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== Monte Carlo Simulations == |
== Monte Carlo Simulations == |
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| − | Simulations that use a statistical range of results rather than actual test work results. In general, the users specifies test results as a mean and a standard deviation and the model generates random work index results from a suitable Gaussian distribution. |
+ | Simulations that use a statistical range of results rather than actual test work results. In general, the users specifies test results as a mean and a standard deviation and the model generates random work index results from a suitable Gaussian distribution. Highly recommended to operate Monte Carlo simulations in a different project from normal simulations and not to mix laboratory work index results with Monte Carlo simulation inputs. |
* [[:Category:Monte_Carlo|Monte Carlo simulations]] |
* [[:Category:Monte_Carlo|Monte Carlo simulations]] |
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** [[Bond/Barratt Monte Carlo simulations]] |
** [[Bond/Barratt Monte Carlo simulations]] |
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== Administrator functions == |
== Administrator functions == |
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Revision as of 17:28, 29 August 2023
SAGMILLING.COM Wiki. Theory and equations for power based modelling of mineral comminution circuits.
This work is licensed under a Commons Attribution 4.0 International License.
Contents
Grinding Circuit Modelling Documentation
This documentation describes the operating and underlying mathematics of the SAGMILLING.COM grinding circuit modelling program. It is intended to be used by the model users who want to operate and understand the functioning of grinding models.
Quick start video to get familiar with the operation of the software is available from here: https://youtu.be/J5XbCEJ6-sE
Operating the Software
- General concepts
- Entering test results
- Creating a circuit model
- Creating/modifying a mill
- How to show a sub-set of results
- How to copy data out of a table and into a spreadsheet
- How to design a new circuit using grindability testing
- How to make temporary changes to models or grindability test data and without saving
- Design choices: HPGR or SAG?
- Design choices: Pebble crushing or not?
- Design implications of Transfer Size
Specific Energy Models
- Theory of power models
- All Model types: specific energy consumption models
- SAG and ball mill circuit models
- Raw Bond/Barratt SAB & SABC model
- Optimized Bond/Barratt Models (including phantom cyclone allowance)
- Optimized Bond/Barratt model SABC (pebble crushing)
- Optimized Bond/Barratt model SAB (pebble recycle)
- Amelunxen SAG Grindability Index (SGI) SAB and SABC model (similar to SPI™)
- Morrell Mi (SMC) SAG & ball mill model
- Single-stage SAG milling models
- Rod mill and ball mill circuit models
- Multi-stage crushing and ball milling models
- Bond/Rowland multi-stage crushing and single-stage ball mill model
- Morrell multi-stage crushing and single-stage ball mill model
- Bond HPGR and ball mill model (using work indices) warning, experimental!
- Morrell HPGR and ball mill model
- Monte Carlo simulations
Mill power draw models
- Measurement of power (motor input versus shell, etc.)
- Media density for rods and balls
- Model types: mill power draw
- Austin SAG model (hybrid phenomenological and empirical model)
- Morrell C-model for SAG & ball mills (phenomenological model with empirical fitting). Sub-divided into:
- a simplified model with 'typical' defaults and
- a full model with all parameters editable.
- Loveday/Barratt SAG model (uses an internal table of empirical "Power Numbers")
- Nordberg ball mill model (largely empirical model)
- Nordberg rod mill model (largely empirical model)
- Crusher model (power allowance)
Laboratory testwork
- Comminution Testwork and test types performed in a laboratory
- Conversions between test types
- Ball mill work index adjustment parameters, also adjustments for Morrell Mib
- Particle size does matter
- Planning a test program for a project that does not have any testing performed yet
Monte Carlo Simulations
Simulations that use a statistical range of results rather than actual test work results. In general, the users specifies test results as a mean and a standard deviation and the model generates random work index results from a suitable Gaussian distribution. Highly recommended to operate Monte Carlo simulations in a different project from normal simulations and not to mix laboratory work index results with Monte Carlo simulation inputs.
Administrator functions
- General administration concepts
- About user IDs
- Add a user
- Add a project
- Assign users to a project
- Add a laboratory to the testwork drop-down menu
Benchmarking
Examples of how the SAGMILLING.COM model compare to published information for actual milling circuits.
- Category:Benchmarking
- Specific Energy Consumption Models
- SAG Mill Power Draw Models, use the IMPC 2016 paper
- Ball Mill Power Draw Models
Bibliography
- Bibliography: Specific energy consumption models
- Bibliography: Mill power draw models
- Bibliography: Testwork programs
