Computational modeling of biomineral carbonate precipitation in porous sand column at micro scale

摘要

Microbial carbonate precipitation (MCP) is a process increasingly studied with the goal to exploit it to adapt mechanical soil properties for desired land uses. Fundamental understanding of key processes in MCP is needed in order to obtain a controllable full-scale application. Besides experimental investigations, mathematical modeling can help by creating a logical framework in which experiments can be interpreted and, if developed enough, predictions can be made. In this study we developed a numerical model that takes into account the various processes governing MCP at micro-scale (mm). Processes investigated include fluid flow, solute transport, crystal growth and deposition, and the clogging of pore spaces. These processes take place at a pore scale and have their effects on overall system properties. Although lab-scale experiments have proven helpful in elucidating the basic mechanisms that govern bacterial transport, crystal growth, deposition and detachment in porous media, the presence of physical-chemical and biological heterogeneities in several physical and engineered micro-systems make them extremely difficult and complex to represent experimentally. To investigate these processes at pore scale, a model is developed in which the porous medium is represented as an idealized two-dimensional structure. An aqueous influent containing urea and calcium chloride flows by through the porous medium. Solutes are transported by convection and diffusion. Bacteria present on grain surfaces hydrolyze urea, forming ammonium and carbonate ions. Microbial carbonate precipitation occurs in the presence of calcium ions, eventually leading to calcium carbonate crystal formation. The model calculates the growth of a layer of crystals on the grain surface, leading to a narrowing of the pore channels with a concomitant increase in pressure drop and average fluid velocity over the 2-D structure. The results show that this simplified model can be used to identify phenomena that occur in physical experiments and have an effect on the larger scale.