IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v144y2018icp851-864.html
   My bibliography  Save this article

Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system

Author

Listed:
  • Dong, Shengming
  • Zhang, Yufeng
  • He, Zhonglu
  • Deng, Na
  • Yu, Xiaohui
  • Yao, Sheng

Abstract

Low temperature power generation system based on organic Rankine cycle (ORC) has been a popular candidate for low grade heat utilization and recovery. To find a way to predict the performance of the ORC system, the exploration and analyses of the Support Vector Machine (SVM) and Back Propagation Artificial Neural Network (BP-ANN) were carried out. For comparison, both Gauss Radial Basis kernel function (SVM-RBF) and linear function (SVM-LF) have been employed in SVM. Additionally, for the sake of comprehensiveness, two division methods for data set called “random division method” and “blocked division method” were studied. Finally, SVM-LF and BP-ANN demonstrated better stability and higher accuracy for both two division methods and for different testing sets while SVM-RBF showed good results for random division method and disappointing results for blocked division method.

Suggested Citation

  • Dong, Shengming & Zhang, Yufeng & He, Zhonglu & Deng, Na & Yu, Xiaohui & Yao, Sheng, 2018. "Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system," Energy, Elsevier, vol. 144(C), pages 851-864.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:851-864
    DOI: 10.1016/j.energy.2017.12.094
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217321370
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.12.094?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. Song, Jian & Song, Yin & Gu, Chun-wei, 2015. "Thermodynamic analysis and performance optimization of an Organic Rankine Cycle (ORC) waste heat recovery system for marine diesel engines," Energy, Elsevier, vol. 82(C), pages 976-985.
    2. Oyeniyi A. Oyewunmi & Christos N. Markides, 2016. "Thermo-Economic and Heat Transfer Optimization of Working-Fluid Mixtures in a Low-Temperature Organic Rankine Cycle System," Energies, MDPI, vol. 9(6), pages 1-21, June.
    3. Shengjun, Zhang & Huaixin, Wang & Tao, Guo, 2011. "Performance comparison and parametric optimization of subcritical Organic Rankine Cycle (ORC) and transcritical power cycle system for low-temperature geothermal power generation," Applied Energy, Elsevier, vol. 88(8), pages 2740-2754, August.
    4. Yu, Haoshui & Feng, Xiao & Wang, Yufei, 2015. "A new pinch based method for simultaneous selection of working fluid and operating conditions in an ORC (Organic Rankine Cycle) recovering waste heat," Energy, Elsevier, vol. 90(P1), pages 36-46.
    5. Wang, E.H. & Zhang, H.G. & Fan, B.Y. & Ouyang, M.G. & Zhao, Y. & Mu, Q.H., 2011. "Study of working fluid selection of organic Rankine cycle (ORC) for engine waste heat recovery," Energy, Elsevier, vol. 36(5), pages 3406-3418.
    6. Madhawa Hettiarachchi, H.D. & Golubovic, Mihajlo & Worek, William M. & Ikegami, Yasuyuki, 2007. "Optimum design criteria for an Organic Rankine cycle using low-temperature geothermal heat sources," Energy, Elsevier, vol. 32(9), pages 1698-1706.
    7. Esen, Hikmet & Inalli, Mustafa & Sengur, Abdulkadir & Esen, Mehmet, 2008. "Modeling a ground-coupled heat pump system by a support vector machine," Renewable Energy, Elsevier, vol. 33(8), pages 1814-1823.
    8. Hong Gao & Chao Liu & Chao He & Xiaoxiao Xu & Shuangying Wu & Yourong Li, 2012. "Performance Analysis and Working Fluid Selection of a Supercritical Organic Rankine Cycle for Low Grade Waste Heat Recovery," Energies, MDPI, vol. 5(9), pages 1-15, August.
    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. Wang, Yongzhen & Li, Chengjun & Zhao, Jun & Wu, Boyuan & Du, Yanping & Zhang, Jing & Zhu, Yilin, 2021. "The above-ground strategies to approach the goal of geothermal power generation in China: State of art and future researches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Shiqi Wang & Zhongyuan Yuan, 2020. "A Hot Water Split-Flow Dual-Pressure Strategy to Improve System Performance for Organic Rankine Cycle," Energies, MDPI, vol. 13(13), pages 1-21, June.
    3. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Xing, Chengda & Yan, Yinlian & Yang, Anren & Wang, Yan, 2023. "Information theory-based dynamic feature capture and global multi-objective optimization approach for organic Rankine cycle (ORC) considering road environment," Applied Energy, Elsevier, vol. 348(C).
    4. Sun, Lei & Liu, Tianyuan & Wang, Ding & Huang, Chengming & Xie, Yonghui, 2022. "Deep learning method based on graph neural network for performance prediction of supercritical CO2 power systems," Applied Energy, Elsevier, vol. 324(C).
    5. 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.
    6. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Zhang, Wujie & Wang, Yan, 2022. "Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection," Applied Energy, Elsevier, vol. 311(C).
    7. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yao, Baofeng & Wang, Yan, 2022. "An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)," Energy, Elsevier, vol. 254(PB).
    8. Tian, Zhen & Gan, Wanlong & Zou, Xianzhi & Zhang, Yuan & Gao, Wenzhong, 2022. "Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 254(PB).

    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. Yang, Min-Hsiung & Yeh, Rong-Hua, 2015. "Thermo-economic optimization of an organic Rankine cycle system for large marine diesel engine waste heat recovery," Energy, Elsevier, vol. 82(C), pages 256-268.
    2. Cavazzini, G. & Bari, S. & Pavesi, G. & Ardizzon, G., 2017. "A multi-fluid PSO-based algorithm for the search of the best performance of sub-critical Organic Rankine Cycles," Energy, Elsevier, vol. 129(C), pages 42-58.
    3. Yang, Min-Hsiung & Yeh, Rong-Hua, 2016. "Economic performances optimization of an organic Rankine cycle system with lower global warming potential working fluids in geothermal application," Renewable Energy, Elsevier, vol. 85(C), pages 1201-1213.
    4. Lecompte, S. & Huisseune, H. & van den Broek, M. & De Schampheleire, S. & De Paepe, M., 2013. "Part load based thermo-economic optimization of the Organic Rankine Cycle (ORC) applied to a combined heat and power (CHP) system," Applied Energy, Elsevier, vol. 111(C), pages 871-881.
    5. Lecompte, S. & Huisseune, H. & van den Broek, M. & De Paepe, M., 2015. "Methodical thermodynamic analysis and regression models of organic Rankine cycle architectures for waste heat recovery," Energy, Elsevier, vol. 87(C), pages 60-76.
    6. Jung, Hyung-Chul & Taylor, Leighton & Krumdieck, Susan, 2015. "An experimental and modelling study of a 1 kW organic Rankine cycle unit with mixture working fluid," Energy, Elsevier, vol. 81(C), pages 601-614.
    7. Ibarra, Mercedes & Rovira, Antonio & Alarcón-Padilla, Diego-César & Blanco, Julián, 2014. "Performance of a 5kWe Organic Rankine Cycle at part-load operation," Applied Energy, Elsevier, vol. 120(C), pages 147-158.
    8. Yang, Fubin & Zhang, Hongguang & Song, Songsong & Bei, Chen & Wang, Hongjin & Wang, Enhua, 2015. "Thermoeconomic multi-objective optimization of an organic Rankine cycle for exhaust waste heat recovery of a diesel engine," Energy, Elsevier, vol. 93(P2), pages 2208-2228.
    9. Chintala, Venkateswarlu & Kumar, Suresh & Pandey, Jitendra K., 2018. "A technical review on waste heat recovery from compression ignition engines using organic Rankine cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 493-509.
    10. Bao, Junjiang & Zhao, Li, 2013. "A review of working fluid and expander selections for organic Rankine cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 325-342.
    11. Ziviani, Davide & Beyene, Asfaw & Venturini, Mauro, 2014. "Advances and challenges in ORC systems modeling for low grade thermal energy recovery," Applied Energy, Elsevier, vol. 121(C), pages 79-95.
    12. Le, Van Long & Feidt, Michel & Kheiri, Abdelhamid & Pelloux-Prayer, Sandrine, 2014. "Performance optimization of low-temperature power generation by supercritical ORCs (organic Rankine cycles) using low GWP (global warming potential) working fluids," Energy, Elsevier, vol. 67(C), pages 513-526.
    13. Habka, Muhsen & Ajib, Salman, 2014. "Investigation of novel, hybrid, geothermal-energized cogeneration plants based on organic Rankine cycle," Energy, Elsevier, vol. 70(C), pages 212-222.
    14. Habka, Muhsen & Ajib, Salman, 2015. "Evaluation of mixtures performances in Organic Rankine Cycle when utilizing the geothermal water with and without cogeneration," Applied Energy, Elsevier, vol. 154(C), pages 567-576.
    15. Hoang, Anh Tuan, 2018. "Waste heat recovery from diesel engines based on Organic Rankine Cycle," Applied Energy, Elsevier, vol. 231(C), pages 138-166.
    16. Kim, Kyeongsu & Lee, Ung & Kim, Changsoo & Han, Chonghun, 2015. "Design and optimization of cascade organic Rankine cycle for recovering cryogenic energy from liquefied natural gas using binary working fluid," Energy, Elsevier, vol. 88(C), pages 304-313.
    17. Li, Tailu & Zhu, Jialing & Hu, Kaiyong & Kang, Zhenhua & Zhang, Wei, 2014. "Implementation of PDORC (parallel double-evaporator organic Rankine cycle) to enhance power output in oilfield," Energy, Elsevier, vol. 68(C), pages 680-687.
    18. Huster, Wolfgang R. & Schweidtmann, Artur M. & Mitsos, Alexander, 2020. "Globally optimal working fluid mixture composition for geothermal power cycles," Energy, Elsevier, vol. 212(C).
    19. Sarkar, Jahar, 2015. "Analyses and optimization of a supercritical N2O Rankine cycle for low-grade heat conversion," Energy, Elsevier, vol. 81(C), pages 344-351.
    20. Subiantoro, Alison & Ooi, Kim Tiow, 2014. "Comparison and performance analysis of the novel revolving vane expander design variants in low and medium pressure applications," Energy, Elsevier, vol. 78(C), pages 747-757.

    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:144:y:2018:i:c:p:851-864. 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.