2020
DOI: 10.1590/0370-44672020730001
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Abstract: Knowing the quantity and the quality of products and tailings generated by a beneficiation plant, even before ore processing, can make the mining operations more sustainable, more profitable, and safer. To forecast these values, it is necessary to submit samples to batch tests which mimic the processing workflow used on an industrial scale. Then, the results need to be analysed with the aim of finding a statistical model able to comprehend how Run of Mine (ROM) characteristics impact the performance at the ben… Show more

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Cited by 2 publications
(1 citation statement)
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“…Based on artifcial neural network technology, the authors in [14] coordinated the model prediction with an amplifcation factor with the plant response and studied the prediction of the material and metallurgical balance of the zinc processing plant. Based on the developed neural network model, the authors in [15] studied forecasts of the mass and metallurgical balance at a gold processing plant. Te authors in [16] tested random forest, SMO (variation of SVR), linear regression, M5 and M5P (variation of the decision tree), and other machine learning techniques to forecast six geometallurgical variables at the Leveäniemi iron ore mine.…”
Section: Introductionmentioning
confidence: 99%
“…Based on artifcial neural network technology, the authors in [14] coordinated the model prediction with an amplifcation factor with the plant response and studied the prediction of the material and metallurgical balance of the zinc processing plant. Based on the developed neural network model, the authors in [15] studied forecasts of the mass and metallurgical balance at a gold processing plant. Te authors in [16] tested random forest, SMO (variation of SVR), linear regression, M5 and M5P (variation of the decision tree), and other machine learning techniques to forecast six geometallurgical variables at the Leveäniemi iron ore mine.…”
Section: Introductionmentioning
confidence: 99%