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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Autores principales:	Wojciech Mueller, Krzysztof Koszela, Sebastian Kujawa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rock bed thermal storage heat transfer artificial neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d9abd7ed485243aa9834dea3483d2852
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spelling	oai:doaj.org-article:d9abd7ed485243aa9834dea3483d28522021-11-25T16:36:00ZNeural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage10.3390/app1122107112076-3417https://doaj.org/article/d9abd7ed485243aa9834dea3483d28522021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10711https://doaj.org/toc/2076-3417Thermal 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.Wojciech MuellerKrzysztof KoszelaSebastian KujawaMDPI AGarticlerock bedthermal storageheat transferartificial neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10711, p 10711 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rock bed thermal storage heat transfer artificial neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	rock bed thermal storage heat transfer artificial neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Wojciech Mueller Krzysztof Koszela Sebastian Kujawa Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage
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.
format	article
author	Wojciech Mueller Krzysztof Koszela Sebastian Kujawa
author_facet	Wojciech Mueller Krzysztof Koszela Sebastian Kujawa
author_sort	Wojciech Mueller
title	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_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_sort	neural identification of a temperature field in the storing phase of thermal energy in rock bed thermal storage
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d9abd7ed485243aa9834dea3483d2852
work_keys_str_mv	AT wojciechmueller neuralidentificationofatemperaturefieldinthestoringphaseofthermalenergyinrockbedthermalstorage AT krzysztofkoszela neuralidentificationofatemperaturefieldinthestoringphaseofthermalenergyinrockbedthermalstorage AT sebastiankujawa neuralidentificationofatemperaturefieldinthestoringphaseofthermalenergyinrockbedthermalstorage
_version_	1718413107131318272

Neural Identification of a Temperature Field in the Storing Phase of Thermal Energy in Rock Bed Thermal Storage

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