Neural control for NOx emissions in a fluidized bed sludge combustor

In this paper, the authors propose a discrete-time neural control scheme to regulate nitrogen oxides (NOx) emissions for a fluidized bed sludge combustor. This scheme ensures carbon monoxide (CO) regulation without decreasing combustion efficiency. In order to obtain the sludge combustion model, it is proposed to use a recurrent high order neural network (RHONN) which is trained with an extended Kalman filter (EKF) algorithm. The proposed neural controler performance is illustrated via simulation.