Methodology for Using a Non-Linear Parameter Estimation Technique for Reactive Multi-Component Solute Transport Modeling in Ground-Water Systems

Abstract

For a numerical or analytical model to be useful it should be ensured that the model outcome matches the observations or field measurements during calibration. This process has been typically done by manual perturbation of the model input parameters. This research investigates a methodology for using non linear parameter estimation technique (the Marquardt-Levenberg technique) with the multi component reactive solute transport model SEAM3D. The reactive multi-component solutes considered in this study are chlorinated ethenes. Previous studies have shown that this class of compounds can be degraded by four different biodegradation mechanisms, and the degradation path is a function of the prevailing oxidation reduction conditions.

Tests were performed in three levels; the first level utilized synthetic model-generated data. The idea was to develop a methodology and perform preliminary testing where "observations" can be generated as needed. The second level of testing involved performing the testing on a single redox zone model. The methodology was refined and tested using data from a chlorinated ethenes-contaminated site. The third level involved performing the tests on a multiple redox zone model. The methodology was tested, and statistical validation of the recommended methodology was performed.

The results of the tests showed that there is a statistical advantage for choosing a subgroup of the available parameters to optimize instead of the optimizing the whole available group. Therefore, it is recommended to perform a parameter sensitivity study prior to the optimization process to identify the suitable parameters to be chosen. The methodology suggests optimizing the oxidation-reduction species parameters first then calibrating the chlorinated ethenes model. The results of the tests also proved the advantage of the sequential optimization of the model parameters, therefore the parameters of the parent compound are optimized, updated in the daughter compound model, for which the parameters are then optimized so on. The test results suggested considering the concentrations of the daughter compounds when optimizing the parameters of the parent compounds. As for the observation weights, the tests suggest starting the applied observation weights during the optimization process at values of one and changing them if needed.

Overall the proposed methodology proved to be very efficient. The optimization methodology yielded sets of model parameters capable of generating concentration profiles with great resemblance to the observed concentration profiles in the two chlorinated ethenes site models considered.