{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:33:04Z","timestamp":1760149984542,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T00:00:00Z","timestamp":1696464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Competitiveness Operational Program","award":["121420"],"award-info":[{"award-number":["121420"]}]},{"name":"European Regional Development Fund","award":["121420"],"award-info":[{"award-number":["121420"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/en16196953","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T09:14:22Z","timestamp":1696497262000},"page":"6953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations"],"prefix":"10.3390","volume":"16","author":[{"given":"Ioan Mihail","family":"Savaniu","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering and Robotics in Construction, Technical University of Civil Engineering Bucharest, 59 Plevnei Str., 010223 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru-Polifron","family":"Chiri\u021b\u0103","sequence":"additional","affiliation":[{"name":"National Institute of Research & Development for Optoelectronics\/INOE 2000, Subsidiary Hydraulics and Pneumatics Research Institute\/IHP, Cutitul de Argint 14, 040558 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oana","family":"Tonciu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering and Robotics in Construction, Technical University of Civil Engineering Bucharest, 59 Plevnei Str., 010223 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magdalena","family":"Culcea","sequence":"additional","affiliation":[{"name":"Faculty of Building Services, Technical University of Civil Engineering Bucharest, 66 Pache Protopopescu Blvd., 020396 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6139-3571","authenticated-orcid":false,"given":"Ancuta","family":"Neagu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering and Robotics in Construction, Technical University of Civil Engineering Bucharest, 59 Plevnei Str., 010223 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hasan, H., Faris, M.A.-I.E., Mohamad, M.N., Al Dhaheri, A.S., Hashim, M., Stojanovska, L., Al Daour, R., Rashid, M., El-Farra, L., and Alsuwaidi, A. (2021). Consumption, Attitudes, and Trends of Vending Machine Foods at a University Campus: A Cross-Sectional Study. Foods, 10.","DOI":"10.3390\/foods10092122"},{"key":"ref_2","unstructured":"Anker (2023, July 20). How Can Solar Powered Vending Machines Help Get More Profit?. Available online: https:\/\/www.anker.com\/blogs\/solar\/solar-powered-vending-machines."},{"key":"ref_3","unstructured":"EcoFriend (2023, July 20). Solar Energy Powers Awesome Vending Machines. Available online: https:\/\/ecofriend.com\/solar-energy-powers-awesome-vending-machines.html."},{"key":"ref_4","unstructured":"Research and Markets (2023, July 20). Intelligent Vending Machines: Global Strategic Business Report. Available online: https:\/\/www.researchandmarkets.com\/reports\/3301146\/intelligent-vending-machines-global-strategic?gclid=EAIaIQobChMI8pqNmdrogAMVxepRCh08qALVEAAYASAAEgIz8vD_BwE#product--toc."},{"key":"ref_5","unstructured":"(2023, July 20). EU Green Public Procurement Criteria for Food, Catering Services and Vending Machines. Available online: https:\/\/circabc.europa.eu\/ui\/group\/44278090-3fae-4515-bcc2-44fd57c1d0d1\/library\/9cd7f542-d33c-43f6-91af-b3838c08c395\/details."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103240","DOI":"10.1016\/j.ifset.2022.103240","article-title":"Heating performance of microwave ovens powered by magnetron and solid-state generators","volume":"83","author":"Zhou","year":"2023","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jfoodeng.2006.05.013","article-title":"Porous media approaches to studying simultaneous heat and mass transfer in food processes. I: Problem formulations","volume":"80","author":"Datta","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_8","unstructured":"Verma, D.K., Mahanti, N.K., Thakur, M., Chakraborty, S., and Srivastav, P.P. (2020). Emerging Thermal and Nonthermal Technologies in Food Processing, Apple Academic Press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/07373937.2015.1037889","article-title":"Temperature Control During Microwave Heating Process by Sliding Mode Neural Network","volume":"34","author":"Li","year":"2015","journal-title":"Dry. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, S., Cho, S., Kim, S.-H., Kim, J., Chae, S., Jeong, H., and Kim, T. (2021). Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses. Energies, 14.","DOI":"10.3390\/en14010122"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E., and Shahab, S. (2020). Deep Learning for Stock Market Prediction. Entropy, 22.","DOI":"10.20944\/preprints202003.0256.v1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S0260-8774(02)00301-1","article-title":"Analysis of critical control points in deviant thermal processes using artificial neural networks","volume":"57","author":"Chen","year":"2003","journal-title":"J. Food Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kollia, I., Stevenson, J., and Kollias, S. (2021). AI-Enabled Efficient and Safe Food Supply Chain. Electronics, 10.","DOI":"10.20944\/preprints202105.0254.v1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sharma, S., Gahlawat, V.K., Rahul, K., Mor, R.S., and Malik, M. (2021). Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics. Logistics, 5.","DOI":"10.3390\/logistics5040066"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Calota, R., Savaniu, M., Girip, A., Nastase, I., Georgescu, M.R., and Tonciu, O. (2022). Study on Energy Efficiency of an Off-Grid Vending Machine with Compact Heat Exchangers and Low GWP Refrigerant Powered by Solar Energy. Energies, 15.","DOI":"10.3390\/en15124433"},{"key":"ref_16","unstructured":"Culcea, M., Darie, E., Gheorghe, S., Pecsi, R., and Savaniu, M.I. (2023). IOP Conference Series: Earth and Environmental Science, Proceedings of the 8th Conference of the Sustainable Solutions for Energy and Environment EENVIRO 2022, Bucharest, Romania, 16\u201321 October 2022, IOP Publishing Ltd."},{"key":"ref_17","first-page":"1185","article-title":"Study on the heat transfer with regard to an off-grid vending machine having a low impact on the environment","volume":"Volume 1185","author":"Girip","year":"2023","journal-title":"IOP Conference Series: Earth and Environmental Science, Proceedings of the 8th Conference of the Sustainable Solutions for Energy and Environment EENVIRO 2022, Bucharest, Romania, 16\u201321 October 2022"},{"key":"ref_18","unstructured":"Victron Energy, B.V. (2021). MPPT Solar Charger Manual, Victron Energy Manuals Publishing House."},{"key":"ref_19","unstructured":"(2023, July 20). Available online: http:\/\/www.chinaxhwb.com\/."},{"key":"ref_20","unstructured":"(2023, July 20). Available online: https:\/\/www.hioki.com\/global\/products\/pqa\/power-quality\/id_5824."},{"key":"ref_21","unstructured":"(2023, July 20). Available online: https:\/\/www.hioki.com\/global\/support\/download\/software\/versionup\/detail\/id_562."},{"key":"ref_22","unstructured":"(2009). Electromagnetic Compatibility (EMC)\u2014Part 4-30: Testing and Measurement Techniques\u2014Power Quality Measurement Methods (Standard No. EN 61000-4-30:2009)."},{"key":"ref_23","first-page":"259","article-title":"Theoretical aspects of the aeration drying process with application in the hay technology","volume":"45","author":"Zaica","year":"2015","journal-title":"Ann. Univ. Craiova\u2014Agric. Mont. Cadastre Ser."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ikeuchi, D., Vargas-Uscategui, A., Wu, X., and King, P.C. (2021). Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing. Appl. Sci., 11.","DOI":"10.3390\/app11041654"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, C., Coen-Pirani, P., and Jiang, X. (2023). Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis. Cancers, 15.","DOI":"10.3390\/cancers15071969"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113485","DOI":"10.1016\/j.cma.2020.113485","article-title":"Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization","volume":"373","author":"Zhang","year":"2021","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ryu, S., Noh, J., and Kim, H. (2017). Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies, 10.","DOI":"10.3390\/en10010003"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bilal, M.A., Wang, Y., Ji, Y., Akhter, M.P., and Liu, H. (2023). Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN). Appl. Sci., 13.","DOI":"10.3390\/app13148121"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, C.-J., and Kuo, P.-H. (2018). A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors, 18.","DOI":"10.3390\/s18072220"}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/16\/19\/6953\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:01:30Z","timestamp":1760130090000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/16\/19\/6953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,5]]},"references-count":30,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["en16196953"],"URL":"https:\/\/doi.org\/10.3390\/en16196953","relation":{},"ISSN":["1996-1073"],"issn-type":[{"type":"electronic","value":"1996-1073"}],"subject":[],"published":{"date-parts":[[2023,10,5]]}}}