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

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  • 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
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    References listed on IDEAS

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    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.
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    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).

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