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

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 BPNNGA is proved as a promising methodology, which could provide direct guidance for the determination of relevant parameters in experimental analysis and control strategy optimization. (C) 2022 Published by Elsevier Ltd.

Automated summary

In this paper, a performance prediction model of the cryogenic ORC was presented based on the BPNN-GA, which was optimized by the genetic algorithm. The model was verified for accuracy through experimental validation and further parametric study.