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.
In this post, we present a brief overview of our recently published book chapter titled “Methylene Blue Adsorption Utilizing Enhanced Chitosan Beads: A Response Surface Methodology and Artificial Neural Network Study.”
Water pollution is a ubiquitous and ongoing consequence of industrial development across various sectors. Pollution emanating from cationic dyes in the environment causes many health problems. Cationic dyes, including Methylene Blue (MB), dissociate into positively charged ions in aqueous solutions. Chemically, Methylene Blue (C₁₆H₁₈N₃ClS) is a thiazine and cationic dye. It is predominantly used as a colourant in the textile industry, particularly in wool, silk, and cotton dyeing. It is also widely applied in industrial processes, including biomedical pigments for cell staining, paper printing and pulp processing, rubber, plastics, leather, cosmetics, and textile colouring.
Industrial and agricultural practices are major contributors to dye-contaminated wastewater. The food, paper, and leather industries are among the primary sources of highly industrialised wastewater. The release of MB into natural water bodies is destructive to aquatic organisms and ecosystems.
Over the years, several methods such as membrane separation (including reverse osmosis), flocculation and coagulation, electrochemical processes, advanced oxidation, adsorption, ion exchange, precipitation, photocatalytic reduction, and biological treatments have been used for dye removal from wastewater. Among these, adsorption is considered one of the most effective techniques due to its low cost, environmentally friendly materials, and ease of operation. In adsorption, pollutants accumulate on the surface of a solid adsorbent mainly through physical interactions.
Various active adsorbents, including zeolites, chitosan, and other materials, have been used for MB removal. Chitosan offers additional advantages such as a high surface area, excellent adsorption capacity, appropriate pore structure, accessibility, cost-effectiveness, mechanical stability, and environmental friendliness.
In this study, the developed materials were characterised using analytical techniques such as Brunauer–Emmett–Teller (BET) analysis and X-ray diffraction (XRD). The main objective of this work was to develop a neural network model to predict the removal of MB from synthetic wastewater using a chitosan derivative as an adsorbent. Using a design matrix from Response Surface Methodology/Central Composite Design (RSM-CCD), the necessary input and output data were collected through a four-level experimental design to construct both RSM and Artificial Neural Network (ANN) models.
This study adopts a unique approach to removing harmful dyes from wastewater by enhancing a natural adsorbent material and integrating advanced modelling tools. The combination of material science and computational modelling demonstrates strong potential for developing practical and sustainable water treatment solutions.