Artificial Neural Network Simulation of Energetic Performance for Sorption Thermal Energy Storage Reactors

Carla Delmarre, Marie-Anne Resmond, Frédéric Kuznik, Christian Obrecht, Bao Chen and Kévyn Johannes
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Carla Delmarre: Université de Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon1, CETHIL UMR5008, 69621 Villeurbanne, France
Marie-Anne Resmond: Université de Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon1, CETHIL UMR5008, 69621 Villeurbanne, France
Frédéric Kuznik: Université de Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon1, CETHIL UMR5008, 69621 Villeurbanne, France
Christian Obrecht: Université de Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon1, CETHIL UMR5008, 69621 Villeurbanne, France
Bao Chen: Université de Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon1, CETHIL UMR5008, 69621 Villeurbanne, France
Kévyn Johannes: Université de Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon1, CETHIL UMR5008, 69621 Villeurbanne, France

Energies, 2021, vol. 14, issue 11, 1-12

Abstract: Sorption thermal heat storage is a promising solution to improve the development of renewable energies and to promote a rational use of energy both for industry and households. These systems store thermal energy through physico-chemical sorption/desorption reactions that are also termed hydration/dehydration. Their introduction to the market requires to assess their energy performances, usually analysed by numerical simulation of the overall system. To address this, physical models are commonly developed and used. However, simulation based on such models are time-consuming which does not allow their use for yearly simulations. Artificial neural network (ANN)-based models, which are known for their computational efficiency, may overcome this issue. Therefore, the main objective of this study is to investigate the use of an ANN model to simulate a sorption heat storage system, instead of using a physical model. The neural network is trained using experimental results in order to evaluate this approach on actual systems. By using a recurrent neural network (RNN) and the Deep Learning Toolbox in MATLAB, a good accuracy is reached, and the predicted results are close to the experimental results. The root mean squared error for the prediction of the temperature difference during the thermal energy storage process is less than 3 K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90 K and 40 K .

Keywords: sorption thermal energy storage; zeolite; artificial neural network; recurrent neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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