Kunming University of Science and Technology
Project supported by the National Natural Science Foundation of China (Research of flame blowing information features extraction and molten pool carbon temperature continuous re-al-time prediction model for converter steelmaking endpoint control，No.61863018）、the Applied Basic Research Programs of Yunnan Science and Technology Department (Research on real time prediction model of BOF end point blowing data based on dynamic and static feature extraction of flame image，No. 202001AT070038）
Accurate prediction of carbon content and temperature is the crucial to the endpoint control of converter steelmaking. For the sample fluctuation is large, it is difficult to measure the similarity of samples in Just-in time Learning, which leads to the problem of low prediction accuracy, this paper proposes a similarity measurement strategy based on an improved spectral clustering algorithm. Firstly, according to the coupling relationship between process variables and dominant variables, a spectral clustering algorithm with global Weighted KL measurement standards was constructed, thus, the clustering subsets with large between clusters variance and small intra-cluster variance were obtained to eliminate the fluctuation among the furnace samples. Secondly, according to the difference information between class clusters, the Local Weighted KL metric criterion was integrated to calculate the posterior probability of the predicted samples belonging to various clusters, then, a similarity measurement strategy suitable for describing the complex characteristics of converter steelmaking process is constructed. Finally, this measurement strategy is used to calculate a subset of samples that are more similar to the properties of the new furnace, and the RVM model is established to predict the end point carbon content, and temperature. The simulation results of actual converter steelmaking process show that prediction accuracy of carbon content within ±0.02 % error range reach 89%, temperature within ±10℃ error range reach 92%.