IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipcs0360544222027438.html
   My bibliography  Save this article

Operation characteristics and performance prediction of a 3 kW organic Rankine cycle (ORC) with automatic control system based on machine learning methodology

Author

Listed:
  • Feng, Yong-Qiang
  • Zhang, Qiang
  • Xu, Kang-Jing
  • Wang, Chun-Ming
  • He, Zhi-Xia
  • Hung, Tzu-Chen

Abstract

Automatic control system enables the laboratory organic Rankine cycle (ORC) to adapt to variable operating conditions of industrial application. In this study, the operation characteristics of a 3 kW ORC with automatic control system applied to a chemical plant, as well as the performance prediction and optimization using machine learning methodology, are addressed. The dynamic behaviors for startup, operating and stop stages are discussed. The BP-ORC neural network model is established based on 3400 sets of experimental data, while the prediction accuracy is analyzed based on the errors of the training and test samples. The effects of six operation parameters on system performance are examined, while the bi-objective optimization for maximum thermal efficiency and maximum net output work is investigated. Results indicate that the component response times for startup stage and stop stage are 90s and 300s, respectively. Increasing the mass flow rate, decreasing the expander outlet temperature and increasing the expander inlet temperature ensure a higher net output work, while increasing the expander inlet temperature, decreasing the expander outlet temperature and increasing pump outlet pressure enable a higher thermal efficiency. The optimum net output work and thermal efficiency from Pareto-optimal solution are 2.87 kW and 8.855%, respectively.

Suggested Citation

  • Feng, Yong-Qiang & Zhang, Qiang & Xu, Kang-Jing & Wang, Chun-Ming & He, Zhi-Xia & Hung, Tzu-Chen, 2023. "Operation characteristics and performance prediction of a 3 kW organic Rankine cycle (ORC) with automatic control system based on machine learning methodology," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027438
    DOI: 10.1016/j.energy.2022.125857
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222027438
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125857?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. Zhang, Xuanang & Wang, Xuan & Cai, Jinwen & He, Zhaoxian & Tian, Hua & Shu, Gequn & Shi, Lingfeng, 2022. "Experimental study on operating parameters matching characteristic of the organic Rankine cycle for engine waste heat recovery," Energy, Elsevier, vol. 244(PA).
    2. Jin, Yunli & Gao, Naiping & Zhu, Tong, 2022. "Effect of resistive load characteristics on the performance of Organic Rankine cycle (ORC)," Energy, Elsevier, vol. 246(C).
    3. Wang, Zhiqi & Pan, Huihui & Xia, Xiaoxia & Xie, Baoqi & Peng, Deqi & Yang, Huya, 2022. "Experimental investigation on steady and dynamic performance of organic Rankine cycle with R245fa/R141b under different cooling and expander speed conditions," Energy, Elsevier, vol. 241(C).
    4. 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).
    5. 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).
    6. Tian, Zhen & Gan, Wanlong & Qi, Zhixin & Tian, Molin & Gao, Wenzhong, 2022. "Experimental study of organic Rankine cycle with three-fluid recuperator for cryogenic cold energy recovery," Energy, Elsevier, vol. 242(C).
    7. 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).
    8. Xu, Bin & Li, Xiaoya, 2021. "A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications," Applied Energy, Elsevier, vol. 286(C).
    9. Dokl, Monika & Gomilšek, Rok & Čuček, Lidija & Abikoye, Ben & Kravanja, Zdravko, 2022. "Maximizing the power output and net present value of organic Rankine cycle: Application to aluminium industry," Energy, Elsevier, vol. 239(PE).
    10. Yilmaz, Ceyhun & Koyuncu, Ismail, 2021. "Thermoeconomic modeling and artificial neural network optimization of Afyon geothermal power plant," Renewable Energy, Elsevier, vol. 163(C), pages 1166-1181.
    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. 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.
    2. Tao, Hai & Alawi, Omer A. & Kamar, Haslinda Mohamed & Nafea, Ahmed Adil & AL-Ani, Mohammed M. & Abba, Sani I. & Salami, Babatunde Abiodun & Oudah, Atheer Y. & Mohammed, Mustafa K.A., 2024. "Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants," Energy, Elsevier, vol. 292(C).
    3. Lu, Pei & Chen, Kaihuang & Luo, Xianglong & Wu, Wei & Liang, Yingzong & Chen, Jianyong & Chen, Ying, 2024. "Experimental and simulation study on a zeotropic ORC system using R1234ze(E)/R245fa as working fluid," Energy, Elsevier, vol. 292(C).
    4. Attila R. Imre & Sindu Daniarta & Przemysław Błasiak & Piotr Kolasiński, 2023. "Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review," Energies, MDPI, vol. 16(16), pages 1-25, August.
    5. 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).

    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. 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).
    2. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yang, Anren & Yan, Yinlian & Pan, Yachao & Wang, Yan, 2023. "Ensemble of self-organizing adaptive maps and dynamic multi-objective optimization for organic Rankine cycle (ORC) under transportation and driving environment," Energy, Elsevier, vol. 275(C).
    3. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yu, Mingzhe & Wang, Yan, 2023. "Investigation and multi-objective optimization of vehicle engine-organic Rankine cycle (ORC) combined system in different driving conditions," Energy, Elsevier, vol. 263(PB).
    4. 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).
    5. Li, Tailu & Qiao, Yuwen & Wang, Zeyu & Zhang, Yao & Gao, Xiang & Yuan, Ye, 2024. "Experimental study on dynamic power generation of three ORC-based cycle configurations under different heat source/sink conditions," Renewable Energy, Elsevier, vol. 227(C).
    6. 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.
    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. Lu, Bowen & Zhang, Zhifu & Cai, Jinwen & Wang, Wei & Ju, Xueming & Xu, Yao & Lu, Xun & Tian, Hua & Shi, Lingfeng & Shu, Gequn, 2023. "Integrating engine thermal management into waste heat recovery under steady-state design and dynamic off-design conditions," Energy, Elsevier, vol. 272(C).
    9. 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).
    10. 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).
    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. Hu, Yige & Wang, Hang & Chen, Hu & Ding, Yang & Liu, Changtian & Jiang, Feng & Ling, Xiang, 2023. "A novel hydrated salt-based phase change material for medium- and low-thermal energy storage," Energy, Elsevier, vol. 274(C).
    13. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    14. Huang, Xinyu & Du, Zhao & Li, Yuanji & Li, Ze & Yang, Xiaohu & Li, Ming-Jia, 2024. "Optimal design on fin-metal foam hybrid structure for melting and solidification phase change storage: An experimental and numerical study," Energy, Elsevier, vol. 302(C).
    15. Vaccari, Marco & Pannocchia, Gabriele & Tognotti, Leonardo & Paci, Marco, 2023. "Rigorous simulation of geothermal power plants to evaluate environmental performance of alternative configurations," Renewable Energy, Elsevier, vol. 207(C), pages 471-483.
    16. 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).
    17. Hashemian, Nasim & Noorpoor, Alireza, 2022. "A geothermal-biomass powered multi-generation plant with freshwater and hydrogen generation options: Thermo-economic-environmental appraisals and multi-criteria optimization," Renewable Energy, Elsevier, vol. 198(C), pages 254-266.
    18. Li, Xiaoya & Xu, Bin & Tian, Hua & Shu, Gequn, 2021. "Towards a novel holistic design of organic Rankine cycle (ORC) systems operating under heat source fluctuations and intermittency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    19. Oliveira, Augusto Cesar Laviola de & Renato, Natalia dos Santos & Martins, Marcio Arêdes & Mendonça, Isabela Miranda de & Moraes, Camile Arêdes & Lago, Lucas Fernandes Rocha, 2023. "Renewable energy solutions based on artificial intelligence for farms in the state of Minas Gerais, Brazil: Analysis and proposition," Renewable Energy, Elsevier, vol. 204(C), pages 24-38.
    20. Johannes Petrus Bester & Martin Van Eldik & Philip van Zyl Venter, 2023. "Energy Recovery Maximisation Modelling Subject to Constrained Cooling," Energies, MDPI, vol. 17(1), pages 1-23, December.

    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:263:y:2023:i:pc:s0360544222027438. 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.