Heuristic Optimization Techniques for Air Gap Membrane Distillation System

Abstract

Heuristic approaches are proposed and implemented to optimize the air gap membrane distillation (AGMD) system for water desalination. A mathematical model to predict the permeate flux of the AGMD system was developed based on the analysis of heat and mass transfers within the module. The developed mathematical model is linked to two optimization algorithms, namely the ant colony optimization (ACO) and particle swarm optimization (PSO). These algorithms have been developed and employed to find the optimal set of variables for the maximum permeate flux of distilled water. The considered variables included feed water temperature, coolant water temperature, air gap width, feed flow rate, and coolant flow rate. The PSO provides comparatively better solution and is computationally less intensive. The differences between the optimum values of flux from the two techniques were found to be less than 3%. Effects of extending the limits (ranges) of different variables were considered.