Abstract
Ore hardness plays a critical role in comminution circuits. Ore hardness is usually characterized at sample support in order to populate geometallurgical block models. However, the required attributes are not always available and suffer for lack of temporal resolution. We propose an operational relative-hardness definition and the use of real-time operational data to train a Long Short-Term Memory, a deep neural network architecture, to forecast the upcoming operational relative-hardness. We applied the proposed methodology on two SAG mill datasets, of one year period each. Results show accuracies above 80% on both SAG mills at a short upcoming period of times and around 1% of misclassifications between soft and hard characterization. The proposed application can be extended to any crushing and grinding equipment to forecast categorical attributes that are relevant to downstream processes.
Highlights
In mining operations, the primary energy consumer is the comminution system, responsible for more than half of the entire mine consumption [1]
We know from Equation (1) that the operational relative-hardness (ORH) labelling process requires as input (i) the one-step forward differences on energy consumption (∆ECt ) and feed tonnage (∆FTt ), and (ii) a lambda (λ) value
This work proposes the use of Long Short-Term Memory networks to forecast relative operational hardness in two semi-autogenous grinding mill (SAG) mills using operational data
Summary
The primary energy consumer is the comminution system, responsible for more than half of the entire mine consumption [1]. Most theoretical and empirical models [4,5,6] demand input feed characteristics, such as hardness, size distribution and inflow rate, SAG characteristics, such as sizing and product size distribution, and operational variables such as bearing pressure, water addition and grinding charge level They are suitable to provide adequate design guidelines, they lack accurate in-situ inference since most assume steady-state and isolation from up and downstream processes. Ore hardness can be characterized at sample support by combining the logged geological properties and the result of standardized comminution tests They can be used to predict the hardness of each block sent to the process.
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