Scientific Reports (Jul 2024)

Early-stage recovery of lithium from spent batteries via CO2-assisted leaching optimized by response surface methodology

  • Ksenija Milicevic Neumann,
  • Muhammad Ans,
  • Bernd Friedrich

DOI
https://doi.org/10.1038/s41598-024-67761-9
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 14

Abstract

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Abstract Recycling lithium (Li) from spent lithium-ion batteries (LIBs) due to the depletion of natural resources and potential toxicity is becoming a progressively favourable measure to realize green sustainability. Presently, the prevalent recycling technique relying on pyrometallurgy lacks the capability to extract lithium. Meanwhile, conventional hydrometallurgical processes frequently employ robust acidic solutions like sulfuric acid and precipitation agents such as sodium carbonate. Unfortunately, this approach tends to result in the extraction of lithium at the end of a lengthy process chain, leading to associated losses and creating challenges in managing complex waste. This study addresses a cost-effective and environmentally friendly early-stage lithium recovery from the thermally conditioned black mass. In this sense, a thermally conditioned black mass is subjected to the carbonization process in a water solution to transform the water-insoluble Li phase into soluble lithium bicarbonate (LiHCO3) and carbonate (Li2CO3) facilitating its selective separation from other elements. Response surface methodology (RSM)—a statistical tool integrated with central composite design (CCD) is employed to optimize the parameters for Li recovery. Temperature, solid–liquid (S/L) ratio, leaching time and CO2 flow rate are considered as variable factors in modelling the optimum recycling process. A quadratic regression model is developed for Li recovery and based on ANOVA analysis, (S/L) ratio, temperature and time are identified as statistically significant factors. Experimental results demonstrate a maximum leaching efficiency of lithium with optimized parameter set, achieving a recovery rate of 97.18% with a fit response of 93.54%.

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