Flotation Monitoring and Optimization

A particle-by-particle view of operational effectiveness

Severe political and economic challenges, as well as increasing costs for production resources, are driving a renewed focus on operational efficiencies in mining and mineral processing. Additionally, as ore grades decline, metallurgists are challenged to determine recovery potential from grade information alone. Prompting the question: which minerals contribute to grade? Adding to the complexity of the issue, recovery variations are more abundant, from pit to pit or from stock pile to stock pile, and from month to month, sometimes from week to week. This always prompts the question: “is recovery good or bad?” During challenging days, the dispute always comes up *is poor recovery caused by the ore (often the view of the metallurgists), or by the operation (often the view of the geologists)? Learn how near real-time mineralogical and textural ore measurements can help monitor and improve the efficiency of flotation plants.

Analysis Techniques in Flotation

Typically, routine tracking of flotation and recovery performance has been relegated primarily to chemical-based observations using on-stream analyzers (OSA) and daily assay balances. These techniques cannot easily account for fluctuations in stream mineralogy and texture. The application of automated mineralogy for auditing mineral processing plant performance has become an industry standard over the last twenty years. Data generated using automated mineralogy are invaluable for investigating the relationship between ore mineralogy and metallurgical performance. 

Automated mineralogy data has, to this point, only been available based on either a snapshot of daily plant liberation and recovery performance, or as a trended data set based on archived samples such as monthly composites. 

As such, data are typically only available long after samples were taken and often represent "ancient history" in terms of the trends they may have been obtained to diagnose. Barriers to broader adoption have been (a) the need for highly skilled operators, (b) an intolerance of the machines to typical milling environments and (c) poor understanding of how to use the data to assess and optimize mill performance. The new automated mineralogy MineSite solution is designed to address this, bringing routine on-site mineralogy-based plant monitoring within reach.

Integrating daily liberation and mineral association data from on-site automated mineralogy analysis with traditional plant recovery and balance information provides unprecedented fidelity in diagnosing the root causes of plant upsets as well as building protocols for increasing daily recoveries.

Matrix Method: Optimizing Flotation Performance

A data analysis methodology, referred to as the matrix method, is designed to present a simple distribution of the minerals of interest over a 3x3 matrix grid of size fraction by liberation class (feed matrix), with a corresponding matrix showing how well the minerals in each matrix category were actually recovered (recovery matrix). As such these two simple matrices conveniently assembles hundreds of thousands feed, concentrate and tailings particle data into the two key parameters driving mineral processing - mineral liberation and particle size.

Changes in the feed matrix from day to day, assuming a relatively consistent grind size, can indicate changes in the ore itself (e.g., a day that has a significant increase in the amount of mineral units occurring in the fine locked fraction, despite a similar grind size, may indicate a finer-grained and potentially more challenging ore).

Changes in the recovery matrix represent changes in how the flotation circuit dealt with similarly prepared material (e.g., what proportion of the coarse liberated galena was recovered that day).

In essence, the combination of the mineral feed matrix and recovery matrix, which multiply together to show the stage recovery of the system in question, discern the difference between how the mineral was prepared for flotation in grinding (feed quality), and how it was then actually recovered in flotation (flotation performance). This can be a single flotation cell or a whole flotation circuit, as long as the circuit contains no further size reduction (such as regrinding). Cumulative sum analysis can then be applied to measure and trend the specific role of both feed quality and flotation performance on overall recovery, and which of these two is responsible for variations in recovery.

The matrix method was developed in close collaboration with Blue Coast Group, a leading provider of metallurgical consulting, flow sheet development and laboratory test work services.


Automated Mineralogy and Petrography Brochure

Automated Mineralogy & Petrography is a technology solution that images rocks. It provides valuable information on which minerals are present in a sample (mineralogy) and how they occur spatially (microtexture). Measurements are performed ultrafast and unattended (automated) to allow high sample throughput.

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