Partha S. Ghosal

Multivariate optimization of process parameters in the synthesis of calcined Ca‒Al (NO3) LDH for defluoridation using 33 factorial, central composite and Box–Behnken design

ABSTRACT Response surface methodology was applied for the first time in the optimization of the preparation of layered double hydroxide (LDH) for defluoridation. The influence of three vital process parameters (viz. pH, molar ratio and calcination temperature) in the synthesis of the adsorbent ‘Calcined Ca‒Al (NO3) LDH’ was thoroughly examined to maximize its fluoride scavenging potential. The process parameters were optimized using the 33 factorial, face centered central composite and Box–Behnken designs and a comparative assessment of the methods was conducted. The maximum fluoride removal efficiency was achieved at a calcination temperature of approximately 500ºC; however, the efficiency decreased with increasing pH and molar ratio. The outcome of the comparative assessment clearly delineates the case specific nature of the models. A better predictability over the entire experimental domain was obtained with the 33 factorial method, whereas the Box–Behnken design was found to be the most efficient model with lesser number of experimental runs. The desirability function technique was performed for optimizing the response, wherein face centered central composite design exhibited a maximum desirability. The calcined Ca‒Al (NO3) LDH, synthesized under the optimum conditions, demonstrated the removal efficiencies of 95% and 99% for the doses of 3 g L−1 and 5 g L−1, respectively.

Modeling and Optimization of Defluoridation by Calcined Ca-Al-(NO3)-LDH Using Response Surface Methodology and Artificial Neural Network Combined with Experimental Design

AbstractA face-centered central composite design was applied as an input to the artificial neural network (ANN), demonstrating the significance of statistical design for an efficient performance with a lesser number of data. The influence of the initial fluoride concentration, adsorbent dose, and reaction time on the fluoride adsorption capacity of the calcined Ca-Al-(NO3) layered double hydroxide was determined through the response surface methodology (RSM) and ANN. A significant variation of the adsorption capacity (3.17–22.16  mg/g) confirmed the importance of the selected process parameters. The adsorption capacity was found to be increased with the increase in the initial fluoride concentration, whereas a reverse trend was observed with the variation of the adsorbent dose. A significant interactive effect was found between the adsorbent dose and the initial fluoride concentration. The mean square of error and R2 associated with the RSM and ANN model are 0.139, 0.135 and 0.993, 0.995, respectively. Th...

Modeling Adsorption Isotherm for Defluoridation by Calcined Ca-Al-(NO3)-LDH: State-of-the-Art Technique

AbstractThe calcined Ca-Al-(NO3) layered double hydroxide (LDH) was synthesized by a coprecipitation method and was applied for the adsorptive removal of fluoride from the aqueous solution. The iso...

Sorptive equilibrium profile of fluoride onto aluminum olivine [(FexMg1−x)2SiO4] composite (AOC): Physicochemical insights and isotherm modeling by non-linear least squares regression and a novel neural-network-based method

ABSTRACT 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 R2 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 R2 value of 0.998 compared to regression models, exhibiting the F value and the R2 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 AlF3 in the spent material after defluoridation.