IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8497-d972126.html
   My bibliography  Save this article

Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants

Author

Listed:
  • Guilherme Jesus

    (Department of Electromechanical Engineering, Univerity of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, Portugal)

  • Martim L. Aguiar

    (Department of Electromechanical Engineering, Univerity of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, Portugal
    C-MAST—Center for Mechanical and Aerospace Science and Technologies, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, Portugal)

  • Pedro D. Gaspar

    (Department of Electromechanical Engineering, Univerity of Beira Interior, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, Portugal
    C-MAST—Center for Mechanical and Aerospace Science and Technologies, Rua Marquês de D’Ávila e Bolama, 6201-001 Covilhã, Portugal)

Abstract

There have been consequences regarding the increment of the greenhouse effect, such as the rise in the planet’s global temperature, and climate change. Refrigerants have an important contribution to the aforementioned environmental impact. In particular, hydrofluorocarbons (HFCs) contribute to the destruction of the ozone layer and the increase of the greenhouse effect. Protocols, international agreements, and legislation were developed to slow down the emission of greenhouse gases. Prohibition and definition of deadlines for the gradual elimination of various refrigerants have been proposed to replace them with others that are environmentally sustainable. Soon, the refrigeration sector will have to replace some refrigerants with others that are alternative and/or sustainable with minimal or zero environmental impact. A computational tool to support decision-making regarding the selection of alternative and/or sustainable refrigerant to replace the old one is developed to be used by refrigeration companies, manufacturers, and installers. A suggestion of refrigerants with reduced environmental impact is provided, ensuring similar thermal performance and energy efficiency, considering the safety level and renovation cost of the installation and refrigerant itself. This decision support system (DSS) uses an objective function that includes the technical specifications and properties of alternative and sustainable refrigerants. The computational tool is applied in the agri-food sector in three case studies. The results show not only the consistency of the computational tool, but also its flexibility, objectivity, and simplicity. Its use allows companies to choose refrigerants with reduced environmental impact, reduced or zero ozone depletion potential and global warming impact, thus contributing to environmental sustainability.

Suggested Citation

  • Guilherme Jesus & Martim L. Aguiar & Pedro D. Gaspar, 2022. "Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants," Energies, MDPI, vol. 15(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8497-:d:972126
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8497/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8497/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ara, Iffat & Turner, Lydia & Harrison, Matthew Tom & Monjardino, Marta & deVoil, Peter & Rodriguez, Daniel, 2021. "Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review," Agricultural Water Management, Elsevier, vol. 257(C).
    2. Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
    3. Khadijeh Alibabaei & Pedro D. Gaspar & Tânia M. Lima, 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling," Energies, MDPI, vol. 14(11), pages 1-21, May.
    4. Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
    5. Zinkernagel, Jana & Maestre-Valero, Jose. F. & Seresti, Sogol Y. & Intrigliolo, Diego S., 2020. "New technologies and practical approaches to improve irrigation management of open field vegetable crops," Agricultural Water Management, Elsevier, vol. 242(C).
    Full references (including those not matched with items on IDEAS)

    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. Imran Ali Lakhiar & Haofang Yan & Chuan Zhang & Guoqing Wang & Bin He & Beibei Hao & Yujing Han & Biyu Wang & Rongxuan Bao & Tabinda Naz Syed & Junaid Nawaz Chauhdary & Md. Rakibuzzaman, 2024. "A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints," Agriculture, MDPI, vol. 14(7), pages 1-40, July.
    2. Bounajra, Afaf & Guemmat, Kamal El & Mansouri, Khalifa & Akef, Fatiha, 2024. "Towards efficient irrigation management at field scale using new technologies: A systematic literature review," Agricultural Water Management, Elsevier, vol. 295(C).
    3. Eduardo Assunção & Pedro D. Gaspar & Khadijeh Alibabaei & Maria P. Simões & Hugo Proença & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application," Future Internet, MDPI, vol. 14(11), pages 1-12, November.
    4. Ruiqi Zhang & Chunguang Hu & Yucheng Sun, 2024. "Decoding the Characteristics of Ecosystem Services and the Scale Effect in the Middle Reaches of the Yangtze River Urban Agglomeration: Insights for Planning and Management," Sustainability, MDPI, vol. 16(18), pages 1-26, September.
    5. Monjardino, Marta & Harrison, Matthew T. & DeVoil, Peter & Rodriguez, Daniel & Sadras, Victor O., 2022. "Agronomic and on-farm infrastructure adaptations to manage economic risk in Australian irrigated broadacre systems: A case study," Agricultural Water Management, Elsevier, vol. 269(C).
    6. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
    7. Michael Gbenga Ogungbuyi & Juan P. Guerschman & Andrew M. Fischer & Richard Azu Crabbe & Caroline Mohammed & Peter Scarth & Phil Tickle & Jason Whitehead & Matthew Tom Harrison, 2023. "Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning," Land, MDPI, vol. 12(6), pages 1-25, May.
    8. Dina Pereira & Joao Carlos Correia Leitao & Pedro Dinis Gaspar & Cristina Fael & Isabel Falorca & Wael Khairy & Nadya Wahid & Hicham El Yousfi & Bassou Bouazzama & Jan Siering & Harald Hansmann & Jele, 2023. "Exploring Irrigation and Water Supply Technologies for Smallholder Farmers in the Mediterranean Region," Sustainability, MDPI, vol. 15(8), pages 1-25, April.
    9. Isakwisa Gaddy Tende & Kentaro Aburada & Hisaaki Yamaba & Tetsuro Katayama & Naonobu Okazaki, 2023. "Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    10. Yeboah, Samuel, 2023. "Unlocking the Potential of Technological Innovations for Sustainable Agriculture in Developing Countries: Enhancing Resource Efficiency and Environmental Sustainability," MPRA Paper 118215, University Library of Munich, Germany, revised 26 Jul 2023.
    11. Li Bin & Muhammad Shahzad & Hira Khan & Muhammad Mehran Bashir & Arif Ullah & Muhammad Siddique, 2023. "Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    12. Jakhongirmirzo Mirzaqobulov & Kedar Mehta & Sana Ilyas & Abdulkhakim Salokhiddinov, 2024. "The Role of Collector-Drainage Water in Sustainable Irrigation for Agriculture in the Developing World: An Experimental Study," World, MDPI, vol. 6(1), pages 1-19, December.
    13. Stavros Sakellariou & Marios Spiliotopoulos & Nikolaos Alpanakis & Ioannis Faraslis & Pantelis Sidiropoulos & Georgios A. Tziatzios & George Karoutsos & Nicolas R. Dalezios & Nicholas Dercas, 2024. "Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
    14. Cheng Miao & Jing Xie & Yingdong Li, 2024. "Evaluating the risk and its acceptability of crop yield reduction in Northwest China under the impact of wind hail disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(15), pages 14027-14048, December.
    15. Eltarabily, Mohamed Galal & Mohamed, Abdelmoneim Zakaria & Begna, Sultan & Wang, Dong & Putnam, Daniel H. & Scudiero, Elia & Bali, Khaled M., 2024. "Simulated soil water distribution patterns and water use of Alfalfa under different subsurface drip irrigation depths," Agricultural Water Management, Elsevier, vol. 293(C).
    16. Li, Yinan & Huang, Yuxin, 2023. "Enhancing resources efficiency: Studying economic development in resource-rich regions for long-term sustainability of China," Resources Policy, Elsevier, vol. 86(PA).
    17. Muhammad Alkaff & Abdullah Basuhail & Yuslena Sari, 2025. "Optimizing Water Use in Maize Irrigation with Reinforcement Learning," Mathematics, MDPI, vol. 13(4), pages 1-21, February.
    18. Yuanzhen Zhang & Guofang Wang & Lingzhi Li & Mingjing Huang, 2025. "A Monitoring Method for Agricultural Soil Moisture Using Wireless Sensors and the Biswas Model," Agriculture, MDPI, vol. 15(3), pages 1-18, February.
    19. Luis Carlos Soares da Silva Junior & David de Andrade Costa & Clifford B. Fedler, 2024. "From Scarcity to Abundance: Nature-Based Strategies for Small Communities Experiencing Water Scarcity in West Texas/USA," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    20. Guo, Shibo & Zhao, Jin & Zhao, Chuang & Guo, Erjing & Liu, Zhijuan & Harrison, Matthew Tom & Liu, Ke & Zhang, Tianyi & Yang, Xiaoguang, 2024. "Adapting crop land-use in line with a changing climate improves productivity, prosperity and reduces greenhouse gas emissions," Agricultural Systems, Elsevier, vol. 217(C).

    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:15:y:2022:i:22:p:8497-:d:972126. 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.