Leaching state prediction for battery black mass using online sensor data
Green Chemistry, vol. 27, pp. 9578–9596
Abstract
An innovative process monitoring strategy during battery black mass leaching, using correlation analysis, regression modeling, and mechanistic interpretation.
Authors 5
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Affiliation as printed
IME Process Metallurgy and Metal Recycling, Institute of RWTH Aachen University, Intzestr. 3, 52056 Aachen, Germany
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Affiliation as printed
IME Process Metallurgy and Metal Recycling, Institute of RWTH Aachen University, Intzestr. 3, 52056 Aachen, Germany
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Affiliation as printed
IME Process Metallurgy and Metal Recycling, Institute of RWTH Aachen University, Intzestr. 3, 52056 Aachen, Germany
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Tobias Kleinert Aachen Chair of Information and Automation Systems for Process and Material Technology
Affiliation as printed
Chair of Information and Automation Systems for Process and Material Technology, RWTH Aachen University, 52064 Aachen, Germany
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Affiliation as printed
IME Process Metallurgy and Metal Recycling, Institute of RWTH Aachen University, Intzestr. 3, 52056 Aachen, Germany
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