Multiple Objective Optimization Of A Coal Grinding Process Via Simple Genetic Algorithm (SGA)

This paper presents the application of a simple genetic algorithm (SGA) to a coal slurry grinding process in order to find the optimum set of conditions under which the desired slurry properties can be attained. The operating parameters of the process were; slurry % solids, dispersant dosage, ball size, and mill speed. The optimization algorithm, which is based upon the mechanics of natural genetics, was used to find near-global optimum points specific to responses measured In terms of fineness, specific energy consumption, viscosity coefficient, and viscosity exponent. In order to minimize the viscosity of the slurry and simultaneously achieve a condition of maximum packing density, optimization was carried out by the penalty method using an overall objective function incorporating several responses. Computer results and analysis demonstrate the ability of the genetic algorithm to perform better than a traditional technique such as response surface methodology (RSM) and to rapidly locate "the global optimum".