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19 October 2023 BP (back propagation) neural network-based performance prediction and optimization of a low-grade heat recovery system
Lili Shen, Bin Cheng, Qiangqiang Li, Xiaoxia Sun, Min Lin, Li Zhou, Chunwei Zhao
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Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270935 (2023) https://doi.org/10.1117/12.2684656
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
The Organic Rankine Cycle (ORC) system with the advantages of high thermal efficiency, environmental friendliness, simple structure and availability of components has been used to recover medium-low grade renewable energy. However, the ORC system involves many operation parameters, and the system performance is the embodiment of the coordinated matching of various parameters. In order to analyse and optimize the low temperature ORC system with the evaporation temperature from 346K to 400K, the ORC performance prediction model using BP (back propagation) neural network has been carried out. The power output of the system has been analysed by combining BP neural network model and genetic algorithm (GA). The results showed that the system power output increases with the increase of the evaporation temperature and mass flow rate, while with the decrease of the condensation temperature and pressure. The maximum power output of the system is 16.3kW, and the corresponding condensation pressure, rotational speed, condensation temperature, mass flow rate and evaporation temperature are 0.1 MPa, 3126 rpm, 290.5 K, 0.4 kg/s and 389.9 K, respectively.
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Lili Shen, Bin Cheng, Qiangqiang Li, Xiaoxia Sun, Min Lin, Li Zhou, and Chunwei Zhao "BP (back propagation) neural network-based performance prediction and optimization of a low-grade heat recovery system", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270935 (19 October 2023); https://doi.org/10.1117/12.2684656
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KEYWORDS
Neural networks
Mathematical optimization
Performance modeling
Genetic algorithms
Solar energy
Thermal efficiency
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