Development of a chemical kinetic model for a biosolids fluidized-bed gasifier and the effects of operating parameters on syngas quality.

In an effort to decrease the land disposal of sewage sludge biosolids and to recover energy, gasification has become a viable option for the treatment of waste biosolids. The process of gasification involves the drying and devolatilization and partial oxidation of biosolids, followed closely by the reduction of the organic gases and char in a single vessel. The products of gasification include a gaseous fuel composed largely of N2, H2O, CO2, CO, H2, CH4, and tars, as well as ash and unburned solid carbon. A mathematical model was developed using published devolatilization, oxidation, and reduction reactions, and calibrated using data from three different experimental studies of laboratory-scale fluidized-bed sewage sludge gasifiers reported in the literature. The model predicts syngas production rate, composition, and temperature as functions of the biosolids composition and feed rate, the air input rate, and gasifier bottom temperature. Several data sets from the three independent literature sources were reserved for model validation, with a focus placed on five species of interest (CO, CO2, H2, CH4, and C6H6). The syngas composition predictions from the model compared well with experimental results from the literature. A sensitivity analysis on the most important operating parameters of a gasifier (bed temperature and equivalence ratio) was performed as well, with the results of the analysis offering insight into the operations of a biosolids gasifier.

Determination of first-order landfill gas modeling parameters and uncertainties.

Using first-order kinetic empirical models to estimate landfill gas (LFG) generation and collection rates is well recognized in the literature. The uncertainty in the estimated LFG generation rates is a major challenge in evaluating performance of LFG collection and LFG to energy facilities. In this investigation, four methods for quantifying first-order LFG generation model parameters, methane generation potential, L(0), and methane generation rate constant, k, were evaluated. It was found that the model is insensitive to the approach taken in quantifying the parameters. However, considering the recognition of using the model in the literature, the optimum method to estimate L(0) and k is to determine L(0) using disposed municipal solid waste composition and laboratory component specific methane potential values. The k value can be selected by model fitting and regression using the first-order model if LFG collection data are available. When such data are not available, k can be selected from technical literature, based on site conditions. For five Florida case-study landfills L(0) varied from 56 to 77 m(3) Mg(-1), and k varied from 0.04 to 0.13 yr(-1) for the traditional landfills and was 0.10 yr(-1) for the wet cell. Model predictions of LFG collection rates were on average lower than actual collection. The uncertainty (coefficient of variation) in modeled LFG generation rates varied from ±11% to ±17% while landfills were open, ±9% to ±18% at the end of waste placement, and ±16% to ±203% 50 years after waste placement ended.

A robust method for estimating landfill methane emissions.

Because municipal solid waste (MSW) landfills emit significant amounts of methane, a potent greenhouse gas, there is considerable interest in quantifying surficial methane emissions from landfills. The authors present a method to estimate methane emissions, using ambient air volatile organic compound (VOC) measurements taken above the surface of the landfill. Using a hand-held monitor, hundreds of VOC concentrations can be taken easily in a day, and simple meteorological data can be recorded at the same time. The standard Gaussian dispersion equations are inverted and solved by matrix methods to determine the methane emission rates at hundreds of point locations throughout a MSW landfill. These point emission rates are then summed to give the total landfill emission rate. This method is tested on a central Florida MSW landfill using data from 3 different days, taken 6 and 12 months apart. A sensitivity study is conducted, and the emission estimates are most sensitive to the input meteorological parameters of wind speed and stability class. Because of the many measurements that are used, the results are robust. When the emission estimates were used as inputs into a dispersion model, a reasonable scatterplot fit of the individual concentration measurement data resulted.