Abstract proceedings for the 7th ICLRS, Sunderbyn, Sweden, June 25-27.
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Quantification of (bio)geochemical heterogeneous activity in full-scale
landfills
Andre Gerard van Turnhout, Delft University of Technology, A.G.vanTurnhout@tudelft.nl Timo Heimovaara, Delft University of Technology, T.J.Heimovaara@tudelft.nl
Robbert Kleerebezem, Delft University of Technology, R.Kleerebezem@tudelft.nl
Introduction
The main focus of this paper is the assessment of landfill leachate/gas emissions and long-term prediction of these emissions. Due to variation in environmental conditions, like rainfall events, leachate concentrations are very dynamic on a short-time scale. The measured concentrations in the leachate oscillate between maximum and minimum concentrations. Our assumption is that the maximum concentrations occur under slow flow conditions when the leachate is in equilibrium with the relatively immobile water fraction present in the bulk of the waste. The minimum concentrations are the result of strong dilution during heavy rainfall events, where the bulk of the waste is by-passed due to preferential flow.. For accurate data-analysis and further modeling it is important to obtain a good estimation of these two types of concentrations. With these two bounding concentration values the effectiveness of natural and induced landfill stabilization (by recirculation or aeration) can be monitored and assessed. In addition we aim to develop a method to predict long-term emissions in which these bounding concentration values play an important role.
Research question
Until now prediction of long-term emissions are made by extrapolation of empirical relations with a high degree of uncertainty. These predictions cannot be used for assessment of landfill after care. More accurate predictions can be made when models are based on the landfill processes responsible for these emissions. But the problem for a process-based modeling approach is that landfills are considered as black boxes. It is known that important processes, influencing emissions, are biological degradation, speciation/precipitation,
adsorption/leaching, diffusive/convective transport (influenced by preferential flow). But information on these processes can only be obtained through emission and waste sample measurements. The key research question is to model the processes (opening the black box) in such a way that calculated emission data is comparable to measured emission data. Once these processes are modeled sufficiently accurate, the model can be extrapolated for estimation of future emissions. Our hypothesis is that the processes in a landfill, due to the heterogeneity, have to be modeled by a stochastic approach. This way every process has its own probability of occurrence. The combination of process probabilities will result in an emission pattern specific for each landfill. This stochastic modeling approach is illustrated in more detail and is presented in Figure 1.
In order to model its heterogeneity in processes the landfill is divided in many cells or sub-models. Each cell represents a particular state of the landfill. For example, one cell can be dominated by organic acid fermentation in combination with diffusive transport while another cell is dominated by aerobic (bio)degradation in combination with convective transport. Parameters to model such particular cell/states (like pH, Eh, DOC, flow etc.) are obtained from databases like LeachXS or experiments. All these cells are combined in a stochastic
Abstract proceedings for the 7th ICLRS, Sunderbyn, Sweden, June 25-27
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model with probabilities assigned to each cell. This lumped model will yield a emission pattern specific for its cell probability distribution. To obtain this cell probability distribution for a landfill, model parameters are fitted such that the calculated emissions correlate well with measured baseline emissions. If a good correlation between the measured and calculated emissions exists, this will indicate that the modeled processes and their probabilities are a representation of the processes occurring in the landfill. Then future emissions can be predicted with this stochastic model by process-based extrapolation.
Figure1: Representation of a heterogeneous landfill by a set of cells (sub-models). Good correlation of the calculated and measured leachate/gas emissions proves a good description of the dominant processes in the landfill.
Within each cell (sub-model) Java-based modeling programs, each specialized to model certain processes, are coupled. For instance, geochemical equilibria in cells can be calculated very accurate with ORCHESTRA, while transport or biological degradation is better simulated with COMSOL. MATLAB serves as a general platform to transform and exchange the simulated data between the different programs for each time step. A schematic representation of this coupling is represented in Figure 2.
Figure 2: Coupling scheme of Java-based modelling programs for a sub-model.
Preliminary results of sub-models, databases and experiments will be presented during the congress. For example a simple sub-model (cell) was constructed representing glucose (batch) fermentation buffered with calcium carbonate. In this model biological degradation was simulated with MATLAB coupled to geochemical equilibrium calculations in ORCHESTRA.
