Markov models and the ensemble Kalman filter for estimation of sorption rates. Page: 4 of 30
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sition probability model for solute transport is used in conjunction with a new con-
ditional probability-based model of the sorption and desorption rates based on break-
through curve data. Two models for prediction of spatially varying sorption and des-
orption rates along a one-dimensional streamline are developed. These models are
a Markov model that utilizes conditional probabilities to determine the rates and an
ensemble Kalman filter (EnKF) applied to the conditional probability method. Both
approaches rely on a previously developed Markov-model of mass transfer, and both
models assimilate the observed concentration data into the rate estimation at each ob-
servation time. Initial values of the rates are perturbed from the true values to form
ensembles of rates and the ability of both estimation approaches to recover the true
rates is examined over three different sets of perturbations. The models accurately
estimate the rates when the mean of the perturbations are zero, the unbiased case. For
the cases containing some bias, addition of the ensemble Kalman filter is shown to
improve accuracy of the rate estimation by as much as an order of magnitude.
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Vugrin, Eric D.; McKenna, Sean Andrew (Sandia National Laboratories, Albuquerque, NM) & Vugrin, Kay White. Markov models and the ensemble Kalman filter for estimation of sorption rates., report, September 1, 2007; United States. (digital.library.unt.edu/ark:/67531/metadc899135/m1/4/: accessed December 10, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.