Comparison and Classification of Heavy Metal Ions for Purification of Synthetic Waste-Water

Abstract

In the current study, fabrication of nanoparticle (NPs) impregnated cellulose for removal of metal ions from synthetic waste-water was demostrated. Co-precipitation method was adopted to impregnate cotton fibers (cellulose) with silver, iron, and zinc oxide NPs. The impregnated cellulose NPs was characterised through FTIR, XRD and SEM techniques. Nanoparticles impregnated cotton was exposed to water containing Mercury ion (Hg2+) , Nickel ion (Ni2+), Chromium ion (Cr3+), Cobalt ion (C02+), and lead ion (Pb2+), at 25°C (pH 5.5) for 400 min, separately. While comparing the adsorptive capacity of various heavy metal ions (H g2+, Ni2+, Cr3+, C02+, and Pb2+) for a specific nanocomposite (FeCt, AgCt, ZnCt) at both concentration (0.5 and 1 gm), the results reveal that H g2+ is efficient ion, being adsorbed by nanocomposite AgCt and ZnCt. However, by comparing various nanocomposite (FeCt, AgCt, and ZnCt) in both concentrations (0.5 and 1 gm) for adsorption capability of a specific metal ion. It was concluded that nanocomposite FeCt and AgCt can be a good source for metal ion adsorption or purification of synthetic waste-water. Furthermore, by introducing different classifiers for appropriate classification of these heavy metals to purify the synthetic waste-water, It was observed that the Linear discriminant analysis (LDA) gives an accuracy of 96.8%. However, the quadratic discriminant analysis (QDA), Random forest (RF), and Artificial neural network (ANN) classify these heavy metals more accurately than LDA with an accuracy rate of 100%.