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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Digitalizing the Circular Economy (CE): From Reactor Simulation to System Models of the CE
Author(s) Markus A. Reuter, Neill Bartie
On-Site Speaker (Planned) Markus A. Reuter
Abstract Scope Embracing the circular economy, this paper will discuss recent work that scales reactor technology to system models with relevant digital twins. By the combination of different tools and methods (from AI, CFD, mass- and heat transfer, kinetics, industrial experience etc.) and integrating these into suitable digital platforms this paper will analyze different circular economy systems also in terms of complete supply chains. With a focus on for example thermoeconomics, exergy dissipation of the systems will be quantified by suitable digital twins for PV cell manufacture, battery technology etc. Integration with impact assessment approaches will show how to minimize the impact of complete supply chains and show which systems produce the lowest footprint products. In addition the link of the digital twins to the sustainability development goals (SDGs) of the United Nations will be elaborated on.
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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