Blast furnace condition data clustering based on combination of T-distributed stochastic neighbor embedding and spectral clustering

T-distributed stochastic neighbor embedding (t-SNE) dimension reduction technique are combined with Spectral clustering algorithm to mine and analyze the historical condition data of the blast furnace, and the clustering results are divided into different furnace conditions. The nonlinear dimensionality reduction algorithm tSNE finds the rule in the data by recognizing the observed pattern based on the similarity of data points with multiple features. Most nonlinear techniques except tSNE can not retain local structure and global structure of the data. At the same time, the clustering problem of dataset is transformed into the optimal partitioning problem of graph by spectral clustering, which reduces the impact of blast furnace operating point drift. The test based on the field history library shows its accuracy. At the same time, the data of the blast furnace condition data can be visualized, and the difference between different furnace conditions is demonstrated, which provides convenience for the research of the blast furnace condition in the future. Finally, based on historical data, the well-trained model can also be used for prediction, and it is effective and convenient for the blast furnace operator to adjust the abnormal furnace condition.