Non-invasive methods applied to the case of Municipal Solid Waste landfills (MSW): analysis of long-term data

摘要

This work presents and discusses a methodology for modeling the behavior ofa landfill system in terms of biogas release to the atmosphere, relatingthis quantity to local meteorological parameters. One of the most importantgoals in the study of MSW sites lies in the optimization of biogascollection, thus minimizing its release to the atmosphere.After an introductory part, that presents the context of non-invasivemeasurements for the assessment of biogas release, the concepts of surveymapping and automatic flux monitoring are introduced.Objective of this work is to make use of time series coming from long-termflux monitoring campaigns in order to assess the trend of gas release fromthe MSW site. A key aspect in processing such data is the modeling of theeffect of meteorological parameters over such measurements; this isaccomplished by modeling the system behavior with a set of Input/Output datato characterize it without prior knowledge (system identification).The system identification approach presented here is based on an adaptivesimulation concept, where a set of Input/Output data help training a "blackbox" model, without necessarily a prior analytical knowledge. The adaptiveconcept is based on an Artificial Neural Network scheme, which is trained byreal-world data coming from a long-term monitoring campaign; such data arealso used to test the real forecasting capability of the model.In this particular framework, the technique presented in this paper appearsto be very attractive for the evaluation of biogas releases on a long termbasis, by simulating the effects of meteorological parameters over the fluxmeasurement, thus enhancing the extraction of the useful information interms of a gas "flux" quantity.