Optimization of the Evaluation Method for Bentonite Used in Iron Ore Pelletizing

Abstract

Bentonite is an essential binder in the iron ore pelletization process. However, limited research has been conducted on the correlation between the physical and chemical properties of bentonite and its pelletizing performances, while the evaluation criteria for pelletizing bentonite have not been standardized. To optimize the current evaluation methods, this study tested the physical and chemical properties of five representative bentonites, as well as their green balling performance after pelletizing. Additionally, a multiple regression model was constructed using R. Stepwise regression and relative weight analysis were used to optimize and evaluate the indicators of bentonite. The results showed that the raw ball performance was mainly affected by water absorption (WA), swelling index (SI), and swelling capacity (SC). The dry ball performance was mainly affected more by methylene blue index (MBI) and cation exchange capacity (CEC). The following stepwise regression analysis revealed that WA, CEC, and SC were significant predictors for green ball drop strength; WA and SI for green ball compressive strength; and WA, MBI, and SC for dry ball compressive strength. The multiple regression model developed in this study exhibits high goodness of fit and accuracy, making it a valuable way for assessing the impact of different quality bentonites on pelletizing performance as well as optimizing the evaluation methodology of bentonite’s performance in iron ore pelletization.