Improved estimation of the langmulr adsorption isotherm using multlscale filtering

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1 Citation (Scopus)

Abstract

Adsorption isotherms play an important role in the design and analysis of adsorption processes. These isotherms are estimated empirically from measurements of 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 Langmuir adsorption isotherm in the presence of measurement noise in the data by developing a multiscale isotherm estimation algorithm. 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 represent 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.

Original languageEnglish
Pages (from-to)2510-2525
Number of pages16
JournalSeparation Science and Technology
Volume44
Issue number11
DOIs
Publication statusPublished - 2009

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Adsorption isotherms
Isotherms
Adsorption
Mean square error

Keywords

  • Adsorption
  • Estimation
  • Filtering
  • Langmuir isotherm
  • Multiscale
  • Wavelets

ASJC Scopus subject areas

  • Chemistry(all)
  • Process Chemistry and Technology
  • Chemical Engineering(all)
  • Filtration and Separation

Cite this

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title = "Improved estimation of the langmulr adsorption isotherm using multlscale filtering",
abstract = "Adsorption isotherms play an important role in the design and analysis of adsorption processes. These isotherms are estimated empirically from measurements of 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 Langmuir adsorption isotherm in the presence of measurement noise in the data by developing a multiscale isotherm estimation algorithm. 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 represent 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.",
keywords = "Adsorption, Estimation, Filtering, Langmuir isotherm, Multiscale, Wavelets",
author = "Mohamed Nounou and Hazem Nounou and Ahmed Abdel-Wahab",
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AU - Nounou, Hazem

AU - Abdel-Wahab, Ahmed

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N2 - Adsorption isotherms play an important role in the design and analysis of adsorption processes. These isotherms are estimated empirically from measurements of 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 Langmuir adsorption isotherm in the presence of measurement noise in the data by developing a multiscale isotherm estimation algorithm. 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 represent 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.

AB - Adsorption isotherms play an important role in the design and analysis of adsorption processes. These isotherms are estimated empirically from measurements of 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 Langmuir adsorption isotherm in the presence of measurement noise in the data by developing a multiscale isotherm estimation algorithm. 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 represent 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.

KW - Adsorption

KW - Estimation

KW - Filtering

KW - Langmuir isotherm

KW - Multiscale

KW - Wavelets

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