Modeling and analysis of SCR denitration system in power plant based on ensemble of extreme learning machine
Aiming at the problem that the selective catalytic reduction(SCR)denitration reaction commonly used in thermal power units is easily affected by environmental factors,and has the characteristics of nonlinearity,large delay and strong disturbance,so it is difficult to establish an accurate reaction model,a modeling method of SCR denitration system based on ensemble of extreme learning machine is proposed.Firstly,four kinds of extreme learning machines and kernel extreme learning machines with different activation functions are selected as the base learners to establish the SCR denitration system model respectively,and then the results of each base learner are integrated by using the partial least squares algorithm.Finally,the SCR denitration system model is established by combining the actual operation data of a 1 000 MW ultra supercritical unit with the proposed modeling method.The experimental results verify the effectiveness of the model.Compared with other modeling methods,the results show that the SCR denitration system model based on integrated limit learning machine has better model generalization ability.
thermal power unitselective catalytic reductionextreme learning machineensemble
NETL
NSTL
万方数据
维普
