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Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes

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

Abstract

One of the key issues of the sustainable development in all countries is industrial productivity improvement, especially the production capacity improvement and energy-saving of complex chemical processes. Therefore, this paper proposes a production prediction and energy-saving model based on Extreme Learning Machine (ELM) integrated Interpretative Structural Modeling (ISM) and Analytic Hierarchy Process (AHP). The factors that affect the productivity are divided into different levers by the ISM. And then the attributes of each layer are fused by the AHP based on the entropy weight, which greatly reduces the complexity of the input attributes. Moreover, the production prediction and energy-saving model is established based on the ELM. Compared with the traditional ELM, the validity and the practicability of the proposed method are verified by University of California Irvine (UCI) datasets. Finally, the proposed method is applied in the production capacity prediction and energy-saving of ethylene production systems and Purified Terephthalic Acid (PTA) production systems. The experimental results show the proposed method could reduce the number of hidden layer nodes and improve the training time of the ELM. Furthermore, the prediction accuracy of the ethylene production and the PTA production reaches about 99% to improve the energy efficiency of complex chemical processes.

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  • 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.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:898-909
    DOI: 10.1016/j.energy.2018.07.077
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    1. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
    2. Ren, Jingzheng & Tan, Shiyu & Goodsite, Michael Evan & Sovacool, Benjamin K. & Dong, Lichun, 2015. "Sustainability, shale gas, and energy transition in China: Assessing barriers and prioritizing strategic measures," Energy, Elsevier, vol. 84(C), pages 551-562.
    3. Haddad, Brahim & Liazid, Abdelkrim & Ferreira, Paula, 2017. "A multi-criteria approach to rank renewables for the Algerian electricity system," Renewable Energy, Elsevier, vol. 107(C), pages 462-472.
    4. Sindhu, Sonal & Nehra, Vijay & Luthra, Sunil, 2017. "Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 496-511.
    5. Geng, ZhiQiang & Qin, Lin & Han, YongMing & Zhu, QunXiong, 2017. "Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP," Energy, Elsevier, vol. 122(C), pages 350-362.
    6. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    7. 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.
    8. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
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    Cited by:

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    4. 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).
    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. Won, Jonghan & Baek, Seung Wook & Kim, Hyemin, 2018. "Autoignition and combustion behavior of emulsion droplet under elevated temperature and pressure conditions," Energy, Elsevier, vol. 163(C), pages 800-810.
    7. 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).
    8. 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).
    9. Han, Yongming & Du, Zilan & Hu, Xuan & Li, Yeqing & Cai, Di & Fan, Jinzhen & Geng, Zhiqiang, 2023. "Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE-LSTM," Applied Energy, Elsevier, vol. 352(C).

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