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Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq

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  • Homod, Raad Z.

Abstract

In a hot and humid climate, HVAC (heating, ventilating and air conditioning) systems go through rigorous coupling procedures as a result of indoor conditions, which are significantly affected by the outdoor environment. Hence, a traditional method for addressing a coupling setback in HVAC systems is to add a reheating coil. However, this technique consumes a significant amount of energy. Three different strategies are designed in a hot and humid climate region, such as Basra, for AHUs (air handling unit), and their evaluations of decoupling are compared. The first and second strategies use the same feedback control references (temperature and relative humidity), except the second one also uses a reheating coil and a wet main cooling coil. The AHU (air handling unit) of the third (proposed) strategy is equipped with a dry main cooling coil and a wet pre-cooling coil to dehumidify fresh air, which allows the controller to handle the coupling problem. Furthermore, the proposed strategy utilises the PMV (predicted mean vote) index as a feedback control reference to increase optimisation parameters that provide more flexibility in meeting the thermal comfort sensation. The adaptive control algorithm of nonlinear multivariable systems is adopted to coordinate these three policies of optimisation. The results of the three strategies show that the proposed scheme achieved the desired thermal comfort, superior performance, adaptation, robustness and implementation without using a reheating coil.

Suggested Citation

  • Homod, Raad Z., 2014. "Assessment regarding energy saving and decoupling for different AHU (air handling unit) and control strategies in the hot-humid climatic region of Iraq," Energy, Elsevier, vol. 74(C), pages 762-774.
    • Handle: RePEc:eee:energy:v:74:y:2014:i:c:p:762-774
    DOI: 10.1016/j.energy.2014.07.047
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    References listed on IDEAS

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    Cited by:

    1. Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
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    4. Badis Bakri & Hani Benguesmia & Ahmed Ketata & Slah Driss & Haythem Nasraoui & Zied Driss, 2024. "Enhancing Sustainable Development: Assessing a Solar Air Heater (SAH) Test Bench through Computational and Experimental Methods," Sustainability, MDPI, vol. 16(14), pages 1-19, July.
    5. Khan, Muhammad Waqas & Choudhry, Mohammad Ahmad & Zeeshan, Muhammad & Ali, Ahsan, 2015. "Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit," Energy, Elsevier, vol. 81(C), pages 477-488.
    6. Homod, Raad Z. & Munahi, Basil Sh. & Mohammed, Hayder Ibrahim & Albadr, Musatafa Abbas Abbood & Abderrahmane, AISSA & Mahdi, Jasim M. & Ben Hamida, Mohamed Bechir & Alhasnawi, Bilal Naji & Albahri, A., 2024. "Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings," Applied Energy, Elsevier, vol. 356(C).
    7. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Adaptive full-range decoupled ventilation strategy and air-conditioning systems for cleanrooms and buildings requiring strict humidity control and their performance evaluation," Energy, Elsevier, vol. 168(C), pages 883-896.
    8. Homod, Raad Z. & Togun, Hussein & Ateeq, Adnan A. & Al-Mousawi, Fadhel Noraldeen & Yaseen, Zaher Mundher & Al-Kouz, Wael & Hussein, Ahmed Kadhim & Alawi, Omer A. & Goodarzi, Marjan & Ahmadi, Goodarz, 2022. "An innovative clustering technique to generate hybrid modeling of cooling coils for energy analysis: A case study for control performance in HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    9. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    10. Awais Shah & Deqing Huang & Yixing Chen & Xin Kang & Na Qin, 2017. "Robust Sliding Mode Control of Air Handling Unit for Energy Efficiency Enhancement," Energies, MDPI, vol. 10(11), pages 1-21, November.
    11. Alexandre Correia & Luís Miguel Ferreira & Paulo Coimbra & Pedro Moura & Aníbal T. de Almeida, 2022. "Smart Thermostats for a Campus Microgrid: Demand Control and Improving Air Quality," Energies, MDPI, vol. 15(4), pages 1-21, February.
    12. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).

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