IIT-BHU Research on Metal Removal from Water

Varanasi, September 5 (IANS): IIT-BHU has carried out research on the removal of copper, nickel and zinc from contaminated water using fired and unfired balls.

The research presents a comparative study between fired and unfired beads to remove copper, nickel and zinc ions from the aqueous phase.

This study revealed that fired and unfired beads can be reused for four adsorption-desorption cycles.

The comparison of the adsorption capacities revealed that the unfired beads removed copper, nickel and zinc ions better than the fired beads.

Assistant Professor Dr. Vishal Mishra, Principal Investigator from the School of Biochemical Engineering provided insight into this research stating that machine learning algorithms were used to identify the scaling criterion and the reactor configuration for bead reactors.

Heavy metals are non-biodegradable, harmful and persistent pollutants in the environment. The presence of toxic heavy metals like copper, zinc and nickel in wastewater requires their removal.

Zinc is the 23rd most abundant element in the earth’s crust and its concentration in wastewater continues to increase. Zinc contamination occurs in water from plating and mining operations, fertilizer and fiber mills, and paper mills.

Natural and artificial contamination of copper in water is documented. It is toxic to aquatic animals even in small amounts.

An overdose of copper causes convulsions, cramps, vomiting and even death. Forging, mineral processing, steam power plants and paint formulation all use nickel.

Discharges from these industries are a major source of nickel pollution. Overexposure to nickel inhibits oxidative enzyme activity, damages the lungs, kidneys, causes dermatitis, and causes gastrointestinal disturbances.

He said drinking water containing 1.3 mg/L copper, 0.1 mg/L nickel and 5 mg/L zinc is allowed by international standards.

Recent research on metal ion adsorption prediction has focused on machine learning and artificial intelligence. This reduces the number of experiments, time and complexity involved in estimating adsorption capacity. It also saves time and resources. Decision trees and random forest are two machine learning models used here.

Current research advances our understanding of metal ion adsorption on new beads and will contribute to future applications through the accumulation of reliable data in the scientific literature.

This research is published in the Journal of Environmental Chemical Engineering, published by Taylor and Francis online.

About William J. Harris

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