Center for Environment and Energy Research and Studies (CEERS)
Abstract
Adsorption plays an important role in water and wastewater treatment.
The analysis and design of processes that involve adsorption rely on
the availability of isotherms that describe these adsorption processes.
Adsorption isotherms are usually estimated empirically from
measurements of the adsorption process variables. Unfortunately, these
measurements are usually contaminated with errors that degrade the
accuracy of estimated isotherms. Therefore, these errors need to be
filtered for improved isotherm estimation accuracy. Multiscale wavelet
based filtering has been shown to be a powerful filtering tool. In this
work, multiscale filtering is utilized to improve the estimation
accuracy of the Freundlich adsorption isotherm in the presence of
measurement noise in the data by developing a multiscale algorithm for
the estimation of Freundlich isotherm parameters. The idea behind the
algorithm is to use multiscale filtering to filter the data at
different scales, use the filtered data from all scales to construct
multiple isotherms and then select among all scales the isotherm that
best represents the data based on a cross validation mean squares error
criterion. The developed multiscale isotherm estimation algorithm is
shown to outperform the conventional time-domain estimation method
through a simulated example