Optimizing the Sorption of Mn2+ ion from Aqueous Solution onto Zinc Chloride Activated Sawdust Using Response Surface Methodology (RSM)

Abstract

Activated carbon from sawdust was prepared and characterized using Fourier transform infra-red (FTIR) and scanning electron microscope (SEM) to determine the presence of functional groups and visualize its microstructural arrangement in other to ascertain its potential for the removal of Mn²⁺ ion from aqueous solution. Statistical design of experiment (DOE) using central composite design was then employed to randomized the levels of selected input parameters in order to determine their optimum values that will guarantee maximum adsorption. To optimize the process, response surface methodology based on numerical optimization was employed. The behaviour of the system which was used to evaluate the relationship between the input and the response variables was explained using the empirical second-order polynomial equation. To validate the optimization results, selected goodness of fit statistics, namely; coefficient of determination, adjusted coefficient of determination and predicted coefficient of determination were employed. Results obtained revealed the adequacy of response surface methodology in optimizing adsorption systems. Analysis of variance test revealed that the model developed is significant at 0.05df with computed p-value < 0.0001. Computed goodness of fit statistics revealed that the predicted R² value of 0.7998 is in reasonable agreement with the adjusted R² value of 0.9062. In addition, numerical optimization results indicate that for 50 mL aqueous solution containing 11.39 mg/L of manganese, 1.0 g zinc chloride activated sawdust, pH of 5.0 and a contact time of 120 minutes will be required to obtain a sorption efficiency of 84.04% with amount removed (qe) of 714mg/g. The outcome of this study justifies the use of sawdust as adsorbent for the treatment of water and wastewater containing divalent metal ions.

Keywords: Response surface methodology, central composite design, ANOVA, numerical optimization.

DOI: 10.7176/CER/11-9-06

Publication date:October 31^st 2019