IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v334y2018icp214-226.html
   My bibliography  Save this article

A modified online sequential extreme learning machine for building circulation fluidized bed boiler's NOx emission model

Author

Listed:
  • Ma, Yunpeng
  • Niu, Peifeng
  • Yan, Shanshan
  • Li, Guoqiang

Abstract

In the last decade, the online sequential extreme learning machine (OS-ELM) has become an effective online modeling tool for the regression problem and time series prediction areas. However, the random initialization input-weights remain unchanged when the new large testing data arrive, which maybe reduce its training accuracy and generalization ability gradually. In this paper, based on the conventional OS-ELM, a kind of input data sample increment online sequential extreme learning machine is proposed, namely SIOS-ELM. As its name suggests, the sample increment is the actual error value between the present training input data sample and the new arriving input data sample. In SIOS-ELM, the parameters of hidden layer nodes (the input-weights and threshold values of hidden layer) are calculated in real time based on the sample increment by twice least square method when the new input data arrives one by one or chunk by chunk. In addition, the output weights are adjusted online as the OS-ELM. Compared with OS-ELM and its variants on benchmark problems, the proposed SIOS-ELM possesses better model accuracy and generalization ability. Additionally, the SIOS-ELM is applied to build the NOx emissions model of one 330 MW circulating fluidized bed boiler. The experiment result reveals that the SIOS-ELM is an effective online machine learning tool.

Suggested Citation

  • Ma, Yunpeng & Niu, Peifeng & Yan, Shanshan & Li, Guoqiang, 2018. "A modified online sequential extreme learning machine for building circulation fluidized bed boiler's NOx emission model," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 214-226.
    • Handle: RePEc:eee:apmaco:v:334:y:2018:i:c:p:214-226
    DOI: 10.1016/j.amc.2018.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300318301759
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2018.03.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Taotao & Tang, Peng & Ye, Taohong, 2023. "Machine learning based heat release rate indicator of premixed methane/air flame under wide range of equivalence ratio," Energy, Elsevier, vol. 263(PE).
    2. Yunpeng Ma & Chenheng Xu & Hua Wang & Ran Wang & Shilin Liu & Xiaoying Gu, 2022. "Model NOx, SO 2 Emissions Concentration and Thermal Efficiency of CFBB Based on a Hyper-Parameter Self-Optimized Broad Learning System," Energies, MDPI, vol. 15(20), pages 1-19, October.
    3. Yuansheng, Huang & Mengshu, Shi, 2021. "What are the environmental advantages of circulating fluidized bed technology? ——A case study in China," Energy, Elsevier, vol. 220(C).
    4. Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
    5. Grochowalski, Jaroslaw & Jachymek, Piotr & Andrzejczyk, Marek & Klajny, Marcin & Widuch, Agata & Morkisz, Pawel & Hernik, Bartłomiej & Zdeb, Janusz & Adamczyk, Wojciech, 2021. "Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions," Energy, Elsevier, vol. 237(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    2. Zhao, Yi & Wang, Shuqin & Shen, Yanmei & Lu, Xiaojuan, 2013. "Effects of nano-TiO2 on combustion and desulfurization," Energy, Elsevier, vol. 56(C), pages 25-30.
    3. Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
    4. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    5. Yun Chen & Chengwei Liang & Dengcheng Liu & Qingren Niu & Xinke Miao & Guangyu Dong & Liguang Li & Shanbin Liao & Xiaoci Ni & Xiaobo Huang, 2022. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction," Energies, MDPI, vol. 16(1), pages 1-20, December.
    6. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    7. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
    8. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    9. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
    10. Wang, Yingnan & Chen, Xu & Zhao, Chunhui, 2024. "A data-driven soft sensor model for coal-fired boiler SO2 concentration prediction with non-stationary characteristic," Energy, Elsevier, vol. 300(C).
    11. Wen, Xiaoqiang & Li, Kaichuang & Wang, Jianguo, 2023. "NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners," Energy, Elsevier, vol. 264(C).
    12. Halil Akbaş & Gültekin Özdemir, 2020. "An Integrated Prediction and Optimization Model of a Thermal Energy Production System in a Factory Producing Furniture Components," Energies, MDPI, vol. 13(22), pages 1-29, November.
    13. Liukkonen, Mika & Hälikkä, Eero & Hiltunen, Teri & Hiltunen, Yrjö, 2012. "Dynamic soft sensors for NOx emissions in a circulating fluidized bed boiler," Applied Energy, Elsevier, vol. 97(C), pages 483-490.
    14. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    15. Xu, Yingjie & Mao, Chengbin & Huang, Yuangong & Shen, Xi & Xu, Xiaoxiao & Chen, Guangming, 2021. "Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis," Energy, Elsevier, vol. 216(C).
    16. Wang, Chunlin & Liu, Yang & Zheng, Song & Jiang, Aipeng, 2018. "Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process," Energy, Elsevier, vol. 153(C), pages 149-158.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:334:y:2018:i:c:p:214-226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.