Optimization of Coke Rate and Sinter Quality by Genetic Algorithm for Two-Layer Sintering of Iron Ore

Two layer sintering by charging green sinter mix with normal coke rate in the upper layer and reduced coke rate in the lower layer, can substantially reduce the coke rate and also improve the sinter quality by producing more uniform thermal profile through out the bed height. The two-layer sintering process have been analyzed by numerical simulation of the process by a detailed CFD based model, considering all the important phenomena like gas-solid reaction, melting and solidification, flow through porous bed, heat and mass transfer etc. Optimization techniques like genetic algorithm, differential evolution is then applied to evaluate the optimum coke rate in the two layers of the bed to produce the ideal thermal profile and melting fraction in the sinter bed for optimum sinter quality. Through this optimization method both high quality sinter with minimum return fines can be achieved along with reduced coke rate. Application of a genetic algorithm for this type of process optimization has several advantages over the traditional optimization techniques since it can identify global optimum condition and perform multi-objective optimization much easily for complex industrial process like iron ore sintering. Keywords: Two-layer Sintering, Iron Ore Sinter Quality, Ergun's Equation, Gas-solid reaction, Melting Fraction, Multi Objective Optimization, Pareto Set, Genetic Algorithm