Corresponding author: Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran , m.shirmardi@mubabol.ac.ir
Abstract: (316 Views)
Background: Removing dyes from wastewater is crucial for environmental protection and public health. Dyes used in various industries can be toxic and persistent, posing threats to aquatic ecosystems and human health.
Methods: This study examined the efficiency of beech wood-derived activated carbon (BW-AC) in removing Acid Red 18 (AR18) and Methylene Blue (MB) dyes. A batch adsorption procedure was used to investigate the impacts of various operational variables, including pH, contact time, adsorbent dosage, and initial dye concentration.
Results: Changes in pH did not significantly affect removal efficiency. However, increasing contact time and adsorbent dosage notably enhanced removal rates, achieving nearly complete removal after 180 minutes. Various statistical metrics were used to identify the best-fitting model, including the corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC). The general order kinetic model provided the best fit to the experimental data based on its AICc values (range: -45.42 to 12.435) and BIC values (range: -48.16 to 6.67) for AR18. For MB dye, the ranges were -5.81 to 7.057 for AICc and -9.58 to 3.282 for BIC. Regarding the adsorption isotherms, the correlation capabilities of the models, as assessed by AICc and BIC, were ranked as follows for AR18: Freundlich, Liu, and Langmuir; while for MB dye, the ranking was Langmuir, Liu, and Freundlich.
Conclusion: The results demonstrate that BW-AC is a highly promising adsorbent for dye removal from aqueous solutions, offering a sustainable, environmentally friendly, and cost-effective solution for water and industrial wastewater treatment.
Asgharnia H, Haddadi M, Gerinasab Z, Vosoughi M, Aligholizadeh F, Tabarinia H et al . Sustainable activated carbon prepared from beech wood residues for the adsorption of dyes: A Bayesian assessment of experimental data. Environ. Health Eng. Manag. 2025; 12 : 1498 URL: http://ehemj.com/article-1-1650-en.html