Phenomenological mathematical modeling of heavy metal biosorption in fixed-bed columns

- Phenomenological biosorption modeling was successfully applied.
- Dynamics and equilibrium were evaluated.
- The biomass efficiently removed Cu(II), Ni(II) and Zn(II) ions.
- The phenomenological model was validated in different experimental conditions.
- The process is rate limited by the internal mass transfer resistance.

A phenomenological mathematical modeling has been applied in order to describe the dynamic behavior of the biosorption of heavy metals in fixed-bed columns. The mathematical model hereby proposed was evaluated on experimental breakthrough curves of the ions Cu(II), Ni(II) and Zn(II) in monocomponent systems using the residue of alginate extraction from Sargassum filipendula as biosorbent. Some authors have studied mathematical models to describe the biosorption of heavy metals, however, in most cases, they have fitted the models to the experimental data without evaluating the prediction capacity of the model in different operational conditions. This capacity is inherent of phenomenological models. Therefore, in addition to the breakthrough curves used to obtain the kinetic parameters of the models, an extra set of breakthrough curves has been studied outside the range evaluated for the fitted parameters in order to validate the model. The equilibrium data for the three studied ions was adequately represented by the Langmuir isotherm. The quantity of Ni(II) removed was very similar to the quantity related to the Zn(II) removal and the removal of Cu(II) ions was far superior than the other ions. The high values for the breakthrough time obtained for the Cu(II) ions represent a high removal efficiency and affinity with the biomass. The mathematical model was able to predict the dynamic behavior of removal for the three investigated ions, indicating that the internal mass transfer resistance is the rate limiting-step. The validation of the model demonstrated the prediction capacity in the different evaluated conditions, characterizing it as a useful tool when analyzing and designing heavy metal biosorption processes in fixed-bed columns.

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