Abstract
Melting and recycling of Al alloys involve large amounts of energy and CO2 release. In order to minimize energy consumption and environment impact, a novel approach has been developed and tested for this industrial sector, but it can be extended to other processes and materials. The approach is based on on-line data acquisition and efficient numerical modeling of heat exchanges within a melting furnace . The fast and efficient numerical model , which includes the physical mechanisms of combustion , radiation , conduction and convection , has a few adjustable parameters which are calibrated on-line by a few data acquisition values. A friendly user-interface allows furnace operators to monitor the melting process and optimize mass loading, door opening, heating sequences, etc. The main features of this tool are presented.
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Notes
- 1.
According to the Swiss Federal Office of Energy , the total energy consumed in Switzerland in 2016 is 854300 TJ, corresponding to about 102 GJ for each Swiss (or 3.2 kW over the whole year), i.e., equivalent to the production of 600 kg of primary aluminum .
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Acknowledgements
The development of SmartMelt has been made possible thanks to the support of Constellium Valais SA, Constellium C-TEC, Innosuisse—Swiss Innovation Agency, Foundation for Technological Innovation (FIT) and Climate-KIC.
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© 2019 The Minerals, Metals & Materials Society
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Rostamian, A., Lesquereux, S., Bertherat, M., Rappaz, M. (2019). Artificial Intelligence to Optimize Melting Processes: An Approach Combining Data Acquisition and Modeling. In: Chesonis, C. (eds) Light Metals 2019. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-05864-7_142
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DOI: https://doi.org/10.1007/978-3-030-05864-7_142
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