Project preparation of ANN model using adsorption

Author(s)	Mr. Omkar Bajarang Pawar
Country	India
Abstract	Adsorption is one of the most efficient and economically viable techniques for the removal of pollutants from industrial wastewater; however, the adsorption process is inherently complex due to the simultaneous influence of multiple operating parameters and their nonlinear interactions. Conventional kinetic and isotherm models often fail to accurately predict adsorption performance under varying process conditions. In this study, an Artificial Neural Network (ANN)–based modeling approach was developed to predict adsorption removal efficiency (%) using key process variables, including initial contaminant concentration, adsorbent dose, contact time, solution pH, and operating temperature.
Keywords	In this study, an ANN model is developed to predict adsorption removal efficiency (%) using experimentally generated adsorption data Rapid industrialization and urbanization have led to a significant increase in the discharge of contaminated wastewater into natural water bodies. Industries such as textile, electroplating, chemical manufacturing, pharmaceutical, and mining release effluents containing dyes, heavy metals, and organic pollutants, which pose serious risks to human health and the environment. Effective treatment of wastewater has therefore become an essential requirement for sustainable industrial development.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2026
Published On	2026-08-13

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