In the present article, Genetic Algorithm (GA) has been applied to estimate the rate parameters from experimental data during reduction of iron-ore/graphite composite pellet in a tailor made high temperature, high mass laboratory scale packed bed reactor. The reduction of iron-ore has been considered in three elementary steps, namely hematite to magnetite, magnetite to wustite and wustite to iron. Assuming all the above conversions follow the first order kinetics, the mass balance equations have been setup to define the rate of conversion of the various phases of oxide, as well as metallic iron. The unknown in the mass balance equations, namely three sets of frequency factors and values of activation energy, have been estimated by coupling the mass balance equations with the GA. The optimum values of the rate parameters have been obtained by minimizing the difference between the predicted and experimental degree of reduction. After optimization, a very close agreement between the experimental and predicted degree of reduction is obtained. The model has been utilized to calculate the evolution of various phases of oxide as well as metallic iron during reduction. Based on model predictions, it was found that hematite depletes fastest in the pellet, followed by magnetite, which passes through a maxima. Wustite initially build up and the gradually decreases while iron increases steadily. It is also seen that amount of metallic iron production increased with temperature. Temporal evolution of various oxide phases and metallic iron was also obtained through XRD and microstructural studies, which were found to be consistent with those predicted through kinetic model. The application of GA to estimate the rate parameters for iron ore reduction is unique and applied successfully for the first time.

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