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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Evolution of Process Models to Digital Twins
Author(s) Alex Holtzapple
On-Site Speaker (Planned) Alex Holtzapple
Abstract Scope Process simulation in the mining and metals industry was introduced in the early 1960s, with the invention of the computer, and has been rapidly evolving since that time. Programming languages and calculation methodologies have been constantly expanding to encompass innovative technology and equipment into metallurgical software for use by engineers and operators. Essentially no longer bound by data storage space or model convergence time, the next stage of this evolution is the incorporation of valuable process models into operations. “Digital twin” is the commonly accepted term for this, and certainly defines the overall simulation system accurately, though does not stress the importance of the well-calibrated, robust process models required. Constructing a multi-disciplined metallurgical, engineering and operational team ensures reliable results and recommendations from any operation’s digital twin.
Proceedings Inclusion? Planned:


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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