Markov models and the ensemble Kalman filter for estimation of sorption rates.

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Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption 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 ... continued below

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30 p.

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Vugrin, Eric D.; McKenna, Sean Andrew (Sandia National Laboratories, Albuquerque, NM) & Vugrin, Kay White September 1, 2007.

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Description

Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption 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 observation 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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30 p.

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  • Report No.: SAND2007-5975
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/920758 | External Link
  • Office of Scientific & Technical Information Report Number: 920758
  • Archival Resource Key: ark:/67531/metadc899135

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Creation Date

  • September 1, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 6, 2016, 12:48 p.m.

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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/: accessed November 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.