The Integrated Virtual Blast Furnace: Enabling Physics-Based Operational Guidance

As part of a DOE-supported research effort, Purdue University Northwest researchers are collaborating with Oak Ridge National Laboratory and United States Steel Corporation to develop a tool to provide blast furnace operators and engineers with process performance insight comparable to high-fidelity computational fluid dynamics modeling, accelerated to provide “what-if” scenarios at near-real-time speed. This is accomplished by pre-simulating a baseline case and a range of potential operating scenarios to establish how the furnace responds to changing inputs, then training a neural network-based Reduced Order Model to accelerate the speed at which predictions of key parameters can be generated.

Tyamo Okosun | Purdue University Northwest
 

Ricardo Calix | Purdue University Northwest
 

Orlando Ugarte | Purdue University Northwest
 

Hong Wang | Oak Ridge National Laboratory
 

Kosta Leontaras | United States Steel Corporation
 

Joseph Morey | United States Steel Corporation
 

Jason Entwistle | United States Steel Corporation
 

Brian Rogers | United States Steel Corporation
 

Chenn Zhou | Purdue University Northwest