Congo red dye removal from aqueous environment by cationic surfactant modified-biomass derived carbon: Equilibrium, kinetic, and thermodynamic modeling, and forecasting via artificial neural network approach

Ceren Karaman, Onur Karaman, Pau Loke Show, Hassan Karimi-Maleh, Najmeh Zare

Research output: Contribution to journalArticlepeer-review

176 Scopus citations

Abstract

Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl2 and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N2 adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g−1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R2 = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.

Original languageBritish English
Article number133346
JournalChemosphere
Volume290
DOIs
StatePublished - Mar 2022

Keywords

  • Artificial neural network
  • Biomass-derived carbon
  • Biosorption
  • Chemical-activation
  • Congo Red
  • Orange peel

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