Bioremediation potential of Dicentrarchus labrax fish scales for dye-based emerging contaminants by ANN-GA hybrid modeling

This work investigates the possibility of using scales of sea bass Dicentrarchus labrax as a low-cost material for the adsorptive removal of methylene blue (MB) cationic dye in aqueous solutions. The physical-chemical characterizations of fish scales in natura (FS-in natura) revealed through thermogravimetry that they are composed of inorganic (hydroxyapatite) and organic (collagen) phases in relatively similar amounts. Spectroscopy analyses show that the interactions of MB with FS-in natura occur mainly in the organic phase layer of the adsorbent. The effects of initial MB concentration (5.0 x 10(-4) and 5.0 x 10(-3) mol L-1) and temperature (25-55 degrees C) on the adsorption efficiency of FS-in natura were evaluated. FS-in natura at MB concentration (5.0 x 10(-3) and 5.0 x 10(-4) mol L-1) exhibited the maximum adsorption capacities of 2.2 x 10(-3) mol g(-1) at 25 degrees C and 2.8 x 10(-5) mol g(-1) at 55 degrees C, respectively. The pseudo-second-order model represented the adsorption kinetics well, and the equilibrium isotherm data were better correlated using the Langmuir equation. The newly developed neural model demonstrated a high predictive capacity with an R-value greater than 0.99 and reduced values for mean squared error, root mean squared error, and mean absolute error equal to 0.003, 0.055, and 0.0348, respectively. The genetic algorithm was used to optimize the experimental conditions of the process. In conclusion, the sea bass scales have promising prospects as a low-cost alternative material for removing cationic dyes from aqueous solutions.

Automated summary

This study investigates the use of sea bass scales as a low-cost adsorbent for removing methylene blue dye from water. The composition and interactions of the scales are analyzed, and the adsorption efficiency is evaluated under different conditions. A neural model is developed to predict the adsorption behavior, and a genetic algorithm is used for process optimization.