Artificial intelligence and regression analysis for Cd(II) ion biosorption from aqueous solution by Gossypium barbadense waste

Manal Fawzy, Mahmoud Nasr, Heba Nagy, Shacker Helmi

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this study, batch biosorption experiments were conducted to determine the removal efficiency of Cd(II) ion from aqueous solutions by Gossypium barbadense waste. The biosorbent was characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) connected with energy dispersive X-ray (EDX). The sorption mechanism was described by complexation/chelation of Cd2+ with the functional groups of O–H, C=O, –COO–, and C–O, as well as, cation-exchange with Mg2+ and K+. At initial Cd(II) ion concentration (Co), 50 mg/L, the adsorption equilibrium of 89.2% was achieved after 15 min under the optimum experimental factors of pH 6.0, biosorbent dosage 10 g/L, and particle diameter 0.125–0.25 mm. Both Langmuir and Freundlich models fitted well to the sorption data, suggesting the co-existence of monolayer coverage along with heterogenous surface biosorption. Artificial neural network (ANN) with a structure of 5–10–1 was performed to predict the Cd(II) ion removal efficiency. The ANN model provided high fit (R2 0.923) to the experimental data and indicated that Co was the most influential input. A pure-quadratic model was developed to determine the effects of experimental factors on Cd(II) ion removal efficiency, which indicated the limiting nature of pH and biosorbent dosage on Cd(II) adsorption. Based on the regression model (R2 0.873), the optimum experimental factors were pH 7.61, biosorbent dosage 24.74 g/L, particle size 0.125–0.25 mm, and adsorption time 109.77 min, achieving Cd2+ removal of almost 100% at Co 50 mg/L.

Original languageEnglish
Pages (from-to)5875-5888
Number of pages14
JournalEnvironmental Science and Pollution Research
Volume25
Issue number6
DOIs
Publication statusPublished - 1 Feb 2018

Fingerprint

Gossypium
Biosorption
artificial intelligence
Artificial Intelligence
Regression analysis
Artificial intelligence
regression analysis
aqueous solution
Regression Analysis
Ions
Adsorption
ion
adsorption
artificial neural network
Sorption
sorption
Neural networks
chelation
Neural Networks (Computer)
Fourier Transform Infrared Spectroscopy

Keywords

  • Agricultural waste biosorbent
  • Artificial neural network
  • Cd(II) ion removal
  • Isotherm and kinetics
  • Pure quadratic model

ASJC Scopus subject areas

  • Environmental Chemistry
  • Pollution
  • Health, Toxicology and Mutagenesis

Cite this

Artificial intelligence and regression analysis for Cd(II) ion biosorption from aqueous solution by Gossypium barbadense waste. / Fawzy, Manal; Nasr, Mahmoud; Nagy, Heba; Helmi, Shacker.

In: Environmental Science and Pollution Research, Vol. 25, No. 6, 01.02.2018, p. 5875-5888.

Research output: Contribution to journalArticle

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AB - In this study, batch biosorption experiments were conducted to determine the removal efficiency of Cd(II) ion from aqueous solutions by Gossypium barbadense waste. The biosorbent was characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) connected with energy dispersive X-ray (EDX). The sorption mechanism was described by complexation/chelation of Cd2+ with the functional groups of O–H, C=O, –COO–, and C–O, as well as, cation-exchange with Mg2+ and K+. At initial Cd(II) ion concentration (Co), 50 mg/L, the adsorption equilibrium of 89.2% was achieved after 15 min under the optimum experimental factors of pH 6.0, biosorbent dosage 10 g/L, and particle diameter 0.125–0.25 mm. Both Langmuir and Freundlich models fitted well to the sorption data, suggesting the co-existence of monolayer coverage along with heterogenous surface biosorption. Artificial neural network (ANN) with a structure of 5–10–1 was performed to predict the Cd(II) ion removal efficiency. The ANN model provided high fit (R2 0.923) to the experimental data and indicated that Co was the most influential input. A pure-quadratic model was developed to determine the effects of experimental factors on Cd(II) ion removal efficiency, which indicated the limiting nature of pH and biosorbent dosage on Cd(II) adsorption. Based on the regression model (R2 0.873), the optimum experimental factors were pH 7.61, biosorbent dosage 24.74 g/L, particle size 0.125–0.25 mm, and adsorption time 109.77 min, achieving Cd2+ removal of almost 100% at Co 50 mg/L.

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