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Prediction of gas‐phase adsorption isotherms using neural nets
Author(s) -
Basu Sukanta,
Henshaw Paul F.,
Biswas Nihar,
Kwan Hon K.
Publication year - 2002
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450800322
Subject(s) - adsorption , methane , propane , activated carbon , artificial neural network , thermodynamics , hydrocarbon , approximation error , sorption isotherm , chemistry , materials science , mathematics , computer science , organic chemistry , physics , artificial intelligence
This study investigated a number of models (the modified Sips', Dubinin‐Astakhov's, VSM theory, the generalized Khan et al.'s model and a simple artificial neural network (ANN)) to predict the effect of temperature on equilibrium adsorption of hydrocarbon gases and vapors on activated carbon. Published data on the adsorption of methane, ethane and propane on activated carbon at 311 K to 505 K were used to estimate the parameters of the conventional models and train the network. Then, the conventional models and the ANN were used to predict the isotherm at a single temperature for each adsorbate, and these results were compared with experimental data. It was found that the ANN model had a lower mean relative error than the conventional models.