Solution

Advanced analytics for minerals processing

Description

Declining ore grades and volatile metals prices present a special challenge for mining operations. It is important to maximise productivity and metal recovery of mineral processing plants. In addition, an efficient operation is becoming more difficult due to the growing need to mine more complex ores. This solution assists plant operators in identifying the optimal approach in terms of controlling grinding and flotation parameters to increase throughput, improve recovery and increase revenue.

Problems solved

Grinding and flotation circuits are the key to achieving optimal performance of a metal concentrator. Precise grinding is required to progressively reduce ore particle size and liberate mineral particles, so they can be efficiently separated from the gangue. Flotation is the most versatile and important mineral separation technique, with a multitude of process parameters requiring meticulous tuning. In terms of both capital and operating costs, grinding and flotation are very important for the plant performance, with significant consumption of process chemicals, grinding media, and electricity.


We believe that the recent progress in machine learning as well as greater availability of process data enables the use of advanced analytics to optimise mineral processing. We start by analysing your current process to identify areas where machine learning-based solutions may bring value. As part of this survey, we look at all major aspects of plant operation, from ore mineralogy to instrumentation, control, and data maintenance. We also examine your control objectives and strategies.


As a result of analysis, we will select a number of pre-developed, industry proven candidate models to predict and improve performance of your processing circuits. Candidate models will be trained based on all types of available data, ranging from sensor readings to video fed directly from flotation cells or ore conveyor belts. The model would typically function as a recommender system, providing plant operator with advice on how to change manipulated variables, such as ore feed rates, reagent dosages, or air rates, in order to achieve optimum plant performance.


We believe in partnerships. Once we have agreed with you on the areas to target, we jointly create an implementation plan, support the delivery and measure the improvements. We are prepared to offer flexible delivery schemes, from remote diagnostics, to on-premises rollout of recommender and automated control systems.


Problems solved:

  • Sub-optimal plant throughput and recovery
  • High processing cost

Outcomes

  • Increased revenue
  • Increased profitability
  • Reduced OPEX
  • Better recovery of minerals
  • Access to expert knowledge on flotation optimisation to refine the process


Quote Solution Number: AX01099

Key facts

3%

Improvement in throughput

1%

Improvement in ore recovery

$1.9bn

Saving for the mining industry

Tags

advanced analytics
AI
Artificial Intelligence
costs
extracting
extraction
flotation
machine learning
metals and mining
optimising process parameters
rock fragmentation analysis

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