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Artificial Neural Network (ANN) Approach for Modeling of Lead (II) Adsorption from Wastewater using a Ragi Husk Powder

D. Krishna*, G. Santhosh Kumar**, D. R. Prasada Raju***

*-*** Department of Chemical Engineering, M. V. G. R. College of Engineering, Vizianagaram, India.

Periodicity:November - January'2022
DOI : https://doi.org/10.26634/jfet.17.2.18553

Abstract

The main sources of lead in the environment are effluent industries such as electroplating, alloying, smelting, mining, refining, pigmenting, plastic manufacture, and metallurgical industries. A batch experiment as well as an Artificial Neural Network (ANN) coupled with a Genetic Algorithm model for the extraction of lead from wastewater was conducted. In the development of the ANN model, a tan sigmoid transfer function for input and a purelin for output layers have been employed. A feed-forward back propagation with a single layer was used with thirteen neurons in the hidden layer. The optimized process parameters, viz., pH, adsorbent dosage, and initial concentration of lead, have been obtained. Based on the regression coefficient value of R2 of 0.998, it was confirmed that the ANN model predicted data and the experimental value data were a perfect match. The maximum percentage removal of lead was obtained at optimum conditions by means of a pattern search algorithm in GA.

Keywords

Ragi Husk Powder, Adsorption, Lead, Artificial Neural Network (ANN) and Pattern Search Algorithm, Modeling of Lead.

How to Cite this Article?

Krishna, D., Kumar, G. S., and Raju, D. R. P. (2022). Artificial Neural Network (ANN) Approach for Modeling of Lead (II) Adsorption from Wastewater using a Ragi Husk Powder. i-manager’s Journal on Future Engineering & Technology, 17(2), 1-6. https://doi.org/10.26634/jfet.17.2.18553

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