Enhanced performance evaluation and operational regulation of a novel combined cooling and power system using machine learning

Juwei Lou, Jiangfeng Wang, Fang Luo, Weidong Chen, Liangqi Chen, M.R. Islam and K.J. Chua

Energy, 2025, vol. 333, issue C

Abstract: The combined power and cooling system based on the S-CO2 Brayton cycle is a proven solution for meeting the multi-energy needs of distributed energy systems. By reusing the working medium from the refrigeration system for further power generation, energy utilization efficiency is markedly improved. This paper proposes a combined cooling and power system with high-pressure mixing, which facilitates the reuse of the working medium and reduces the mass flow rate of the main compressor in the S-CO2 recuperation Brayton cycle. Machine learning models, utilizing two-layered feedforward neural networks, are judiciously developed and employed to predict the off-design performance of turbomachines. The operational characteristics and regulation of the high-pressure mixing (HPM) and low-pressure mixing (LPM) systems are evaluated and compared using multi-objective optimization with a genetic algorithm. The results indicate that the HPM system excels in converted thermal efficiency, while the LPM system is superior in refrigeration performance. The optimal converted thermal efficiencies are 47.6 % and 32.3 % for HPM and LPM systems under constant turbomachine performance. Based on the machine learning model, corrected optimal converted thermal efficiencies of 48.02 % and 32.88 % are achieved for the HPM and LPM systems, respectively. This research presents an innovative concept for distributed energy systems with diverse energy requirements.

Keywords: Combined cooling and power; S-CO2 Brayton cycle; Transcritical CO2 compression refrigeration; Machine learning; Operation characteristic (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225029482

DOI: 10.1016/j.energy.2025.137306

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