
Metallurgical Systems
Enterprise metallurgical accounting, digital twin, and process optimisation software purpose-built for minerals processing plants.
About Metallurgical Systems
Metallurgical Systems is a Sydney-based company founded in 2010 by John Vagenas, developing the Metallurgical Intelligence (MI) software suite for minerals processing plants. The company occupies a distinctive niche — providing dynamic metallurgical accounting, plant-wide mass balance, and process optimisation through a digital twin approach that performs 720 mass balance iterations per plant per day. This level of computational frequency transforms traditional monthly or weekly reconciliation into near real-time process intelligence.
The company is relatively small but serves a critical function: connecting process plant data into a coherent metallurgical model that satisfies AMIRA P754 metallurgical accounting code requirements while simultaneously enabling ML-powered process optimisation. Key deployments include Paladin Energy's Langer Heinrich uranium mine, and one gold mining client reportedly recovered US$116 million (~2.1 tonnes of gold) previously lost to tailings using MI Core analytics.
— Written by the MSR editorial team
Products by Metallurgical Systems (2)
Sorted alphabeticallyMI Core
by Metallurgical Systems
MI Core is the foundational product of the Metallurgical Intelligence suite — a full plant digital twin that connects directly to source data systems and performs dynamic plant-wide mass and energy balance calculations every hour. The platform monitors inventory changes over time using machine learning, provides automated production reporting, tracks ESG emissions (Scope 1/2/3), and supports unlimited data source integration. MI Core V6 introduced parallel processing for 90% faster data import and 80% faster calculations.
MI Process Optimiser
by Metallurgical Systems
MI Process Optimiser sits on top of MI Core, capturing data from millions of data points across the plant to identify optimum processing conditions using machine learning. The system handles time-lag dependencies (critical in minerals processing where cause and effect are often separated by hours), non-linear calculations, and multi-variable optimisation. It continuously learns plant operating patterns and compares theoretical optimal conditions against actual performance, quantifying the gap and recommending adjustments.
Company Details
- HQ
- Surry Hills, Australia
- Company Size
- 11-50
- Website
- metallurgicalsystems.com
- View Profile