Discrete element method (DEM) has been an increasingly used tool to get better understanding of charging process and flow behaviors of granular materials in metallurgical reactors. However, validation and precision of DEM must be verified and calibrated. In this paper, a calibration approach is proposed for the sphere equivalence of irregular particles in DEM simulation of charging process. In this approach, the non-sphere behavior of irregular particles is characterized by a pair of apparent sliding and rolling resistance coefficients obtained by quantitative comparison of the angle of repose and discharging time of hopper based on laboratory measurement of physical benchmarking experiments. The calibration approach is applied in the DEM simulation of the charging process of a shaft furnace in COREX 3000. Validation of simulation results for flow trajectory and stream width after leaving chute and burden distribution and profile is investigated through comparison of DEM and experiments. The results show that, with such a calibration approach, DEM can be easily used to simulate solid flow of irregular particles.

Due to the lack of simple and effective data filtering method for multi‐variable and numerous samples in BOF endpoint forecasting model, a method of outlier identification and judgment was introduced and applied to data screens for improving BOF endpoint forecasting model. The outside values as potential outliers are calculated using the method of five‐number summary which is a robust estimation of the population parameter, and then the potential outliers are judged with the clustering method. By comparing the exceptional data from clustering analysis with the outside values from the five‐number summary, the intersection of these two groups is regarded as the final outliers to be deleted; in addition, the exceptional data but not outside values are regarded as final exceptional data to be further analyzed; and the outside values but not exceptional data are regarded as final outliers to be deleted too. Finally, to verify the data selection, an improved BP‐based neural network model is used to predict the end‐point carbon content and temperature. By using this data pretreatment method, the absolute values of the mean and maximum training residuals of endpoint carbon and temperature decreased by 26.7%, 41% and 17.3%, 34.5% respectively; and those of the prediction decreased by 10%, 44.9% and 9.4%, 22.9% respectively. It is shown that the proposed method improves effectively the neural network model for BOF endpoint forecasting.

No abstract

No abstract

No abstract