As a precursor to calculating an "Amenability Index" is the step where incremental perfect or ideal separation is applied at each fraction.
Consider a fractional size dataset where the gangue is present in the finest fractions, such as a typical iron ore.
First we up-sample, per this example. in order to avoid linear segments in the response curve.
Once up-sampled we need to incrementally remove each up-sampled fraction starting at the finest size and recalculate the retained (now coarser) sample. This is essentially applying a perfect partition at every fraction boundary. The dataset can then be used to demonstrate the amenability of the ore in a general sense, independent of a selected cut-point or partition, by way of the amenability index (to be calculated separately).
The objective of this method is to return a dataframe of "Ideal Incremental Recovery", though it is likely we'd also expect to also return the "Ideal Incremental mass-composition".
As a precursor to calculating an "Amenability Index" is the step where incremental perfect or ideal separation is applied at each fraction.
Consider a fractional size dataset where the gangue is present in the finest fractions, such as a typical iron ore.
First we up-sample, per this example. in order to avoid linear segments in the response curve. Once up-sampled we need to incrementally remove each up-sampled fraction starting at the finest size and recalculate the retained (now coarser) sample. This is essentially applying a perfect partition at every fraction boundary. The dataset can then be used to demonstrate the amenability of the ore in a general sense, independent of a selected cut-point or partition, by way of the amenability index (to be calculated separately).
The objective of this method is to return a dataframe of "Ideal Incremental Recovery", though it is likely we'd also expect to also return the "Ideal Incremental mass-composition".