Identification and Evaluation of Blast Furnace Condition Based on Spatio-Temporal Feature Extraction.

As the core equipment in ironmaking process, it is very important to ensure the stable and smooth status of blast furnace (BF). The furnace condition reflects the running status of the BF, so the identification and evaluation of BF condition is of great significance. In this paper, the BF condition identification and evaluation model is established based on BF detection parameters. Considering the characteristics of temporal continuity and spatial distribution of BF detection parameters, spiking neural network (SNN) is used to extract the spatio-temporal feature and establish the above model. The model is verified by the actual production process data.