A Digital Twin to Manage Metals

Alex Sidorenko, head of risk and insurance, Serra Verde Group

The global race to diversify secure rare earth supply chains has thrust a new generation of mining and processing operations into uncharted territory. For decades, ionic clay rare earth processing has been left to a small number of companies with strict controls over information disclosure— leaving other companies with the challenge of building operations elsewhere without a playbook, peer benchmarks, or operational data to validate their assumptions. At the same time, it is also critical for companies such as Serra Verde to deliver the highest standard of environmental performance.

That reality has created a profound risk management challenge: how do you accurately forecast the performance, capital requirements, and exposures of a plant that has no precedent? For most operators, the answer has historically been static engineering models — spreadsheet-based mass balances built on single-point averages. But those tools were never designed to capture the dynamic, compounding nature of real-world variability.

“Static models cannot see dynamic risk,” said Alex Sidorenko, Head of Risk and Insurance at Serra Verde Group, the company behind the first ionic clay rare earth mine and processing plant to operate outside of China. “The only way to understand how variability propagates through a complex, interconnected system is to simulate that system as it actually behaves.”

A First-of-Kind Operation Demanded a First-of-Kind Approach

For Serra Verde, the stakes of getting risk modeling right were enormous. Ionic clay rare earth processing involves a sequence of chemical and mechanical steps where variability in ore characteristics, equipment performance, and environmental conditions compound through the system. A shortfall at one stage cascades into the next.

The conventional industry approach, a single-point flowsheet, was, in his view, structurally incapable of representing that complexity.

The downstream consequences of misrepresenting ore characteristics and equipment performance, Sidorenko noted, were not theoretical. They would have fed directly into suboptimal design, flawed capital allocation, incorrect insurance valuations, missed offtake commitments, and a business model less viable than its own assumptions suggested.

Building a Risk-Based Digital Twin From the Ground Up

Rather than refine the existing modeling approach, operational, engineering, mining and risk teams Serra Verde rebuilt it from scratch. They commissioned Business Science Corporation (BSC) the development of a risk-based digital twin — a fully dynamic simulation of the mine and processing plant constructed using discrete event simulations.

By stress-testing designs virtually before physical commitments were made, the platform identified opportunities to improve design, suggested infrastructure sizing, and flagged equipment vulnerabilities. Across procurement decisions, infrastructure sizing, process buffer additions, mine design, and fleet optimizations, the simulation has informed, and in several cases fundamentally changed, decisions involving hundreds of millions of dollars. &

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