ANAEROBIC DIGESTION MODEL (ADM1)
The ADM1 as proposed by the IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes (Batstone et al., 2002a) is a structured but highly complex model which describes 7 groups of bacteria and archaea (included in a total of 32 dynamic state concentration variables) catalyzing 19 biochemical kinetic processes coupled to 105 kinetic and stoichiometric parameters. The set of differential equations (DE) of the ADM1 for the calculation of the variables include 10 DE to model the evolution of soluble matter concentrations in the liquid phase and two DE to model inorganic carbon and inorganic nitrogen levels in the liquid phase. Particulate matter and biomass concentrations in liquid phase are described …show more content…
The main reason for this situation is that the mechanisms ruling these processes are not adequately understood to formulate reliable nonlinear mathematical models because AD is a complex biological process involving decomposition by several major populations of microorganisms.
However, ADM1 model was assumed to simulate a constant volume completely mixed system (Batstone et al., 2002). In real commercial scale, it is hard to find an idea mixing in any reactors. The complex flow behavior in the anaerobic digesters would affect the accuracy of ADM1 model in the prediction of large-scale digesters. Furthermore, ADM1 requires a large number of input parameters due to the complex model structure leading to a high amount of both kinetic and stoichiometric expressions. In most cases, only a limited amount of parameters is significant for model performance, depending on the substrate chosen. The use of complex models such as ADM1, although valuable for general process simulation, has severe shortcomings if they are intended to be used for process control and optimization (Stamatelatou et al., 2009). This is because there are difficulties in determining the numerous model parameters (non-identifiability of parameters), while manipulating a large number of equations limits the applicability for the dynamic analysis, process simulation and control. In addition, although the model assumptions reflect quite well our current understanding of the physical processes involved, many of the individual steps may actually be so fast so that they do not influence the overall process
The ADM1 as proposed by the IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes (Batstone et al., 2002a) is a structured but highly complex model which describes 7 groups of bacteria and archaea (included in a total of 32 dynamic state concentration variables) catalyzing 19 biochemical kinetic processes coupled to 105 kinetic and stoichiometric parameters. The set of differential equations (DE) of the ADM1 for the calculation of the variables include 10 DE to model the evolution of soluble matter concentrations in the liquid phase and two DE to model inorganic carbon and inorganic nitrogen levels in the liquid phase. Particulate matter and biomass concentrations in liquid phase are described …show more content…
The main reason for this situation is that the mechanisms ruling these processes are not adequately understood to formulate reliable nonlinear mathematical models because AD is a complex biological process involving decomposition by several major populations of microorganisms.
However, ADM1 model was assumed to simulate a constant volume completely mixed system (Batstone et al., 2002). In real commercial scale, it is hard to find an idea mixing in any reactors. The complex flow behavior in the anaerobic digesters would affect the accuracy of ADM1 model in the prediction of large-scale digesters. Furthermore, ADM1 requires a large number of input parameters due to the complex model structure leading to a high amount of both kinetic and stoichiometric expressions. In most cases, only a limited amount of parameters is significant for model performance, depending on the substrate chosen. The use of complex models such as ADM1, although valuable for general process simulation, has severe shortcomings if they are intended to be used for process control and optimization (Stamatelatou et al., 2009). This is because there are difficulties in determining the numerous model parameters (non-identifiability of parameters), while manipulating a large number of equations limits the applicability for the dynamic analysis, process simulation and control. In addition, although the model assumptions reflect quite well our current understanding of the physical processes involved, many of the individual steps may actually be so fast so that they do not influence the overall process