An Adaptive Extreme Learning Machine for Modeling NOx Emission of a 300 MW Circulating Fluidized Bed Boiler

Extreme learning machine (ELM) provides high learning speed, but generalization performance needs to be further improved. Therefore, we propose an adaptive ELM with a relaxation factor \(\lambda \) (A-ELM). In A-ELM, according to the nonlinear degree of actual data, the output layer obtains adaptively \(1-\lambda \) rate information through the hidden layer and \(\lambda \) rate information through the input layer. Since the relaxation factor \(\lambda \) is bound up with the input weights and hidden biases of A-ELM, in order to obtain the optimal \(\lambda \), \(\lambda \), input weights and hidden biases are obtained together by teaching–learning-based optimization (A-ELM-TLBO). Then, 15 benchmark regression data sets verify the performance of A-ELM-TLBO. Finally, A-ELM-TLBO is applied to set up the mapping relation between NOx emission and operational conditions of a 300 MW circulating fluidized bed (CFB) boiler. Compared with six other models, experimental results show that A-ELM-TLBO has good approximation ability and generalization performance. So, A-ELM-TLBO provides a good basis for tuning CFB boiler operating parameters to reduce NOx emission.