Dynamics of spatial heterogeneity in a landfill with interacting phase densities:a stochastic analysis


A landfill represents a complex and dynamically evolving structure that can be stochastically perturbed by exogenous factors. Both thermodynamic (equilibrium) and time varying (non-steady state) properties of a landfill are affected by spatially heterogenous and nonlinear sub-processes that combine with constraining initial and boundary conditions arising from the associated surroundings. While multiple approaches have been made to model landfill statistics by incorporating spatially dependent parameters on the one hand (data based approach) and continuum dynamical mass-balance equations on the other (equation based modelling), practically no attempt has been made to amalgamate these two approaches while also incorporating inherent stochastically induced fluctuations affecting the process overall. In this article, we will implement a minimalist scheme of modelling the time evolution of a realistic three dimensional landfill through a reaction–diffusion based approach, focusing on the coupled interactions of four key variables –solid mass density, hydrolysed mass density, acetogenic mass density and methanogenic mass density, that themselves are stochastically affected by fluctuations, coupled with diffusive relaxation of the individual densities, in ambient surroundings. Our results indicate that close to the linearly stable limit, the large time steady state properties, arising out of a series of complex coupled interactions between the stochastically driven variables, are scarcely affected by the biochemical growth–decay statistics. Our results clearly show that an equilibrium landfill structure is primarily determined by the solid and hydrolysed mass densities only rendering the other variables as statistically “irrelevant”inthis (largetime) asymptotic limit. The other major implication of incorporation of stochasticity in the land- fill evolution dynamics is in the hugely reduced production times of the plants that are now approximately 20–30 years instead of the previous deterministic model predictions of 50 years and above. The predictions from this stochastic model are in conformity with available experimental observations.

Publication DOI: https://doi.org/10.1016/j.apm.2016.08.026
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School > Aston India Foundation for Applied Research
College of Business and Social Sciences > Aston Business School
Additional Information: © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: stochastic differential equations,waste management,modelling
Publication ISSN: 1872-8480
Last Modified: 06 May 2024 07:16
Date Deposited: 01 Sep 2016 10:45
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Related URLs: https://www.sci ... 4504?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2017-01-01
Published Online Date: 2016-08-31
Accepted Date: 2016-08-24
Authors: Chattopadhyay, Amit K. (ORCID Profile 0000-0001-5499-6008)
Dey, Prasanta K. (ORCID Profile 0000-0002-9984-5374)
Ghosh, Sadhan K.


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