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Non-invasive Methods Applied to the Case of Municipal Solid Waste Landfills (Msw): Analysis of Long-term Data : Volume 19, Issue 19 (14/11/2008)

By Scozzari, A.

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Book Id: WPLBN0003998784
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Non-invasive Methods Applied to the Case of Municipal Solid Waste Landfills (Msw): Analysis of Long-term Data : Volume 19, Issue 19 (14/11/2008)  
Author: Scozzari, A.
Volume: Vol. 19, Issue 19
Language: English
Subject: Science, Advances, Geosciences
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Scozzari, A. (2008). Non-invasive Methods Applied to the Case of Municipal Solid Waste Landfills (Msw): Analysis of Long-term Data : Volume 19, Issue 19 (14/11/2008). Retrieved from

Description: CNR Institute of Geoscience and Earth Resources, Via Moruzzi 1, 56124 Pisa, Italy. 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.

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

Hu, Y. H. and Hwang, J. N.: Handbook of Neural Network Signal Processing, CRC Press, 2001.; Chauvet, P. and Galli A.: Universal Kriging, Pubblication n. C – 96, Centre de Géostatistique, Ecole des Mines de Paris, Fontainebleau, 1982.; Luo, F. L. and Unbehauen, R.: Applied neural networks for signal processing, Cambridge Press, 1997.; Raco, B., Scozzari, A., Guidi, M., Lelli M., and Lippo, G.: Comparison of two non-invasive methodologies to monitor diffuse biogas emissions from MSW landfills soil: a case study, Sardinia Symposium 2005.; Krige, D. G.: A statistical approach to some basic mine valuation problems on the Witwatersrand, J. Chem. Metall. Min. Soc. S. Afr., 52, 119–139, 1951.


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