IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v160y2018icp898-909.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218313744
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.07.077?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    2. 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.
    3. Cui, Ye & E, Hanyu & Pedrycz, Witold & Fayek, Aminah Robinson, 2022. "A granular multicriteria group decision making for renewable energy planning problems," Renewable Energy, Elsevier, vol. 199(C), pages 1047-1059.
    4. 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.
    5. 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).
    6. Zhao, Bin & Ren, Yi & Gao, Diankui & Xu, Lizhi & Zhang, Yuanyuan, 2019. "Energy utilization efficiency evaluation model of refining unit Based on Contourlet neural network optimized by improved grey optimization algorithm," Energy, Elsevier, vol. 185(C), pages 1032-1044.
    7. 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).
    8. 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).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naeini, Mina Alavi & Zandieh, Mostafa & Najafi, Seyyed Esmaeil & Sajadi, Seyed Mojtaba, 2020. "Analyzing the development of the third-generation biodiesel production from microalgae by a novel hybrid decision-making method: The case of Iran," Energy, Elsevier, vol. 195(C).
    2. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    3. 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).
    4. 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).
    5. Konstantinos Ioannou & Georgios Tsantopoulos & Garyfallos Arabatzis & Zacharoula Andreopoulou & Eleni Zafeiriou, 2018. "A Spatial Decision Support System Framework for the Evaluation of Biomass Energy Production Locations: Case Study in the Regional Unit of Drama, Greece," Sustainability, MDPI, vol. 10(2), pages 1-22, February.
    6. 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.
    7. BumChoong Kim & Juhan Kim & Jinsoo Kim, 2019. "Evaluation Model for Investment in Solar Photovoltaic Power Generation Using Fuzzy Analytic Hierarchy Process," Sustainability, MDPI, vol. 11(10), pages 1-23, May.
    8. Shuang Yao & Donghua Yu & Yan Song & Hao Yao & Yuzhen Hu & Benhai Guo, 2018. "Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain," Sustainability, MDPI, vol. 10(12), pages 1-19, November.
    9. Priom Mahmud & Sanjoy Kumar Paul & Abdullahil Azeem & Priyabrata Chowdhury, 2021. "Evaluating Supply Chain Collaboration Barriers in Small- and Medium-Sized Enterprises," Sustainability, MDPI, vol. 13(13), pages 1-28, July.
    10. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    11. Mohamed Ali Elleuch & Marwa Mallek & Ahmed Frikha & Wafik Hachicha & Awad M. Aljuaid & Murad Andejany, 2021. "Solving a Multiple User Energy Source Selection Problem Using a Fuzzy Multi-Criteria Group Decision-Making Approach," Energies, MDPI, vol. 14(14), pages 1-16, July.
    12. Mostafa Shaaban & Jürgen Scheffran & Jürgen Böhner & Mohamed S. Elsobki, 2018. "Sustainability Assessment of Electricity Generation Technologies in Egypt Using Multi-Criteria Decision Analysis," Energies, MDPI, vol. 11(5), pages 1-25, May.
    13. Finn, Thomas & McKenzie, Paul, 2020. "A high-resolution suitability index for solar farm location in complex landscapes," Renewable Energy, Elsevier, vol. 158(C), pages 520-533.
    14. Sward, Jeffrey A. & Nilson, Roberta S. & Katkar, Venktesh V. & Stedman, Richard C. & Kay, David L. & Ifft, Jennifer E. & Zhang, K. Max, 2021. "Integrating social considerations in multicriteria decision analysis for utility-scale solar photovoltaic siting," Applied Energy, Elsevier, vol. 288(C).
    15. Desantes, J.M. & García-Oliver, J.M. & Vera-Tudela, W. & López-Pintor, D. & Schneider, B. & Boulouchos, K., 2016. "Study of the auto-ignition phenomenon of PRFs under HCCI conditions in a RCEM by means of spectroscopy," Applied Energy, Elsevier, vol. 179(C), pages 389-400.
    16. Alkan, Ömer & Albayrak, Özlem Karadağ, 2020. "Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA," Renewable Energy, Elsevier, vol. 162(C), pages 712-726.
    17. He, Chao & Zhang, Quanguo & Ren, Jingzheng & Li, Zhaoling, 2017. "Combined cooling heating and power systems: Sustainability assessment under uncertainties," Energy, Elsevier, vol. 139(C), pages 755-766.
    18. Simona Stojanova & Nina Cvar & Jurij Verhovnik & Nataša Božić & Jure Trilar & Andrej Kos & Emilija Stojmenova Duh, 2022. "Rural Digital Innovation Hubs as a Paradigm for Sustainable Business Models in Europe’s Rural Areas," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    19. Geng, ZhiQiang & Dong, JunGen & Han, YongMing & Zhu, QunXiong, 2017. "Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes," Applied Energy, Elsevier, vol. 205(C), pages 465-476.
    20. Dorokhov, V.V. & Kuznetsov, G.V. & Vershinina, K.Yu. & Strizhak, P.A., 2021. "Relative energy efficiency indicators calculated for high-moisture waste-based fuel blends using multiple-criteria decision-making," Energy, Elsevier, vol. 234(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:160:y:2018:i:c:p:898-909. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.