Adaptive neuro‐fuzzy inference system modeling of 2,4‐dichlorophenol adsorption on wood‐based activated carbon

Phenolic compounds cause significant problems both in drinking water and wastewater due to their toxicity, high oxygen requirements, and low biodegradability. They are listed as primary pollutants by the United States Environmental Protection Agency and the European Union. In this study, the adsorpt...

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Bibliographic Details
Published in:Environmental progress 2020-09, Vol.39 (5), p.n/a
Main Authors: Alver, Alper, Baştürk, Emine, Tulun, Şevket, Şimşek, İsmail
Format: Article
Language:English
Subjects:
Activated carbon
adaptive neuro‐fuzzy inference system
Adaptive systems
Adsorbents
Adsorption
Biodegradability
Biodegradation
Correlation analysis
Dosage
Drinking water
Environmental protection
Inference
isotherm
kinetic behavior
Modelling
Oxygen requirement
pH effects
phenol
Phenolic compounds
Phenols
Pollutant removal
Pollutants
Pollution control
Sensitivity analysis
Toxicity
Wastewater
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Summary:Phenolic compounds cause significant problems both in drinking water and wastewater due to their toxicity, high oxygen requirements, and low biodegradability. They are listed as primary pollutants by the United States Environmental Protection Agency and the European Union. In this study, the adsorption efficiency of 2,4‐dichlorophenol (2,4‐DCP) on activated carbon, which is commonly used in treatment plants, was investigated under different experimental conditions including adsorbent dose, initial phenol concentration, initial pH, and contact time. As a result of experimental studies, it was determined that the adsorption isotherm and kinetics could be perfectly fitted to Langmuir and the assumption of pseudo‐second order model, respectively. Then, the adaptive neuro‐fuzzy inference system (ANFIS) model was developed, which was the primary purpose of this study. The correlation between training and testing data and the ANFIS output was over 0.999. The generalization ability of the model was found to be 0.999. The input variables such as adsorbent dosage (14.2%), initial concentration (14.6%), initial pH (13.9%), and the contact time (57.2%) showed a higher effect on 2,4‐DCP removal efficiency in the sensitivity analysis. To summarize, modeling studies that are frequently preferred in treatment plants for the removal of different pollutants will reduce the number of experiments harmful to human health and save time, labor, and economy.
ISSN:1944-7442
1944-7450
DOI:10.1002/ep.13413