Forward and Inverse Bio-Geochemical Modeling of Microbially Induced Calcite Precipitation in Half-Meter Column Experiments

Microbially induced calcite precipitation (MICP) offers an alternative solution to a wide range of civil engineering problems. Laboratory tests have shown that MICP can immobilize trace metals and radionuclides through co-precipitation with calcium carbonate. MICP has also been shown to improve the...

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Veröffentlicht in:Transport in porous media 2011-10, Vol.90 (1), p.23-39
Hauptverfasser: Barkouki, T. H., Martinez, B. C., Mortensen, B. M., Weathers, T. S., De Jong, J. D., Ginn, T. R., Spycher, N. F., Smith, R. W., Fujita, Y.
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Sprache:eng
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Zusammenfassung:Microbially induced calcite precipitation (MICP) offers an alternative solution to a wide range of civil engineering problems. Laboratory tests have shown that MICP can immobilize trace metals and radionuclides through co-precipitation with calcium carbonate. MICP has also been shown to improve the undrained shear response of soils and offers potential benefits over current ground improvement techniques that may pose environmental risks and suffer from low “certainty of execution.” Our objective is to identify an effective means of achieving uniform distribution of precipitate in a one-dimensional porous medium. Our approach involves column experiments and numerical modeling of MICP in both forward and inverse senses, using a simplified reaction network, with the bacterial strain Sporoscarcina pasteurii . It was found that the stop-flow injection of a urea- and calcium-rich solution produces a more uniform calcite distribution as compared to a continuous injection method, even when both methods involve flow in opposite direction to that used for bacterial cell emplacement. Inverse modeling was conducted by coupling the reactive transport code TOUGHREACT to UCODE for estimating chemical reaction rate parameters with a good match to the experimental data. It was found, however, that the choice of parameters and data was not sufficient to determine a unique solution, and our findings suggest that additional time and space-varying analytical data of aqueous species would improve the accuracy of numerical modeling of MICP.
ISSN:0169-3913
1573-1634
DOI:10.1007/s11242-011-9804-z