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

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  • 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
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    8. 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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