Abstract

Estimation of power plant fuel input fractions based on unscented Kalman filtering using a first principles simulation model of the furnace is considered. The approach is described, together with experimental results using data from a full scale circulating fluidized bed power plant. The results encourage the fusion of machine learning and physical models in monitoring of industrial processes.