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Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries

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  • Geng, Zhiqiang
  • Zeng, Rongfu
  • Han, Yongming
  • Zhong, Yanhua
  • Fu, Hua

Abstract

Data envelopment analysis (DEA) has been widely used in the energy efficiency analysis of industrial production processes. However, the traditional DEA model is not high in the division of the efficiency value of decision making units (DMUs), and produces a large number of DMUs with an efficiency value equal to 1, making it difficult to identify their merits and demerits. Therefore, a novel DEA model based on the affinity propagation (AP) clustering algorithm (AP-DEA) is proposed. Through the AP clustering algorithm, high influence input data of the energy efficiency can be obtained. The merits and demerits of DMUs can then be identified with a high degree of discrimination to obtain better efficiency groups. Finally, the proposed model is applied to evaluate the energy efficiency and optimize the energy configuration of the ethylene and pure terephthalic acid (PTA) production processes in complex petrochemical industries. The experimental results show that this proposed model can improve the efficiency value discrimination of efficiency values by effective DMUs better than the traditional DEA. Moreover, the energy saving potentials of ethylene and PTA production systems are approximately 0.49% and 24.74%, respectively, and the carbon emission reduction of the ethylene production system is approximately 10.04%.

Suggested Citation

  • Geng, Zhiqiang & Zeng, Rongfu & Han, Yongming & Zhong, Yanhua & Fu, Hua, 2019. "Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries," Energy, Elsevier, vol. 179(C), pages 863-875.
  • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:863-875
    DOI: 10.1016/j.energy.2019.05.042
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    References listed on IDEAS

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    Cited by:

    1. Hongwei Liu & Ronglu Yang & Zhixiang Zhou & Dacheng Huang, 2020. "Regional Green Eco-Efficiency in China: Considering Energy Saving, Pollution Treatment, and External Environmental Heterogeneity," Sustainability, MDPI, Open Access Journal, vol. 12(17), pages 1-19, August.
    2. Ge Huang & Wei Pan & Cheng Hu & Wu-Lin Pan & Wan-Qiang Dai, 2021. "Energy Utilization Efficiency of China Considering Carbon Emissions—Based on Provincial Panel Data," Sustainability, MDPI, Open Access Journal, vol. 13(2), pages 1-14, January.
    3. Han, Yongming & Liu, Shuang & Geng, Zhiqiang & Gu, Hengchang & Qu, Yixin, 2021. "Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model," Energy, Elsevier, vol. 218(C).
    4. Yongyou Nie & Jinbu Zhao & Yiyi Zhang & Jizhi Zhou, 2020. "Risk Evaluation of “Not-In-My-Back-Yard” Conflict Potential in Facilities Group: A Case Study of Chemical Park in Xuwei New District, China," Sustainability, MDPI, Open Access Journal, vol. 12(7), pages 1-18, March.
    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. Wang, Jianyi & Qin, Weiwei & Zhu, Xixi & Teng, Yongqiang, 2020. "Covalent organic frameworks (COF)/CNT nanocomposite for high performance and wide operating temperature lithium–sulfur batteries," Energy, Elsevier, vol. 199(C).

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