Soft-sensing method for endpoint prediction of BOF carbon content and temperature based on WGKSOM-DRCA adaptive JITL
Accurate endpoint prediction of Basic Oxygen Furnace(BOF)carbon content and temperature is the key to realize endpoint control.To solve the problem of low quality of similar samples by traditional Just-In-Time Learning(JITL)measurement due to the high volatility and nonlinear characteristics of BOF process data,an adaptive JITL soft-sensing method based on Weighted Gaussian Kernel Self-Organization Map Dynamic Relevant Component Anal-ysis(WGKSOM-DRCA)was proposed for endpoint prediction of BOF carbon content and temperature.The WGK-SOM clustering algorithm was proposed by using the WGK metric criterion introducing label information to guide the clustering direction and improve the clustering quality of algorithm and reduce the influence of data volatility.The Gaussian posterior probability was used to calculate the membership degree of the test samples and the appro-priate learning set was selected adaptively to predict the endpoint carbon content and temperature by introducing dy-namic factors and using DRCA metric learning strategy.Results showed that the proposed algorithm performed bet-ter than other algorithms in predicting endpoint carbon content and temperature of BOF steelmaking.The prediction accuracy of carbon content was 92％within the error range of±0.02％,and the prediction accuracy of temperature was 93.5％within the error range of±10℃.
万方数据
维普
