IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v213y2018icp322-333.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918300473
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.01.046?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. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    2. Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
    3. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    4. Wade D. Cook & Joe Zhu, 2015. "DEA Cross Efficiency," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 2, pages 23-43, Springer.
    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. Han, Yongming & Geng, Zhiqiang & Zhu, Qunxiong & Qu, Yixin, 2015. "Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry," Energy, Elsevier, vol. 83(C), pages 685-695.
    7. Ren, Tao & Patel, Martin & Blok, Kornelis, 2006. "Olefins from conventional and heavy feedstocks: Energy use in steam cracking and alternative processes," Energy, Elsevier, vol. 31(4), pages 425-451.
    8. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    9. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    10. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    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, 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. 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.
    3. 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.
    4. 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).
    5. 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.
    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).

    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. 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.
    2. Deng, Yuanwang & Liu, Huawei & Zhao, Xiaohuan & E, Jiaqiang & Chen, Jianmei, 2018. "Effects of cold start control strategy on cold start performance of the diesel engine based on a comprehensive preheat diesel engine model," Applied Energy, Elsevier, vol. 210(C), pages 279-287.
    3. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, vol. 11(8), pages 1-21, August.
    4. Shermeh, H. Ebrahimzadeh & Najafi, S.E. & Alavidoost, M.H., 2016. "A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies," Energy, Elsevier, vol. 112(C), pages 686-697.
    5. Bian, Yiwen & Hu, Miao & Wang, Yousen & Xu, Hao, 2016. "Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 990-998.
    6. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    7. Tao Xu & Jianxin You & Hui Li & Luning Shao, 2020. "Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review," Energies, MDPI, vol. 13(14), pages 1-20, July.
    8. Yuan, Qianqian & Fang Chin Cheng, Charles & Wang, Jiayu & Zhu, Tian-Tian & Wang, Ke, 2020. "Inclusive and sustainable industrial development in China: An efficiency-based analysis for current status and improving potentials," Applied Energy, Elsevier, vol. 268(C).
    9. Ding, Li-Li & Lei, Liang & Zhao, Xin & Calin, Adrian Cantemir, 2020. "Modelling energy and carbon emission performance: A constrained performance index measure," Energy, Elsevier, vol. 197(C).
    10. Hosseini, Keyvan & Stefaniec, Agnieszka, 2019. "Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure," Energy, Elsevier, vol. 189(C).
    11. He, Yan-Lin & Wang, Ping-Jiang & Zhang, Ming-Qing & Zhu, Qun-Xiong & Xu, Yuan, 2018. "A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry," Energy, Elsevier, vol. 147(C), pages 418-427.
    12. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali & Toloo, Mehdi & Ghazizadeh, Mohammad Sadegh, 2016. "Eco-efficiency considering the issue of heterogeneity among power plants," Energy, Elsevier, vol. 111(C), pages 722-735.
    13. Roychaudhuri, Pritam Sankar & Kazantzi, Vasiliki & Foo, Dominic C.Y. & Tan, Raymond R. & Bandyopadhyay, Santanu, 2017. "Selection of energy conservation projects through Financial Pinch Analysis," Energy, Elsevier, vol. 138(C), pages 602-615.
    14. Xu, Xin & Cui, Qiang, 2017. "Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure," Energy, Elsevier, vol. 122(C), pages 274-286.
    15. 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.
    16. Bühler, Fabian & Guminski, Andrej & Gruber, Anna & Nguyen, Tuong-Van & von Roon, Serafin & Elmegaard, Brian, 2018. "Evaluation of energy saving potentials, costs and uncertainties in the chemical industry in Germany," Applied Energy, Elsevier, vol. 228(C), pages 2037-2049.
    17. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    18. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(C).
    19. De Clercq, Djavan & Wen, Zongguo & Caicedo, Luis & Cao, Xin & Fan, Fei & Xu, Ruifei, 2017. "Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China," Applied Energy, Elsevier, vol. 205(C), pages 1231-1243.
    20. Jindal, Abhinav & Nilakantan, Rahul, 2021. "Falling efficiency levels of Indian coal-fired power plants: A slacks-based analysis," Energy Economics, Elsevier, vol. 93(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:appene:v:213:y:2018:i:c:p:322-333. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.