Analysis of Factors Influencing Cu(II) Sorption By Clinoptiolite
AuthorsŠljivić-Ivanović, Marija Z.
Smičiklas, Ivana D.
Marković, Jelena P.
Milenkovic, Aleksandra S.
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Experimental design methodology represents a powerful tool for the analysis and optimization of various processes. Immobilization of toxic substances by sorption onto low-cost materials has gained a lot of attention in the last decade. Fundamental knowledge about sorption processes and their practical use can be improved by experimental planning and statistical analysis. In this study, the effects of initial metal concentration and pH, as well as the sorbent mass and particle size, on Cu(II) sorption by natural clinoptilolite were evaluated and compared. Full factorial experimental design at two levels was applied. Statistically significant factors were determined considering residual Cu(II) concentrations as a system response. The Pareto graphs of standardized effects, Main effect plots and Interaction plots were created using statistical software. Initial sorbate concentration, sorbent mass and their interaction were recognized as statistically significant, at 95% confidence level. M...ain effect plot approved that sorbent mass increase and initial Cu(II) concentration decrease caused reduction of residual Cu(II) concentration in solution. On the other hand, the change of initial solution pH and sorbent particle size didnt provoke significant response changes. Bearing in mind that pH is a factor with high effect on heavy metal sorption, the insignificant influence of initial pH detected in this study can be explained by buffering properties of the applied clinoptilolite and relatively narrow pH range chosen in order to prevent sorbent dissolution on one side and sorbate precipitation on the other. By regression analysis, the mathematical model for process description was derived. The correlation between predicted and experimental values was high (R-2 GT 0.99). In the investigated ranges of parameters, the obtained empirical equation can be applied for the prediction of system response.