On-line steam production prediction for a municipal solid waste incinerator by fully tuned minimal RBF neural networks

The paper describes an on-line prediction algorithm able to estimate, over a specified time horizon, the steam production of a municipal solid waste incinerator. The algorithm has to work on-line due to the heavy disturbances acting on the incineration process. The learning algorithm is based on radial basis function networks and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks.