Comparison of multiple linear regression and group method of data handling models for predicting sunset yellow dye removal onto activated carbon from oak tree wood

Abdol Mohammad Ghaedi, Mohammad Mehdi Baneshi, Azam Vafaei, Alireza Rayegan Shirazi Nejad, Inderjeet Tyagi, Nitish Kumar, E. Galunin, Alexey G. Tkachev, Shilpi Agarwal, Vinod Kumar Gupta

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Activated carbon from oak tree is used as adsorbent for the removal of noxious anionic dye sunset yellow. The prepared adsorbent is characterized using X-ray diffraction, Scanning Electron microscopy equipped with Energy-Dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy. In addition to this, parameters like initial concentration, adsorbent dosage, contact time, pH, and particle size on the uptake of SY dye from wastewater is well investigated and optimized. For maximum adsorption, the initial concentration of 10 mg/L; adsorbent dose of 0.25 g; pH =1; contact time = 35 min and particle size = 150–250 μm is found to be optimal value. The adsorption isotherm data at different adsorbent dosage of 0.05–0.25 g is in agreement with the Langmuir isotherm having Qmax = 5.8377–30.1205 mg/g. On the other hand, models like, Group Method of Data Handling and multiple linear regression were used to forecast the removal efficiency of noxious anionic dye sunset yellow and from results, it is specified that the GMDH model possess a high performance than MLR model for forecasting removal percentage of SY dye. Hence, activated carbon from oak tree can be efficiently used as adsorbent for the removal of SY dye from wastewater.

Original languageEnglish
Pages (from-to)262-275
Number of pages14
JournalEnvironmental Technology and Innovation
Volume11
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Keywords

  • Adsorption
  • Adsorption kinetics
  • Anionic dye SY
  • Isotherms
  • Modeling

ASJC Scopus subject areas

  • Environmental Science(all)
  • Soil Science
  • Plant Science

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