Prediction model of sintering bed temperature based on lognormal distribution function: construction and application

The monitoring of sintering bed temperature is crucial for controlling physical and chemical reactions during the sintering process and achieving low carbonization trends. However, accurately monitoring this temperature is challenging due to the "black box" nature of the sintering bed, which includes the sintering trolley and cup. As a result, a theoretical model needs to be developed urgently to predict the sintering bed's temperature profile. In this investigation, we propose a mathematical model that utilizes the characteristic value of the lognormal distribution to estimate indirectly the temperature of the sintering bed based on the measured temperature of the sintering exhaust gas. The sintering exhaust gas temperature and sintering bed temperature conform to the function: T=T0+A2πw⋅te−(ln(t−tc))2w22. To further enhance the accuracy of the sintering bed temperature prediction, the longitudinal positions of the sintering bed were expanded from three points to multiple points, enabling the construction of a temperature prediction function for the entire sinter bed and obtaining a cloud map of the sintering bed temperature. By determining the sintering terminal point and controlling the sintering machine's speed according to the location of the high-temperature region on the cloud map, the performance of the temperature prediction model was optimized. Through an analysis of the mathematical model and construction of the sintering temperature profile, a predictive route of exhaust gas temperature → three-point sintering bed → total sintering bed was established. This predictive method can significantly enhance the understanding of the sintering process and can be employed in industrial plants.