Precision In Progress

However, why is this the case? The trends the industry is exposed to – the increasing importance of sustainability, persisting labour and skills shortages, and continuous cost pressures – mean that technology in mining simply cannot afford to remain stagnant.

Specifically, when it comes to the processing of ores, the same drivers remain ever present – if not more so. Zero-entry mine trends now expand into processing plants, where even control is no longer executed from the site itself.

The processes of separation, leaching, and even flotation continue to be the most efficient methods of separating ores from gangue efficiently, cost-effectively, and sustainably. In the lab, where the flowsheets are refined by piloting processes and research organisations, measurement and instrumentation play an important role in accurately controlling these processes to maximise value.

In the field, the future of sensing technology is already here. Field instrumentation that can measure parameters previously only possible in a lab has already been deployed in mining processes. This now brings the level of optimisation and control from what was possible at the pilot stage into the larger-scale processing plant.

This article delves into the pivotal role of advanced data collection methods and instrumentation in enhancing mining processes, with a spotlight on separation, leaching, and flotation and their positive impact on product quality, energy efficiency, resource utilisation, safety, environmental compliance, data-driven decision-making, and fostering a deeper understanding of process mechanisms and innovation.

By exploring the synergy between laboratory insights and field instrumentation, this article uncovers not only the challenges faced, but the groundbreaking solutions that industry leaders, including Endress+Hauser and Analytik Jena, bring to the table.

Two of these emerging technologies that are being implemented by the most innovative mining operators are machine learning (ML) and artificial intelligence (AI) for process optimisation. By leveraging AI and ML technologies, there can be considerable gains in process efficiency.

Machine learning in leaching

Leaching is a process used to extract valuable metals from ore by dissolving them in a suitable solvent. The effectiveness of leaching is highly dependent on factors such as ore composition, particle size, and the choice of leaching agent. Traditionally, accurate measurements in the laboratory facilitate the selection of optimal leaching conditions. However, with the improvements in measuring technology, these measurements can now be reliably taken in the field. Parameters such as pH, dissolved oxygen, redox potential, slurry density, chemical dosing concentration, and temperature gradients can now all be read. These parameters, if fed into the ML optimisation systems, can deliver greater insights and much faster returns than if the ML solution was reliant on slow, old laboratory data, or even with inaccurate field data.

Any information which can also be fed into a feedforward control philosophy solution will be able to reduce any variation during operation by minimising disturbances. Online measurements of brine composition into lithium processing applications can help with this. For example, the use of ICP-OES can deliver online lithium composition every 30 seconds without any sample preparation.

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Read the article online at: https://www.globalminingreview.com/technology-digitalisation/31052024/precision-in-progress/