Solvent extraction of cobalt from spent lithium-ion batteries: Dynamic optimization of the number of extraction stages using factorial design of experiments and response surface methodology

Vieceli, Nathália; Ottink, Thomas; Stopić, Srečko; Dertmann, Christian; Swiontek, Thomas; Vonderstein, Claudia; Sojka, Reiner; Reinhardt, Niclas; Ekberg, Christian; Friedrich, Bernd; Petraniková, Martina

doi:10.1016/j.seppur.2022.122793

Cited by 26 publications

(4 citation statements)

References 27 publications

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“…The coded equation is valuable for assessing the relative influence of these factors by comparing their coefficients. Another crucial aspect of the ANOVA analysis is the F‐value (Fisher's Variation ratio), which assesses the adequacy of the model and compares it to residual errors [30] . The model's F‐values, namely 9912.08 for Fe, 2878.5 for Co, and 0.8573 for Ni, indicate the significance of the regression model.…”

Section: Resultsmentioning

confidence: 99%

“…Another crucial aspect of the ANOVA analysis is the F-value (Fisher's Variation ratio), which assesses the adequacy of the model and compares it to residual errors. [30] The model's Fvalues, namely 9912.08 for Fe, 2878.5 for Co, and 0.8573 for Ni, indicate the significance of the regression model. For Fe and Co, there is only a 0.01 % chance, and for Ni, a 0.19 % chance that such a large F-value could occur due to random noise.…”

Section: Statistical Analysis Extraction Data For Fe Co and Nimentioning

confidence: 95%

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Process Optimization Using Box‐Behnken Design for the Solvent Extraction and Separation of Iron, Cobalt, and Nickel from Simulated Crud Solution

Panda,

Mishra

2024

ChemistrySelect

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The corrosion product deposit (crud) found on the surface of the fuel rod of the pressurized water reactors (PWRs) chiefly made up of nickel ferrite. Fe, Co, Ni and Cr are the major elements exist in PWR crud. Ascribable to the simplicity in execution, handling, and option for recyclability, solvent extraction technique has been broadly used in nuclear energy sector. In the present investigation, the recovery of iron, cobalt, and nickel has been studied from simulated crud solution in nitrate medium using a novel hydrophobic menthol‐decanoic acid‐based deep eutectic solvent. Process parameters such as pH, DES concentration and shaking time were considered as input variables, and concentration of Fe, Co, and Ni as response variables for the design of experiments. Response surface methodology via Box‐Behnken design using polynomial quadratic model was selected for optimization of solvent extraction experiments. A total of 17 runs were conducted and the results were used for optimization purpose. Complete separation of Co/Fe and Co/Ni is achievable under various extraction conditions. As per the BBD analysis, the optimization conditions obtained were pH=1.9, [DES]=2.99 mol/L and shaking duration=18.27 min. Under these conditions,the amount of Fe, Co and Ni extracted were found as 32.29 g/L, 0.2111 g/L, and 19.83 g/L, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Statistical Analysis Extraction Data For Fe Co and Nimentioning

confidence: 95%

Process Optimization Using Box‐Behnken Design for the Solvent Extraction and Separation of Iron, Cobalt, and Nickel from Simulated Crud Solution

Panda,

Mishra

2024

ChemistrySelect

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“…Furthermore, the ∆H and ∆G of the reaction are calculated, and the reaction equation is shown in Equation ( 5), which corresponds to the substitution of the ethylenediamine group in en/SG for two molecules in the hydrated metal ions. The ∆H and ∆G of the adsorption process are calculated by Equations ( 6) and (7), respectively. As shown in Comparing the ∆G of the two metal ion adsorption processes, it can be seen that when en/SG adsorbs [Co(H 2 O) 6 ] 2+ , the ∆G of the reaction system decreases more, indicating that the reaction is easier to proceed and the formed complex is more stable.…”

Section: Co(ii) and Ni(ii) Stable Species In Sulfate Solutionmentioning

confidence: 99%

“…With the growth of the electric vehicle rechargeable battery market, the consumption of cobalt and nickel is expected to increase dramatically, which may lead to a significant increase in their supply risks [1][2][3][4]. Therefore, improving the recovery efficiency of cobalt and nickel in secondary resources is one of the sustainable strategies to alleviate its supply risk [5][6][7][8]. Generally, cobalt and nickel coexist (e.g., spent lithium-ion batteries) and are transferred into aqueous solution via leaching using reagents such as H 2 SO 4 [9,10].…”

Section: Introductionmentioning

confidence: 99%

Insight into the Mechanism of Cobalt-Nickel Separation Using DFT Calculations on Ethylenediamine-Modified Silica Gel

Yang

Yuan

et al. 2023

Materials

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The separation of Co(II) and Ni(II) from leaching solution is gaining interest because Co(II) and Ni(II) are increasingly used in emerging strategic areas, such as power batteries. Herein, the surface of silica gel is functionalized with 1,2-ethylenediamine and used for the separation of Co(II) and Ni(II). The Co(II) removal efficiency of the modified silica is 80.2%, with a 4-fold improvement in the separation factor. The geometry, frequency, and electrostatic potential of the ethylenediamine modified silica gel (en/SG) are calculated. The corresponding properties of M2+ (M-Co, Ni) adsorbed on en/SG in an aqueous solution are simulated and analyzed. The results show that ethylenediamine tends to form [Men(H2O)4]2+ after binding to M2+, and the binding ability of Co(II) to ethylenediamine is stronger. Besides, the thermodynamic calculations show that en/SG has a more negative Gibbs free energy when absorbing Co(II) in aqueous solution, so en/SG is more inclined to bind with Co(II) preferentially. It is the difference in complexation ability between Ni, Co, and ethylenediamine that enlarges the difference in the original physical adsorption, thus strengthening the separation performance. This work will provide guidance for a rational design of high-performance nickel-cobalt adsorption materials.

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Preparation, Characterization, and Adsorption Performance of H₂TiO₃ Lithium Ion Sieves with Homogeneous Nanoparticles: Kinetics, Thermodynamics, and Mechanism Analysis

Liu,

Xu,

Shi

et al. 2024

ChemistrySelect

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Li2TiO3 precursor with layered structure is successfully prepared by template‐free method using anatase TiO2 and Li2CO3, followed by the treatment of dilute hydrochloric acid, which leads to obtaining H2TiO3 lithium ion sieve nanoparticles with uniform dispersion and small size. The adsorption performance and mechanism of the H2TiO3 are being studied. The experimental results show that the H2TiO3 lithium ion sieve has a large BET specific surface area (19.10 m2 g−1) and good thermal stability. At the same time, it also has a high adsorption rate and adsorption capacity (55.13 mg g−1) towards Li+ in solution due to its high dispersibility and uniformity of particle size, which can expose more adsorption sites in LiCl solution. The adsorption capacity of the H2TiO3 lithium ion sieve can reach 46.63 mg g−1, which achieves 84.58% of the equilibrium adsorption capacity. The equilibrium adsorption parameters follow the Langmuir model perfectly, demonstrating that Li+ undergoes monolayer adsorption. The results of thermodynamic and kinetic analysis show that the adsorption behavior of H2TiO3 is spontaneous and conforms to the pseudo‐second‐order kinetic model, indicating the adsorption process is controlled by chemosorption. In addition, after five adsorption/desorption cycles, the H2TiO3 lithium ion sieve still shows a high adsorption capacity of 46.15 mg g−1, and the dissolution loss of titanium is only 0.14%, which shows that the cycle stability is excellent. XPS and zeta potential analyses illustrate that the adsorption mechanism of Li+ on the H2TiO3 is an ion exchange reaction between Li+ and H+, combined with the electrostatic attraction of the adsorbent surface. Additionally, the adsorption performance of H2TiO3 lithium ion sieve in West Taijinaier Salt Lake brine was tested, and the adsorbent material proves itself is a perspective candidate for lithium extraction from aqueous lithium resources.

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Solvent extraction of cobalt from spent lithium-ion batteries: Dynamic optimization of the number of extraction stages using factorial design of experiments and response surface methodology

Cited by 26 publications

References 27 publications

Process Optimization Using Box‐Behnken Design for the Solvent Extraction and Separation of Iron, Cobalt, and Nickel from Simulated Crud Solution

Process Optimization Using Box‐Behnken Design for the Solvent Extraction and Separation of Iron, Cobalt, and Nickel from Simulated Crud Solution

Insight into the Mechanism of Cobalt-Nickel Separation Using DFT Calculations on Ethylenediamine-Modified Silica Gel

Preparation, Characterization, and Adsorption Performance of H₂TiO₃ Lithium Ion Sieves with Homogeneous Nanoparticles: Kinetics, Thermodynamics, and Mechanism Analysis

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Solvent extraction of cobalt from spent lithium-ion batteries: Dynamic optimization of the number of extraction stages using factorial design of experiments and response surface methodology

Cited by 26 publications

References 27 publications

Process Optimization Using Box‐Behnken Design for the Solvent Extraction and Separation of Iron, Cobalt, and Nickel from Simulated Crud Solution

Process Optimization Using Box‐Behnken Design for the Solvent Extraction and Separation of Iron, Cobalt, and Nickel from Simulated Crud Solution

Insight into the Mechanism of Cobalt-Nickel Separation Using DFT Calculations on Ethylenediamine-Modified Silica Gel

Preparation, Characterization, and Adsorption Performance of H2TiO3 Lithium Ion Sieves with Homogeneous Nanoparticles: Kinetics, Thermodynamics, and Mechanism Analysis

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Preparation, Characterization, and Adsorption Performance of H₂TiO₃ Lithium Ion Sieves with Homogeneous Nanoparticles: Kinetics, Thermodynamics, and Mechanism Analysis