Abstract:
Canada's per capita solid waste disposal rates are among the highest in the world.
Landfill gas generation requires more accurate modelling in order to properly compare
future emission mitigation or energy production projects. The EPA software LandGEM
was selected for its common use in the literature. Two alternative methods to increase
accuracy in methane and carbon dioxide estimates were studied.
Real-time methane collection data from a municipal landfill in Regina's cold, semi-arid
climate were consolidated to fit a linear-interpolated form of LandGEM. LandGEM
defaults were found invalid for this landfill due to significant overestimation (76.5%
error). Seasonal variations in gas collection were explored, and found that optimal
seasonal k and Lo collection parameters had 7.3% error compared to field data, compared
to 15.5% error using optimal annual parameters. The optimal kwinter was 0.0082 year-1
and the ksummer was 0.0095 year-1 (14.7% difference). Three pseudo-second order
iterative methods were used to fit the model estimates to the daily data, and they were
evaluated using RSS and literature values. Optimized parameters were applied to a
simple study using LFGcost-Web. The default parameters overestimated the net present
worth by 57-107% for three of the four projects.
LandGEM assumes that carbon dioxide estimates are a function of methane, and that the
two gases make up nearly 100% of gas content. This can lead to oversights in collection
system design. A total of 25 cases (five formulas, five approaches) were compared for
carbon dioxide collection at four western Canadian landfills. The existing Default with
Traces approach overestimated production in 3 of the 4 sites, resulting in the highest RSS. LandGEM's governing formula yielded the most accurate results under this
approach (mean RSS increased by 7.0 to 49.9% using other equations). Optimization
resulted in the most accurate results for all formulas and approaches, and had the greatest
reduction in RSS over the default approach (73.0 to 98.0%). The 1.4 ratio approach
yielded the second most accurate results (mean RSS reduction of 66.5%). The annual k
formula calculated k’s via two empirical formulas (based on precipitation), and yielded
the lowest accuracy in 12 of 20 approaches.
Description:
A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Environmental Systems Engineering, University of Regina. ix, 89 p.