IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v73y2014icp898-907.html
   My bibliography  Save this article

Modeling and optimization of a chiller plant

Author

Listed:
  • Wei, Xiupeng
  • Xu, Guanglin
  • Kusiak, Andrew

Abstract

A data-driven approach is utilized to model a chiller plant that has four chillers, four cooling towers, and two chilled water storage tanks. The chillers have varying energy efficiency. Since the chiller plant model derived from data-driven approach is nonlinear and non-convex, it is not practical to solve it by using the traditional gradient-based optimization algorithm. A two-level intelligent algorithm is developed to solve the model aiming at minimizing the total cost of the chilled water plant. The proposed algorithm can effectively search the optimum under the non-convex and nonlinear situation. A simulation case is conducted and the corresponding results are discussed.

Suggested Citation

  • Wei, Xiupeng & Xu, Guanglin & Kusiak, Andrew, 2014. "Modeling and optimization of a chiller plant," Energy, Elsevier, vol. 73(C), pages 898-907.
  • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:898-907
    DOI: 10.1016/j.energy.2014.06.102
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214008019
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2014.06.102?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. Janghorban Esfahani, I. & Yoo, C.K., 2013. "Exergy analysis and parametric optimization of three power and fresh water cogeneration systems using refrigeration chillers," Energy, Elsevier, vol. 59(C), pages 340-355.
    2. Kusiak, Andrew & Li, Mingyang & Zheng, Haiyang, 2010. "Virtual models of indoor-air-quality sensors," Applied Energy, Elsevier, vol. 87(6), pages 2087-2094, June.
    3. Powell, Kody M. & Cole, Wesley J. & Ekarika, Udememfon F. & Edgar, Thomas F., 2013. "Optimal chiller loading in a district cooling system with thermal energy storage," Energy, Elsevier, vol. 50(C), pages 445-453.
    4. Ashok Kumar (ed.), 2010. "Air Quality," Books, IntechOpen, number 787.
    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. Bornman, Waldo & Dirker, Jaco & Arndt, Deon C. & Meyer, Josua P., 2016. "Operational energy minimisation for forced draft, direct-contact bulk air cooling tower through a combination of forward and first-principle modelling, coupled with an optimisation platform," Energy, Elsevier, vol. 114(C), pages 995-1006.
    2. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    3. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    4. Daishi Sagawa & Kenji Tanaka, 2023. "Machine Learning-Based Estimation of COP and Multi-Objective Optimization of Operation Strategy for Heat Source Considering Electricity Cost and On-Site Consumption of Renewable Energy," Energies, MDPI, vol. 16(13), pages 1-26, June.
    5. Elnazeer Ali Hamid Abdalla & Perumal Nallagownden & Nursyarizal Bin Mohd Nor & Mohd Fakhizan Romlie & Sabo Miya Hassan, 2018. "An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus," Energies, MDPI, vol. 11(4), pages 1-24, March.
    6. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    7. Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Jinhua Hu & Meng Wang, 2017. "Modeling and Optimization of a CoolingTower-Assisted Heat Pump System," Energies, MDPI, vol. 10(5), pages 1-18, May.
    8. Grágeda, Mario & González, Alonso & Alavia, Wilson & Ushak, Svetlana, 2015. "Development and optimization of a modified process for producing the battery grade LiOH: Optimization of energy and water consumption," Energy, Elsevier, vol. 89(C), pages 667-677.
    9. Verma, Anoop & Asadi, Ali & Yang, Kai & Tyagi, Satish, 2015. "A data-driven approach to identify households with plug-in electrical vehicles (PEVs)," Applied Energy, Elsevier, vol. 160(C), pages 71-79.
    10. Galea, Matthew & Sant, Tonio, 2016. "Coupling of an offshore wind-driven deep sea water pump to an air cycle machine for large-scale cooling applications," Renewable Energy, Elsevier, vol. 88(C), pages 288-306.
    11. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    12. Suryakant, & Tyagi, Satish, 2015. "Optimization of a platform configuration with generational changes," International Journal of Production Economics, Elsevier, vol. 169(C), pages 299-309.
    13. Zhang, Xu & Wang, Yujie & Yang, Duo & Chen, Zonghai, 2016. "An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model," Energy, Elsevier, vol. 115(P1), pages 219-229.
    14. Ho, W.T. & Yu, F.W., 2021. "Improved model and optimization for the energy performance of chiller system with diverse component staging," Energy, Elsevier, vol. 217(C).
    15. Bo Gao & Ji Ni & Zhongyuan Yuan & Nanyang Yu, 2023. "Pump-Valve Combined Control of a HVAC Chilled Water System Using an Artificial Neural Network Model," Energies, MDPI, vol. 16(5), pages 1-16, March.
    16. Wang, Huijie & Qiu, Baoyun & Zhao, Fangling & Yan, Tianxu, 2023. "Method for increasing net power of power plant based on operation optimization of circulating cooling water system," Energy, Elsevier, vol. 282(C).

    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. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    2. Kelly, Scott & Shipworth, Michelle & Shipworth, David & Gentry, Michael & Wright, Andrew & Pollitt, Michael & Crawford-Brown, Doug & Lomas, Kevin, 2013. "Predicting the diversity of internal temperatures from the English residential sector using panel methods," Applied Energy, Elsevier, vol. 102(C), pages 601-621.
    3. Janghorban Esfahani, Iman & Kang, Yong Tae & Yoo, ChangKyoo, 2014. "A high efficient combined multi-effect evaporation–absorption heat pump and vapor-compression refrigeration part 1: Energy and economic modeling and analysis," Energy, Elsevier, vol. 75(C), pages 312-326.
    4. Rismanchi, B., 2017. "District energy network (DEN), current global status and future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 571-579.
    5. Powell, Kody M. & Kim, Jong Suk & Cole, Wesley J. & Kapoor, Kriti & Mojica, Jose L. & Hedengren, John D. & Edgar, Thomas F., 2016. "Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market," Energy, Elsevier, vol. 113(C), pages 52-63.
    6. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    7. Shaikh, Faisal Karim & Zeadally, Sherali, 2016. "Energy harvesting in wireless sensor networks: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1041-1054.
    8. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    9. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    10. Blanco-Marigorta, A.M. & Lozano-Medina, A. & Marcos, J.D., 2017. "A critical review of definitions for exergetic efficiency in reverse osmosis desalination plants," Energy, Elsevier, vol. 137(C), pages 752-760.
    11. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).
    12. Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
    13. Araghi, Alireza Hosseini & Khiadani, Mehdi & Hooman, Kamel, 2016. "A novel vacuum discharge thermal energy combined desalination and power generation system utilizing R290/R600a," Energy, Elsevier, vol. 98(C), pages 215-224.
    14. Dufour, Thomas & Hoang, Hong Minh & Oignet, Jérémy & Osswald, Véronique & Clain, Pascal & Fournaison, Laurence & Delahaye, Anthony, 2017. "Impact of pressure on the dynamic behavior of CO2 hydrate slurry in a stirred tank reactor applied to cold thermal energy storage," Applied Energy, Elsevier, vol. 204(C), pages 641-652.
    15. Lake, Andrew & Rezaie, Behanz & Beyerlein, Steven, 2017. "Review of district heating and cooling systems for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 417-425.
    16. Guelpa, Elisa & Verda, Vittorio, 2019. "Thermal energy storage in district heating and cooling systems: A review," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Long, R. & Bao, Y.J. & Huang, X.M. & Liu, W., 2014. "Exergy analysis and working fluid selection of organic Rankine cycle for low grade waste heat recovery," Energy, Elsevier, vol. 73(C), pages 475-483.
    18. Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2017. "Development of an optimization based design framework for microgrid energy systems," Energy, Elsevier, vol. 140(P1), pages 340-351.
    19. Neri, Manfredi & Guelpa, Elisa & Verda, Vittorio, 2022. "Design and connection optimization of a district cooling network: Mixed integer programming and heuristic approach," Applied Energy, Elsevier, vol. 306(PA).
    20. Gadhamshetty, Venkataramana & Gude, Veera Gnaneswar & Nirmalakhandan, Nagamany, 2014. "Thermal energy storage system for energy conservation and water desalination in power plants," Energy, Elsevier, vol. 66(C), pages 938-949.

    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:73:y:2014:i:c:p:898-907. 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.