Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage
Thermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of different...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10711 |
| _version_ | 1797511389350199296 |
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| author | Wojciech Mueller Krzysztof Koszela Sebastian Kujawa |
| author_facet | Wojciech Mueller Krzysztof Koszela Sebastian Kujawa |
| author_sort | Wojciech Mueller |
| collection | DOAJ |
| description | Thermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of differential equations into algebraic equations, as well as knowledge about the initial and boundary conditions. Furthermore, a lack of information or incomplete information about the initial conditions makes it difficult or impossible to evaluate the volume of stored energy, or can cause significant errors during evaluation. Such situations occur in systems equipped with a rock battery, in which solar collectors act as source of energy. Considering the above, as well as the lack of a model for batteries in a vertical setting, we identified the need for research into the storage phase of rock bed thermal storage systems, working in a horizontal setting, and generating MLP-type neural models. Among these models, MLP 4-7-1 turned out to be the best both in terms of the values of regression statistics and possibilities of generalization. According to the authors, artificial neural models depicting temperature changeability in storage phase will be helpful in the development of a new methodology that can predict the heat volume in rock bed thermal storage systems. |
| first_indexed | 2024-03-10T05:44:35Z |
| format | Article |
| id | doaj.art-d9abd7ed485243aa9834dea3483d2852 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:35Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d9abd7ed485243aa9834dea3483d28522023-11-22T22:17:47ZengMDPI AGApplied Sciences2076-34172021-11-0111221071110.3390/app112210711Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal StorageWojciech Mueller0Krzysztof Koszela1Sebastian Kujawa2Faculty of Biosystems Engineering, Poznań University of Life Sciences, 60-625 Poznań, PolandFaculty of Biosystems Engineering, Poznań University of Life Sciences, 60-625 Poznań, PolandFaculty of Biosystems Engineering, Poznań University of Life Sciences, 60-625 Poznań, PolandThermal energy stored within a rock bed thermal storage system, which is mostly used in agriculture, can be identified during the storage phase using mathematical models based on heat transfer, which concerns batteries running in a vertical setting. However, this requires the conversion of differential equations into algebraic equations, as well as knowledge about the initial and boundary conditions. Furthermore, a lack of information or incomplete information about the initial conditions makes it difficult or impossible to evaluate the volume of stored energy, or can cause significant errors during evaluation. Such situations occur in systems equipped with a rock battery, in which solar collectors act as source of energy. Considering the above, as well as the lack of a model for batteries in a vertical setting, we identified the need for research into the storage phase of rock bed thermal storage systems, working in a horizontal setting, and generating MLP-type neural models. Among these models, MLP 4-7-1 turned out to be the best both in terms of the values of regression statistics and possibilities of generalization. According to the authors, artificial neural models depicting temperature changeability in storage phase will be helpful in the development of a new methodology that can predict the heat volume in rock bed thermal storage systems.https://www.mdpi.com/2076-3417/11/22/10711rock bedthermal storageheat transferartificial neural network |
| spellingShingle | Wojciech Mueller Krzysztof Koszela Sebastian Kujawa Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage Applied Sciences rock bed thermal storage heat transfer artificial neural network |
| title | Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage |
| title_full | Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage |
| title_fullStr | Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage |
| title_full_unstemmed | Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage |
| title_short | Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage |
| title_sort | neural identification of a temperature field in the storing phase of thermal energy in rock bed thermal storage |
| topic | rock bed thermal storage heat transfer artificial neural network |
| url | https://www.mdpi.com/2076-3417/11/22/10711 |
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