No abstract

No abstract

No abstract

Chitosan is a natural polymer largely studied for heavy metal adsorption applications, since the amino and hydroxyl groups present in its structure can act as chelation sites. However, this material presents some drawbacks as an adsorbent due to its low mechanical strength. Studies have shown that the use of immobilized chitosan on glass beads can improve the mechanical strength of adsorbent and enhance mass transfer properties. For this reason, stirred batch experiments of copper adsorption on chitosan immobilized onto glass beads were performed to estimate the surface diffusion coefficient and the chitosan film thickness, considering an inert solid glass core. The kinetic data were modelled by a surface diffusion model incorporating the external film mass transfer resistance. Column experiments were also performed for copper solution at different flow rates and a film‐surface diffusion model was used to describe the breakthrough adsorption experiments, using the chitosan film thickness estimated from the batch experiment (φ = 2.5 µm). The input parameters for this model were determined by batch experiments or estimated from correlations available in the open literature. The surface diffusion coefficients (0.98–1.72 × 10−10 cm2 · min−1) of copper in the chitosan film for different flow rates were estimated. The experimental data and the model agreed, indicating that the film thickness and the mass transfer parameters were well predicted.

A high-accuracy modeling of the mass front evolution in a fixed bed is determined by considering the equilibrium information of the components’ concentration adsorbed on the stationary phase. Thus, adsorption isotherms based on thermodynamic principles are required in the mass balance. It is challenging to include isotherms with a high level of detail due to the necessity to compile it on each instant and position, drastically increasing the computation time. Pattern identification methods may be a solution to use a high-level isotherms model quickly and efficiently. Here, we structure a Deep Neural Network aiming to train a surrogate model using the solution of a non-linear Poisson-Boltzmann equation as a data set, modified to include the ionic dispersion potential from the Lifshitz Theory. Thus, the surrogate model output is used in the mass balance model while solving the partial differential equations which describe a column mass front. This approach generates a hybrid model in a serial identification strategy. For a case study, we analyze the mass front behavior of lysozyme in a silica-packed-bed column from pH 6 to pH 10, using different salts: NaCl, NaBr, and NaI. The results indicate a low retention time for the pHs near the isoelectric points for lysozyme (pH 10.8) and silica (pH 5.7) [1]. In addition, differences in salt types allow a significant decay in retention time and an increase in mass front compressibility in the following order: I– > Br– > Cl– due to the differences in anion polarizability. The results here indicate that a link between different scales is successfully achieved using DNN.