Fourier Neural Operator-driven transient analysis and control for supercritical CO2 cycles

Huaitao Zhu, Gongnan Xie, Abdallah S. Berrouk and Panos Liatsis

Energy, 2025, vol. 323, issue C

Abstract: The dynamic performance and control strategies of supercritical carbon dioxide (SCO2) systems constitute a fundamental research area that demands thorough scientific exploration. This study systematically addresses several critical gaps in the transient analysis of SCO2 systems, particularly highlighting the insufficient theoretical exploration of the system's dynamic behavior, the predominant reliance on conventional Proportional-Integral-Derivative (PID) control approaches, and the significant computational challenges in solving transient dynamics and control problems. To this end, the following research plan is proposed: first, the intrinsic principles of SCO2 dynamic characteristics are analyzed based on the printed circuit heat exchanger (PCHE) transient model; second, system identification is performed for the SCO2 system; and then, Model Predictive Control (MPC) and PI control-based reinforcement learning (RL-PI) suitable for the SCO2 cycle is designed and compared with traditional PI control; and finally, a Fourier Neural Operator (FNO)-based model for the open-loop response of the SCO2 system and its MPC controller is trained. The results shows that, at the microscopic level, the PCHE outlet temperature consists of an inertial system driven by wall heat transfer and a non-minimum phase system driven by inlet enthalpy. At the macroscopic level, efficiency exhibits non-minimum phase behavior, while net output power does not. A backpropagation (BP) neural network achieved optimal system identification, with training set fits of 97.56 % (efficiency) and 99.19 % (net output power), and validation set fits of 92.79 % and 97.75 %, respectively. MPC outperformed PI, RL-PI, and open-loop control, showing shorter response times, reduced overshoot, stronger noise immunity, and improved signal tracking. FNO successfully predicted the SCO2 cycle's open-loop response, achieving relative errors of 10−4 for net output power and control outputs compared to MPC. The system performance was validated through numerical simulations, and results indicated that FNO has a significant advantage in computational efficiency, with memory and time reduction by 3 orders of magnitude.

Keywords: SCO2 brayton cycle; Transient analysis; Model predictive control; Reinforcement learning; Fourier neural operator (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225014707
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:323:y:2025:i:c:s0360544225014707

DOI: 10.1016/j.energy.2025.135828

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().