The Synergy of Real-time AI, Reporting, and Decision-Making in Mining

Real-time AI is crucial in optimizing processes by predicting metallurgical performance based on material characteristics.  In continuation of our recent blog post ‘Empowering Geometallurgical Excellence with Real-time AI: A Game-Changing Approach’, geometallurgical excellence is characterized by the capability to leverage geological insights for optimizing metallurgical performance and making well-informed decisions promptly. 

This cyclical approach entails retrospectively analyzing geological data to comprehend its influence on metallurgy and, prospectively, utilizing this knowledge to forecast and shape future processes.

Optimizing Processes in Three Steps

IntelliSense.io applications technology has gained significant traction by integrating real-time artificial intelligence (AI), reporting systems, and decision-making processes. This powerful combination empowers businesses to harness data-driven insights, optimize operations, and make informed decisions promptly.

Let’s delve into the synergy of these components and explore how they are transforming modern business landscapes:

Customer implementation

1. Harnessing the Power of Scientific AI

Even more significant, the groundbreaking Scientific AI technology pioneered by IntelliSense.io, which integrates mechanistic (first principle or physics) models with machine learning techniques, uniquely delivers real-time predictive intelligence by attempting to model the complexities of the industrial real world.

By integrating real-time data streams and advanced machine learning algorithms, the real-time feedback loop enables proactive adjustments to optimize metallurgical performance and maximize resource value.

 

2. Revolutionizing Reporting Practices

Even more significant, the groundbreaking Scientific AI technology pioneered by IntelliSense.io, which integrates mechanistic (first principle or physics) models with machine learning techniques, uniquely delivers real-time predictive intelligence by attempting to model the complexities of the industrial real world.

By integrating real-time data streams and advanced machine learning algorithms, the real-time feedback loop enables proactive adjustments to optimize metallurgical performance and maximize resource value.

3. Enhanced Traceability for Strategic Decision-Making

The enhanced traceability facilitated by real-time AI connects upstream geological data to downstream metallurgical outcomes. This link provides a clear understanding of the impact of geological variability on resource value, streamlining approval processes for financing. Moreover, it empowers mining professionals to anticipate market fluctuations in advance, making strategic decisions with their resources.

A Mining Example: Optimizing Flotation 

The Flotation Optimizer performs numerous simulations every minute to identify the best solution and recommend optimal targets aligned with the site’s objectives.

The initial step towards progress involves live tracking material movement and its properties, such as from stockpiles to plant feed. On the grinding side, this knowledge helps reduce downtime and maintenance through liner wear optimization and increases grinding circuit throughput. 

Further on, anticipating incoming material and feedstream characteristics (e.g. mineralogy, mass flow, grade feed)  better enables the control recommendations of air flow rate and froth depth, hence helping in the optimization of flotation’s metal recovery, gangue rejection, reagent usage, and circuit stability.

Customer implementation

Future Mining Landscape

Scientific Real-time AI is reshaping the landscape of mining (specifically geometallurgy) by automating tasks, providing predictive insights, and enhancing traceability while accounting for the variabilities of the real industrial world. 

This transformative technology empowers mining professionals to optimize metallurgical performance, maximize resource value, and make well-informed decisions in real-time. 

Many miners around the world have successfully employed IntelliSense.io applications to achieve results such as a 1% ($38M) increase in metal production in flotation, 5-8% decrease in plat feed variability from stockpiles, 16% reduction in chemical use in Thickening1% increase in grinding throughput and more.

Central to the IntelliSense.io core values 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 (with no upfront Capex). And real, tangible value can be quickly delivered that will help you guarantee a boost in mineral production.

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