IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v216y2021ics0360544220323392.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220323392
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.119232?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Mohsen Ghaderi & Christopher Reddick & Mikhail Sorin, 2023. "A Systematic Heat Recovery Approach for Designing Integrated Heating, Cooling, and Ventilation Systems for Greenhouses," Energies, MDPI, vol. 16(14), pages 1-22, July.
    3. 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.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohanraj, M. & Belyayev, Ye. & Jayaraj, S. & Kaltayev, A., 2018. "Research and developments on solar assisted compression heat pump systems – A comprehensive review (Part A: Modeling and modifications)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 90-123.
    2. Ignacio López Paniagua & Ángel Jiménez Álvaro & Javier Rodríguez Martín & Celina González Fernández & Rafael Nieto Carlier, 2019. "Comparison of Transcritical CO 2 and Conventional Refrigerant Heat Pump Water Heaters for Domestic Applications," Energies, MDPI, vol. 12(3), pages 1-17, February.
    3. Xiufang Liu & Changhai Liu & Ze Zhang & Liang Chen & Yu Hou, 2017. "Experimental Study on the Performance of Water Source Trans-Critical CO 2 Heat Pump Water Heater," Energies, MDPI, vol. 10(6), pages 1-14, June.
    4. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    5. Wang, Wenyi & Zhao, Zhongfan & Zhou, Qun & Qiao, Yiyuan & Cao, Feng, 2021. "Model predictive control for the operation of a transcritical CO2 air source heat pump water heater," Applied Energy, Elsevier, vol. 300(C).
    6. Schlosser, F. & Jesper, M. & Vogelsang, J. & Walmsley, T.G. & Arpagaus, C. & Hesselbach, J., 2020. "Large-scale heat pumps: Applications, performance, economic feasibility and industrial integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Wahiba Yaïci & Michela Longo & Evgueniy Entchev & Federica Foiadelli, 2017. "Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System," Sustainability, MDPI, vol. 9(8), pages 1-14, August.
    8. Yang, Fubin & Cho, Heejin & Zhang, Hongguang & Zhang, Jian, 2017. "Thermoeconomic multi-objective optimization of a dual loop organic Rankine cycle (ORC) for CNG engine waste heat recovery," Applied Energy, Elsevier, vol. 205(C), pages 1100-1118.
    9. Badiei, A. & Golizadeh Akhlaghi, Y. & Zhao, X. & Shittu, S. & Xiao, X. & Li, J. & Fan, Y. & Li, G., 2020. "A chronological review of advances in solar assisted heat pump technology in 21st century," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    10. 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.
    11. Abdul Mujeebu, Muhammad & Alshamrani, Othman Subhi, 2016. "Prospects of energy conservation and management in buildings – The Saudi Arabian scenario versus global trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1647-1663.
    12. Singh Gaur, Ankita & Fitiwi, Desta & Curtis, John, 2019. "Heat pumps and their role in decarbonising heating Sector: a comprehensive review," Papers WP627, Economic and Social Research Institute (ESRI).
    13. Keshtkar, Mohammad Mehdi & Talebizadeh, Pouyan, 2017. "Multi-objective optimization of cooling water package based on 3E analysis: A case study," Energy, Elsevier, vol. 134(C), pages 840-849.
    14. Rajib Uddin Rony & Huojun Yang & Sumathy Krishnan & Jongchul Song, 2019. "Recent Advances in Transcritical CO 2 (R744) Heat Pump System: A Review," Energies, MDPI, vol. 12(3), pages 1-35, January.
    15. 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.
    16. Sun, Zhili & Wang, Qifan & Xie, Zhiyuan & Liu, Shengchun & Su, Dandan & Cui, Qi, 2019. "Energy and exergy analysis of low GWP refrigerants in cascade refrigeration system," Energy, Elsevier, vol. 170(C), pages 1170-1180.
    17. 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.
    18. Abbas, Sajid & Yuan, Yanping & Zhou, Jinzhi & Hassan, Atazaz & Yu, Min & Yasheng, Ji, 2022. "Experimental and analytical analysis of the impact of different base plate materials and design parameters on the performance of the photovoltaic/thermal system," Renewable Energy, Elsevier, vol. 187(C), pages 522-536.
    19. Song, Zhiying & Ji, Jie & Cai, Jingyong & Zhao, Bin & Li, Zhaomeng, 2021. "Investigation on a direct-expansion solar-assisted heat pump with a novel hybrid compound parabolic concentrator/photovoltaic/fin evaporator," Applied Energy, Elsevier, vol. 299(C).
    20. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323392. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.