Jump to: navigation, search

Testwork and Test Types

The SAGMILLING.COM database is configured to handle the following types of tests and related geological information:

Laboratory tests of grindability, impact, abrasion and compression.

  • wibm: Bond ball mill work index (sometimes called "BWI")
  • wirm: Bond rod mill work index (sometimes called "RWI")
  • wic: Bond crushing work index (low energy impact, sometimes called "LEIT", "IWI" or "CWI")
  • dwt: Drop Weight tests, SMC Test ® of SMCC Pty Ltd. and JK DWT (values stored include A×b and DWI)
  • sgi: SAG Grindbility Index (also SAG Power Index™ or SPI™ of SGS Mineral Services; originally called a "Starkey SAG test"Starkey et al, 1994)
  • ai: Bond abrasion index
  • ucs: Unconfined Compressive Strength, a detailed geotechnical test conducted on drill core
  • pli: Point Load Index, a field-test conducted on drill core

Geological tables, used for organizing and partitioning samples, or as proxies for test results.

  • litho: Lithological, alteration and sample acquisition data, including drill hole intervals

Automatically generated tables. Assembled by the program out of the tests entered into the laboratory test tables.

  • Testwork summary: Summarizes all the test results entered for a project, including samples with inadequate information for modelling.

Extra table, accessible to your administrators

  • Laboratory Listing: Listing of laboratories and their colour schemes for plotting results.

Tests Used By Models

All specific energy consumption models depend on laboratory or pilot/full-scale mill results. The exact mixture of tests required varies by method, but these can be generalized into the follow categories based on the dimensions of the samples tested:

Model Fine size Medium size Coarse size
Bond/Barratt WiBM WiRM WiC
SMC Mib Mia Mic
SAGDesign WiBM(modified) SAGDesign
JK SimMet WiBM, ta A, b

The Mic is not a true measurement of coarse hardness because the measurement is conducted at a medium size and extrapolated based on a database.

JK SimMet is actually a population-balance model rather than a power model. CEET 2 is a hybrid model having properties of a population-balance model and a power model. Both tend to be more complicated and have more "tuning factors" that are appropriate to fitting mill surveys as opposed to initial design. Specialized software is needed to operate both models.

Planning a pre-feasibility test program

Pick two of the methods outlined above and select the samples required to satisfy those methods. Identify the major lithology/alteration combinations in each distinct orebody and select 10-20 samples representative of that combination. Make sure each combination and orebody is represented spatially as it is necessary to sample the harder and softer zones that will be present.

Pick one method as the "main" and use the other as the "check". Perform the "main" testwork set on all samples and perform the "check" on half to a third of the samples. The choice of which method to use is largely personal preference on the part of the engineer in charge of the program, but the following guidelines are offered for consideration:

  • Orebodies with coarse fractures should use methods that include coarse measurements (Bond/Barratt, CEET 2)
  • The WiC measurement requires whole-diameter PQ or HQ diameter core.

The desired outcomes of this level of study are:

  • the range of specific power consumption in the overall "likely to be mined" orebody;
  • the range of specific power consumption in a likely arbitrary "payback pit" or "upper part" of the orebody;
  • a relative ranking of the hardness of the major ore types;
  • a milling circuit design that can achieve a desired throughput on a specified proportion (typically 75%) of the samples.

Planning a feasibility test program

The first steps in the procedure are the same as outlined for the pre-feasibility program, except more samples are collected based on geostatistical targets. Perform the "main" and "check" methods on all samples. If the pre-feasibility stage demonstrated good agreement in predictions between the two modelling methods selected, then continue with two; otherwise, add a subset of 30%-50% of a third calculation method (the "tie-breaker").

Have the geostatisticians review grindability results from the pre-feasibility level and see they can establish the "area of influence" of a particular sample using variograms or other techniques. If so, use half that dimension to determine the number of samples required. If not, use the following rules-of-thumb:

  • Large tonnage disseminated porphyry deposits: area of influence is 250 m; sample every 125 m in ore.
  • Medium tonnage massive sulphide deposits: area of influence is 50 m; sample every 25 m in ore.
  • Small tonnage vein-hosted gold deposits: grindability will be controlled by the vein host rock and not necessarily the veins themselves (unless the veins are of mineable width). Sample every 10-20 m down the vein trend where each sample is a mineable-width-long piece of drill core.

Perform duplicates on 10%-15% of the "main" method tests in a second laboratory. The results should be within 10% of the original laboratory for that test to be considered "checked by external duplicate".

The desired outcomes of this level of study are:

  • entry of specific power consumption values into the mining block model;
  • a mill design that can achieve desired throughput (or higher) on the pay-back pit;
  • a mine design that uses variable throughput by mining year to "keep the mill full";
  • quality control checks on laboratories;
  • comparison of throughput estimate distributions by two (or three) methods as another quality control check.

Planning a geometallurgy test program

Where an operating mill exists, establish an orebody-specific relationship between one or two of the methods with the actual operation of the mill. This will involve mill surveys and SAG feed sample collection.

After a relationship to a method is established, conduct a drilling program to target future ore production. The quantity of samples to take generally varies with more samples being taken in nearer-term production and fewer samples in longer-term production. An example program of 158 samples at Goldstrike for a CEET 1 model (and the mill benchmarking) is described in Custer et al, 2001.

The desired outcomes of this program are:

  • fitting of specific power consumption models and mill power draw models to the operating plant;
  • identification of plant bottlenecks and, optionally, a plant optimization program;
  • entry of specific power consumption values into the mining block model;
  • an optimized mine plan using variable throughput to "keep the mill full".