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Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes

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  • Geng, Zhiqiang
  • Yang, Xiao
  • Han, Yongming
  • Zhu, Qunxiong

Abstract

Energy optimization and analysis of complex chemical processes play a significant role in the sustainable development procedure. In order to deal with the high-dimensional and noise data in complex chemical processes, we present an energy optimization and analysis method based on extreme learning machine integrating the index decomposition analysis. First, index decomposition analysis has been used to decompose the high-dimensional data to three energy performance indexes of the activity effect, the structure effect and the intensity. And then, those indexes and the production/conductivity of the chemical process are defined as inputs and outputs of the extreme learning machine respectively to build energy optimization and analysis model. Finally, the proposed method has been applied to optimizing and analyzing energy status of the ethylene system and the purified terephthalic acid solvent system in complex chemical processes. The experiment results show that the proposed method has the characteristics of fast learning, stable network outputs and high model accuracy in handling with the high-dimensional data. Moreover, it can optimize energy of chemical processes and guide the production operation. In our experiment, the production of ethylene plants can be increased by 5.33%, and the conductivity of purified terephthalic acid plants can be reduced by 0.046%.

Suggested Citation

  • Geng, Zhiqiang & Yang, Xiao & Han, Yongming & Zhu, Qunxiong, 2017. "Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes," Energy, Elsevier, vol. 120(C), pages 67-78.
    • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:67-78
    DOI: 10.1016/j.energy.2016.12.090
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    3. Najafi, Bahman & Akbarian, Eivaz & Lashkarpour, S. Mehdi & Aghbashlo, Mortaza & Ghaziaskar, Hassan S. & Tabatabaei, Meisam, 2019. "Modeling of a dual fueled diesel engine operated by a novel fuel containing glycerol triacetate additive and biodiesel using artificial neural network tuned by genetic algorithm to reduce engine emiss," Energy, Elsevier, vol. 168(C), pages 1128-1137.
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    5. 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).
    6. Geng, Zhiqiang & Li, Hongda & Zhu, Qunxiong & Han, Yongming, 2018. "Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes," Energy, Elsevier, vol. 160(C), pages 898-909.
    7. 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.
    8. 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).
    9. Azarpour, Abbas & Mohamadi-Baghmolaei, Mohamad & Hajizadeh, Abdollah & Zendehboudi, Sohrab, 2022. "Systematic energy and exergy assessment of a hydropurification process: Theoretical and practical insights," Energy, Elsevier, vol. 239(PC).
    10. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.

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