Research on removal of metal from water

IIT-BHU has done research on the removal of copper, nickel and zinc from contaminated water using fired and non-fired beads.

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

This study found that both fired and non-fired beads can be reused for four cycles of adsorption-desorption.

Comparing adsorption capacities revealed that non-fired beads removed copper, nickel and zinc ions better than fired beads.

Assistant Professor Dr Vishal Mishra, principal researcher of the School of Biochemical Engineering, provided information about this research stating that machine learning algorithms were used to identify the scale-up criterion and reactor configuration for beads-mediated reactors.

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

Zinc is the 23rd most abundant element in the earth's crust, and its concentration in wastewater is steadily increasing. Zinc contamination occurs in water from plating and mining operations, fertilizer and fiber plants, and paper mills.

Naturally occurring and man-made copper contamination in water is documented. It is toxic to aquatic animals even in small amounts.

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

These industries discharges are a major source of nickel pollution. Nickel overexposure inhibits oxidative enzyme activity, damages the lungs, kidneys, causes dermatitis, and causes gastrointestinal distress.

He said that 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 predicting metal ion adsorption 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.

The present research work advances our understanding of metal ion adsorption on novel beads and will assist in future applications through the accumulation of reliable data in the scientific literature.