Methylene Blue Adsorption Utilising Enhanced Chitosan Beads: A Response Surface Methodology and Artificial Neural Network Study | Chapter 01 | Engineering Research: Perspectives on Recent Advances Vol. 12

 

Pollution emanating from cationic dyes, including Methylene Blue (MB), in the environment causes many health problems. The release of MB into natural water bodies is destructive to natural creatures and ecosystems. Adsorption is one such technique, which has advantages such as low cost, environmentally friendly material for dye removal, and ease of use. Chitosan is a cost-effective, environmentally friendly adsorbent with high surface area, excellent adsorption capacity, and good mechanical stability. A method was established to assess the adsorption of methylene blue (MB) from synthetic wastewater.  Chitosan beads (CS) were synthesised, cross-linked with glutaraldehyde (CCS), and subsequently grafted with aniline (GCCS).  The characteristics of the synthesised materials were evaluated using XRD and BET techniques.  The research examined pH, contact time, adsorbent amount, and starting concentration.  The input data consisted of these parameters, whereas the output data was determined by MB removal efficiency.  Response surface methodology/central composite design (RSM-CCD) and artificial neural network (ANN) were utilised to predict and optimise MB adsorption.  Statistical indicators evaluated the significance of these models.  In developing the ANN model, 70% of the data was designated for training, 15% for validation, and 15% for testing.  The RSM-CCD data indicate that the optimal process parameters were achieved at a pH of 7, an adsorbent dose of 6 g, a contact period of 55 minutes, and a 125 mg/L starting concentration.  Thus, training, testing, and validation phases characterise a well-trained neural network, with R2 values recorded at 1, 0.96837, and 0.96146, respectively.  The statistical results indicated that the ANN method surpasses the RSM model technique.

 

Author(s) Details :-

 

Ephraim Igberase
Department of Chemical Engineering, Durban University of Technology, Steve Biko, Durban, South Africa.

 

Innocentia G. Mkhize
Department of Chemical Engineering, Durban University of Technology, Steve Biko, Durban, South Africa.

 

Please see the book here :- https://doi.org/10.9734/bpi/erpra/v12/6428