Centralized anaerobic digestion bioprocess plant identification and direct I-term neural control using second order learning

The paper proposed to use a recurrent neural network model, and a real-time Levenberg-Marquardt algorithm of its learning for centralized modeling, identification and I-term control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator, represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points plus one- in the recirculation tank. The paper proposed to use centralized direct I-term adaptive neural control based on centralized neural identification of the plant. The comparative graphical simulation results of the digestion wastewater treatment system identification and control, exhibited a good convergence and precise reference tracking, giving slight priority to the direct control with respect to the optimal control applied.