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

作者：Li, Xia; Niu, Peifeng^*; Li, Guoqiang; Liu, Jianping

来源：Neural Processing Letters, 2017, 46(2): 643-662.

DOI：10.1007/s11063-017-9611-9

• Extreme learning machine
• Relaxation factor
• Teaching-learning-based optimization
• NOx emission
• 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. 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.

• 出版日期2017-10
• 单位燕山大学; 河北科技师范学院