IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i13p2544-d244997.html
   My bibliography  Save this article

Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model

Author

Listed:
  • Jens Baetens

    (Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium)

  • Greet Van Eetvelde

    (Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium
    INEOS Group, 1180 Rolle, Switzerland)

  • Gert Lemmens

    (INEOS Group, 1180 Rolle, Switzerland)

  • Nezmin Kayedpour

    (Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium)

  • Jeroen D. M. De Kooning

    (Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium)

  • Lieven Vandevelde

    (Electrical Energy Laboratory (EELAB), Department of Electrical Energy, Metals, Mechanical Constructions & Systems (EEMMeCS), Ghent University, Tech Lane Ghent Science Park—Campus A, Technologiepark-Zwijnaarde 131, 9052 Ghent, Belgium
    EEDT-DC Flanders Make, 9052 Ghent, Belgium)

Abstract

The shift from fossil fuel to more renewable electricity generation will require the broader implementation of Demand Side Response (DSR) into the grid. Utility processes in industry are suited for this, having a large thermal time constant or buffer, and large electricity consumption. A widespread utility system in industry is an induced draft evaporative cooling tower. Considering the safety aspect, such a process needs to maintain cooling water temperature within predefined safe boundaries. Therefore, in this paper, two modelling methods for the prediction of the basin temperature of an induced draft evaporative cooling tower are proposed. Both a white box and a black box methodology are presented, based on the physical principles of fluid dynamics and adaptive neuro-fuzzy interference system (ANFIS) modelling, respectively. By analysing the accuracy of both models with a focus to cooling tower fan state changes, i.e., DSR purposes, it is shown that the white box model performs best. Fostering the idea of using such a system for DSR purposes, the concept of design for flexibility is also touched upon, discussing the thermal mass. Pre-cooling, where the temperature of the cooling water basin is lowered before a fan switch off period, was simulated with the white box model. It was shown that beneficial pre-cooling (to lower the temperature peak) is limited in time.

Suggested Citation

  • Jens Baetens & Greet Van Eetvelde & Gert Lemmens & Nezmin Kayedpour & Jeroen D. M. De Kooning & Lieven Vandevelde, 2019. "Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model," Energies, MDPI, vol. 12(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2544-:d:244997
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/13/2544/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/13/2544/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Mahdi Ghadiri & Azam Marjani & Samira Mohammadinia & Saeed Shirazian, 2021. "An insight into the estimation of relative humidity of air using artificial intelligence schemes," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10194-10222, July.
    2. Hamzah Ali Alkhazaleh & Navid Nahi & Mohammad Hossein Hashemian & Zohreh Nazem & Wameed Deyah Shamsi & Moncef L. Nehdi, 2022. "Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms," Sustainability, MDPI, vol. 14(21), pages 1-14, November.

    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. 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.

    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:jeners:v:12:y:2019:i:13:p:2544-:d:244997. 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.