Modeling Lead and Cadmium Sorption onto Sepiolite Using Response Surface Methodology

Introduction: Some of the heavy metals such as cadmium (Cd) and lead (Pb) are toxic and represent hazardous pollutants due to their persistence in the environment. These metals have adverse effects on human health, which include growth retardation, cancer, damage to the nervous and heart system. Heavy metals can cause malfunctioning of the cellular processes via the displacement of essential metals from their respective sites. Mainly heavy metals discharge into the environment from industrial and urban sewage. There are different methods to reduce water pollution and the removal of heavy metals from water that one of them is sorption by using organic and inorganic adsorbents such as sepiolite. The low cost of sepiolite along with the high specific surface area, chemical and mechanical stability, and layered structure have made these clay minerals as excellent adsorbent materials for the removal of heavy metals from wastewaters. This study aims to investigate the sorption of Cd and Pb by sepiolite as an inorganic absorbent and optimize process variables (initial concentration, pH and ionic strength) using Response Surface Methodology (RSM) and Box–Behnken design (BBD). Materials and Methods: Response Surface Methodology (RSM) is a statistical method that uses quantitative data from appropriate experiments to determine regression model equations and operating conditions. RSM is a collection of mathematical and statistical techniques for modeling and analysis of problems in which a response of interest is influenced by several variables. A standard RSM design called Box-Behnken Design (BBD) was applied in this work to study the variables for sorption of Cd and Pb by sepiolite from aqueous solution using a batch process. BBD for three variables (initial Cd and Pb concentrations, pH and ionic strength), each with two levels (the minimum and maximum), was used as an experimental design model. Sepiolite sample used in this study was taken from a mine in Fariman region, northeastern Iran. In the experimental design model, initial concentration (0-200 mg­ L-1), pH (3-6) and ionic strength (0.01-0.06­ mol L-1) were taken as input variables. Design-Expert program was used for regression and graphical analysis of the data obtained. The optimum values of the selected variables were obtained by solving the regression equation and by analyzing the response surface contour plots. The variability independent variables were explained by the multiple coefficients of determination, R­2 and the model equation was used to predict the optimum value and subsequently to elucidate the interaction between the factors within the specified range.Results: The results showed that the sorption of Cd and Pb intensified by increasing initial concentration and pH but ionic strength had an inverse effect. The sorption of Pb and Cd ions onto the sepiolite minerals were lowest at pH =3 and IS=0.06 but increased with an increase in pH and initial concentration of the solution. High value for R2 (0.99) and adjusted R2 (0.99) showed that the removal of Cd and Pb can be described by the response surface method. One-way ANOVA showed (p< 0.0001) that the quadratic model is the best model for determining the interaction variables. According to optimization results, the sorption of Cd and Pb are maximized when pH: 6, concentration: 200 mg.L-1 and ionic strength: 0.02 mol.L-1. The predicted adsorption at these settings for Pb and Cd are 44.4 and 34.28 mg.g-1, respectively. It was found that the initial concentration is the most effective parameter in the sorption of Cd and Pb by sepiolite. Sepiolite adsorbed more lead ions than cadmium ions from aqueous solution.Conclusion: Response surface methodology using BBD, proved a very effective and time-saving model for studying the influence of process parameters (pH, initial concentration and ionic strength) on response factor (sorb). This model significantly reduces the number of experiments and hence facilitating the optimum conditions. The experimental values and the predicted values are in perfect match with an R2 value of 0.99. The high correlation coefficient between the model and experimental data (R2=0.99) showed that the model was able to predict the removal of Cd and Pb from aqueous solution by using sepiolite. The model revealed that concentration, metal type and pH were the most effective parameters on the response yield (adsorption by sepiolite), respectively. According to the results, sepiolite showed a greater efficiency for sorption of Cd and Pb from aqueous solution, also usage of sepiolite as an inorganic absorbent due to its low cost and abundance can be economically justified.