Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle

Jintao He, Lingfeng Shi, Hua Tian, Xuan Wang, Xiaocun Sun, Meiyan Zhang, Yu Yao and Gequn Shu

Energy, 2023, vol. 285, issue C

Abstract: The CO2 combined cooling and power cycle (CCP) is a promising alternative for waste heat recovery due to its environmental friendliness and excellent performance. However, the transient dynamic behavior analysis and control of CCP systems are challenged by the instability of waste heat sources. In transient dynamic modeling, artificial neural networks, with their nonlinear mapping capabilities and relatively low computational requirements, prove advantageous over dynamic simulation models. In this study, six commonly used artificial neural network architectures are employed for approximating and predicting the transient dynamic behavior of CCP systems and subjected to preliminary applications. Results show that the multilayer feedforward neural network is the most suitable among the six networks for predicting and approximating the CCP system's transient dynamic behavior. Based on this model, a trajectory optimization control strategy is designed, leading to a 5.3 % improvement in CCP net power. This research underscores the effectiveness of artificial neural networks in the field of CCP dynamic modeling, offering valuable guidance for its application.

Keywords: Artificial neural network; Transient dynamic behavior prediction; Combining cooling and power system; Trajectory optimization control (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028451

DOI: 10.1016/j.energy.2023.129451

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