Application of response surface methodology for optimization of cadmium biosorption in an aqueous solution by Saccharomyces cerevisiae

Optimization of a cadmium biosorption process was performed by varying three independent parameters (initial pH, initial cadmium ion concentration, Saccharomyces cerevisiae dosage) using a central composite design (CCD) under response surface methodology (RSM). For the maximum biosorption of cadmium ion in an aqueous solution by S. cerevisiae, a total of 20 experimental runs were set and the experimental data fitted to the empirical second-order polynomial model of a suitable degree. The potential of S. cerevisiae as a bioadsorbent was evaluated as a pretreated material with 700 g/L of ethanol. Furthermore, the quantitative relationship between the heavy metal uptake (q) and different levels of these factors was used to work out optimized levels of these parameters by a full factorial design (23). The analysis of variance (ANOVA) of the quadratic model demonstrates that the model was highly significant. The best set required 5 as initial pH, 3.8 g/L S. cerevisiae and 19 mg/L cadmium ion concentration within 240 min of contact time. Three dimensional plots demonstrate relationships between the cadmium ion uptake with the paired factors (when other factor was kept at its optimal level), describing the behavior of biosorption system in a batch process. The model showed that cadmium uptake in aqueous solution was affected by all the three factors studied. An optimum cadmium uptake of 6.71 mg/g biomass was achieved at initial cadmium ion concentration of 26.46 mg/L and S. cerevisiae dosage of 2.13 g/L. The process kinetic was also evaluated by isotherm, pseudo-second-order and intra-particle diffusion models. It showed that both monolayer adsorption and intra-particle diffusion mechanisms were effective in the cadmium biosorption process. Therefore, it is apparent that the response surface methodology not only gives valuable information on interactions between the factors but also leads to identification of feasible optimum values of the studied factors.