Coupling model of aerobic waste degradation considering temperature, initial moisture content and air injection volume

Abstract

A quantitative description of aerobic waste degradation is important in evaluating landfill waste stability and economic management. This research aimed to develop a coupling model to predict the degree of aerobic waste degradation. On the basis of the first-order kinetic equation and the law of conservation of mass, we first developed the coupling model of aerobic waste degradation that considered temperature, initial moisture content and air injection volume to simulate and predict the chemical oxygen demand in the leachate. Three different laboratory experiments on aerobic waste degradation were simulated to test the model applicability. Parameter sensitivity analyses were conducted to evaluate the reliability of parameters. The coupling model can simulate aerobic waste degradation, and the obtained simulation agreed with the corresponding results of the experiment. Comparison of the experiment and simulation demonstrated that the coupling model is a new approach to predict aerobic waste degradation and can be considered as the basis for selecting the economic air injection volume and appropriate management in the future.

An Aerobic Degradation Model for Landfilled Municipal Solid Waste

“…The expression of the temperature influence function was originally proposed by Rosso et al (1993). Ma (2018) used this temperature expression to build the degradation kinetic model and got a good fitting result. The influence factor of temperature can be expressed by Equation (12):…”

“…With regard to the underlying theory, first-order [15][16][17][18][19][20][21] or Monod-type equations [22][23][24][25][26] are usually used to calculate respiration rates as a function of the organic waste degradation rate. The main factors that control the degradation process include temperature, moisture content, pH, aeration rate, C/N ratio, particle size, and nutrient content [27][28][29][30][31]. However, it is inappropriate to include too many parameters in models, so it is important to select those that are most influential.…”