HR: 0800h
AN: H41E-0831    [Abstracts]
TI: Coupled inverse geochemical and microbial reactive transport models in porous media
AU: * Samper, J
EM: jsamper@udc.es
AF: University of A Coruna, ETS Ingenieros de Caminos Campus Elviņa s/n, Coruņa, 15192, Spain
AU: Yang, C
EM: Yangcb@gmail.com
AF: Utah State University, Dept. Plants, Soils, and Biometeorology, Logan, UT 84322, United States
AB: Microbial processes play a major role in controlling geochemical conditions in subsurface systems. Various laboratory and in situ experiments have been performed to evaluate the relevance of microbial processes and derive key microbial parameters. Such experiments are often interpreted by suboptimal trial-and-error curve fitting. Here we present an inverse model for coupled flow, reactive solute transport, geochemical and microbial processes which overcomes the limitations of trial-and-error methods by making data interpretation in a systematic, objective, and efficient manner. It extends the capabilities of existing inverse models which deal mostly with flow and chemically-reactive solute transport. Our inverse model relies on the microbial reactive transport code BIOCORE of Samper et al. (2006a) and improves the inverse reactive transport model INVERSE- CORE of Dai and Samper (2004) by allowing the simultaneous estimation of geochemical and microbial parameters. The inverse model has been implemented in a finite element code, INVERSE-BIOCORE2D and its capabilities have been verified and tested with a synthetic experiment involving equilibrium speciation, kinetic sorption/desorption and kinetic biodegradation reactions. Model results indicate that both chemical and microbial parameters can be estimated accurately for error-free data. Estimation errors of microbial parameters are larger than those of kinetic sorption parameters and generally increase with increasing standard deviation of data noise. Estimation error of yield coefficient is the smallest among all microbial parameter and which does not depend on data noise. The inverse model has been used also to estimate microbial parameters of a laboratory experiment involving sucrose fermentation by yeast. Inverse estimation improves significantly the fit to measured data.
DE: 1846 Model calibration (3333)
DE: 1847 Modeling
DE: 1849 Numerical approximations and analysis
SC: Hydrology [H]
MN: 2007 Fall Meeting