Improved Estimation of the Langmuir Adsorption Isotherm Using Multiscale Filtering
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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.