Sorptive equilibrium profile of fluoride onto aluminum olivine [(FeMg)SiO] composite (AOC): Physicochemical insights and isotherm modeling by non-linear least squares regression and a novel neural-network-based method.

A novel aluminum/olivine composite (AOC) was prepared by wet impregnation followed by calcination and was introduced as an efficient adsorbent for defluoridation. The adsorption of fluoride was modeled with one-, two- and three-parameter isotherm equations by non-linear regression to demonstrate the adsorption equilibrium. The FI was the best-fitted model among the two-parameter isotherms with a R value of 0.995. The three-parameter models were found to have better performance with low values of the error functions and high F values. The neural-network-based model was applied for the first time in the isotherm study. The optimized model was framed with eight neurons in hidden layer with a mean square of error of 0.0481 and correlation coefficient greater than 0.999. The neural-based model has the better predictability with a higher F value of 9484 and R value of 0.998 compared to regression models, exhibiting the F value and the R in the range of 86-3572 and 0.835-0.995, respectively. The material characterization established the formation of the aluminum oxide, silicate, etc. onto the olivine which is conducive of the removal of fluoride by the formation of aluminum fluoride compounds, such as AlF in the spent material after defluoridation.