Scientific AI Provides a Much Needed Boost Amidst the Warning of a ‘Huge Crisis’ for the World Mineral Supply

The Shift Toward Clean Energy Relies on Minerals, and What Does a Substantial Supply of Minerals Depend On?

With the spiraling demand for crucial minerals like copper, nickel, and lithium driven by the clean energy revolution, the probability of supply needing to come from low-grade deposits and high-cost of operating expenditure (OPEX) mines is growing.

Additionally, as covered in Bloomberg’s latest article any substantial new greenfield deposits are slow (circa twenty years) to firstly locate and subsequently bring into production. These projects often exist beyond the traditional mineral jurisdictions in locations such as Mongolia and the Democratic Republic of Congo, operating under the watchful eye of governments zealously safeguarding their natural resources. 

This further contributes to the challenges involved in addressing the widening gap between global demand and supply, which, according to the International Energy Agency (IEA), is expected to be around 66% by 2030 and will ultimately slow down our transition to a clean energy world. 

BHP’s Oyu mine - Mongolia

BHP’s Oyu mine, featured in the Bloomberg article mentioned above, serves as a notable example. Situated in the Mongolian desert, it is already 20 years old and is only just getting production started. 

 

With these challenges in mind, it will come as no surprise that most of the major mining companies are attempting to adopt AI to enhance their throughput and recovery. However, it is a vexing problem to solve due to the inherent variability in mining. The industry is littered with many attempts to apply AI and for good reason they have failed to deliver meaningful and sustainable results. The lessons are clear and we have covered them in a previous blog and whitepaper here:
The solution necessitates a new approach and advanced technology like IntelliSense.io’s Scientific AI solutions – a unique fusion of Physics-based models with Machine Learning techniques – to address the complexity of the harsh mining environment. 

Many miners around the world have successfully employed IntelliSense.io applications to achieve results such as a 3% increase in metal production, a 16% reduction in chemical use, an 18% decrease in energy consumption, and more.

How Can Scientific AI Help Secure More Metals?

In the relentless pursuit of continuous operational improvement, a digital transformation journey through the adoption of cutting-edge technologies such as Scientific AI helps by unveiling patterns to optimize mineral processes

This new approach can provide valuable insights into Stockpile contents and ore properties, enabling more informed blending decisions and accuracy on what is coming to the plant. While AI-led recommendations on setpoints can enhance downstream processes like Grinding, resulting in increased throughput. Moreover, AI can optimize operating parameters in the Flotation and Leaching circuit, leading to incremental improvements in metal recovery. This is just a taste of what IntelliSense.io customers experience.

Improve Overall Cu Recovery by 1% ($38M)

Practical Example

Digitalization holds the potential to narrow the gap between the supply and demand of critical minerals in the upcoming years. Drawing from our earlier data on copper mine production, implementing AI applications for a 3% to 5% increase in metal recovery could enhance our capability to supply an extra 450,000 tons annually, valued at $3.2 billion.

This quantity is equivalent to the annual production capacity of the Las Bambas mine in Peru, a prominent copper producer ranking among the top ten copper mines globally.

 

Pre-built Solutions to a Timely Urgency

Adopting cutting-edge technologies like Scientific-AI presents a revolutionary strategy to enhance production in existing mines. This approach aims to boost productivity, lower operational costs, and promote sustainability.

Central to our implementation process is the capacity to manage the change associated with the artificial intelligence solutions while meeting the scope of deployment (generally 4-5 months), whilst minimizing risk to our clients with pre-built applications. And real, tangible value can be quickly delivered that will help guarantee a boost in mineral production to fuel the energy transition.

Interested in knowing more?

Mine to Market: Value Chain Optimization

The Stockpile & Inventory Optimization Application is one of a suite of real-time decision-making applications that uses AI to optimize each process; from mine to market.

Our Material Handling model connects these applications to drive even greater efficiency.

Our process optimization apps can be deployed on a specific process bottleneck or expanded across the entire value chain. They are powered by our Industrial AI Decision Intelligence Platform, brains.app.

Stockpile and Inventory Optimization

Stockpile & Inventory Optimization

Grinding Optimization App

Grinding Optimization

Thickener Circuit Optimization

Thickener Optimization

Flotation Circuit

Flotation Optimization

Solvent Extraction Optimization App

Solvent Extraction (SX) Optimization

Leaching Optimization App

Leaching Optimization