Fixed bed adsorption of tetracycline on a mesoporous activated carbon: Experimental study and neuro-fuzzy modeling

• Published in: Journal of applied research and technology Vol. 15; no. 5; pp. 454 - 463
• Main Authors: Marzbali, Mojtaba Hedayati, Esmaieli, Mohamad
• Format: Journal Article
• Language: English
• Published: Elsevier España, S.L.U 01.10.2017
• Subjects: Apricot shell; Column adsorption; Machine learning; Neuro-fuzzy; Tetracycline; Column adsorption; Apricot shell; Neuro-fuzzy; Machine learning; Tetracycline
• ISSN: 1665-6423
• DOI: 10.1016/j.jart.2017.05.003

[Display omitted] This study investigates the use of synthesized mesoporous carbon in the fixed bed adsorption, as a promising process, to eliminate tetracycline from wastewater. In order to study the adsorptive capability of adsorbent, particles were embedded in a laboratory-scale Pyrex glass tube. An increase in initial concentration and decrease in bed height and flow rate led to the higher adsorption capacity. The highest bed capacity of 76.97mgg−1 was obtained using 4cm bed depth, 4mLmin−1 and 50mgL−1 influent concentration. The initial part of breakthrough curve perfectly matched the Adams–Bohart model at all experimental conditions. However, it was anticipated that Yoon–Nelson model could predict the whole curve acceptably, the results showed an inaccurate fitting. Therefore, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the breakthrough curve using data series of adsorption experiments. This model indicated a good statistical prediction in terms of relative errors.