Thermodynamics and exergoeconomics evaluations of a new solar–biomass–natural gas integrated energy system coupled with a heat recovery system: biofuel yield prediction under two different machine-learning models
Li X. Chen Z. Gao J. Ghadimi N.
2025Oxford University Press
International Journal of Low-Carbon Technologies
2025#201936 - 1950 pp.
This study addresses the challenge of developing low-carbon, efficient energy systems by proposing an integrated solar–biomass–natural gas configuration coupled with a heat recovery system and hydrogen production. Thermodynamic and exergoeconomic analyses evaluate performance, while random forest and Kernel Ridge regression models predict biofuel yield with high accuracy. The optimized system achieves energy and exergy efficiencies of 39.5% and 34.5%, producing 11.22 MW electricity and 0.0442 kg/s H2, with CO2 emissions of 106.92 kg/h. This conceptual design reduces natural gas consumption, enhances renewable integration, and demonstrates a scalable pathway toward sustainable, multioutput power generation.
biofuel yield prediction , biomass-natural gas combustion , exergoeconomics , integrated energy system , parabolic trough solar collector , random forest and kernel ridge regression
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Higher School of Economics and Business, Al-Farabi Kazakh National University, Almaty, 050043, Kazakhstan
School of Business, Liaocheng University, Shandong, Liaocheng, 252000, China
Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
Higher School of Economics and Business
School of Business
Young Researchers and Elite Club
10 лет помогаем публиковать статьи Международный издатель
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