Artificial Intelligence: The Road to an Equitable and Sustainable Cobalt Supply

Cobalt Institute and Stanford Mineral-X’s groundbreaking paper “Artificial Intelligence: The Road to an Equitable and Sustainable Cobalt Supply”, maps both existing and emerging AI applications across the cobalt value chain and sets out a forward-looking vision for the future.

Authored by renowned Stanford University Professor and Mineral-X co-founder Jef Caers, in partnership with Cobalt Institute, the paper explores how Artificial Intelligence (AI) can reshape the cobalt supply chain – from exploration to production – while advancing equity and sustainability.

AI will play a pivotal role in creating an equitable and sustainable cobalt supply chain

Autonomous AI is not yet possible in critical mineral value chains, including cobalt.

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An equitable and sustainable cobalt supply chain requires new technology investments, as well as policy changes at the country and international levels.

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An AI-assisted approach has already transformed the way companies conduct critical mineral exploration, with exploration becoming more cost-effective as a result.

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AI has the potential to help reduce unexpected and costly surprises in both mining and processing by optimizing operational planning.

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AI can help transform the current cobalt supply chain in line with chosen ESG standards

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AI is optimally placed to help reduce uncertainty on the possible presence of economically viable deposits

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AI can aid cobalt exploration by accounting for the various, unique cobalt-hosting mineral systems, and then making recommendations on project development to help reduce exploration cost by factoring in both geological risk and ESG standards.

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AI provides optimization opportunities in processing of cobalt ore in the areas currently ripe for innovation.

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Reprocessing cobalt from mine tailings has gained attention to recover cobalt while reducing environmental impacts associated with mining.

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Mineral-X is a research and innovation initiative based at Stanford University’s Doerr School of Sustainability that focuses on technological innovations to create a resilient & sustainable critical mineral supply chain. It develops new pathways in the mineral supply chain, from upstream mineral exploration to processing, in collaboration with industry partners. It develops novel data science and integrated decision-making under uncertainty across the critical mineral value chain.

Autonomous AI is not yet possible in critical mineral value chains, including cobalt. 

Humans will still lead, and an ‘AI-assisted approach’ is the way forward, with AI helping us in the coming years to complete and optimize tasks and processes.

An equitable and sustainable cobalt supply chain requires new technology investments, as well as policy changes at the country and international levels.

AI can help recommend optimal actions that mining companies, investors, governments and NGOs should take that account for the complex interaction of geological, economic, environmental, sustainability and geopolitical factors.

An AI-assisted approach has already transformed the way companies conduct critical mineral exploration, with exploration becoming more cost-effective as a result.

AI in critical mineral exploration actually requires human intelligence. While AI is strong in exploring various plans computationally using numerical modeling, it lacks a geologist’s creativity and intuitive reasoning based on past experience in interpreting data.

AI has the potential to help reduce unexpected and costly surprises in both mining and processing by optimizing operational planning.

Such planning is optimized by knowing an orebody’s significant 3D variations in terms of key mining and metallurgical factors.

AI can help transform the current cobalt supply chain in line with chosen ESG standards.

An AI-assisted approach outperforms human activity alone in making complex and sequential, in time decisions that involve many competing criteria. AI also unlocks more efficient quantification of investments involving geological, environmental and geopolitical risk.

AI is optimally placed to help reduce uncertainty on the possible presence of economically viable deposits.

Reducing such uncertainty is achieved through sequentially performing multiple data acquisition campaigns while factoring in the 3-D subsurface in all of its complexities.

AI can aid cobalt exploration by accounting for the various, unique cobalt-hosting mineral systems, and then making recommendations on project development to help reduce exploration cost by factoring in both geological risk and ESG standards.

This approach has significant potential in the Democratic Republic of the Congo (DRC), where the country remains largely underexplored.

AI provides optimization opportunities in processing of cobalt ore in the areas currently ripe for innovation

- introducing new processing technology and the combined optimization of processing steps such as grinding, flotation, leaching and extraction.

Reprocessing cobalt from mine tailings has gained attention to recover cobalt – often co-occurring in Ni and Cu mines – while reducing environmental impacts associated with mining.

AI can recommend the type and quantity of data to acquire to assess the economic potential of cobalt extraction from tailings.