Anele Shamase, Willie Nheta
This study employs response surface methodology (RSM) with a custom optimal design to develop and optimize iron (Fe) grade and recovery through magnetization roasting followed by induced dry-roll magnetic separation. The relationships between the independent and dependent variables are investigated. The effect of roasting temperature, magnetization roasting time, magnetic field intensity, rotor speed, and product splitter position for the induced dry-roll magnetic separator on Fe grade and recovery are studied. Suitable models are generated to predict the optimum operating conditions. An analysis of Variance (ANOVA) is employed to validate the developed regression models’ adequacy and assess the main and interaction-related effects on Fe grade and recovery. During magnetization roasting, a satisfactory Fe grade of 66.8% with a recovery of 16.7% was obtained under optimal conditions of 1050 ◦C for 97 min. Conversely, after the induced dry-roll magnetic separator optimization, an Fe grade of 66.1% with a recovery of 60.2% was achieved under optimum conditions of 0.105 T for magnetic field intensity, 70 Hz for rotor speed, and an 11 mm product splitter position. This study effectively illustrates how RSM can model the processes of magnetization roasting and induced dry-roll magnetic separation, particularly concerning the operating parameters.
@article{fd9f0d59-5544-47d5-917f-5c158ec3dca7,
title={Optimization of Magnetization Roasting and Magnetic Separation for the Recovery of Iron from Low-Grade Iron Plant Tailings},
author={Anele Shamase and Willie Nheta},
year={2025},
language={English}
}TY - JOUR TI - Optimization of Magnetization Roasting and Magnetic Separation for the Recovery of Iron from Low-Grade Iron Plant Tailings AU - Anele Shamase AU - Willie Nheta PY - 2025 LA - English ER -
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