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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace
Author(s) Vivek Rao, Vineet Kumar, Alexandra Anderson, Joseph Grogan
On-Site Speaker (Planned) Alexandra Anderson
Abstract Scope Lead is the most recycled metal and our goal in this work is to improve energy efficiencies in the smelting processes used for recycling. In that effort, Oak Ridge National Laboratory is partnering with Gopher Resource, the second largest independent lead recycling company in the United States, to develop a high-fidelity Computational Fluid Dynamics (CFD) model of a direct-fired secondary lead furnace. These high-performance simulations are being developed using first principles modeling with pilot and operational empirical validation. A three staged process is used to tackle the problem. Firstly, a CFD model of an experimental furnace is constructed to validate the combustion model. Secondly, a solid phase furnace feed is added using a Discrete Element Model. Thirdly, and lastly, the full furnace operation is simulated which includes the combustion flow field, particle loading, smelting and melting processes, and formation of slag and lead.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, Recycling and Secondary Recovery, Pyrometallurgy

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

AI/Data Mining in Materials Manufacturing
Audio Signal Processing for Quantitative Moulding Material Regeneration
Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace
Determining the Bubble Dynamics of a Top Submerged Lance Smelter
Development of Virtual Die Casting Simulator for Workforce Development
Digitalization for Advanced Manufacturing through Simulation, Visualization and Machine Learning
Digitalizing the Circular Economy (CE): From Reactor Simulation to System Models of the CE
Evolution of Process Models to Digital Twins
Factors to Consider when Designing Aluminium Alloys for Increased Scrap Usage
NOW ON-DEMAND ONLY - An Automated Recycling Process of End-of-life Lithium-ion Batteries Enhanced by Online Sensing and Machine Learning Techniques
Refractory Lifetime Prediction in Industrial Processes with Artificial Intelligence
Steel Production Efficiency Improvements by Digitalization

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