LAUSR

Abstract Thermal state of iron ore sintering in iron and steel production cannot be revealed straightforward, which is unfavorable for field operations. In this paper, the soft-measuring models were established to extract the feature points through curve fitting method and evaluate the whole state via random forest algorithm. All the models proposed were validated by the industrial data, and the results show that feature extraction model can identify the variation of reaction zones, and evaluation model possesses a classification accuracy over 95%. The soft-measuring models were integrated into the automatic control system developed for sintering plant. Running results illustrate that the system can enhance the stable control and reduce the power consumption of sintering process.