Foam Flow Model of Municipal Solid Waste and Its Application in Landfill Gas Pressure Prediction
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 149, Issue 3
Abstract
Foam, as a group of disconnected gas bubbles wrapped with liquid film, also exists in municipal solid waste (MSW) landfills. In this study, a foam flow model in MSW is established, in which the population balance equation is adopted to calculate the dynamic process of foam generation, bursting, and transport. The status of foam is characterized using the number density, which is defined as the number of bubbles per unit volume of pore. The relative permeability and viscosity of foam are corrected according to its number density. Then, the proposed model is verified through the foam displacement experiment conducted in an MSW column. The foam flow model can accurately simulate the transient change of pressure drop and liquid saturation during foam displacement. The values of coefficients of foam generation and bursting rates are fitted to be suitable for MSW. Finally, the foam flow model is applied to predict the gas pressure development in an MSW landfill. Foam is able to maintain the landfill gas pressure at a high value even for aged MSW with low gas generation rate. The S-shaped curve of gas pressure versus depth suggests that the foam flow model is capable of simulating the phenomenon of high gas pressure accumulated in the deep layer of landfills because it takes into account the nonuniform distribution of foam number density. The advantage of the foam flow model over the gas flow model is discussed with respect to landfill gas pressure prediction.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The work is funded by the National Natural Science Foundation of China (Grant Nos. 52108348 and 51988101), the National Key Research and Development Program of China (Grant No. 2019YFC1806000), the Key R&D Program of Zhejiang Province (Grant No. 2022C03095), and the China Postdoctoral Science Foundation (Grant No. 2021M692836), which are gratefully acknowledged.
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Received: Nov 30, 2021
Accepted: Sep 23, 2022
Published online: Jan 12, 2023
Published in print: Mar 1, 2023
Discussion open until: Jun 12, 2023
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