Adsorption of Lead (Pb(II)) from Contaminated Water onto Activated Carbon: Kinetics, Isotherms, Thermodynamics, and Modeling by Artificial Intelligence

Badr Abd El-wahaab, Walaa H. El-Shwiniy (), Raid Alrowais, Basheer M. Nasef and Noha Said
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Badr Abd El-wahaab: Chemistry Department, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Walaa H. El-Shwiniy: Department of Chemistry, College of Science, University of Bisha, Bisha 61922, Saudi Arabia
Raid Alrowais: Department of Civil Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
Basheer M. Nasef: Network Systems Management Department, Faculty of Applied College, Shaqra University, Al Quwaiiyah 11961, Saudi Arabia
Noha Said: Environmental Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt

Sustainability, 2025, vol. 17, issue 5, 1-19

Abstract: Heavy metals, extensively used in various industrial applications, are among the most significant environmental pollutants due to their hazardous effects on human health and other living organisms. Removing these pollutants from the environment is essential. In this study, activated carbon (AC) (Carbon C) was employed to eliminate Pb(II) from water. The optimal removal conditions were determined as follows: a 50 mg dose of activated carbon, an initial Pb(II) concentration of 100 mg/L, pH 4, a temperature of 30 °C, and a contact time of 60 min Under these conditions, activated carbon achieved a Pb(II) removal efficiency of approximately 97.86%. The adsorption data for Pb(II) closely aligned with the 2nd-order kinetic model, and the equilibrium data were effectively described by the Langmuir isotherm equation. The maximum adsorption capacity of Pb(II), as determined by the Langmuir model, was 48.75 mg/g. These methods were successfully applied to remove Pb(II) from various environmental and industrial wastewater samples. To accurately predict the percentage of Pb(II) removal based on parameters such as pollutant type, carbon dosage, pH, initial concentration, temperature, and treatment duration, feed-forward neural networks (FFNNs) were utilized. The FFNN model demonstrated outstanding predictive accuracy, achieving a root mean square error (RMSE) of 0.03 and an R 2 value of 0.996.

Keywords: lead (II); water treatment; spectrophotometric; analytical applications; adsorption behavior; activated carbon; neural networks modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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