IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6953-d1253846.html
   My bibliography  Save this article

Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations

Author

Listed:
  • Ioan Mihail Savaniu

    (Faculty of Mechanical Engineering and Robotics in Construction, Technical University of Civil Engineering Bucharest, 59 Plevnei Str., 010223 Bucharest, Romania)

  • Alexandru-Polifron Chiriță

    (National Institute of Research & Development for Optoelectronics/INOE 2000, Subsidiary Hydraulics and Pneumatics Research Institute/IHP, Cutitul de Argint 14, 040558 Bucharest, Romania)

  • Oana Tonciu

    (Faculty of Mechanical Engineering and Robotics in Construction, Technical University of Civil Engineering Bucharest, 59 Plevnei Str., 010223 Bucharest, Romania)

  • Magdalena Culcea

    (Faculty of Building Services, Technical University of Civil Engineering Bucharest, 66 Pache Protopopescu Blvd., 020396 Bucharest, Romania)

  • Ancuta Neagu

    (Faculty of Mechanical Engineering and Robotics in Construction, Technical University of Civil Engineering Bucharest, 59 Plevnei Str., 010223 Bucharest, Romania)

Abstract

This article presents novel research on the utilization of a neural-network-based time control system for microwave oven heating of food items within a solar-powered vending machine. The research aims to explore the control of heating time for various food products, considering multiple variables. The neural network controller is calibrated through extensive experimentation, allowing it to accurately predict optimal heating times based on input parameters such as food type, weight, initial temperature, water content, and desired doneness level. The results demonstrate that the neural-network-controlled microwave oven achieves precise and desirable heating durations, mitigating the risk of overheating and ensuring superior food quality and taste. Moreover, the solar-powered vending machine showcases a commitment to sustainable energy sources, effectively reducing dependence on non-renewable energy and minimizing greenhouse gas emissions. To maintain food quality and freshness, a food refrigeration unit is integrated into the vending machine, employing load-balancing technology to control the refrigeration chamber’s temperature effectively. Energy efficiency is prioritized in both the refrigeration unit and the microwave oven through intelligent algorithms and system optimization. The combination of a neural-network-controlled microwave oven, a solar-powered vending machine, and a food refrigeration unit introduces a novel and sustainable approach to food preparation and energy management.

Suggested Citation

  • Ioan Mihail Savaniu & Alexandru-Polifron Chiriță & Oana Tonciu & Magdalena Culcea & Ancuta Neagu, 2023. "Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations," Energies, MDPI, vol. 16(19), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6953-:d:1253846
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6953/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6953/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saurabh Sharma & Vijay Kumar Gahlawat & Kumar Rahul & Rahul S Mor & Mohit Malik, 2021. "Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics," Logistics, MDPI, vol. 5(4), pages 1-16, September.
    2. Răzvan Calotă & Mihai Savaniu & Alina Girip & Ilinca Năstase & Matei Răzvan Georgescu & Oana Tonciu, 2022. "Study on Energy Efficiency of an Off-Grid Vending Machine with Compact Heat Exchangers and Low GWP Refrigerant Powered by Solar Energy," Energies, MDPI, vol. 15(12), pages 1-26, June.
    3. Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
    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. Andreea-Alina CORNEA, 2023. "Big Data in Food Industry: A Technical Summary of Modern Approaches Used in Data Extraction," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 27(2), pages 25-35.
    2. Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021. "Stock price prediction using BERT and GAN," Papers 2107.09055, arXiv.org.
    3. Ramakrishnan Ramanathan & Yanqing Duan & Tahmina Ajmal & Katarzyna Pelc & James Gillespie & Sahar Ahmadzadeh & Joan Condell & Imke Hermens & Usha Ramanathan, 2023. "Motivations and Challenges for Food Companies in Using IoT Sensors for Reducing Food Waste: Some Insights and a Road Map for the Future," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    4. Suya Jin & Guiyan Liu & Qifeng Bai, 2023. "Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    5. Mohammed El Amine Senoussaoui & Mostefa Brahami & Issouf Fofana, 2021. "Transformer Oil Quality Assessment Using Random Forest with Feature Engineering," Energies, MDPI, vol. 14(7), pages 1-15, March.
    6. Arvind Kumar Sinha & Pradeep Shende, 2024. "Uncertainty Optimization Based Feature Selection Model for Stock Marketing," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 357-389, January.
    7. Damian Ślusarczyk & Robert Ślepaczuk, 2023. "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers 2023-17, Faculty of Economic Sciences, University of Warsaw.
    8. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.
    9. Mufhumudzi Muthivhi & Terence L. van Zyl, 2022. "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers 2203.05673, arXiv.org.
    10. Juan C. King & Roberto Dale & Jos'e M. Amig'o, 2024. "Blockchain Metrics and Indicators in Cryptocurrency Trading," Papers 2403.00770, arXiv.org.
    11. Mohit Malik & Vijay Kumar Gahlawat & Rahul S Mor & Vijay Dahiya & Mukheshwar Yadav, 2022. "Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review," Logistics, MDPI, vol. 6(4), pages 1-16, October.
    12. Helen Onyeaka & Phemelo Tamasiga & Uju Mary Nwauzoma & Taghi Miri & Uche Chioma Juliet & Ogueri Nwaiwu & Adenike A. Akinsemolu, 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    13. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    14. Yiyang Zheng, 2022. "Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract," Papers 2203.12457, arXiv.org.
    15. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
    16. Li-Chen Cheng & Yu-Hsiang Huang & Ming-Hua Hsieh & Mu-En Wu, 2021. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    17. Johannes Hangl & Viktoria Joy Behrens & Simon Krause, 2022. "Barriers, Drivers, and Social Considerations for AI Adoption in Supply Chain Management: A Tertiary Study," Logistics, MDPI, vol. 6(3), pages 1-22, September.

    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:16:y:2023:i:19:p:6953-:d:1253846. 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.