scholarly journals Solute mixing regulates heterogeneity of mineral precipitation in porous media

2017 ◽  
Vol 44 (13) ◽  
pp. 6658-6666 ◽  
Author(s):  
Mehmet B. Cil ◽  
Minwei Xie ◽  
Aaron I. Packman ◽  
Giuseppe Buscarnera
2020 ◽  
Vol 24 (5) ◽  
pp. 1865-1882 ◽  
Author(s):  
Mehrdad Ahkami ◽  
Andrea Parmigiani ◽  
Paolo Roberto Di Palma ◽  
Martin O. Saar ◽  
Xiang-Zhao Kong

2012 ◽  
Author(s):  
Chi Zhang ◽  
Lee Slater ◽  
George Redden ◽  
Yoshiko Fujita ◽  
Timothy Johnson ◽  
...  

1999 ◽  
Vol 39 (7) ◽  
pp. 57-64 ◽  
Author(s):  
A. J. Cooke ◽  
R. K. Rowe ◽  
B. E. Rittmann ◽  
I. R. Fleming

A numerical model links the build-up of mineral precipitate (primarily CaCO3) and the anaerobic activity of biofilms, which occur in granular material permeated with leachate from a municipal solid waste landfill. The model represents the porous-media flow system as a collection of elements in which each element acts as a separate, fixed-film reactor. The model represents biofilm growth for microorganisms carrying out acetogenesis of propionate and methanogenesis of acetate. It also directly links substrate utilization to mineral precipitation and accounts for the accumulation of inert biomass on the porous media at any time or position along the length of the column. Thus, the model describes the ecological interactions among fermenters, methanogens, inert biomass, and mineral precipitate. Although substrate utilization by the active microorganisms drives the entire system, mineral precipitate becomes a dominant component in the biofilm.


Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Elise Wright ◽  
Nicole Sund ◽  
David Richter ◽  
Giovanni Porta ◽  
Diogo Bolster

In this work, we develop a novel Lagrangian model able to predict solute mixing in heterogeneous porous media. The Spatial Markov model has previously been used to predict effective mean conservative transport in flows through heterogeneous porous media. In predicting effective measures of mixing on larger scales, knowledge of only the mean transport is insufficient. Mixing is a small scale process driven by diffusion and the deformation of a plume by a non-uniform flow. In order to capture these small scale processes that are associated with mixing, the upscaled Spatial Markov model must be extended in such a way that it can adequately represent fluctuations in concentration. To address this problem, we develop downscaling procedures within the upscaled model to predict measures of mixing and dilution of a solute moving through an idealized heterogeneous porous medium. The upscaled model results are compared to measurements from a fully resolved simulation and found to be in good agreement.


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