Regression Analysis for Hydrocarbon Adsorption Isotherm on Activated Carbon

Adsorption process refers to the capability of all solid substances to attract to
their surfaces molecules of gases or solutions with which they are in contact. This
process is very useful for purification and separation. It is widely used in industry such
as in the separation and fractionation of hydrocarbons for example adsorption of phenol
using activated carbon.
Adsorption isotherm is a relationship between the partial pressure of an
adsorbate gas and the surface coverage of the adsorbent at constant temperature. There
are a lot of adsorption isotherms available in the literature, however not all the
correlations can best fit all the data which covers wide range of the temperature and
pressure found in most process situations. This project deals primarily with the
regression analysis on adsorption isotherm correlation of hydrocarbon adsorption.
The objectives of this project are to identify the appropriate correlation for
adsorption data and to use existing regression analysis software which can cover wide
range of temperature and pressure. Based onthe analysis, comparison study on different
types of adsorption isotherms is conducted. The comparison is also done for different
type of regression method from Statistical Analysing System (SAS) and MATLAB
programming.
The scope covers the study of five selected adsorption isotherms, which are
Langmuir, Sips (Langmuir-Freundlich), Toth, Dubinin-Radushkevich and Dubinin-
Astakhov isotherms. MATLAB programming is used for nonlinear regression of
adsorption data at wide range of temperature and pressure. The data used for simulation
is taken from published literature.
From the result, the best fit of adsorption isotherm data is determined by the
statistical analysis using residual. The best fit adsorption isotherm is identified based on
the lowest value of residual obtained. Verification of the method used is done by
comparing the result with published literature.
From this study, it can be concluded the nonlinear regression tool from
MATLAB can provide better selection of adsorption isotherm model of hydrocarbon
adsorption systems. This method is capable for fitting adsorption data which covers
wide range of temperature and pressure. Sips equation is the best correlation for the
representation of octane adsorption on activated carbon as it gives the smallest value of
residual. The findings of this project are useful for preliminary studies of adsorption
isotherm and plays important role inthe design of adsorption unit in the industry.