Selective Recovery of Zinc from Alkaline Batteries via a Basic Leaching Process and the Use of a Machine Learning-Based Digital Twin for Predictive Purposes

Noelia Muñoz García, José Luis Valverde, Beatriz Delgado Cano (), Michèle Heitz and Antonio Avalos Ramirez ()
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Noelia Muñoz García: Department of Chemical and Biotechnological Engineering, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
José Luis Valverde: Faculty of Chemical Sciences and Technology, University of Castilla-La Mancha, 13005 Ciudad Real, Spain
Beatriz Delgado Cano: Centre National en Électrochimie et en Technologies Environnementales—CNETE, Shawinigan, QC G9N 6V8, Canada
Michèle Heitz: Department of Chemical and Biotechnological Engineering, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Antonio Avalos Ramirez: Department of Chemical and Biotechnological Engineering, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

Energies, 2024, vol. 17, issue 24, 1-15

Abstract: Recycling the metals found in spent batteries offers both environmental and economic benefits, especially when extracted and purified using environmentally friendly processes. Two basic leaching agents were tested and compared: ammonium hydroxide (NH 4 OH) and sodium hydroxide (NaOH). Using NH 4 OH 4 M at 25 °C, 30.5 ± 0.7 wt. % of zinc (Zn) was dissolved for a solid/liquid (S/L) ratio of 1/10 (g of black mass (BM)/mL of solution); meanwhile, with NaOH 6 M at 70 °C, and an S/L ratio of 1/5 (g of BM/mL of solution), 69.9 ± 2.8 wt. % of the Zn initially present in the BM of alkaline batteries was leached. A virtual representation of the experimental data through digital twins of the alkaline leaching process of the BM was proposed. For this purpose, 90% of the experimental data were used for training a supervised learning procedure involving 600 different artificial neural networks (ANNs) and using up to 12 activation functions. The application was able to choose the most suitable ANN using an ANOVA analysis. After the training step, the network was tested by predicting the outputs of inputs that were not used in the training process, to avoid overfitting in a validating process with 10% of the data. The best model was employed for estimating the degree of leaching of different metals that can be obtained from BM, obtaining a data deviation of less than 10% for highly concentrated compounds such as Zn.

Keywords: hydrometallurgy; alkaline batteries; selective Zn extraction; digital twins; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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