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Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry

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  • Zhu, Qun-Xiong
  • Zhang, Chen
  • He, Yan-Lin
  • Xu, Yuan

Abstract

Comprehensive energy modeling and saving potential analysis play a key role in sustainable development of complex petrochemical industry. However, it is difficult to make effective energy modeling and saving potential analysis due to the characteristics of uncertainty, high nonlinearity, and with noise of the data collected from the practical production. To deal with this problem, an energy modeling and saving potential analysis method using a novel extreme learning fuzzy logic network is proposed. In the proposed method, Mamdani type fuzzy inference system and multi-layer feedforward artificial neural network are integrated. First, the original ethylene production data is fused into a comprehensive energy consumption index. Then the index is fuzzified as outputs instead of precise values. Finally, an extreme learning algorithm based on Moore-Penrose Inverse is utilized to train the network efficiently. Three levels of energy efficiency of “low efficiency, median efficiency and high efficiency” can be effectively classified using the proposed method. For “low efficiency”, valid slack variables are predicted for finding the direction of improving energy efficiency and then analyzing the energy saving potential. The performance and the practicality of the proposed method are confirmed through an application of China ethylene industry. Simulation results show that low-efficiency samples can be effectively improved to be high-efficiency samples and the energy saving potential in terms of the crude oil reduction amount is indicted as 8.82%.

Suggested Citation

  • Zhu, Qun-Xiong & Zhang, Chen & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry," Applied Energy, Elsevier, vol. 213(C), pages 322-333.
    • Handle: RePEc:eee:appene:v:213:y:2018:i:c:p:322-333
    DOI: 10.1016/j.apenergy.2018.01.046
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
    2. Yang, Dan & Peng, Xin & Ye, Zhencheng & Lu, Yusheng & Zhong, Weimin, 2021. "Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes," Applied Energy, Elsevier, vol. 303(C).
    3. Gong, Shixin & Shao, Cheng & Zhu, Li, 2019. "Multi-level and multi-granularity energy efficiency diagnosis scheme for ethylene production process," Energy, Elsevier, vol. 170(C), pages 1151-1169.
    4. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
    5. 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.
    6. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    7. Meng, Di & Shao, Cheng & Zhu, Li, 2022. "Two-level comprehensive energy-efficiency quantitative diagnosis scheme for ethylene-cracking furnace with multi-working-condition of fault and exception operation," Energy, Elsevier, vol. 239(PA).
    8. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
    9. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).

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