(239c) Landfill Modeling Using Ensemble Kalman Filter

Authors: 
Li, H., University of Southern California
Qin, S. J., University of Southern California
Sahimi, M., University of Southern California
Tsotsis, T. T., University of Southern California


In the United States, landfill remains to be the primary choice for disposal of solid waste. The municipal solid waste in landfills undergoes anaerobic decomposition and produces a gaseous mixture called landfill gas (LFG), which mostly consists of methane and carbon dioxide, which are greenhouse gases that are responsible for climate changes. Due to the composition of LFG, landfills have recently become a promising source of green energy. The key obstacle of using LFG as renewable energy is how to predict and control the rate of LFG generated by a given landfill and its quality. Therefore, mathematical modeling is a critical path to achieve this objective.

Several models have been proposed for describing gas generation and migration in a landfill. In this work, we use the three-dimensional (3D) model developed by Sanchez et al, 2006. Because of the scarcity of real data for physical properties, we first use genetic algorithms (GA) to generate initial permeability distribution based on the limited production data. Then sequential Gaussian simulation (SGS) is implemented to obtain the initial ensembles for an Ensemble Kalman Filter (EnKF) to achieve real time model adaptation. Lastly, we apply EnKF for updating permeability distribution in a landfill by assimilating real-time production data. Our results demonstrate the efficiency and accuracy of EnKF in data assimilation in the landfill problem. Practical issues about EnKF are also discussed in this work.

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