Assessment of municipal solid waste settlement models based on field-scale data analysis

Assessment of municipal solid waste settlement models based on field-scale data analysis

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Article ID: iaor201527168
Volume: 42
Issue: 9
Start Page Number: 101
End Page Number: 117
Publication Date: Aug 2015
Journal: Waste Management
Authors: ,
Keywords: datamining, statistics: inference, performance, optimization, decision, management
Abstract:

An evaluation of municipal solid waste (MSW) settlement model performance and applicability was conducted based on analysis of two field‐scale datasets: (1) Yolo and (2) Deer Track Bioreactor Experiment (DTBE). Twelve MSW settlement models were considered that included a range of compression behavior (i.e., immediate compression, mechanical creep, and biocompression) and range of total (2–22) and optimized (2–7) model parameters. A multi‐layer immediate settlement analysis developed for Yolo provides a framework to estimate initial waste thickness and waste thickness at the end‐of‐immediate compression. Model application to the Yolo test cells (conventional and bioreactor landfills) via least squares optimization yielded high coefficient of determinations for all settlement models (R 2 >0.83). However, empirical models (i.e., power creep, logarithmic, and hyperbolic models) are not recommended for use in MSW settlement modeling due to potential non‐representative long‐term MSW behavior, limited physical significance of model parameters, and required settlement data for model parameterization. Settlement models that combine mechanical creep and biocompression into a single mathematical function constrain time‐dependent settlement to a single process with finite magnitude, which limits model applicability. Overall, all models evaluated that couple multiple compression processes (immediate, creep, and biocompression) provided accurate representations of both Yolo and DTBE datasets. A model presented in Gourc et al. (2010) included the lowest number of total and optimized model parameters and yielded high statistical performance for all model applications (R 2⩾0.97).

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