Soft-measuring method of iron ore sintering process using transient model

Abstract

To achieve efficient sintering machine operation in the steel industry, we developed an online soft-measuring method that can visualize the temperature distribution in the sintering process using a two-dimensional (2D) transient model. Although various numerical simulation models of the sintering process have been proposed, the conventional models suffer from estimation errors caused by unmeasurable disturbances, such as the fluctuations in raw material characteristics, when these models are applied for online control in actual plants over a long period. In this study, to reduce the estimation errors, the model parameters were adjusted successively by moving horizon estimation (MHE), considering the effects of the disturbances. The validation results with actual plant data showed that the estimation errors of the burn rising point (BRP) and the exhaust gas compositions were reduced significantly by MHE. In particular, the root mean square error (RMSE) of the BRP estimation was only 1.48 m. In addition, a correlation was confirmed between the estimated high-temperature holding time of the material and the product yield. The developed soft-measuring method is beneficial for process automation to improve product yield.