Parameters estimability analysis and identification for adsorption equilibrium models of carbon dioxide
This research work deals with the modeling of the CO 2 adsorption on a commercial adsorbent, based on the temperature dependent Sips and Toth models. In fact, these semi-empirical approaches can properly predict the equilibrium of adsorption of CO 2 , and more specifically in the case of energetical...
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Veröffentlicht in: | Adsorption : journal of the International Adsorption Society 2017-04, Vol.23 (2-3), p.373-380 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
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Zusammenfassung: | This research work deals with the modeling of the CO
2
adsorption on a commercial adsorbent, based on the temperature dependent Sips and Toth models. In fact, these semi-empirical approaches can properly predict the equilibrium of adsorption of CO
2
, and more specifically in the case of energetically heterogeneous surfaces. In addition, the investigated adsorption models involve several unknown parameters to be estimated from the available adsorption equilibrium experimental data, measured between 303 and 343 K and up to a CO
2
partial pressure of 101 kPa. An estimability analysis was therefore carried out in order to evaluate which parameters are estimable and those that can be fixed either from literature or from previous studies. According to the estimability analysis, whatever the adsorption model which is used, only one over the six unknown parameters is considered as nonestimable. The estimable model parameters are especially the maximum amount adsorbed (q
m0
), the equilibrium constant (b
0
), the heterogeneity factors (s
0
and t
0
) assessed at the reference temperature (T
0
) as well as the constant model parameter (α), which is related to the heterogeneity factors (s and t). The estimable parameters were then identified and their optimized values were used in the comparison of the model predictions and the experimental measurements of CO
2
adsorption equilibrium. The results show finally that the model predictions fit well with the experimental data. |
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ISSN: | 0929-5607 1572-8757 |
DOI: | 10.1007/s10450-017-9864-7 |