4.7 Article

Fixed-bed columns mathematical modeling for selective nickel and copper recovery from industrial spent acids by chelating resins

Journal

SEPARATION AND PURIFICATION TECHNOLOGY
Volume 313, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.seppur.2023.123457

Keywords

Industrial spent acids; Adsorption and desorption; Nickel and copper recovery; Chelating resins; Modeling of fixed-bed columns

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Chelating resins are used to recover valuable metals from spent acid streams containing heavy metals. In this study, a two-column system with BPA resins was used to selectively separate and recover nickel and copper. Mathematical models based on mass transfer were developed to describe the adsorption and desorption stages.
Spent acid streams generated in industry containing high concentrations of heavy metals are potential secondary sources of raw materials. Chelating resins are excellent candidates to recover valuable metals from complex mixtures at very low pH conditions. In particular, previous works reported high recoveries of nickel and copper from real industrial acids (3400 mg Cu2+ L-1, 8700 mg Ni2+ L-1 and 24000 mg Fe L-1) using commercial bispicolyamine (BPA)-based resins. In this work, adsorption and desorption using two in-series fixed-bed columns with BPA resins have been proposed to carry out the selective and independent separation and recovery of nickel and copper. Under the selected operating conditions, it was possible to recover 90% of the copper and 80% of the nickel present in the problem solution. A mathematical model based on mass transfer was developed in order to describe the adsorption and desorption stages. Adsorption chemical reactions were modeled as equilibrium reactions, fitting to Langmuir's and Freundlich's isotherms for copper and nickel respectively. The chemical reactions for both metals in desorption fitted into first order reactions. Finally, the kinetic constants k(de)=0.81 kg(dryresin) L-1 h(-1) for copper and k(de)=1.10 kg(dryresin) L-1 h(-1) for nickel were estimated using the software Aspen Custom modeler. The predicted values agreed with the experimental data.

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