Activated Carbon Fabricated from Biomass for Adsorption/Bio-Adsorption of 2,4-D and MCPA: Kinetics, Isotherms, and Artificial Neural Network Modeling

Raid Alrowais (), Mahmoud M. Abdel Daiem (), Basheer M. Nasef and Noha Said
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Raid Alrowais: Department of Civil Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
Mahmoud M. Abdel Daiem: Environmental Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Basheer M. Nasef: Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Noha Said: Environmental Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt

Sustainability, 2023, vol. 16, issue 1, 1-22

Abstract: Adsorption is an effective and economical alternative to remove herbicides from polluted water. The aim of this study is to investigate the adsorption of the most common herbicides (2,4-dichlorophenoxy-acetic acid (2,4-D) and 4-chloro-2-methylphenoxyacetic acid (MCPA)) onto activated carbon (AC) fabricated from wheat straw under different conditions. The adsorption of MCPA and 2,4-D onto the selected AC (CLW) and the effects of the ionic strength, the solution pH, and the presence of microorganisms in the medium were investigated. The results showed that the selected AC had a high surface area (1437 m 2 /g). The adsorption rate increased with an increase in the AC mass. The selected AC had a higher adsorption capacity (1.32 mmol/g) for 2,4-D compared to MCPA (0.76 mmol/g). The adsorption of 2,4-D and MCPA was not affected by variation in the solution pH. However, the presence of electrolytes exerted a major effect on adsorption. The presence of microorganisms enhanced adsorption onto the AC by 17% and 32% for 2,4-D and MCPA, respectively. Moreover, a radial basis function neural network (RBFNN) was employed to accurately predict the adsorption capacity based on the pollutant type, carbon dose, initial concentration, pH, ionic strength, and presence of bacteria. The RBFNN showed excellent accuracy in predicting the adsorption capacity, with an R 2 value of 0.96 and a root mean square error (RMSE) of 0.054. These findings showed that the AC fabricated from biomass residues of wheat straw is a promising option to recycle this type of biomass waste and reduce environmental threats, consequently contributing to achieving sustainability.

Keywords: adsorption; bio-adsorption; activated carbon; wheat straw; herbicides; artificial intelligence; radial basis function neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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