Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks
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DOI: 10.1016/j.apenergy.2019.113525
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Cited by:
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- An, G.L. & Wu, S.F. & Wang, L.W. & Zhang, C. & Zhang, B., 2022. "Comparative investigations of sorption/resorption/cascading cycles for long-term thermal energy storage," Applied Energy, Elsevier, vol. 306(PA).
- Salins, Sampath Suranjan & Kota Reddy, S.V. & Shiva Kumar,, 2021. "Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier," Applied Energy, Elsevier, vol. 293(C).
- Saikia, Pranaynil & Bastida, Héctor & Ugalde-Loo, Carlos E., 2024. "An effective predictor of the dynamic operation of latent heat thermal energy storage units based on a non-linear autoregressive network with exogenous inputs," Applied Energy, Elsevier, vol. 360(C).
- Carla Delmarre & Marie-Anne Resmond & Frédéric Kuznik & Christian Obrecht & Bao Chen & Kévyn Johannes, 2021. "Artificial Neural Network Simulation of Energetic Performance for Sorption Thermal Energy Storage Reactors," Energies, MDPI, vol. 14(11), pages 1-12, June.
- Ding, Zhixiong & Wu, Wei, 2022. "Type II absorption thermal battery for temperature upgrading: Energy storage heat transformer," Applied Energy, Elsevier, vol. 324(C).
- Scapino, Luca & De Servi, Carlo & Zondag, Herbert A. & Diriken, Jan & Rindt, Camilo C.M. & Sciacovelli, Adriano, 2020. "Techno-economic optimization of an energy system with sorption thermal energy storage in different energy markets," Applied Energy, Elsevier, vol. 258(C).
- Tzinnis, Efstratios & Baldini, Luca, 2021. "Combining sorption storage and electric heat pumps to foster integration of solar in buildings," Applied Energy, Elsevier, vol. 301(C).
- Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Anna Kulakowska & Anna Zylka & Karolina Grabowska & Katarzyna Ciesielska & Wojciech Nowak, 2020. "Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)," Energies, MDPI, vol. 13(24), pages 1-16, December.
- Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
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Keywords
Artificial neural networks; Sorption heat storage; Energy efficiency; Thermal energy storage;All these keywords.
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