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Energy efficiency evaluation in ethylene production process with respect to operation classification

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  • Gong, Shixin
  • Shao, Cheng
  • Zhu, Li

Abstract

It is significant to increase energy efficiency of ethylene production process for petrochemical enterprise, in terms of the production level and productive benefits. But it is noticed from the actual production data that the energy efficiency of ethylene production has a strong relationship with the complex production conditions. It is necessary to combine the ethylene production states analysis with energy efficiency evaluation and improvement. With regard to the efficiency evaluation methods, data envelopment analysis (DEA) concentrate on a single working condition mode and fails to take into account the complicated working conditions. Therefore, a new energy efficiency evaluation method is presented with respect to operation classification. First, the typical working conditions of the ethylene production are determined corresponding to the key factors, including crude material composition and cracking depth, and the working conditions of production data are classified by k-means clustering algorithm. On the basis of the multiple working conditions, DEA is used to evaluate the performance of decision making units (DMUs) for different working conditions respectively. In addition, the advice on energy new allocation is suggested to the operators. Finally, the accuracy and effectiveness of the proposed method are illustrated by applying in a practical ethylene production.

Suggested Citation

  • Gong, Shixin & Shao, Cheng & Zhu, Li, 2017. "Energy efficiency evaluation in ethylene production process with respect to operation classification," Energy, Elsevier, vol. 118(C), pages 1370-1379.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:1370-1379
    DOI: 10.1016/j.energy.2016.11.012
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    1. Gong, Shixin, 2023. "Multi-scale energy efficiency recognition and diagnosis scheme for ethylene production based on a hierarchical multi-indicator system," Energy, Elsevier, vol. 267(C).
    2. Yu, Kunjie & While, Lyndon & Reynolds, Mark & Wang, Xin & Liang, J.J. & Zhao, Liang & Wang, Zhenlei, 2018. "Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization," Energy, Elsevier, vol. 148(C), pages 469-481.
    3. 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).
    4. Zhu, Li & Li, Zhe & Chen, Junghui, 2021. "Evaluating and predicting energy efficiency using slow feature partial least squares method for large-scale chemical plants," Energy, Elsevier, vol. 230(C).
    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).

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