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Showing 2 results for Methane Emission
Development of ILGAM model to predict generation and methane emission from landfill
Sayyed Hossein Khazaei, Mazaher Moeinaddini, Reza Rafiee, Nematollah Khorasani, Melanie L. Sattler,
Volume 15, Issue 3 (12-2022)
Abstract
Background and Objective: Various models have been developed to predict methane generation and emissions from landfills. Due to their simplicity, the minimum number of required data, and the accuracy of the outputs, First-order decay are the most common models to predict methane generation in landfill,. Three important parameters in modeling landfill gas generation using a first-order model are the total weight of waste buried in the landfill, the methane generation potential, and the methane generation rate constant. The purpose of this research was to accurately estimate the parameters of the first-order model and to optimize it for estimating methane generation in the landfill and also to develop the ILGAM software.
Materials and Methods: ILGAM model consists of two submodels: 1) the gas generation sub-model and 2) the methane oxidation sub-model. The methane oxidation sub-model is based on the MOT model. The gas generation sub-model is based on a first-order equation with an emphasis on the contribution of the aerobic process in the estimation of the ultimate methane potential of waste. The parameters of the equation were modeled using the latest available results in the literature. To evaluate the model, the actual methane emission and methane oxidation were measured in the Karaj landfill. The results of the model, along with a few common models, were compared with actual data obtained from the Karaj landfill.
Results: The ILGAM model predicted the gas emission from the Karaj landfill with an error of 5.8%. In contrast, LandGem, IPCC and CLEEN models predicted the methane gas emission from the Karaj landfill with an error of 74.4%, 40.2%, and 27.1%, respectively.
Conclusion: When compared to other models, the ILGAM model estimated the closest values to actual measurements for methane emission and methane oxidation in the Karaj landfill.  Owing to its user-friend Graphical User Interface (GUI), the model can be easily executed in a wide range of landfills by entering a few easy-to-measure data in the field.
 

Estimation of Methane gas emission rate from landfill using inverse dispersion modeling (case study of landfill in Alborz province)
Ehsan Mohammad Hassani, Reza Rafei, Mazaher Moeinaddini, Niki Aghapour,
Volume 17, Issue 1 (6-2024)
Abstract
Background and Objective: One of the largest sources of methane emissions is landfills, and various models have been developed to predict landfill methane production and emissions. The main goal of this research is to utilize the inverse Gaussian model to estimate g methane greenhouse gas emissions and model it using field data. This study introduces a simple method to estimate the amount of methane emissions based on ambient methane concentrations.
Materials and Methods: In this research, the methane emission rates from landfill were estimated for warm (July) and cold (February) seasons using a sampling campaign from 27 stations and standard inverse Gaussian dispersion equations. Monte Carlo simulation was also employed. To determine the model, an optimization-based method, along with inverse scattering modeling, was utilized to process surface emission monitoring data.
Results: The model results indicated during the cold (February) and warm (July) seasons, the methane emission rates were estimated at 1696.99 and 16.53 g/s, respectively. These findings confirm that the methane production and emission during the cold season were lower than in the warm season, likely due to reduced temperature and bacterial activity.
Conclusion: The method used in this study, the inverse Gaussian dispersion model, can be applied to estimate methane gas emission rates from other landfills. However, it necessitates the permanent recording of data and the use of daily or weekly averages in calculations to mitigate potential errors and enhance the accuracy of modeling.
 

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