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A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems

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  • Geng, Zhiqiang
  • Li, Yanan
  • Han, Yongming
  • Zhu, Qunxiong

Abstract

Single layer feed-forward network (SLFN) is well applied to find mapping relationships between the input data and the output data. However, the SLFN has two obvious shortcomings of the indetermination structure and parameters. Therefore, this paper proposes a novel self-organizing cosine similarity learning network (SO-CSLN), which can obtain a stable structure and suitable parameters. The hidden layer nodes of the SO-CSLN are determined by the rank of the sample covariance matrix based on the central limit theorem. And then the weights are obtained by the entropy theory and the cosine similarity theory. Moreover, compared with the SLFN, the proposed algorithm can overcome the shortcomings of the SLFN and provide better performance with faster convergence and smaller generalization error through different UCI data sets. Finally, the proposed method is applied to building the production prediction model of the ethylene production system in petrochemical industries. The experiment results show that the effectiveness and the practicality of the proposed method. Meanwhile, it can guide ethylene production and improve the energy efficiency.

Suggested Citation

  • Geng, Zhiqiang & Li, Yanan & Han, Yongming & Zhu, Qunxiong, 2018. "A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems," Energy, Elsevier, vol. 142(C), pages 400-410.
  • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:400-410
    DOI: 10.1016/j.energy.2017.10.017
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    Cited by:

    1. Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
    2. Alexander Kramer & Fernando Morgado‐Dias, 2020. "Artificial intelligence in process control applications and energy saving: a review and outlook," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(6), pages 1133-1150, December.
    3. Mafakheri, Aso & Sulaimany, Sadegh & Mohammadi, Sara, 2023. "Predicting the establishment and removal of global trade relations for import and export of petrochemical products," Energy, Elsevier, vol. 269(C).
    4. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    5. Geng, Zhiqiang & Zhang, Yanhui & Li, Chengfei & Han, Yongming & Cui, Yunfei & Yu, Bin, 2020. "Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature," Energy, Elsevier, vol. 194(C).

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