|Title (Primary)||Numerical modelling of water sorption isotherms of zeolite 13XBF based on sparse experimental data sets for heat storage applications|
|Author||Semprini, S.; Lehmann, C.; Beckert, S.; Kolditz, O. ; Gläser, R.; Kerskes, H.; Nagel, T.|
|Journal||Energy Conversion and Management|
|Keywords||Water/zeolite adsorption; Equilibrium characterization; Thermal energy storage; Sparse experimental data|
|UFZ wide themes||RU5;|
This study analyzes the possibility of determining the parameters of an adsorption equilibrium model based on a reduced number of isotherms for the working pair water/zeolite 13X. The employed models rely on the Dubinin-Polanyi theory of micropore adsorption. The reliability of the adsorption equilibrium model based on sparse data is evaluated in terms of the error in the adsorption equilibrium and in terms of the error in loading lift and heat storage density for an adsorption cycle typical for heat storage applications.
It is found that as little as three measured adsorption isotherms are sufficient to yield a description of the adsorption equilibrium of zeolite 13X in a wide pressure and temperature range, if the following criteria are obeyed: (i) the measured isotherms should cover the entire range of the characteristic curve and (ii) it is recommended to include isotherms at temperatures close to the working cycle limits.
Based on these considerations, temperature ranges for the experimental determination of a reduced set of adsorption isotherms are recommended that yield a reliable description of the adsorption equilibrium in a wide pressure and temperature range. Thereby it is demonstrated that the experimental effort can be reduced significantly while maintaining the predictive capability of the theoretical model.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=19345|
|Semprini, S., Lehmann, C., Beckert, S., Kolditz, O., Gläser, R., Kerskes, H., Nagel, T. (2017):
Numerical modelling of water sorption isotherms of zeolite 13XBF based on sparse experimental data sets for heat storage applications
Energy Conv. Manag. 150 , 392 - 402