This work presents and discusses a methodology for modeling the behavior of
a landfill system in terms of biogas release to the atmosphere, relating
this quantity to local meteorological parameters. One of the most important
goals in the study of MSW sites lies in the optimization of biogas
collection, thus minimizing its release to the atmosphere.
After an introductory part, that presents the context of non-invasive
measurements for the assessment of biogas release, the concepts of survey
mapping and automatic flux monitoring are introduced.
Objective of this work is to make use of time series coming from long-term
flux monitoring campaigns in order to assess the trend of gas release from
the MSW site. A key aspect in processing such data is the modeling of the
effect of meteorological parameters over such measurements; this is
accomplished by modeling the system behavior with a set of Input/Output data
to characterize it without prior knowledge (system identification).
The system identification approach presented here is based on an adaptive
simulation concept, where a set of Input/Output data help training a "black
box" model, without necessarily a prior analytical knowledge. The adaptive
concept is based on an Artificial Neural Network scheme, which is trained by
real-world data coming from a long-term monitoring campaign; such data are
also used to test the real forecasting capability of the model.
In this particular framework, the technique presented in this paper appears
to be very attractive for the evaluation of biogas releases on a long term
basis, by simulating the effects of meteorological parameters over the flux
measurement, thus enhancing the extraction of the useful information in
terms of a gas "flux" quantity. |