Circular convolution parallel extreme learning machine for modeling boiler efficiency for a 300 MW CFBB

Abstract

Aiming at the accuracy prediction of combustion efficiency for a 300 MW circulating fluidized bed boiler (CFBB), a circular convolution parallel extreme learning machine (CCPELM) which is a double parallel forward neural network is proposed. In CCPELM, the circular convolution theory is introduced to map the hidden layer information into higher-dimension information; in addition, the input layer information is directly transmitted to its output layer, which makes the whole network into a double parallel construction. In this paper, CCPELM is applied to establish a model for boiler efficiency though data samples collected from a 300 MW CFBB. Some comparative simulation results with other neural network models show that CCPELM owns very high prediction accuracy with fast learning speed and very good repeatability in learning ability.