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

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  • 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
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    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)

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    2. Feng, Yong-Qiang & Wu, Yu-Zhe & Zhang, Qiang & Liu, Zhi-Nan & Wang, Xing-Xing & Hung, Tzu-Chen & Yu, Hao-Shui & He, Zhi-Xia, 2025. "Experiment investigation and machine learning prediction of a biomass-fired organic Rankine cycle combined heating and power system under various heat source temperatures and mass flow rates," Energy, Elsevier, vol. 324(C).
    3. Wang, Hai-Xiao & Lei, Biao & Wu, Yu-Ting & Zhang, Ye-Qiang & Du, Yan-Jun & Zhang, Xiao-Ming & Yang, Pei-Hong, 2025. "Performance improvement and multi-objective optimization of energy, economic, and environmental factors in organic Rankine cycle using machine learning-driven quasi-two-stage single screw expander," Energy, Elsevier, vol. 324(C).
    4. Vignesh Kumar, V. & Madhesh, K. & Sanjay, K. & Guru Prasath, P. & Pavish Karthik, A. & Praveen Kumar, G., 2025. "A novel ensemble machine learning approach for optimizing sustainability and green hydrogen production in hybrid renewable-based organic Rankine cycle-operated proton exchange membrane electrolyser sy," Renewable Energy, Elsevier, vol. 242(C).
    5. 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.
    6. Zhang, Yuan & Li, Yifan & Tian, Zhen & Yang, Chao & Peng, Hao & Kan, Ankang & Gao, Wenzhong, 2025. "Thermodynamic performance prediction and optimization of a 1 kW ocean thermal energy cogeneration system based on artificial neural network," Energy, Elsevier, vol. 314(C).
    7. 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).
    8. 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.
    9. 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).

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