A Reactive Transport Model for Biogrout Compared to Experimental Data

Abstract

Biogrout is a method for reinforcement of granular soil. In the Biogrout process, calcium carbonate is produced. This solid connects the grains, and therefore the strength of the soil is increased. The calcium carbonate is formed with the use of micro-organisms. Experiments and numerical simulations have been performed to demonstrate the process under various conditions. In this paper, it has been examined whether a reactive transport model can be used to describe a Biogrout experiment that was performed in a column with a length of 5 m. Four different models for the course of the reaction rate are considered. The concentration of micro-organisms and the reaction rate are fine-tuned in order to find a description of the experiment that is a best fit for the particular model. This is done by minimizing the error between the experimental and numerical results for the concentration of calcium carbonate and the by-product of the reaction. List of symbolsInjected concentration of micro-organisms (normalized) (1) S bac Ratio of micro-organisms that is fixated (with respect to the injected concentration) (1)

Assessing the Kinetics and Pore‐Scale Characteristics of Biological Calcium Carbonate Precipitation in Porous Media using a Microfluidic Chip Experiment

“…Pore clogging either could be the result of an agglomeration of multiple crystals (label c), not only due to a single large crystal (label d) but also due to a trapped gas bubble, or could reduce pore connectivity and affect the transport and distribution of substrates and resulting precipitates in subsequent flushes. Local pore clogging may potentially explain observed variations in engineering properties of treated soils reported in the literature (e.g., van Wijngaarden et al, ; Whiffin et al, ).…”

Field-scale modeling of microbially induced calcite precipitation

“…Applications found in the literature include the interaction of microbes with the subsurface transport of contaminants, [e.g., 37,63,69,74], microbially enhanced oil recovery [e.g., 44,53,54,72], or biomineralization, of which especially the engineered application of MICP has received considerable attention. Most numerical models for MICP are, similarly to the model used in this study, formulated at the REV scale (or Darcy scale) [e.g., 2,17,46,51,[76][77][78], while [64] and [80][81][82] use pore-network and pore-scale models, respectively.…”

“…On the other hand, they account for the impact of the calcite precipitated during MICP on hydrodynamics. Michaelis-Menten kinetics are used to model the ureolysis rate in [76][77][78] and, like Cuthbert et al [17], they assume that the precipitation rate is proportional to the ureolysis rate. The permeability change is accounted for by a Kozeny-Carman relationship, but only calcite is assumed to have an effect.…”

“…The permeability change is accounted for by a Kozeny-Carman relationship, but only calcite is assumed to have an effect. Bacteria are assumed to be homogeneously distributed in [76], while van Wijngaarden et al [77] account for attachment, detachment, and bacterial transport and van Wijngaarden et al [78] investigate the effect of various decay and biomass removal rates. For special cases, [76,77] propose analytical solutions.…”

A Numerical Model for Enzymatically Induced Calcium Carbonate Precipitation

“…One step towards overcoming this difficulty is the numerical model that we provide for EICP. For MICP, numerical models have been shown to be capable of capturing the complex interplay of hydraulics, precipitation reactions, and the change in hydraulic properties [12,[18][19][20].…”