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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Refractory Lifetime Prediction in Industrial Processes with Artificial Intelligence
Author(s) Nikolaus Voller, Christoph Pichler, Christine Wenzl, Gregor Lammer
On-Site Speaker (Planned) Nikolaus Voller
Abstract Scope This paper deals with refractory lifetime prediction by using artificial intelligence (AI). Through the effective use of process parameters, obtained from various operational processes within the Industrial (Cement/Lime, Non-Ferrous Metals, Process Industries, Foundry) and Steel sector, an AI model is generated. With the assistance of modern surveying technology, a correlation can be identified between process parameters and refractory wear. Using this method, suitable prediction of the refractory lifetime, as well as the wear mechanism, is possible. In addition, maintenance cycles can be adjusted and the optimal maintenance intensity of the operated furnaces can be ensured. With our intelligent Automated Process Optimization (APO) solution the prediction of refractory lifetime and wear mechanism can be done in real-time. Thus, we can provide value-added service to our customers.
Proceedings Inclusion? Planned:
Keywords Pyrometallurgy, Ceramics, Machine Learning


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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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