REGRESSION ANALYSIS OF FACTORS AFFECTING ZINC RECOVERY IN ROTARY KILNS

Abstract

With the rapid increase in global industrialization, the demand for zinc constantly increases while primary zinc resources gradually decrease. This situation makes the recovery of zinc from secondary sources a strategic priority from a circular economic perspective. Electric arc furnace (EAF) dust, an essential iron and steel industry waste, is a valuable secondary resource due to its high zinc content. Therefore, zinc recovery is an attractive option, given its low production cost. This study conducted an experimental analysis of the zinc recovery process from EAF dust in rotary kilns. Operational data obtained from an industrial-scale plant were used. The data set included EAF dust amount, iron content in EAF dust, lime content, coal used, slag amount, and zinc grade in slag. Multiple Linear Regression (MLR) analysis was applied to determine the relationships between the parameters. The MLR analysis calculated the multiple correlation coefficient (R) as 0.661876, and statistically significant relationships were determined between process parameters and slag formation. The findings provide a systematic and innovative approach for optimizing process parameters and controlling slag formation in industrial-scale zinc recovery processes. The analysis results indicated that raw material quantities and iron content in EAF dust significantly affect slag formation, contributing to zinc recovery efficiency and waste minimization. The developed model allows the systematic understanding and control of the effects of process parameters on slag formation while also providing a solid basis for future optimization studies.