Adsorptive bubble separation method in the recovery of a biomolecule

Abstract

The present work aims to separate an amino acid (l-phenylalanine) from a highly dilute aqueous solution by the 'adsorptive bubble separation method'. Separation is based on the principle of adsorption at the interphase of the gas bubble and aqueous phase and subsequent amino acid extraction in the top layered nonaqueous phase. The operation was carried out in a long glass column with a comprehensive solvent chamber at the top. Design-Expert software optimizes the process parameters, and the process involves three variables: gas (nitrogen) flow rate, extractant, and solvent quantity, which control responses, recovery percentage, and the ratio of enrichment. The design models are found to be significant. The maximum recovery of amino acid observed was 75.545% and the value of the enrichment ratio was 3.87. In addition, two models were generated by the software, which was statistically significant (P < 0.05). Therefore, this process is adopted as it is of straightforward construction, eco-friendly, and less costly than other methods previously known to recover l-phenylalanine from a dilute solution.

Maximizing methyl red dye removal efficiency: a targeted parameter approach

“…Likewise, the dependent variable typically decreases as the favorable coefficient terms decrease, and vice versa. 28,29,32 The responses are typically maximized when the coefficients in the linear terms are positive and the coefficients in the quadratic terms are negative, as shown in Eqns ( 6) and (7). 'Linear against Block' is the weight of taking the mean and combining it with the linear terms.…”

“…According to the principle of maxima and minima, a decrease in the response is expected when the coefficients are positive in the quadratic terms and negative in the linear terms, and the opposite also is true. Likewise, the dependent variable typically decreases as the favorable coefficient terms decrease, and vice versa 28,29,32 . The responses are typically maximized when the coefficients in the linear terms are positive and the coefficients in the quadratic terms are negative, as shown in Eqns (6) and (7).…”

“…By leveraging response surfaces, second‐order polynomial equations can be derived with minimal reliance on trial and error. Additionally, this method enables the examination of interactions between independent components, providing a comprehensive understanding of the studied system 9,28‐31 . This methodology has two variants: the BBD and the Box–Wilson design, also known as the center composite design.…”

“…Additionally, this method enables the examination of interactions between independent components, providing a comprehensive understanding of the studied system. 9,[28][29][30][31] This methodology has two variants: the BBD and the Box-Wilson design, also known as the center composite design. The number of experiments executed for the Box-Wilson design is…”