. School of Environment, Tsinghua University, Beijing 100084, China . School of Chemical and Environmental Engineering, China University of Mining and Technology, Beijing 100083, China
Aeration is pivotal in accelerating landfill stabilization. Biodegradation kinetic models of landfills have not fully accounted for the uneven distribution of oxygen during aerobic in situ stabilization, owing to the high heterogeneity of landfills. In this study, a successive degradation of organic matter (SDOM) model is proposed to calculate the reaction rate constant of municipal solid waste (MSW). The SDOM model assumes that organic matter (OM) is composed of n independent shares, with each share starting to degrade at different times. However, all fractions degrade according to first-order kinetics once they enter the reaction phase. In this study, degradation tests of typical organic matter in landfills were conducted under varying oxygen concentrations, and the reaction rates for each degradation test were calculated using the SDOM model. Subsequently, a model was developed to simulate the variation in the reaction rate constant with the oxygen concentration. Superposition tests on multiple types of organic matter were conducted to further validate the superposition principle of the degradation process. Model verification using real waste data revealed a reaction rate constant of 0.12, demonstrating a better fit compared to the Monod model and traditional first-order kinetic model, as well as the highest accuracy in the calculation of CO2 produced in the degradation process. The SDOM model can help to understand the degradation mechanism of the aerobic in situ stabilization of landfills in a better manner.
Fangming Xu,Junlong Huang,Zhenjiang Zhuo, et al. A novel time-series-based kinetic model for degradation of municipal solid waste under different oxygen concentrations[J]. Front. Environ. Sci. Eng.,
2025, 19(2): 19.
Fig.1 Diagram of the laboratory-scale reactor of (a) individual OM degradation, (b) simulated aerobic degradation.
Organic fraction
Protein
Fat
Total sugar
Cellulose
Lignin
(%, Dry basis)*
8.5
4.21
14.5
8.47
9.89
Tab.1 Initial chemical composition of waste
Fig.2 Calculation procedure and diagram of SDOM model.
Fig.3 Gas generation rates at the oxygen concentrations of 2%, 5%, 10%, and 21% at t = 55 °C of (a) starch, (b) protein, (c) fat, and (d) cellulose.
O2 concentration
Protein
Starch
Cellulose
Fat
2%
0.123
0.086
0.046
0.148
5%
0.144
0.104
0.054
0.173
10%
0.164
0.14
0.088
0.182
21%
0.178
0.15
0.105
0.188
Tab.2 Reaction rate constant k of organic matters at different oxygen concentration
Fig.4 Relationship between the reaction rate constant and oxygen concentration.
Fig.5 Gas generation characteristics between individual and mixed OM with time: (a) protein and fat, (b) protein and starch, (c) fat and starch, (d) cellulose and fat, (e) protein and cellulose, (f) cellulose and starch; (g) simulated and measured values of gas generation characteristics of protein and starch at 10% oxygen concentration.
Fig.6 Effect of SDOM model verification (a) comparison of gas production of individual OM and real waste, (b) comparison of SDOM model versus first-order and Monod model of gas production rate with time.
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