Hybrid neural modelling of anaerobic wastewater treatment processes

This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feed-forward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data

Advanced monitoring and control of anaerobic treatment plants: I – Survey and process description

The development of an advanced monitoring and control system for industrial scale anaerobic treatment of wood processing factories’ wastewaters was the aim of a Research Project (AMOCO) developed by a number of groups (Belgium, France, Portugal and Spain) during the last three years. In this paper, the characteristics of the wastewater treatment plants used in this project, as well as their instrumentation are described. Furthermore, the experimental protocol adopted by the partners to obtain information for developing control tools (based on mathematical models, fuzzy models or heuristic rules) and the main results of their application to the treatment processes are presented