Abstract
It is required to maintain silicon content in hot metal ([Si]) at a stable level to ensure smooth operation of the blast furnace ironmaking process. However, current blast furnace control strategy always leads to frequent fluctuation of silicon content in hot metal. To stabilize blast furnace operation, this article attempts to identify the optimum control centre of silicon content through exploring the operational data of blast furnace ironmaking process. A quantitative analysis of the impact of thermal state on the smelting efficiency and intensity is presented by combining wavelet denoising and fuzzy c-means (FCM) clustering. Simulation results show that the commonly adopted mean value of historical data is not necessarily the optimum state of blast furnace operation. There exists some optimum state lower than the mean value, under which higher smelting efficiency and intensity can be achieved. It is also proved that the “low silica smelting practice” attempt in the steel industry is feasible and meaningful.
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