; Optimization; Pressure drop; Spouted bed; Solid particles