IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v306y2022ipbs0306261921013593.html
   My bibliography  Save this article

ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks

Author

Listed:
  • Maiorino, Angelo
  • Del Duca, Manuel Gesù
  • Aprea, Ciro

Abstract

Advanced control methods proved they effectiveness in reducing energy consumption of refrigeration systems equipped with a variable-speed compressor, but they could not be suitable for fixed-speed compressors, which are usually controlled by a simple ON/OFF logic with a mechanical thermostat, which does not allow to optimize the performance of such devices. Hence, a novel control method based on the use of Artificial Neural Networks to optimize the operations of refrigeration systems equipped with a fixed-speed compressor is proposed. This technique uses an Artificial Neural Network, which stem from a three-step process, able to provide the ON/OFF control loop with the optimal hysteresis value accordingly to the requirement of the user, in terms of set-point temperature and optimization priority, and the ambient temperature. The proposed control method was encoded in a microcontroller to test its effectiveness with a refrigeration system. The results of the experimental tests demonstrated the great potential of this approach showing a reduction of energy consumption of 6.8% and 2.2% with no stored material and ambient temperatures of 25 °C and 32 °C, respectively. Then, the introduction of 45 kg of stored material led to energy savings up to 13.4% and 6.6% with ambient temperatures of 25 °C and 32 °C, respectively. Furthermore, it was evidenced that door openings and pick-and-place operations can reduce the positive effect of this approach, reducing the energy saving to 3.7%. The results show that Artificial Neural Networks can be successfully applied to optimize the ON/OFF control loop of refrigeration systems, considering both plug-in and built-in solutions.

Suggested Citation

  • Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013593
    DOI: 10.1016/j.apenergy.2021.118072
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921013593
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118072?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. Bolaji, B.O. & Huan, Z., 2013. "Ozone depletion and global warming: Case for the use of natural refrigerant – a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 49-54.
    2. Mohajan, Haradhan, 2011. "Greenhouse gas emissions increase global warming," MPRA Paper 50839, University Library of Munich, Germany, revised 18 Apr 2011.
    3. Belman-Flores, J.M. & Barroso-Maldonado, J.M. & Rodríguez-Muñoz, A.P. & Camacho-Vázquez, G., 2015. "Enhancements in domestic refrigeration, approaching a sustainable refrigerator – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 955-968.
    4. K. Gnana Sheela & S. N. Deepa, 2013. "Review on Methods to Fix Number of Hidden Neurons in Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, June.
    5. Mota-Babiloni, Adrián & Navarro-Esbrí, Joaquín & Makhnatch, Pavel & Molés, Francisco, 2017. "Refrigerant R32 as lower GWP working fluid in residential air conditioning systems in Europe and the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1031-1042.
    6. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    7. Romano, Antonio A. & Scandurra, Giuseppe & Carfora, Alfonso & Fodor, Mate, 2017. "Renewable investments: The impact of green policies in developing and developed countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 738-747.
    8. Aprea, Ciro & Maiorino, Angelo, 2010. "A flexible numerical model to study an active magnetic refrigerator for near room temperature applications," Applied Energy, Elsevier, vol. 87(8), pages 2690-2698, August.
    9. Kim, Hyung Chul & Keoleian, Gregory A. & Horie, Yuhta A., 2006. "Optimal household refrigerator replacement policy for life cycle energy, greenhouse gas emissions, and cost," Energy Policy, Elsevier, vol. 34(15), pages 2310-2323, October.
    10. Mraz, Miha, 2001. "The design of intelligent control of a kitchen refrigerator," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 56(3), pages 259-267.
    11. Mota-Babiloni, Adrián & Barbosa, Jader R. & Makhnatch, Pavel & Lozano, Jaime A., 2020. "Assessment of the utilization of equivalent warming impact metrics in refrigeration, air conditioning and heat pump systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    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. Li, Chengzhan & Sun, Jian & Zou, Huiming & Cai, Jinghui & Zhu, Tingting, 2023. "Characteristic analysis and energy efficiency of an oil-free dual-piston linear compressor for household refrigeration with various conditions," Energy, Elsevier, vol. 270(C).
    2. Mario Pérez-Gomariz & Antonio López-Gómez & Fernando Cerdán-Cartagena, 2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review," Clean Technol., MDPI, vol. 5(1), pages 1-21, January.
    3. Juan M. Belman-Flores & Donato Hernández-Fusilier & Juan J. García-Pabón & David A. Rodríguez-Valderrama, 2024. "Intelligent Control Based on Usage Habits in a Domestic Refrigerator with Variable Speed Compressor for Energy-Saving," Clean Technol., MDPI, vol. 6(2), pages 1-23, April.
    4. Qi Chen & Yinsong Li, 2022. "Experimental Investigation on Intermittent Operation Characteristics of Dual-Temperature Refrigeration System Using Hydrocarbon Mixture," Energies, MDPI, vol. 15(11), pages 1-19, May.

    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. Albà, C.G. & Alkhatib, I.I.I. & Llovell, F. & Vega, L.F., 2023. "Hunting sustainable refrigerants fulfilling technical, environmental, safety and economic requirements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. Angelo Maiorino & Manuel Gesù Del Duca & Jaka Tušek & Urban Tomc & Andrej Kitanovski & Ciro Aprea, 2019. "Evaluating Magnetocaloric Effect in Magnetocaloric Materials: A Novel Approach Based on Indirect Measurements Using Artificial Neural Networks," Energies, MDPI, vol. 12(10), pages 1-22, May.
    3. Kumma, Nagarjuna & Kruthiventi, S.S Harish, 2024. "Current status of refrigerants used in domestic applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Chen, Qi & Yu, Mengqi & Yan, Gang & Yu, Jianlin, 2022. "Thermodynamic analyses of a modified ejector enhanced dual temperature refrigeration cycle for domestic refrigerator/freezer application," Energy, Elsevier, vol. 244(PA).
    5. Yan, Gang & Bai, Tao & Yu, Jianlin, 2016. "Thermodynamic analysis on a modified ejector expansion refrigeration cycle with zeotropic mixture (R290/R600a) for freezers," Energy, Elsevier, vol. 95(C), pages 144-154.
    6. Belman-Flores, J.M. & Barroso-Maldonado, J.M. & Rodríguez-Muñoz, A.P. & Camacho-Vázquez, G., 2015. "Enhancements in domestic refrigeration, approaching a sustainable refrigerator – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 955-968.
    7. Biglia, Alessandro & Gemmell, Andrew J. & Foster, Helen J. & Evans, Judith A., 2020. "Energy performance of domestic cold appliances in laboratory and home environments," Energy, Elsevier, vol. 204(C).
    8. Kyriakopoulos, Grigorios L. & Arabatzis, Garyfallos & Tsialis, Panagiotis & Ioannou, Konstantinos, 2018. "Electricity consumption and RES plants in Greece: Typologies of regional units," Renewable Energy, Elsevier, vol. 127(C), pages 134-144.
    9. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    10. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    11. Kasaeian, Alibakhsh & Hosseini, Seyed Mohsen & Sheikhpour, Mojgan & Mahian, Omid & Yan, Wei-Mon & Wongwises, Somchai, 2018. "Applications of eco-friendly refrigerants and nanorefrigerants: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 91-99.
    12. Wang, Quan-Jing & Wang, Hai-Jie & Chang, Chun-Ping, 2022. "Environmental performance, green finance and green innovation: What's the long-run relationships among variables?," Energy Economics, Elsevier, vol. 110(C).
    13. Mohajan, Haradhan, 2013. "Greenhouse Gas Emissions from Small Industries," MPRA Paper 50796, University Library of Munich, Germany, revised 26 Feb 2013.
    14. Wu, Xi & Xu, Shiming & Jiang, Mengnan, 2018. "Development of bubble absorption refrigeration technology: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3468-3482.
    15. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    16. Yang, Junqin & Zhao, Hui & Li, Chenchen & Li, Xiuwei, 2021. "A direct energy reuse strategy for absorption air-conditioning system based on electrode regeneration method," Renewable Energy, Elsevier, vol. 168(C), pages 353-364.
    17. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
    18. Mohajan, Devajit & Mohajan, Haradhan, 2023. "Families of Grounded Theory: A Theoretical Structure for Novel Researchers," MPRA Paper 116752, University Library of Munich, Germany, revised 24 Jan 2023.
    19. Mohajan, Devajit & Mohajan, Haradhan, 2023. "Mathematical Model for Nonlinear Budget Constraint: Economic Activities on Increased Budget," MPRA Paper 117299, University Library of Munich, Germany, revised 17 Mar 2023.
    20. Sinha, Avik & Shah, Muhammad Ibrahim & Mehta, Atul & Sharma, Rajesh, 2022. "Impact of Energy Innovation on Greenhouse Gas Emissions: Moderation of Regional Integration and Social Inequality in Asian Economies," ADBI Working Papers 1304, Asian Development Bank Institute.

    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:appene:v:306:y:2022:i:pb:s0306261921013593. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.