Decentralized fuzzy-neural identification and i-term adaptive control of distributed parameter bioprocess plant

Abstract

The chapter proposed to use of a Recurrent Neural Network Model (RNNM) incorporated in a fuzzy-neural multi model for decentralized identification of an aerobic digestion process, carried out in a fixed bed and a recirculation tank anaerobic wastewater treatment system. The analytical model of the digestion bioprocess represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points. The proposed decentralized RNNM consists of five independently working Recurrent Neural Networks (RNN), so to approximate the process dynamics in four different measurement points plus the recirculation tank. The RNN learning algorithm is the second order Levenberg-Marquardt one. The comparative graphical simulation results of the digestion wastewater treatment system approximation, obtained via decentralized RNN learning, exhibited a good convergence, and precise plant variables tracking. The identification results are used for I-term direct and indirect (sliding mode) control obtaining good results.