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About this Symposium
Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Sponsorship TMS Extraction and Processing Division
TMS: Recycling and Environmental Technologies Committee
TMS: Process Technology and Modeling Committee
Organizer(s) Elsa Olivetti, Massachusetts Institute of Technology
Alexandra E. Anderson, Gopher Resource
Mertol Gökelma, Izmir Institute of Technology
Camille Fleuriault, Eramet Norway
Scope Over the last 20 years, the manufacturing landscape has been transformed by the growing take of digital sciences on the improvement of product and processes. Most innovative solutions for advanced materials production are being developed via automation, computerization and digitalization. In this symposium, the role of modelling and programming technologies in waste management, the reduction of environmental footprints and the optimization of industrial processes will be explored. Session topics include:

- Advanced Process Simulation and Visualization Techniques
- Use of Artificial Intelligence for Improved Process Control & Optimization
- Automation of Recycling Processes

Abstracts Due 07/19/2021
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

AI/data mining in materials manufacturing
An automated recycling process of end-of-life lithium-ion batteries enhanced by online sensing and machine learning techniques
Audio Signal Processing for Quantitative Moulding Material Regeneration
Computational Methodology to Simulate Pyrometallurgical processes in a secondary lead furnace
Designing alloys for increase scrap usage using Bayesian optimisation
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
Refractory lifetime prediction in Industrial processes with artificial intelligence
Steel Production Efficiency Improvements by Digitalization


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