Artificial intelligence is moving into mineral-processing operations, where algorithms can increasingly support decisions affecting concentrator performance rather than simply generating operational reports. An August collaboration between Vale and ABB highlighted this development. Vale plans to extend automation, AI and digital technologies developed at its Conceição II Model Plant in Brazil to additional operations.
Mineral-processing plants continuously respond to changing feed conditions. Ore hardness, mineralogy, grades and particle-size distributions can all vary during operation, requiring adjustments to grinding intensity, reagent additions, pump rates and other process variables.
AI Applications in Concentrator Control
AI systems can analyse multiple operating variables simultaneously and recommend adjustments more frequently than conventional manual control. The technology can also potentially progress from providing recommendations to automatically changing plant settings. The potential impact can be significant even when individual improvements are relatively small. At a major mine, a 1% improvement in recovery or plant availability can generate additional annual production without requiring physical expansion of the processing plant.
Automation companies are positioned to develop these applications because they already supply control systems and instrumentation used inside concentrators. AI can add an optimisation layer to existing plant hardware.
Operational Risks and Control Requirements
Greater automation also introduces additional requirements as systems move from monitoring and recommendations toward direct control. A dashboard recommendation can be disregarded by an operator, while an algorithm that automatically changes plant settings can affect recovery, equipment condition and production. This makes human oversight, data quality, cybersecurity and fail-safe systems important elements of AI deployment in mineral processing.
The commercial assessment of AI in mining is therefore increasingly linked to plant performance. Its application in processing can be measured by its ability to produce more metal from the same ore, equipment and labour.
