Statistical Analysis and Modeling of Trivalent Chromium Ion Adsorption by Green-Mediated Iron Nanoparticles

Abstract

In this study, the adsorption of trivalent chromium ions by green-mediated iron nanoparticles was studied statistically. The effect of independent variables such as pH, temperature, time, adsorbent dosage, and initial metal ion concentration on uptake capacity and removal efficiency were examined. Multiple linear regression (MLR), principal component analysis (PCA), partial least squares (PLS), and principal component regression (PCR) are effectively applied for the analysis and modeling of adsorption data. The value of p in Bartlett’s sphericity test was proved to be less than 0.05 which indicates that the principal component analysis could be useful for adsorption data. The AHC analysis showed that among all variables, the contribution of pH was high in the adsorption of trivalent chromium ions by ZVIN and MIN nanoparticles. The value of R2 in statistical modeling of adsorption of trivalent chromium ions by ZVIN particles was high in PCR (0.981) than in MLR (0.945) and PLS (0.752) models. Similarly, for MIN particles, the R2 value of PCR (0.982) was higher than the MLR (0.943) and PLS (0.742) models. The analysis of goodness of fit statistics showed that the PCR model effectively predicted the uptake capacity and removal efficiency more than MLR and PLS models.

Keywords

• trivalent chromium, zero-valent iron nanoparticles, magnetic iron nanoparticles, principal component analysis, partial least squares, agglomerative hierarchical clustering