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

Idowu Rudolph Ilaboya ,Ebierin Akpoebidimiyen Otuaro

doi:10.7176/cer/11-9-06

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 Mn 2+ 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 R 2 value of 0.7998 is in reasonable agreement with the adjusted R 2 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

Highlights

Environmental pollution caused by the discharge of untreated effluents containing toxic metals such as lead, chromium, and manganese has become an issue of concerned and have developed into a widely studied area (Ziemacki et al, 1989)
The majority of which are susceptible to biological degradation, heavy metals will not degrade into harmless end products, and their presence in streams and lakes leads to bioaccumulation in living organism, causing health problems in animals, plants and human beings (Weng et al, 2007, Demirbas et al, 2004)
Artificial neural network (ANN) are one of the many machine learning tools that are capable of performing the task of modelling and prediction of experimental data, determining the optimum values of the input variables required to maximize the efficiency of metal ion removal has continue to pose a challenge to the use of neural network

Summary

Introduction

Environmental pollution caused by the discharge of untreated effluents containing toxic metals such as lead, chromium, and manganese has become an issue of concerned and have developed into a widely studied area (Ziemacki et al, 1989). The toxic effects of lead, chromium and manganese ions in human, especially when present above the threshold limit in the hydrosphere are well documented (Khurshid & Qureshi, 1984) The presence of these heavy metals in the environment is of great concern to scientists and engineers because of their toxic nature (Sekar et al, 2004). Statistical design of experiment using central composite design method and response surface methodology were used to optimize the sorption of Mn2+ from aqueous solution onto zinc chloride activated sawdust with a view to determine the optimum value of selected adsorption variables, namely; initial metal ion concentration, adsorbent dose, contact time and pH that will guarantee maximum adsorption. It is easy to prepare, highly effective and non-toxic (Ikenyiri, et al, 2019)

Methods

Results

Conclusion