Genetic Algorithm-Based Multicriteria Optimization of Ironmaking in the Blast Furnace

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Abstract
A method has been developed for optimizing ironmaking in the blast furnace with the aim to minimize costs and CO2 emissions. These two goals are pursued by a genetic algorithm yielding states of operation on a Pareto-optimal front with nondominated solutions. The blast furnace process is described mathematically by a thermodynamic simulation model, where realistic operational constraints are imposed. The states on the Pareto-optimal fronts evolved are analyzed in more detail, considering the constraints of the process. The solutions are found to give rise to clearly different specific emissions but very similar specific costs as long as the production stays within the limits of the granted CO2 emissions allowances of the plant. However, this also implies that the costs of ironmaking may rise considerably along with increased prices of the allowances or reduced emission rights. The findings of the work are expected to be valuable in the strategic evaluation of future ironmaking options.

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