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Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis

Author

Listed:
  • Xu, Yingjie
  • Mao, Chengbin
  • Huang, Yuangong
  • Shen, Xi
  • Xu, Xiaoxiao
  • Chen, Guangming

Abstract

For the advantages of high efficiency and low impact to the environment, CO2 air source heat pump water heater (ASHPWH) is applied to produce domestic water, which also reveals good potential in cold regions. In order to boost the system performance and practicability under low ambient temperature, optimization for CO2 ASHPWH is conducted using non-dominated sorting genetic algorithm (NSGA-II). A validated artificial neural network (ANN) predicts energy parameters for the optimization. And an economic model provides economic and environmental parameters, which considers the influence of housing price, tank volume, and on/off-peak electricity price, rarely taken into account in published studies. Then the optimizing progress is conducted under −20 °C ambient temperature and 9–65 °C water temperature, in which four optimized variables are selected: gas cooler outlet temperature (Tgc), heat rejection pressure (Pgc), compressor displacement (qvh) and water tank volume (Vwt). The final solution of Tgc = 15 °C, Pgc = 8294.1 kPa, Vwt = 0.3647 m3, qvh = 401.33 mL/s results in two objectives (CO2 emission and total annual cost) of 8599.4 kg and 1626.9 $/year, revealing advantages both in energy and economy. It is noteworthy that the cost of the space occupied by system is the fourth important factor in capital cost. These results lay solid foundation for further studies and system application.

Suggested Citation

  • Xu, Yingjie & Mao, Chengbin & Huang, Yuangong & Shen, Xi & Xu, Xiaoxiao & Chen, Guangming, 2021. "Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis," Energy, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323392
    DOI: 10.1016/j.energy.2020.119232
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    References listed on IDEAS

    as
    1. Gunasekar, N. & Mohanraj, M. & Velmurugan, V., 2015. "Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps," Energy, Elsevier, vol. 93(P1), pages 908-922.
    2. Ohkura, Masashi & Yokoyama, Ryohei & Nakamata, Takuya & Wakui, Tetsuya, 2015. "Numerical analysis on performance enhancement of a CO2 heat pump water heating system by extracting tepid water," Energy, Elsevier, vol. 87(C), pages 435-447.
    3. Hu, Bin & Li, Yaoyu & Cao, Feng & Xing, Ziwen, 2015. "Extremum seeking control of COP optimization for air-source transcritical CO2 heat pump water heater system," Applied Energy, Elsevier, vol. 147(C), pages 361-372.
    4. Austin, Brian T. & Sumathy, K., 2011. "Transcritical carbon dioxide heat pump systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 4013-4029.
    5. Lee, Ungki & Park, Sudong & Lee, Ikjin, 2020. "Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)," Energy, Elsevier, vol. 196(C).
    6. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    7. Shirazi, Ali & Najafi, Behzad & Aminyavari, Mehdi & Rinaldi, Fabio & Taylor, Robert A., 2014. "Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling," Energy, Elsevier, vol. 69(C), pages 212-226.
    8. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
    9. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    10. Yokoyama, Ryohei & Shimizu, Takeshi & Ito, Koichi & Takemura, Kazuhisa, 2007. "Influence of ambient temperatures on performance of a CO2 heat pump water heating system," Energy, Elsevier, vol. 32(4), pages 388-398.
    11. Zhang, Jian-Fei & Qin, Yan & Wang, Chi-Chuan, 2015. "Review on CO2 heat pump water heater for residential use in Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1383-1391.
    12. Selbaş, Reşat & Kızılkan, Önder & Şencan, Arzu, 2006. "Thermoeconomic optimization of subcooled and superheated vapor compression refrigeration cycle," Energy, Elsevier, vol. 31(12), pages 2108-2128.
    13. Sayyaadi, Hoseyn & Mehrabipour, Reza, 2012. "Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger," Energy, Elsevier, vol. 38(1), pages 362-375.
    14. Jain, Vaibhav & Sachdeva, Gulshan & Kachhwaha, Surendra Singh, 2015. "Energy, exergy, economic and environmental (4E) analyses based comparative performance study and optimization of vapor compression-absorption integrated refrigeration system," Energy, Elsevier, vol. 91(C), pages 816-832.
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    2. Zhongkai Wu & Feifei Bi & Jiyou Fei & Zecan Zheng & Yulong Song & Feng Cao, 2023. "The Collaborative Optimization of the Discharge Pressure and Heat Recovery Rate in a Transcritical CO 2 Heat Pump Used in Extremely Low Temperature Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
    3. Ahmed Al-Zahrani, 2023. "Investigating New Environmentally Friendly Zeotropic Refrigerants as Possible Replacements for Carbon Dioxide (CO 2 ) in Car Air Conditioners," Sustainability, MDPI, vol. 16(1), pages 1-28, December.
    4. Zhao, Shuchun & Guo, Junheng & Dang, Xiuhu & Ai, Bingyan & Zhang, Minqing & Li, Wei & Zhang, Jinli, 2022. "Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation," Energy, Elsevier, vol. 240(C).
    5. Aljolani, Osama & Heberle, Florian & Brüggemann, Dieter, 2024. "Thermo-economic and environmental analysis of a CO2 residential air conditioning system in comparison to HFC-410A and HFC-32 in temperate and subtropical climates," Applied Energy, Elsevier, vol. 353(PA).
    6. Yikai Wang & Yifan He & Yulong Song & Xiang Yin & Feng Cao & Xiaolin Wang, 2021. "Energy and Exergy Analysis of the Air Source Transcritical CO 2 Heat Pump Water Heater Using CO 2 -Based Mixture as Working Fluid," Energies, MDPI, vol. 14(15), pages 1-18, July.

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