Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/274
Title: A Study of Copper and Lead Removal from Synthetic Leachate by Photocatalysis
Authors: Soraganvi, Veena S.
Desai, Naveen N.
Keywords: Artificial neural network · Kinetic study · Response Surface Methodology (RSM) · TiO2 · Ag-doped TiO2 nanomaterial
Issue Date: 13-Aug-2024
Publisher: Springer
Series/Report no.: 235:602;
Abstract: Most undeveloped and developing coun- tries have adopted landfill as the ultimate disposal method for their Municipal Solid Waste (MSW). Lea- chate produced by a landfill site is highly dangerous, with high concentrations of organic and inorganic pollutants, ammonia and toxic heavy metals. Heavy metals commonly found in landfill leachate are cop- per, lead, mercury, cadmium, arsenic etc., The main objective of this research is to study the removal of heavy metals copper and lead, from synthetic lea- chate using TiO 2 and Ag-doped TiO 2 nanomateri- als by a heterogeneous photocatalytic process. The photocatalytic experiments were conducted using a compound parabolic collector in sunlight. The design of experiment is used to obtain the minimum num- ber of experiments for the study, to analyze the data and to understand the interaction between the process variables and their responses. Photocatalytic behavior of copper and lead removal has been demonstrated using an Artificial Neural Network (ANN) model. Characterization studies are conducted on TiO2 and Ag-doped TiO 2 nanomaterials by XRD, SEM and EDX. At optimum parameters of the dosage 0.75 g/L, pH 5 and irradiation time of 100 min, copper removal from synthetic leachate was found to be 80.38% and 80.12%, lead removal efficiency was 97.2% and 96.34% for TiO 2 and Ag-doped TiO2, respectively. The determination coefficient value obtained by RSM and ANN ensures that the developed model of copper and lead removal gives an accurate prediction. The kinetic study shows that, copper and lead removal by photocatalytic process are described well by a Lang- muir–Hinshelwood kinetic model.
URI: http://hdl.handle.net/123456789/274
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