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Anaerobic digestion is an excellent technique for the energetic valorisation of various types of biomass including waste forms. Because of its complex nature, the optimisation and further process development of this technology go hand in hand with the availability of mathematical models for both simulation and control purposes. Over the years, the variety of mathematical models developed has increased as have their complexity. This paper reviews the trends in anaerobic digestion modelling, with the main focus on the current state of the art. The most significant simulation and control models are highlighted, and their effectiveness critically discussed. The importance of the availability of models that are less complex, which can be used for control purposes, is assessed. The paper concludes with a discussion on the inclusion of microbial community data in mathematical models, an innovative approach which could drastically improve model performance

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.

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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 di↵erential 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 di cult.

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