Analysis of Heavy Metal Ions in Potable Water Using Soft Computing Technique

Abstract

The water quality analysis has been an intriguing subject in the recent years because of the issues related to water resources. The work presents the application of the genetic algorithm to water samples containing contaminants for the optimal selection of electrode and the corresponding frequency for the better classification of various contaminants. We have used 24 water samples containing 8 different heavy metal ions (Cd, Co, Zn, Ni, Cu, Cr, Ar and Pb) for our experiment. The electrodes used were Gold, Platinum, Glassy Carbon and Silver Nanoparticle electrode. The impedance values of these four electrodes are recorded as Single-Electrode Multi-Frequency (SEMF), Single-Frequency Multi-Electrode (SFME) and Multi-Electrode Multi-Frequency (MEMF). The impedance values are subjected to Principal Component Analysis. Further, the optimal classification of various metal ions present in the water samples is done using Genetic Algorithm and this is validated by the application of Davis Bouldin index. The results show that DBI value may be enhanced by choosing electrode with optimum frequency.