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Prediction Method of NOx from Power Station Boilers Based on Neural Network

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成果类型:
期刊论文
作者:
Zhang, Yuzuo;Li, Yuanhao;Zhang, Xinyan;Zheng, Shijue
通讯作者:
Zhang, Yuzuo(2597117537@qq.com)
作者机构:
[Zhang, Yuzuo; Zheng, Shijue] Cent China Normal Univ, Dept Comp Sci & Technol, Wuhan 430079, Peoples R China.
[Zhang, Xinyan; Li, Yuanhao] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China.
语种:
英文
关键词:
Ammonia;Boilers;Cleaning;Errors;Forecasting;Learning systems;Multilayer neural networks;Nitrogen oxides;Power plants;Predictive analytics;Recurrent neural networks;Statistical tests;Average prediction error;Data standardization;Multi layer perceptron;Numerical processing;Power generation systems;Power station boiler;Prediction methods;Variable operating condition;Learning algorithms
期刊:
Journal of Circuits, Systems and Computers
ISSN:
0218-1266
年:
2021
卷:
30
期:
06
页码:
2150097:1-2150097:28
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61821003, 61832007, 61772222, U1705261]; Major Project of National Science and Technology of China [2017ZX01032-101]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [2019kfyXMBZ037]
机构署名:
本校为第一机构
院系归属:
计算机学院
摘要:
In the coal-fired power generation system, it is necessary to predict the NOx emissions of power station boilers when it comes to the step to spray ammonia to ensure that NOx emissions do not exceed national standards. Using traditional machine learning algorithms in the modeling of power station boilers will require features selection and steady-state extraction, which is not suitable for practical applications. In order to reduce the NOx prediction error rate under variable operating conditions, a multi-model fusion algorithm S3LX combined wi...

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