Multiple criteria in a top gas recycling blast furnace optimized through a k-optimality-based genetic algorithm

A steel plant flow sheet containing a top gas recycling blast furnace is simulated and subjected to multi-objective optimization through an evolutionary approach. A recently proposed k-optimality criterion is used, which allows optimizing a large number of objectives in an evolutionary way, which is difficult to do by other methods. A number of promising optimum results, showing the optimum tradeoffs between several cost factors are identified and analyzed. The results appear to be very significant in the context of CO₂ reduction challenges faced by the steel industries today.