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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
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    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.
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