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

Zhen Tian, Wanlong Gan, Xianzhi Zou, Yuan Zhang and Wenzhong Gao

Energy, 2022, vol. 254, issue PB

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.

Keywords: Cryogenic organic Rankine cycle; Cold energy recovery; Back propagation neural network; Genetic algorithm; Performance prediction (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222009306

DOI: 10.1016/j.energy.2022.124027

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