During the process of blast furnace, auxiliary, such like coal, coke, oxygen, limestone, and refractory can form different resource conversion efficiency when processed with main material resources which are rich of iron, and this has an important impact on resource consumption and environmental emission. Coke rate is one important technical and economical index in the process of blast furnace production as well as the central exemplification of efficiency and consumption of blast furnace production. This discourse will analysis related subjects from the perspective of operative characteristics of auxiliary resource's effects on comprehensive coke rate in blast furnace, using improved BP neural network of 16-20-1 topological structure to predict comprehensive coke rate in blast furnace. Real practices have proved that this model has low chance of predicative error in the blast furnace production, and this means accuracy and effectiveness.
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