Least Square Parallel Extreme Learning Machine for Modeling NOx Emission of a 300MW Circulating Fluidized Bed Boiler

It is very important to establish an accurate combustion characteristics model of a boiler to reduce NOx emission. In this paper, a novel least square parallel extreme learning machine (LSPELM) is firstly proposed, all of whose weights and thresholds are determined by using least square method twice. Then, LSPELM is applied to 11 classical regression problems to test the validity. The experimental results show that, compared with other methods, LSPELM with a few hidden neurons can achieve good generalization and stability. Next, using Moore-Penrose generalized inverse theory and Woodbury formula, an online learning way of LSPELM (OLSPELM) based on sample increment is also proposed. If the samples of the current time are the same as those of the last time, the weights and thresholds of OLSPELM remain unchanged and are not updated. Only when the input samples of two times are different, can the weights and thresholds of OLSPELM be updated adaptively. Finally, LSPELM and OLSPELM are employed to successfully establish offline and online models of NOx emission concentration for a 300WM circulating fluidized bed boiler. The simulation results also show that LSPELM and OLSPELM have better nonlinear generalization ability and stability performance than some other state-of-the-art models. So, the proposed LSPELM and OLSPELM have good application value.