Over the years, modelling frameworks have been proposed to describe and predict the degradation of municipal solid waste in landfills. These frameworks, however, fail to couple the kinetic solutions to the real-time equilibrium state of the system. Because of this, fundamental processes such as pH changes, NH4+ adsorption to the waste surface and changes to the fluid composition are often not (or inadequate) taken into account. In this study, we propose a degradation model that couples a kinetic solver to the chemical equilibrium software, ORCHESTRA. For this, a tool is developed that allows seamless operability between ORCHESTRA and the Python environment. The modelling framework is used to simulate the degradation of solid organic matter in a batch reactor following a simplified, but comprehensive, reaction network. Reaction rates are based on Monod kinetics that includes a wide variety of inhibition functions. The Freundlich equation is used to capture NH4+ adsorption to the waste surface. Results from various case studies show that the modelling framework is suitable for coupling real-time landfill chemistry to degradation kinetics. We found that NH₄⁺ concentrations play a dominant role in the presented model. While low NH₄⁺ concentrations limit biomass growth by substrate inhibition, high concentrations (>0.002 4
mol/L) affect the hydrolysis rate by toxicity inhibition. This translates to a model that is sensitive to a variety of input parameters such as the initial C/N ratio, the Kd (degree of adsorption) value and the initial organic fraction. A comparison with literature studies, however, implies that the model lacks fundamental processes such as gas and fluid transport or changes in volume and temperature. In addition to this, we discuss that the Freundlich isotherms may be inappropriate to capture adsorption in a coupled framework as no ion exchange is taken into account. Models that do so, such as the NICA-Donnan model, could improve the model results significantly.

The deterministic approach for interpreting CPT soil profiles poses the serious limitation of not taking data uncertainty into account. Therefore, a Bayesian model was developed by Wang et al. (2013) that, for a given CPT profile, determines the most probable number of soil layers and most probable soil layer thicknesses by simulating and comparing multiple ‘model classes’ with different complexities. In this study, this proposed model is implemented into the Python coding environment after which the functionality is verified by conducting a case study on a 23 푚 CPT profile from the Groningen area (NE Netherlands). For the given CPT profile, the model distinguishes 6 separate soil layers from which the position and thickness are in agreement with the deterministic analysis and the available borehole data. However, the case study suggests that the model fails to correctly identify the most probable soil types for CPT measurements within the vicinity of the edges of the Robertson chart. This is most-likely related to a “cut-off”-effect of the joint Gaussian distribution describing the uncertainty of a single datapoint. A subsequent study on the integration of the statistical parameters within the model is therefore required. Additionally, the code includes several optimizing strategies, but remains time consuming for very complex model classes. Further optimization is suggested to achieve greater model precision and efficiency.