Optimization of gas cyclone design using evolutionary computing approach

The concept of Evolutionary Computing method covers the process of searching for an optimal solution inspired by natural evolution. It can also be viewed as a family of trial and error problem solvers which can be considered as global optimization methods with a metaheuristic or stochastic optimization concept, characterized by the use of a population of candidate solutions. Such methods include Genetic Algorithm, Particle Swarm Intelligence and Differential Evolution among others. The conventional approach adopted in the design process is to use mathematical models and sensitivity approach to obtain relevant optimal design parameters. Accurate computation and optimization of the design parameters of new equipment is one of the main concerns of design engineers. The goal here is to apply evolutionary computing methods to design a gas cyclone with optimum design parameters taking into cognisance that the optimization process is complicated which requires an extensive search of a very large input space. The motivation of this research effort is the avoidance of complex mathematical models and sensitivity approach for gas cyclone design. The result shows that a hybrid Differential Evolution based Particle Swarm Optimization outperformed standard Genetic Algorithm, Particle Swarm Intelligence and Differential Evolution.