The Multi-Objective Optimization of Blast Furnace Oxygen Enrichment Rate Based on Optimal Carbon Ratio

Abstract

The NSGA-II algorithm was used to establish a multi-objective optimization model for the oxygen enrichment rate of a blast furnace in terms of achieving a lower fuel ratio and higher pulverized coal ratio. The model has the hearth temperature as the constraint condition and the oxygen enrichment rate as the decision variable. The NSGA-II algorithm was used to obtain the Pareto optimal solution of the multi-objective optimization scheme. The prediction effect of the optimization scheme was then tested in industrial experiments. The results show that the optimal setting of the oxygen enrichment rate predicted by the model was 2.70%, which provides an optimal fuel ratio and pulverized coal ratio of 553.86 kg·tHM−1 and 144.58 kg·tHM−1, respectively. In actual production, when the oxygen enrichment rate was set at 2.71%, an optimal fuel ratio and pulverized coal ratio of 553.74 kg·tHM−1 and 148.73 kg·tHM−1 were obtained. The relative error in the oxygen enrichment rate between the model prediction and the actual prediction was 0.003%. The prediction results of the model suggest a reduction in CO2 emissions by 25,770.71 tons per year. The CO2 emission reduction in actual production was approximately 1.09 times the prediction of the model.