Classification of heavy metal ions present in multi-frequency multi-electrode potable water data using evolutionary algorithm

Karkra, Rashmi and Kumar, Prashant and Bansod, B.S. and Bagchi, Sudeshna and Sharma, Pooja and Krishna, C.R. (2016) Classification of heavy metal ions present in multi-frequency multi-electrode potable water data using evolutionary algorithm. Applied Water Science, 6 (22). pp. 1-11. ISSN 2190-5487 (Print) 2190-5495 (Online) (Submitted)

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Official URL: https://link.springer.com/article/10.1007/s13201-0...

Abstract

Access to potable water for the common people is one of the most challenging tasks in the present era. Contamination of drinking water has become a serious problem due to various anthropogenic and geogenic events. The paper demonstrates the application of evolutionary algorithms, viz., particle swan optimization and genetic algorithm to 24 water samples containing eight different heavy metal ions (Cd, Cu, Co, Pb, Zn, Ar, Cr and Ni) for the optimal estimation of electrode and frequency to classify the heavy metal ions. The work has been carried out on multi-variate data, viz., single electrode multi-frequency, single frequency multi-electrode and multi-frequency multi-electrode water samples. The electrodes used are platinum, gold, silver nanoparticles and glassy carbon electrodes. Various hazardous metal ions present in the water samples have been optimally classified and validated by the application of Davis Bouldin index. Such studies are useful in the segregation of hazardous heavy metal ions found in water resources, thereby quantifying the degree of water quality.

Item Type: Article
Uncontrolled Keywords: Genetic algorithm Particle swarm optimization Heavy metal ions Water quality Multi-electrode Multi-frequency
Subjects: CSIO > Agri - Instrumentation
Depositing User: Ms. Jyotsana
Date Deposited: 09 Aug 2018 11:58
Last Modified: 09 Aug 2018 11:58
URI: http://csioir.csio.res.in/id/eprint/624

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