Soft sensor method for endpoint carbon temperature of BOF steelmaking based on FCM-WPPCR collaborative measure

ZHAO An; LIU Hui; CHEN Fu-gang

doi:10.7641/CTA.2024.30357

Abstract：The accurate detection of the endpoint carbon temperature in basic oxygen furnace(BOF)steelmaking is the key to determining the final stage of steelmaking,and the nonlinear and multiworking condition data characteristics are the difficulties in effectively predicting the endpoint carbon temperature using soft sensing.A fuzzy C-means clustering weighted posterior probability and cluster relation(FCM-WPPCR)co metric strategy is proposed to address the problem of inaccurate sample matching caused by the difficulty of directly measuring relevant samples in just-in-time learning(JITL)under complex industrial data and the relatively single measurement basis.This strategy is used to select the optimal sample subset for JITL.This strategy uses fuzzy C-means clustering to partition multicondition sub clusters,and introduces the Earth mover's distance(EMD)criterion to construct a posterior probability pattern to characterize the membership degree of the tested sample in each cluster;Secondly,after determining the correlation between other sub clusters and the maximum membership sub cluster through the maximum mean difference,a collaborative measurement mechanism is constructed by calculating the mean of the EMD distance between the test sample and each cluster sample to construct a weighted posterior probability and cluster relationship,in order to determine the execution criteria for selecting the test sample in each cluster;Finally,selecting relevant samples from each cluster to construct the optimal sample subset,and establishing a local regression model to predict the end-point carbon temperature.Through simulation of actual steelmaking production process data,the prediction accuracy of carbon content reaches 92.60％within the error range of±0.02％,and the prediction accuracy of temperature reaches 93.30％within the error range of±10℃.