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

Abstract

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.

Soil engineering in vivo : harnessing natural biogeochemical systems for sustainable, multi-functional engineering solutions

“…MICP process monitoring is exemplified through results from one-dimensional column experiments (length ¼ 45 cm, diameter ¼ 5 cm) performed on sand (D 50 ¼ 0.21 mm) to improve spatial uniformity (figure 6a) [84]. Similar approaches have been reported by Whiffin et al [85].…”

Large‐Scale Experiments in Microbially Induced Calcite Precipitation (MICP): Reactive Transport Model Development and Prediction

“…Ureolysis driven MICP involves a microbially catalyzed reaction, which can be modeled if the reaction kinetics are known (Burbank et al, ; Chahal et al, ; Krajewska, ; Mobley & Hausinger, ). Models for predicting MICP processes have been developed by a number of authors (Barkouki et al, ; Cuthbert et al, ; Ebigbo et al, ; Martinez et al, ; van Wijngaarden et al, ). The different models reported have focused on addressing complexities in the different interacting processes and often are designed to match a series of experiments usually at small scales.…”

Investigating the Influence of the Initial Biomass Distribution and Injection Strategies on Biofilm-Mediated Calcite Precipitation in Porous Media

“…1); -inverse first-order distribution, as proposed by Barkouki et al (2011), which corresponds to a change of the direction of flow after inoculation. Barkouki et al (2011) propose that this distribution of cells will lead to a more homogeneous distribution of precipitated calcite, as the reduction of reactants along the flow path is counteracted by an increase in catalyzing enzyme. Even though this initial biomass distribution is likely to be unrealistic for subsurface applications, it provides an upper bound on how much the resulting calcite precipitation can be influenced by the initial biomass distribution.…”

“…It has been suggested that the distribution of cells in the porous medium during MICP is a key parameter (Barkouki et al 2011). Yet it is very difficult to control or even reliably measure the concentrations of attached microbial cells (e.g., Bouwer et al 2000;Cunningham et al 2007).…”

“…As a consequence of this lack of information and data, many models assume the resulting distribution of biomass or enzyme based on conceptual considerations or experimental observations, e.g., homogeneous distribution of biomass (van Wijngaarden et al 2011), exponentially decreasing biomass with increasing distance to the injection (Barkouki et al 2011), or biomass being distributed according to a Gamma distribution (Martinez et al 2014). …”