| In the United States, landfilling remains the primary choice for the disposal of municipal solid waste (MSW). The MSW in landfills undergoes anaerobic decomposition by various micro-organisms and produces large amounts of landfill gas (LFG). LFG, a gaseous mixture consisting mainly of CH4 and CO2, can be utilized as a renewable source of green energy to produce electricity, heat and fuels. Even though there is a huge potential of converting LFG into energy, LFG has so far found fairly limited utility, because the economics of landfill energy projects are still not quite favorable due to high operation costs. Among the key obstacle is that landfill engineers are not able to accurately predict the amount and quality of LFG emitted from a certain landfill and thus cannot determine optimal strategies to operate a landfill economically. In this dissertation, we are going to investigate three essential aspects of efficient landfill management: short- and long-term performance prediction, real-time state estimation and model-based production optimization.;Considering the importance of the short-term prediction, we first develop an artificial neural network (ANN) approach in order to make accurate short-term forecasting for several important quantities in a large landfill in Southern California, including the temperature, and the CH4, CO2, and O2 concentration profiles. The ANN, a data-driven method, does not require the access to technical details about a landfill and thus is computationally efficient. It is shown that the ANN can be successfully trained by the experimental data and can provide accurate predictions for the behavior of landfill at least in the short-term. For the long-term prediction, however, a model-based prediction method is commonly used, for which an accurate theoretical landfill model is needed. Hashemi et al. (2002) and Sanchez et al. (2006) presented a comprehensive 3D model to describe the LFG generation and transport, which has been validated by real landfill data. We further extend this landfill model, in conjunction with the ANN method and the genetic algorithm (GA) method, for the long-term prediction. The effectiveness of this novel approach is demonstrated by a synthetic landfill gas model.;Another obstacle to accurately forecasting the amount and content of LFG is the lack of a realistic description of landfills, due to the fact that a landfill is a highly heterogeneous system, whereby the physical properties, such as permeability, porosity and tortuosity, are anisotropic and vary spatially. These properties are critical to determine generation and transport of LFG in a landfill. However, such information is difficult to obtain, because only limited measurement data, such as the gas flow from a landfill and its composition, are available. Therefore, it is important to characterize and estimate these factors that influence the accuracy of a landfill model, by assimilating the measurement data as soon as they become available. A real-time updating approach, based on a combination of the GA and the ensemble Kalman filter (EnKF), is proposed to solve this estimation problem.;We then proceed to the problem of model-based production optimization for a landfill system, using the heterogeneous landfill model obtained from the real-time state estimation. The complexity of a landfill system will cause heavy computation burden to the optimization, where conventional optimization methods might fail. An effective ensemble based method is developed and successfully implemented to maximize the net present value (NPV) of a synthetic landfill model without constraints. The optimization with nonlinear flow rate constraints is handled by a GA based method, called parameterless GA, through modifying the objective function of the GA. |
