, Volume 22, Issue 3, pp 357–370 | Cite as

Multicomponent adsorption modeling: isotherms for ABE model solutions using activated carbon F-400

  • Niloofar Abdehagh
  • F. Handan Tezel
  • Jules ThibaultEmail author


Biobutanol has attracted significant interest in recent decades and is seriously considered as a potential biofuel to partly replace gasoline. However, some production challenges must be addressed to make butanol economically viable such as the low product concentration and product toxicity inhibiting the microorganism. To alleviate these limitations, several in situ or ex situ separation techniques have been investigated in view of their integration to the biobutanol production process to enhance its economic viability. One of these techniques is adsorption which is one of the most energy-efficient techniques used for biobutanol separation. Considering the number of chemical species present in the ABE fermentation broth, it is essential to develop multicomponent adsorption isotherms for all components as a first step to design a high performance adsorption process. Few multicomponent isotherm models have been proposed such as multicomponent Langmuir and Freundlich. In this study, these two models as well as artificial neural networks were used to model the isotherms of each component in an ABE fermentation broth as a function of the equilibrium concentrations of all components for activated carbon F-400. Results showed that the multicomponent Langmuir model was not accurate due to the many simplifying assumptions. The multicomponent Freundlich and feedforward neural network (FFNN) isotherm models were able to predict the behavior of multicomponent systems very well. Indeed, the predictive model of the experimental data had a coefficient of determination (R2) of 0.97 and 0.99, for multicomponent Freundlich and FFNN isotherm models, respectively.


Biobutanol Adsorption Isotherm model Artificial neural network Langmuir adsorption isotherm Freundlich adsorption isotherm 





Artificial neural network


Feed forward neural network


High level


Low level


Single component



Competition coefficient in multicomponent Freundlich isotherm model


Constant in multicomponent Langmuir isotherm model (L/g adsorbate)


Adsorbate concentration at equilibrium (g/L)


Normalized adsorbate concentration at equilibrium (g/L)


Adsorbate concentration in equilibrium with adsorbed phase concentration q (g/L)


Constant in multicomponent Freundlich isotherm model


Constant in multicomponent Freundlich isotherm model (L/g adsorbent)


Adsorption capacity (g adsorbate/g adsorbent)


Adsorption capacity in equilibrium with bulk liquid concentration C (g adsorbate/g adsorbent)


Saturation adsorption capacity (g adsorbate/g adsorbent)


Normalized adsorption capacity (g adsorbate/g adsorbent)


Weights in FFNN model



The authors would like to acknowledge the Natural Science and Engineering Research Council (NSERC) of Canada and Ontario Graduate Scholarship (OGS) for their financial support.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Niloofar Abdehagh
    • 1
  • F. Handan Tezel
    • 1
  • Jules Thibault
    • 1
    Email author
  1. 1.Department of Chemical and Biological EngineeringUniversity of OttawaOttawaCanada

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