IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i11p4752-d1407688.html
   My bibliography  Save this article

Performance Analysis and Optimization of Solar-Coupled Mine Water-Source Heat Pump Combined Heating and Cooling System

Author

Listed:
  • Chang Zhao

    (Energy School, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Jianhui Zhao

    (Energy School, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Mei Wang

    (Energy School, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

To address the energy consumption issue in mining area buildings, this paper proposed a solar-coupled mine water-source heat pump combined heating and cooling (SMWHP-CHC) system, taking the employee dormitory building group of a coal mining enterprise in Tongchuan City, China, as a case study. The system utilizes renewable solar energy and waste heat recovered from mine water as composite heat sources, and utilizes the cold energy in mine water as a cooling source to meet the demands for space heating, space cooling, and annual domestic hot water of the building in a sustainable manner. The simulation model of the system was established by TRNSYS to analyze the system’s annual operational performance. The results indicated that the system exhibited a positive energy efficiency and environmental performance under different operating conditions. The heating coefficients of the performance of the system (COP sys ) during the space heating season and transition season were 3.54 and 18.6, and the cooling energy efficiency ratio of the system (EER sys ) was 3.79. In addition, aiming to minimize the annual cost of the system, multiple crucial device parameters were synchronously optimized employing the PSO-HJ hybrid optimization algorithm through the GenOpt 2 software. The annual cost of the optimized system was reduced by 8.82%, and the investment cost was significantly reduced, while the performance was also improved. This study can provide theoretical support for the sustainable engineering application of the SMWHP-CHC system in mining area buildings.

Suggested Citation

  • Chang Zhao & Jianhui Zhao & Mei Wang, 2024. "Performance Analysis and Optimization of Solar-Coupled Mine Water-Source Heat Pump Combined Heating and Cooling System," Sustainability, MDPI, vol. 16(11), pages 1-20, June.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4752-:d:1407688
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/11/4752/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/11/4752/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Dongwei & Gao, Zhao & Fang, Chenglei & Shen, Chao & Li, Hang & Qin, Xiang, 2022. "Simulation and analysis of hot water system with comprehensive utilization of solar energy and wastewater heat," Energy, Elsevier, vol. 253(C).
    2. Menéndez, Javier & Ordónez, Almudena & Fernández-Oro, Jesús M. & Loredo, Jorge & Díaz-Aguado, María B., 2020. "Feasibility analysis of using mine water from abandoned coal mines in Spain for heating and cooling of buildings," Renewable Energy, Elsevier, vol. 146(C), pages 1166-1176.
    3. Yurim Kim & Jonghun Lim & Jae Yun Shim & Seokil Hong & Heedong Lee & Hyungtae Cho, 2022. "Optimization of Heat Exchanger Network via Pinch Analysis in Heat Pump-Assisted Textile Industry Wastewater Heat Recovery System," Energies, MDPI, vol. 15(9), pages 1-16, April.
    4. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    Full references (including those not matched with items on IDEAS)

    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. Sabina Kordana-Obuch & Michał Wojtoń & Mariusz Starzec & Beata Piotrowska, 2023. "Opportunities and Challenges for Research on Heat Recovery from Wastewater: Bibliometric and Strategic Analyses," Energies, MDPI, vol. 16(17), pages 1-36, September.
    2. Kazimierz Kawa & Rafał Mularczyk & Waldemar Bauer & Katarzyna Grobler-Dębska & Edyta Kucharska, 2024. "Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings," Energies, MDPI, vol. 17(13), pages 1-16, June.
    3. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    4. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
    5. Chi, Fang'ai & Xu, Liming & Peng, Changhai, 2020. "Integration of completely passive cooling and heating systems with daylighting function into courtyard building towards energy saving," Applied Energy, Elsevier, vol. 266(C).
    6. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    7. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    9. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    11. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    12. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    13. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    14. Sarabia Escriva, Emilio José & Hart, Matthew & Acha, Salvador & Soto Francés, Víctor & Shah, Nilay & Markides, Christos N., 2022. "Techno-economic evaluation of integrated energy systems for heat recovery applications in food retail buildings," Applied Energy, Elsevier, vol. 305(C).
    15. Chun-Wei Chen, 2023. "A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    16. James Ogundiran & Ehsan Asadi & Manuel Gameiro da Silva, 2024. "A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
    17. Zhaocheng Li & Yu Song, 2022. "Energy Consumption Linkages of the Chinese Construction Sector," Energies, MDPI, vol. 15(5), pages 1-13, February.
    18. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    19. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
    20. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:16:y:2024:i:11:p:4752-:d:1407688. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.