Centralized neural identification and control of an anaerobic digestion bioprocess

The paper proposed to use a Recurrent Neural Network Model (RNNM), and a dynamic backpropagation algorithm of its learning for centralized modeling, identification and direct adaptive 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 three collocation points plus the recirculation tank. The graphical simulation results of the digestion wastewater treatment system direct adaptive neural control, exhibited a good convergence and precise reference tracking, outperforming the optimal control.