Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction

被引：99

作者：

Tang, Zhenhao ^{[1
]}

Wang, Shikui ^{[1
]}

Chai, Xiangying ^{[1
]}

Cao, Shengxian ^{[1
]}

Ouyang, Tinghui ^{[2
]}

Li, Yang ^{[3
]}

机构：

[1] Northeast Elect Power Univ, Coll Automat Engn, Jilin, Peoples R China

[2] Natl Inst Adv Ind Sci & Technol, Artificial Intelligence Res Ctr, Tokyo, Japan

[3] Northeast Elect Power Univ, Sch Elect Engn, Jilin, Peoples R China

来源：

ENERGY | 2022年 / 256卷

关键词：

Autoencoder; Extreme learning machine; Deep learning; NOx emission concentration prediction; COAL-FIRED BOILERS; COMBUSTION; OPTIMIZATION; EFFICIENCY; REGRESSION;

D O I：

10.1016/j.energy.2022.124552

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission con-centration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models.(c) 2022 Elsevier Ltd. All rights reserved.

引用

页数：10

共 34 条

[1] Prediction of SOx-NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine
Adams, Derrick
Oh, Dong-Hoon
Kim, Dong-Won
Lee, Chang-Ha
Oh, Min
[J]. JOURNAL OF CLEANER PRODUCTION, 2020, 270
[2] Prediction of unburned carbon and NOx in a tangentially fired power station using single coals and blends
Backreedy, RI
Jones, JM
Ma, L
Pourkashanian, M
Williams, A
Arenillas, A
Arias, B
Pis, JJ
Rubiera, F
[J]. FUEL, 2005, 84 (17) : 2196 - 2203
[3] Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems
Bostani, Hamid
Sheikhan, Mansour
[J]. SOFT COMPUTING, 2017, 21 (09) : 2307 - 2324
[4] CFD modeling of hydrodynamics, combustion and NOx emission in a tangentially fired pulverized-coal boiler at low load operating conditions
Chang, Jian
Wang, Xin
Zhou, Zhijian
Chen, Honggang
Niu, Yuguang
[J]. ADVANCED POWDER TECHNOLOGY, 2021, 32 (02) : 290 - 303
[5] Feature fusions for 2.5D face recognition in Random Maxout Extreme Learning Machine
Chong, Lee Ying
Ong, Thian Song
Teoh, Andrew Beng Jin
[J]. APPLIED SOFT COMPUTING, 2019, 75 : 358 - 372
[6] Nox emission estimation in gas turbines via interpretable neural network observer with adjustable intermediate layer considering ambient and boundary conditions
Huang, Dawen
Tang, Shanhua
Zhou, Dengji
Hao, Jiarui
[J]. MEASUREMENT, 2022, 189
[7] Extreme learning machine: Theory and applications
Huang, Guang-Bin
Zhu, Qin-Yu
Siew, Chee-Kheong
[J]. NEUROCOMPUTING, 2006, 70 (1-3) : 489 - 501
[8] Extreme Learning Machine for Regression and Multiclass Classification
Huang, Guang-Bin
Zhou, Hongming
Ding, Xiaojian
Zhang, Rui
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2012, 42 (02): : 513 - 529
[9] Optimization method based extreme learning machine for classification
Huang, Guang-Bin
Ding, Xiaojian
Zhou, Hongming
[J]. NEUROCOMPUTING, 2010, 74 (1-3) : 155 - 163
[10] Climate change and CCS increase the water vulnerability of China's thermoelectric power fleet
Jin, Yi
Scherer, Laura
Sutanudjaja, Edwin H.
Tukker, Arnold
Behrens, Paul
[J]. ENERGY, 2022, 245

← 1 2 3 4 →