IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p5155-d618450.html
   My bibliography  Save this article

An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room

Author

Listed:
  • Mpho J. Lencwe

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • SP Daniel Chowdhury

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Sipho Mahlangu

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Maxwell Sibanyoni

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Louwrance Ngoma

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air conditioning system (HVACS). This is achieved by using a specific range of controllers, which have more than thirty loops of proportional, integral, and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen and oxygen, maintaining the desired temperature of the battery storage room within recommended limits (i.e., 25 ± 1 °C tolerance) without compromising quality, as set out in the user requirement specification. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database, etc. It does this automatically and allows no human interface after all necessary settings and installation are completed. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and to ensure that the HVACS extracts the gas content to the outside environment. The system’s results show that the network control panel operates effectively as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, this also enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead–acid batteries (LAB). The extraction of toxic gases, regulation of temperature, ensuring suitable humidity in UPS battery room is important as it provides longer operational service of equipment, thus reducing frequent maintenance in these rooms. This benefits the electricity supply industry and helps in saving for unplanned maintenance costs.

Suggested Citation

  • Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5155-:d:618450
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/5155/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/5155/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    3. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    4. Pouria Bahramnia & Seyyed Mohammad Hosseini Rostami & Jin Wang & Gwang-jun Kim, 2019. "Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control," Energies, MDPI, vol. 12(24), pages 1-24, December.
    5. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    6. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    7. Tobias Heidrich & Jonathan Grobe & Henning Meschede & Jens Hesselbach, 2018. "Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany," Energies, MDPI, vol. 11(12), pages 1-18, December.
    8. Andrew Izawa & Matthias Fripp, 2018. "Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance," Energies, MDPI, vol. 11(9), pages 1-18, September.
    9. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    10. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    11. 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).
    12. Ruixin Lv & Zhongyuan Yuan & Bo Lei & Jiacheng Zheng & Xiujing Luo, 2021. "Model Predictive Control with Adaptive Building Model for Heating Using the Hybrid Air-Conditioning System in a Railway Station," Energies, MDPI, vol. 14(7), pages 1-22, April.
    13. Jau-Woei Perng & Yi-Chang Kuo & Yao-Tsung Chang & Hsi-Hsiang Chang, 2020. "Power Substation Construction and Ventilation System Co-Designed Using Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-27, May.
    14. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.
    15. Mei, Jun & Xia, Xiaohua, 2017. "Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 195(C), pages 439-452.
    16. Awais Shah & Deqing Huang & Tianpeng Huang & Umar Farid, 2018. "Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems," Energies, MDPI, vol. 11(11), pages 1-18, October.
    17. Nam-Chul Seong & Jee-Heon Kim & Wonchang Choi, 2019. "Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    18. 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.
    19. Wang, Wei & Chen, Jiayu & Huang, Gongsheng & Lu, Yujie, 2017. "Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution," Applied Energy, Elsevier, vol. 207(C), pages 305-323.
    20. Christopher J. Bay & Rohit Chintala & Bryan P. Rasmussen, 2020. "Steady-State Predictive Optimal Control of Integrated Building Energy Systems Using a Mixed Economic and Occupant Comfort Focused Objective Function," Energies, MDPI, vol. 13(11), pages 1-26, June.
    21. Mario Collotta & Antonio Messineo & Giuseppina Nicolosi & Giovanni Pau, 2014. "A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input," Energies, MDPI, vol. 7(8), pages 1-30, July.
    22. Edorta Carrascal-Lekunberri & Izaskun Garrido & Bram Van der Heijde & Aitor J. Garrido & José María Sala & Lieve Helsen, 2017. "Energy Conservation in an Office Building Using an Enhanced Blind System Control," Energies, MDPI, vol. 10(2), pages 1-23, February.
    23. Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2017. "Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands," Applied Energy, Elsevier, vol. 190(C), pages 222-231.
    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. V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.

    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. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    3. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
    5. Löhr, Yannik & Wolf, Daniel & Pollerberg, Clemens & Hörsting, Alexander & Mönnigmann, Martin, 2021. "Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages," Applied Energy, Elsevier, vol. 290(C).
    6. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    7. Ali Hamza & Muhammad Uneeb & Iftikhar Ahmad & Komal Saleem & Zunaib Ali, 2023. "Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones," Energies, MDPI, vol. 16(10), pages 1-27, May.
    8. Amal Azzi & Mohamed Tabaa & Badr Chegari & Hanaa Hachimi, 2024. "Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort," Sustainability, MDPI, vol. 16(5), pages 1-36, March.
    9. Ahmad Esmaeilzadeh & Brian Deal & Aghil Yousefi-Koma & Mohammad Reza Zakerzadeh, 2022. "How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading," Energies, MDPI, vol. 15(22), pages 1-27, November.
    10. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
    11. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    12. Zhang, Sheng & Ai, Zhengtao & Lin, Zhang, 2021. "Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving," Applied Energy, Elsevier, vol. 293(C).
    13. Wald, Dylan & King, Jennifer & Bay, Christopher J. & Chintala, Rohit & Johnson, Kathryn, 2022. "Integration of distributed controllers: Power reference tracking through charging station and building coordination," Applied Energy, Elsevier, vol. 314(C).
    14. Giuseppe Anastasi & Carlo Bartoli & Paolo Conti & Emanuele Crisostomi & Alessandro Franco & Sergio Saponara & Daniele Testi & Dimitri Thomopulos & Carlo Vallati, 2021. "Optimized Energy and Air Quality Management of Shared Smart Buildings in the COVID-19 Scenario," Energies, MDPI, vol. 14(8), pages 1-17, April.
    15. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    16. Jiang, Zixin & Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk," Applied Energy, Elsevier, vol. 334(C).
    17. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
    18. Awais Shah & Deqing Huang & Tianpeng Huang & Umar Farid, 2018. "Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems," Energies, MDPI, vol. 11(11), pages 1-18, October.
    19. Goopyo Hong & Byungseon Sean Kim, 2018. "Response to Comments by Yaolin Lin and Wei Yang “Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy”. Energies 2018, 11 , 407," Energies, MDPI, vol. 11(6), pages 1-2, June.
    20. Anass Berouine & Radouane Ouladsine & Mohamed Bakhouya & Mohamed Essaaidi, 2020. "Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings," Energies, MDPI, vol. 13(12), pages 1-16, June.

    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:14:y:2021:i:16:p:5155-:d:618450. 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.