Will Australia have enough critical minerals to support the clean energy transition?

An Uncertain Future That Depends on Critical Mineral Resources

Apart from significant infrastructure and industry advancements, the role of critical minerals in realizing a low-carbon future is paramount. This is because renewable energy sources like solar, wind, and electric vehicles heavily rely on them. Notable examples of critical minerals found in Australia, vital for the clean energy transition, include:

  • Lithium – a crucial component in batteries for harnessing solar power.
  • Zinc – used as a protective coating for corrosion prevention in wind turbines.
  • Silicon – employed in semiconductors that enable solar panels to convert light into electricity.
  • Vanadium – used in batteries to support large-scale energy storage for the grid.

Australia, blessed with abundant reserves of these key critical minerals, holds a unique position to participate in the clean energy transition actively 

but there are other aspects that need to be considered such as the adoption of artificial intelligence (AI)-based technologies to increase process efficiency.

In light of this time-sensitive opportunity, both government and industry stakeholders are rightfully prioritizing the advancement and rapid integration of AI technologies, workforce training in tech-related fields, and the development of supply chains essential for domestic value-added processing and manufacturing of critical minerals, always with a strong commitment to sustainability.


Mining and AI

Objective-Driven AI: Delivering Real-Time Process Optimization

The endeavor to secure substantial quantities of critical minerals presents many opportunities for companies strategically positioned to provide them. Whether it’s established mineral mining firms or mining companies capable of shifting their operations toward critical minerals, the upsurge in demand is forcing miners to sustainably ‘do more with less’, in other words, get the most out of resources.

Nevertheless, considering the scenario where your business relies on the right technologies is something important to have in mind. How do you plan to increase efficiency or optimize your operations? This concern extends not only to minerals listed on Australia’s critical mineral roster but also to others like copper (which are categorized as critical in the lists of other countries).

brains.app platform in action

Digital technologies, notably Artificial Intelligence (AI), are increasingly indispensable for enhancing efficiency, productivity, and cost-effectiveness. While AI encompasses various problem-solving approaches, a new approach called ‘objective-driven AI’ is emerging as a robust method for real-time process optimization applications within the industrial sector.

Why is this New Approach so Important?

Unlike the exhaustive training requirement of reinforcement learning (RL) or the potential inaccuracies of large language models (LLMs), objective-driven AI bases decisions on a comprehensive understanding of the world. This approach mirrors human reasoning by proactively assessing potential outcomes before action.

In dynamic environments like mining operations, objective-driven AI can mitigate some of the pitfalls of other modern AI approaches and enhance efficiency and safety by offering a combination of first principles and data-driven, flexible decision-making, closely resembling human planning and evaluation.

Scientific AI: Leading from the Front

In our case, Scientific AI is the name that IntelliSense.io has given to our unique and innovative implementation of objective-driven AI in the context of the mining industry. Unique to this approach is its adaptability; objectives can be adjusted in real-time without the need for exhaustive model retraining with new data.

This approach allows for more informed, data-driven decisions that account for multiple factors, making operations safer and more efficient. You can find more information about how IntelliSense.io models work and the outcomes already achieved by global top-tier miners.

Scientific AI

Scientific AI provides trusted  predictive intelligence to operational decision-makers in real-time

A Crucial Tipping Point

Mining operations find themselves at a crucial tipping point in their history. The demand for critical minerals necessitates higher ore outputs, but this must be achieved sustainably to support net-zero goals. Embracing new technology is paramount for the industry to enhance performance and safety standards while aligning with the net-zero initiative.

Objective-driven AI solutions can be an ally in this transition by modeling the mining process in taking into account a more real-world environment. Through our tangible examples like the Australian and Chilean miners’ use of AI and 3D visualization to better manage stockpiles; the use of AI to aid recovery in one of the world’s largest and most complex Flotation circuits in Chile; the incredible results seen by increasing recovery from tailings leach at a flagship polymetallic mine in Australia; or the use of AI to predict, days before, adverse events in the thickening circuit and improve water recovery for a Chilean miner, the mining sector learns the true value of AI through such real-world applications.

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