At the bottom of the Resolution Copper mine, the difference between a safe workday and a dangerous one can hinge on water and heat. To keep underground working conditions safe, engineers must anticipate how fast groundwater will flow into the mine and how hot it will become as operations change. But deep underground, those forces are difficult to predict.
That’s where a digital twin comes in.
Near Superior, Arizona, Rio Tinto, a top global mining group, is developing what could become the largest underground copper mine in North America. The project is critical to strengthening the U.S. copper supply, especially as demand surges for use in electric vehicles, renewable energy and the power grid infrastructure. It is also one of the deepest, hottest and most technically complex mining operations ever attempted in the region. Planning for a mine that may operate for decades demands tools that can predict how underground systems will behave long before problems arise.
To help meet that challenge, three graduate students from the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at Arizona State University, are bringing computer science underground. Working alongside engineers and researchers at Resolution Copper, they are building a digital twin of the mine, which is a virtual replica that blends physics, data and visualization to forecast how water, heat and operations interact over time.
What is a digital twin?
Sandeep Gupta, a Fulton Schools professor of computer science and engineering, leads the Intelligent Mobile & Pervasive Applications & Communication Technologies Lab, or IMPACT Lab, and is guiding the students through the process of creating a digital twin of the mine. A digital twin is more than a 3D model or a dashboard of sensor readings. It is a dynamic computational representation of a physical system that updates as conditions change. By combining real-world data with physics-based simulations and machine learning, a digital twin can test scenarios and anticipate risks before problems play out in reality.
At Resolution Copper, research focuses on hydrothermal behavior. Engineers are watching how groundwater flows into the mine, how pumping alters those flows and how heat is transferred through rock and water. Because the mine is still under development, it has only a few years of historical data — far less than artificial intelligence, or AI, models typically require.
Ayan Banerjee, a Fulton Schools research associate professor, says that this constraint shaped the students’ work from the start.
“Two or three years of data is not much for machine learning,” Banerjee says. “It limits the model’s ability to generalize, which is why we can’t rely on data alone and have to bring in physics and domain knowledge.”