Neural network identification for biomass gasification considering inputs

This paper presents a neural network application to identify a kinetic model for the char reduction zone of a solid fuel gasification process, including input signals. The considered model consists of six differential equations which represent the production of six components (carbon, hydrogen, carbon monoxide, water, carbon dioxide and methane) and are obtained from reaction rate equations of the four main reactions in the char reduction zone of a fluidized bed gasifier. On the other hand, the identification presented in this work is based on a discrete-time high order neural network (RHONN), which is trained with an extended Kalman filter (EKF) algorithm. The objective is to reproduce with the neural network the different components production under various operating conditions. The neural identifier performance is illustrated via simulation.