IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v254y2022ipbs0360544222009306.html
   My bibliography  Save this article

Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm

Author

Listed:
  • Tian, Zhen
  • Gan, Wanlong
  • Zou, Xianzhi
  • Zhang, Yuan
  • Gao, Wenzhong

Abstract

In this paper, a performance prediction model of the cryogenic ORC was presented based on the back propagation neural network optimized by the genetic algorithm (BPNN-GA). Firstly, an experimental setup was established to obtain the database for BPNN-GA model training and test. Then, the expander output power, working fluid mass flow rate, and the cold energy efficiency were selected as the BPNN-GA model outputs and the model structure was determined as 9-12-3. Finally, the accuracy of the BPNN-GA model was verified, and the parametric study was further conducted. The mean absolute relative errors (MARE) are 1.1876%, 0.9037%, and 2.6464%, the root mean square errors (RMSE) are 5.3789 W, 1.0260 kgh−1, and 0.3151%, and the correlation coefficients (R) are 0.9974, 0.9977, and 0.9665 for the expansion work, the working fluid mass flow rate, and the cold energy efficiency, respectively. The BPNN-GA is proved as a promising methodology, which could provide direct guidance for the determination of relevant parameters in experimental analysis and control strategy optimization.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222009306
    DOI: 10.1016/j.energy.2022.124027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222009306
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124027?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, Yaopeng & Jia, Ming & Han, Xu & Bai, Xue-Song, 2021. "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," Energy, Elsevier, vol. 225(C).
    2. Koppauer, H. & Kemmetmüller, W. & Kugi, A., 2017. "Modeling and optimal steady-state operating points of an ORC waste heat recovery system for diesel engines," Applied Energy, Elsevier, vol. 206(C), pages 329-345.
    3. Li, Yung-Ming & Hung, Tzu-Chen & Wu, Chia-Jung & Su, Ting-Ying & Xi, Huan & Wang, Chi-Chuan, 2021. "Experimental investigation of 3-kW organic Rankine cycle (ORC) system subject to heat source conditions: A new appraisal for assessment," Energy, Elsevier, vol. 217(C).
    4. Ziviani, Davide & James, Nelson A. & Accorsi, Felipe A. & Braun, James E. & Groll, Eckhard A., 2018. "Experimental and numerical analyses of a 5 kWe oil-free open-drive scroll expander for small-scale organic Rankine cycle (ORC) applications," Applied Energy, Elsevier, vol. 230(C), pages 1140-1156.
    5. Palagi, Laura & Pesyridis, Apostolos & Sciubba, Enrico & Tocci, Lorenzo, 2019. "Machine Learning for the prediction of the dynamic behavior of a small scale ORC system," Energy, Elsevier, vol. 166(C), pages 72-82.
    6. 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.
    7. Kosmadakis, George & Landelle, Arnaud & Lazova, Marija & Manolakos, Dimitris & Kaya, Alihan & Huisseune, Henk & Karavas, Christos-Spyridon & Tauveron, Nicolas & Revellin, Remi & Haberschill, Philippe , 2016. "Experimental testing of a low-temperature organic Rankine cycle (ORC) engine coupled with concentrating PV/thermal collectors: Laboratory and field tests," Energy, Elsevier, vol. 117(P1), pages 222-236.
    8. Rech, Sergio & Zandarin, Simone & Lazzaretto, Andrea & Frangopoulos, Christos A., 2017. "Design and off-design models of single and two-stage ORC systems on board a LNG carrier for the search of the optimal performance and control strategy," Applied Energy, Elsevier, vol. 204(C), pages 221-241.
    9. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Zhang, Wujie & Song, Gege, 2021. "Introducing machine learning and hybrid algorithm for prediction and optimization of multistage centrifugal pump in an ORC system," Energy, Elsevier, vol. 222(C).
    10. Altun, A.F. & Kilic, M., 2020. "Thermodynamic performance evaluation of a geothermal ORC power plant," Renewable Energy, Elsevier, vol. 148(C), pages 261-274.
    11. Dong, Shengming & Hu, Xiaowei & Huang, Jun Fang & Zhu, Tingting & Zhang, Yufeng & Li, Xiang, 2021. "Investigation on improvement potential of ORC system off-design performance by expander speed regulation based on theoretical and experimental exergy-energy analyses," Energy, Elsevier, vol. 220(C).
    12. Chatzopoulou, Maria Anna & Simpson, Michael & Sapin, Paul & Markides, Christos N., 2019. "Off-design optimisation of organic Rankine cycle (ORC) engines with piston expanders for medium-scale combined heat and power applications," Applied Energy, Elsevier, vol. 238(C), pages 1211-1236.
    13. Kang, Seok Hun, 2012. "Design and experimental study of ORC (organic Rankine cycle) and radial turbine using R245fa working fluid," Energy, Elsevier, vol. 41(1), pages 514-524.
    14. Pili, R. & Eyerer, S. & Dawo, F. & Wieland, C. & Spliethoff, H., 2020. "Development of a non-linear state estimator for advanced control of an ORC test rig for geothermal application," Renewable Energy, Elsevier, vol. 161(C), pages 676-690.
    15. Eyerer, Sebastian & Dawo, Fabian & Kaindl, Johannes & Wieland, Christoph & Spliethoff, Hartmut, 2019. "Experimental investigation of modern ORC working fluids R1224yd(Z) and R1233zd(E) as replacements for R245fa," Applied Energy, Elsevier, vol. 240(C), pages 946-963.
    16. Lin, Shan & Zhao, Li & Deng, Shuai & Ni, Jiaxin & Zhang, Ying & Ma, Minglu, 2019. "Dynamic performance investigation for two types of ORC system driven by waste heat of automotive internal combustion engine," Energy, Elsevier, vol. 169(C), pages 958-971.
    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, Pengfei & Zhu, Ze & Liang, Wenlong & Liao, Longtao & Wan, Jiashuang, 2023. "Hybrid mechanistic and neural network modeling of nuclear reactors," Energy, Elsevier, vol. 282(C).
    2. Tian, Zhen & Chen, Xiaochen & Zhang, Yuan & Gao, Wenzhong & Chen, Wu & Peng, Hao, 2023. "Energy, conventional exergy and advanced exergy analysis of cryogenic recuperative organic rankine cycle," Energy, Elsevier, vol. 268(C).
    3. Zhang, Yuan & Wu, Xiaocheng & Tian, Zhen & Gao, Wenzhong & Peng, Hao & Yang, Ke, 2023. "Comparison of random forest, support vector regression, and long short term memory for performance prediction and optimization of a cryogenic organic rankine cycle (ORC)," Energy, Elsevier, vol. 280(C).
    4. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2023. "Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization," Energies, MDPI, vol. 16(3), pages 1-11, February.

    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. 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).
    2. Chatzopoulou, Maria Anna & Lecompte, Steven & Paepe, Michel De & Markides, Christos N., 2019. "Off-design optimisation of organic Rankine cycle (ORC) engines with different heat exchangers and volumetric expanders in waste heat recovery applications," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. 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).
    4. Hsieh, Jui-Ching & Chen, Yen-Hsun & Hsieh, Yi-Chi, 2023. "Experimental study of an organic Rankine cycle with a variable-rotational-speed scroll expander at various heat source temperatures," Energy, Elsevier, vol. 270(C).
    5. Calise, Francesco & Cappiello, Francesco L. & Dentice d'Accadia, Massimo & Vicidomini, Maria, 2021. "Thermo-economic optimization of a novel hybrid renewable trigeneration plant," Renewable Energy, Elsevier, vol. 175(C), pages 532-549.
    6. Dawo, Fabian & Eyerer, Sebastian & Pili, Roberto & Wieland, Christoph & Spliethoff, Hartmut, 2021. "Experimental investigation, model validation and application of twin-screw expanders with different built-in volume ratios," Applied Energy, Elsevier, vol. 282(PA).
    7. Hoang, Anh Tuan, 2018. "Waste heat recovery from diesel engines based on Organic Rankine Cycle," Applied Energy, Elsevier, vol. 231(C), pages 138-166.
    8. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Zhang, Wujie & Wang, Yan & Yao, Baofeng, 2023. "Dynamic response assessment and multi-objective optimization of organic Rankine cycle (ORC) under vehicle driving cycle conditions," Energy, Elsevier, vol. 263(PA).
    9. Lisheng Pan & Huaixin Wang, 2019. "Experimental Investigation on Performance of an Organic Rankine Cycle System Integrated with a Radial Flow Turbine," Energies, MDPI, vol. 12(4), pages 1-20, February.
    10. Jiménez-Arreola, Manuel & Wieland, Christoph & Romagnoli, Alessandro, 2019. "Direct vs indirect evaporation in Organic Rankine Cycle (ORC) systems: A comparison of the dynamic behavior for waste heat recovery of engine exhaust," Applied Energy, Elsevier, vol. 242(C), pages 439-452.
    11. Shi, Yao & Zhang, Zhiming & Chen, Xiaoqiang & Xie, Lei & Liu, Xueqin & Su, Hongye, 2023. "Data-Driven model identification and efficient MPC via quasi-linear parameter varying representation for ORC waste heat recovery system," Energy, Elsevier, vol. 271(C).
    12. Shi, Yao & Zhang, Zhiming & Xie, Lei & Wu, Xialai & Liu, Xueqin Amy & Lu, Shan & Su, Hongye, 2022. "Modified hierarchical strategy for transient performance improvement of the ORC based waste heat recovery system," Energy, Elsevier, vol. 261(PA).
    13. Li, Zhi & Wang, Lei & Jiang, Ruicheng & Wang, Bingzheng & Yu, Xiaonan & Huang, Rui & Yu, Xiaoli, 2022. "Experimental investigations on dynamic performance of organic Rankine cycle integrated with latent thermal energy storage under transient engine conditions," Energy, Elsevier, vol. 246(C).
    14. Davide Di Battista & Roberto Cipollone, 2023. "Waste Energy Recovery and Valorization in Internal Combustion Engines for Transportation," Energies, MDPI, vol. 16(8), pages 1-28, April.
    15. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Pan, Yachao & Zhang, Wujie & Wang, Yan, 2023. "Nonlinear modeling and multi-scale influence characteristics analysis of organic Rankine cycle (ORC) system considering variable driving cycles," Energy, Elsevier, vol. 265(C).
    16. Ying Zhang & Li Zhao & Shuai Deng & Ming Li & Yali Liu & Qiongfen Yu & Mengxing Li, 2022. "Novel Off-Design Operation Maps Showing Functionality Limitations of Organic Rankine Cycle Validated by Experiments," Energies, MDPI, vol. 15(21), pages 1-19, November.
    17. Witanowski, Łukasz & Ziółkowski, Paweł & Klonowicz, Piotr & Lampart, Piotr, 2023. "A hybrid approach to optimization of radial inflow turbine with principal component analysis," Energy, Elsevier, vol. 272(C).
    18. Li, Jian & Peng, Xiayao & Yang, Zhen & Hu, Shuozhuo & Duan, Yuanyuan, 2022. "Design, improvements and applications of dual-pressure evaporation organic Rankine cycles: A review," Applied Energy, Elsevier, vol. 311(C).
    19. Yan Gao & Qianxi Song & Wen Su & Xinxing Lin & Zhi Sun & Zhisheng Huang & Yaping Gao, 2023. "Experimentally Identifying the Influences of Key Parameters for an Organic Rankine Cycle Using R123," Sustainability, MDPI, vol. 15(1), pages 1-14, January.
    20. Schifflechner, Christopher & Dawo, Fabian & Eyerer, Sebastian & Wieland, Christoph & Spliethoff, Hartmut, 2020. "Thermodynamic comparison of direct supercritical CO2 and indirect brine-ORC concepts for geothermal combined heat and power generation," Renewable Energy, Elsevier, vol. 161(C), pages 1292-1302.

    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:254:y:2022:i:pb:s0360544222009306. 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.