IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2023i1p224-d1311248.html
   My bibliography  Save this article

Review of Energy-Related Machine Learning Applications in Drying Processes

Author

Listed:
  • Damir Đaković

    (Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Miroslav Kljajić

    (Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Nikola Milivojević

    (Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Đorđije Doder

    (Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Aleksandar S. Anđelković

    (Department of Energy and Process Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

Abstract

Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

Suggested Citation

  • Damir Đaković & Miroslav Kljajić & Nikola Milivojević & Đorđije Doder & Aleksandar S. Anđelković, 2023. "Review of Energy-Related Machine Learning Applications in Drying Processes," Energies, MDPI, vol. 17(1), pages 1-38, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:224-:d:1311248
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/224/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/1/224/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    2. Tagnamas, Zakaria & Idlimam, Ali & Lamharrar, Abdelkader, 2023. "Predictive models of beetroot solar drying process through machine learning algorithms," Renewable Energy, Elsevier, vol. 219(P2).
    3. Hamid, Khalid & Sajjad, Uzair & Yang, Kai Shing & Wu, Shih-Kuo & Wang, Chi-Chuan, 2022. "Assessment of an energy efficient closed loop heat pump dryer for high moisture contents materials: An experimental investigation and AI based modelling," Energy, Elsevier, vol. 238(PB).
    4. Beigi, Mohsen & Torki-Harchegani, Mehdi & Tohidi, Mojtaba, 2017. "Experimental and ANN modeling investigations of energy traits for rough rice drying," Energy, Elsevier, vol. 141(C), pages 2196-2205.
    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. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    2. Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    3. Joshua Adeniyi Depiver & Sabuj Mallik, 2023. "An Empirical Study on Convective Drying of Ginger Rhizomes Leveraging Environmental Stress Chambers and Linear Heat Conduction Methodology," Agriculture, MDPI, vol. 13(7), pages 1-28, June.
    4. Uzair Sajjad & Imtiyaz Hussain & Muhammad Sultan & Sadaf Mehdi & Chi-Chuan Wang & Kashif Rasool & Sayed M. Saleh & Ashraf Y. Elnaggar & Enas E. Hussein, 2021. "Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces," Sustainability, MDPI, vol. 13(22), pages 1-19, November.
    5. Prabhakaran, SP Sathiya & Swaminathan, Ganapathiraman & Joshi, Viraj V., 2022. "Combustion and pyrolysis kinetics of Australian lignite coal and validation by artificial neural networks," Energy, Elsevier, vol. 242(C).
    6. Deymi-Dashtebayaz, Mahdi & Davoodi, Vajihe & Khutornaya, Julia & Sergienko, Olga, 2023. "Parametric analysis and multi-objective optimization of a heat pump dryer based on working conditions and using different refrigerants," Energy, Elsevier, vol. 284(C).
    7. Ahmet Beyzade Demirpolat, 2019. "Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System," Energies, MDPI, vol. 12(18), pages 1-14, September.
    8. Liu, Xuexiang & Liu, Haowen & Zhao, Xudong & Han, Zhonghe & Cui, Yu & Yu, Min, 2022. "A novel neural network and grey correlation analysis method for computation of the heat transfer limit of a loop heat pipe (LHP)," Energy, Elsevier, vol. 259(C).
    9. Elif Üstündağlı Erten & Iman Janghorban Esfahani & Pouya Ifaei & Ebru Belkıs Güzeloğlu, 2023. "Dwelling, Habits, and Possessions: Clustering Turkey’s Household Energy Choices through Responsible Consumption and Poverty," Energies, MDPI, vol. 16(24), pages 1-20, December.
    10. Wang, Lijin & Fan, Weipeng & Jiang, Guoqian & Xie, Ping, 2023. "An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms," Energy, Elsevier, vol. 284(C).
    11. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    12. Panda, Brajesh Kumar & Mishra, Gayatri & Panigrahi, Shubham Subrot & Shrivastava, Shanker Lal, 2021. "Microwave-assisted parboiling of high moisture paddy: A comparative study based on energy utilization, process economy and grain quality with conventional parboiling," Energy, Elsevier, vol. 232(C).
    13. Showkat Ahmad Bhat & Nen-Fu Huang & Imtiyaz Hussain & Farzana Bibi & Uzair Sajjad & Muhammad Sultan & Abdullah Saad Alsubaie & Khaled H. Mahmoud, 2021. "On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    14. Adolf Rybka & Petr Heřmánek & Ivo Honzík, 2021. "Effect of drying temperature in hop dryer on hop quality," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 67(1), pages 1-7.
    15. Ghandehariun, Samane & Ghandehariun, Amir M. & Ziabari, Nima Bahrami, 2023. "Performance prediction and optimization of a hybrid renewable-energy-based multigeneration system using machine learning," Energy, Elsevier, vol. 282(C).
    16. Antonio Quijano & Celena Lorenzo & Luis Narvarte, 2023. "Economic Assessment of a PV-HP System for Drying Alfalfa in The North of Spain," Energies, MDPI, vol. 16(8), pages 1-19, April.
    17. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(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:17:y:2023:i:1:p:224-:d:1311248. 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.