Energy, exergy and exergoeconomic analyses and ANN-based three-objective optimization of a supercritical CO2 recompression Brayton cycle driven by a high-temperature geothermal reservoir

Eduardo Ruiz-Casanova, Carlos Rubio-Maya, Víctor M. Ambriz-Díaz and A. Gutiérrez Martínez

Energy, 2024, vol. 311, issue C

Abstract: The use of CO2-based power cycles to harness high-temperature geothermal resources is an interesting application that has been little explored. Therefore, to obtain a more holistic understanding of these systems, in this work, a supercritical CO2 recompression Brayton cycle is assessed. For comparison, the performances of a recuperative cycle and a recompression cycle with intercooling were also computed. Detailed mathematical models were developed and then, artificial neural network-based surrogate models were adopted to conduct multi-objective optimization. Results of the baseline simulations show that, the high temperature recuperator and the turbine are the most important components from both exergy and exergoeconomic approaches. The optimizations reveal that the intercooled cycle is superior to the other layouts in all the cases while the recuperative cycle is not feasible with reinjection temperatures greater or equal to 150 °C. When the reinjection temperature is constrained to 150 °C, the intercooled cycle attains 4472.70 kW, 62.10 % and 14.42 $/GJ for net power, exergy efficiency and product unit cost, respectively, whereas without this constraint, it achieves 5394.09 kW, 61.83 %, 11.79 $/GJ. In conclusion, the adoption of advanced layouts of the CO2 cycle is beneficial for this application from both technical and economic points of view.

Keywords: Geothermal energy; Hot dry rock; Supercritical carbon dioxide; Artificial neural network; Surrogate model; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031578

DOI: 10.1016/j.energy.2024.133381

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