Dynamical model development and parameter identification for an anaerobic wastewater treatment process.

This paper deals with the development and the parameter identification of an anaerobic digestion process model. A two-step (acidogenesis-methanization) mass-balance model has been considered. The model incorporates electrochemical equilibria in order to include the alkalinity, which has to play a central role in the related monitoring and control strategy of a treatment plant. The identification is based on a set of dynamical experiments designed to cover a wide spectrum of operating conditions that are likely to take place in the practical operation of the plant. A step by step identification procedure to estimate the model parameters is presented. The results of 70 days of experiments in a 1-m(3) fermenter are then used to validate the model.

Advanced monitoring and control of anaerobic wastewater treatment plants: software sensors and controllers for an anaerobic digester.

A mass balanced based model representing the dynamical behaviour of anaerobic digester has served as a basis for the design of software sensors for the concentration of inorganic carbon, alkalinity and volatile fatty acids. The predictions of the sensors are close to the actual off-line measurements. The model has also been used to design a model-based adaptive linearizing controller and a fuzzy controller whose objective is to regulate the ratio of the intermediate alkalinity over the total alkalinity below some desired value (0.3) under which the process is assumed to remain in stable conditions and avoid VFA accumulation. Both controllers were calibrated via extensive numerical simulations and implemented. The controllers proved successful in maintaining the ratio of TA over PA below 0.3, even in presence of large variations of the organic load.

Software sensors to monitor the dynamics of microbial communities: application to anaerobic digestion.

A mass balance based model has been derived to represent the dynamical behavior of the ecosystem contained in an anaerobic digester. The model considers two bacterial populations: acidogenic and methanogenic bacteria. It forms the basis for the design of a software sensor considering both a model of the biological system and on-line gaseous measurements. The software sensor computes the concentration of inorganic carbon and volatile fatty acids (VFA) in the digester. Another software sensor is dedicated to the estimation of the bacterial biomasses. The predictions of the software sensors for a real experiment are very close to the actual off-line measurements. The software sensors monitor the accumulation of VFA and thus very early detect a destabilization of the digester due to overloading. The presented methodology demonstrates the usefulness of advanced monitoring techniques for an improved understanding of the internal working of a biological system.