Abstract
ABSTRACT The optimal concentrate assay and weight recovery are among the most important factors in mineral processing plants, particularly iron ore beneficiation plants. The optimal balance between concentrate assay and total recovery is typically determined through chemical analysis and application of the Davis tube test in the laboratory. Consequently, the level of concentrate assay and tonnage is regulated in the plant by (a) sampling and chemical analysis in the lab and (b) application of weighing equipment such as belt scales. Belt scales also have a random error and specified standard deviation in weighing and are not completely reliable. Modifying the assay obscures the variation in concentrate weight. The best subset method can be used to extract the relation between concentrate tonnage and process variables; however, this method is unsuitable due to the variance inflation factor (VIF). In this investigation, to circumvent VIF, simple models using least angle regressions (LARS), Lasso, and LAR are presented utilising 2655 data (for each variable) collected from the Gohar Zamin beneficiation iron ore plant. The highest model’s R-square was 66.51%, and it predicted that if the concentrate assay decreases from 68.41% to 67%, the total concentrate tonnage can increase by 33800–35300 tons per year with a 95% prediction interval for the 2 million-ton capacity plant.
Published Version
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