Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study.

In this work, both the structural and practical identifiability of the Anaerobic Digestion Model no. 1 (ADM1) is investigated, which serves as a relevant case study of large non-linear dynamic network models. The structural identifiability is investigated using the probabilistic algorithm, adapted to deal with the specifics of the case study (i.e., a large-scale non-linear dynamic system of differential and algebraic equations). The practical identifiability is analyzed using a Monte Carlo parameter estimation procedure for a 'non-informative' and 'informative' experiment, which are heuristically designed. The model structure of ADM1 has been modified by replacing parameters by parameter combinations, to provide a generally locally structurally identifiable version of ADM1. This means that in an idealized theoretical situation, the parameters can be estimated accurately. Furthermore, the generally positive structural identifiability results can be explained from the large number of interconnections between the states in the network structure. This interconnectivity, however, is also observed in the parameter estimates, making uncorrelated parameter estimations in practice difficult.

Parameter identification and modeling of the biochemical methane potential of waste activated sludge.

Anaerobic digestion is widely used in waste activated sludge treatment. In this paper, partial least-squares (PLS) is employed to identify the parameters that are determining the biochemical methane potential (BMP) of waste activated sludge. Moreover, a model is developed for the prediction of the BMP. A strong positive correlation is observed between the BMP and volatile fatty acids and carbohydrate concentrations in the sludge. A somewhat weaker correlation with COD is also present. Soluble organics (sCOD, soluble carbohydrates and soluble proteins) were shown not to influence the BMP in the observed region. This finding could be most-valuable in the context of application of sludge pretreatment methods. The obtained model was able to satisfactory predict the BMP.