期刊
ENERGY
卷 190, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2019.116482
关键词
Selective catalytic reduction; NOx sequence prediction; Sequence to sequence model; Long short-term memory
资金
- National Key R&D Program of China [2016YFB0600205]
- Fundamental Research Funds for the Central Universities, China [2019MS019]
As environmental protection policies become more stringent, lower and lower NOx emission targets are required. Accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. This study aims to accurately predict the future sequence of NOx emission in the next horizon. Through the analysis on formation mechanism of NOx and the reaction mechanism of SCR reactor, a sequence to sequence dynamic prediction model is proposed, which can fit multivariable coupling, nonlinear and large delay systems. In particular, considering the different effects of multivariate on NOx, a new attention mechanism is necessary to be put forward. A large amount of historical data is used to fully train this dynamic prediction model. The results show that, the prediction accuracy of the NOx concentration and fluctuation trend based on this model is superior to comparison algorithms. Furthermore, some interesting features of this prediction model, such as error accumulation and bidirectional encoder, are also discussed in depth. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据