Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Feature engineering using homogenization theory with multiscale perturbation analysis for supervised model-based learning of physical clogging condition in seepage filters

Aquifer recharge and recovery systems (ARRS), which can broadly be analysed as seepage depth filters, in natural or engineered aquifers are gaining attention worldwide. Engineering predictions of their complex physical clogging behavior, however, continue to be challenging which has hindered the predictive maintenance of these systems for energy and materials savings. To address this problem statement, we leverage the homogenization theory with the multiscale perturbation analysis as the feature engineering step to reduce the complexity of the physical clogging behavior in ARRS. The analytical approach systematically derives a unique homogenized representation which quantifies the clogging condition at the macroscale. A series of physical parameters are identified from the derived homogenized representation to build a pre-processed input layer into our own multi-layered neural network (NN) architecture for predictive analysis. Measured data extracted from the literature is then used to train and verify the NN model. The trained model yields an average error deviation of 20% between the model's predictions and the respective measurements for an optimized set of hyperparameters tested. We then discuss quantitatively how the model can be adhered to predict the timing for a concerned ARRS to reach its breakthrough stage for a range of operational conditions. Finally, we also demonstrate how the homogenized representation can be useful to determine an arbitrary filter's critical reaction rate and diffusion coefficient responsible for its breakthrough stage.Accepted versio