Macroscopic modelling and identification of an anaerobic waste treatment process

Anaerobic fermentation is an important process used for recycling solid organic waste, which leads to a significant reduction of the waste volume with the production of biogas as a positive side effect. For state observation and control purposes, a mathematical representation of the process is required. However, anaerobic fermentation is far too complex to be described in full metabolic details, due to the variety of responsible microorganisms and the unknown and time-varying waste composition. The level of complexity of the description is limited by the amount and quality of available experimental data, which can be used for model identification. In practice, the derivation of a dynamic process model involves the following steps: (i) the selection of suitable macroscopic reaction schemes and kinetic structures, (ii) the estimation of the unknown model parameters from experimental data by minimizing a maximum-likelihood criterion, (iii) the estimation of the unknown measurement variances, (iv) the estimation of the covariance matrix of the parameter estimates and (v) the validation of the obtained model. In this study, attention is focused on these several steps, and a dynamic model of a complex anaerobic process is inferred from infrequent measurements of global variables. The experimental data are obtained from six experiments carried out in a small-scale continuous bioreactor under different feed and (controlled) acidity conditions.