Modelling and optimization for methylene blue adsorption using graphene oxide/chitosan composites via artificial neural network-particle swarm optimization

Khiam, Goh Kheng, Karri, Rama Rao, Mubarak, Nabisab Mujawar, Khalid, Mohammad, Walvekar, Rashmi, Abdullah, Ezzat Chan and Rahman, Muhammad (2022) Modelling and optimization for methylene blue adsorption using graphene oxide/chitosan composites via artificial neural network-particle swarm optimization. Materials Today Chemistry, 24. p. 100946. ISSN 2468-5194

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Official URL: https://doi.org/10.1016/j.mtchem.2022.100946

Abstract

Water pollution due to dyes from industrial effluents and domestic wastewater is a big environmental issue, so an effective adsorbent is needed. In this study, graphene oxide/chitosan (GO/CS) composites were synthesized and applied for methylene blue (MB) dye removal. Characterization was done on the GO and GO/CS composites using FTIR, EDX, SEM, and TGA. The adsorption studies were conducted to verify the effect of pH, adsorbent dosage and contact time. The interactive effects of the process variables were verified using response surface methodology (RSM), and optimal conditions for higher adsorption efficiency are evaluated by Artificial neural network (ANN)-Particle swarm optimization (PSO). ANN-PSO predictions are in good agreement with the experimental values and hence resulted in higher R2 (=0.998) compared to RSM predictions (R2 = 0.981). The MB adsorption process is found to be obeying the Langmuir isotherm and pseudo 1st order kinetic model. The maximum MB removal efficiency (90.34%) and adsorption amount (7.53 mg/g) can be obtained at an initial dye concentration of 10 mg/L and optimal values of pH (5), adsorbent dosage (0.143 g/L) and contact time (125 min). These results further confirm that the ANN-PSO-based approach is able to capture the inherent mechanisms of the MB adsorption process and can be used as a good modelling approach.

Item Type: Article
Uncontrolled Keywords: Graphene oxide/chitosan compositeMethylene blueResponse surface methodologyParticle swarm optimizationArtificial neural network
Subjects: F200 Materials Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 14 Jun 2022 11:09
Last Modified: 14 Jun 2022 11:09
URI: http://nrl.northumbria.ac.uk/id/eprint/49303

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