Abstract
Spent cathode carbon is a type of waste generated during the maintenance or replacement of aluminum reduction cells, which contains some toxic fluorides and cyanides. If not treated, and through wanton accumulation, these toxic substances will gradually enter the soil and groundwater, causing water pollution, endangering human health, and compromising ecological environment safety. Therefore, this study used spent cathode carbon produced during aluminum electrolysis as raw material. In a steam atmosphere, the removal of fluorine was achieved through high-temperature calcination in a muffle furnace. The effects of several main factors (such as calcination temperature, holding time, particle size, and water vapor flow) on the defluorination rate were studied using the single-factor method and response surface method (RSM). The results showed that the optimum process values were as follows: the calcination temperature was 1100°C, the holding time was 3 h, the particle size was 150–200 mesh, and the water vapor flow was 3 mL min−1. The defluorination rate obtained from the experiment was 96.2%, which was similar to the rate predicted by the response surface model. The entire process is simple, low-cost, and environmentally friendly. Overall, this study provides a new strategy for treating toxic spent cathode carbon.
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Acknowledgements
The research is funded by Yunnan Xingdian Talent Support Project- Industrial innovation talents (2019-1096), Yunnan Xingdian Talent Support Project-Young talents (2018-73).
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WY: Data curation, Formal Analysis, Investigation, Methodology, Writing—original draft. WC: Data curation, Methodology, Writing—original draft. YX: Data curation, Methodology, Writing—original draft. HX: Conceptualization, Funding acquisition, Resources, Software, Supervision, Validation. JT: Data curation, Methodology. LZ: Conceptualization, Funding acquisition, Resources, Software, Supervision, Validation.
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Yang, W., Cai, W., Xu, Y. et al. A Superior Technology for Removing Fluorine from Spent Cathode Carbon: Optimization of the Process by Response Surface Methodology. JOM (2024). https://doi.org/10.1007/s11837-024-06572-9
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DOI: https://doi.org/10.1007/s11837-024-06572-9