Experimental study of a large temperature difference thermal energy storage tank for centralized heating systems

Decreasing the backwater temperature of the primary pipe in a centralized heating system is one successful way to increase the heating capacity and recover different kinds of industrial low-grade heat from the system. A new system combining an energy storage tank and a heat pump is introduced in this study as the key device in this system, so the temperature difference of this thermal storage tank could be over 25?C. To improve the thermal energy storage tank design, a mathematical model considering disturbance factor is given, an experimental system is built, and good agreement is found when the experimental results are compared with simulation results.

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Model predictive control of a large temperature difference refrigerating station with ice cold thermal energy storage

2017 Chinese Automation Congress (CAC) ◽

10.1109/cac.2017.8244104 ◽

2017 ◽

Author(s):

Wei Dong ◽

Liu Kun ◽

Wang Yi

Keyword(s):

Energy Storage ◽

Model Predictive Control ◽

Predictive Control ◽

Thermal Energy ◽

Temperature Difference ◽

Thermal Energy Storage ◽

Large Temperature ◽

Large Temperature Difference

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A Parametric Study of a Solar-Assisted House Heating System with a Seasonal Underground Thermal Energy Storage Tank

Sustainability ◽

10.3390/su12208686 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8686 ◽

Cited By ~ 1

Author(s):

Le Minh Nhut ◽

Waseem Raza ◽

Youn Cheol Park

Keyword(s):

Mathematical Model ◽

Energy Storage ◽

South Korea ◽

Thermal Energy ◽

Thermal Energy Storage ◽

Storage Tank ◽

Fossil Fuels ◽

Weather Conditions ◽

Heating System ◽

Thickness Effect

The requirement for energy is increasing worldwide as populations and economies develop. Reasons for this increase include global warming, climate change, an increase in electricity demand, and paucity of fossil fuels. Therefore, research in renewable energy technology has become a central topic in recent studies. In this study, a solar-assisted house heating system with a seasonal underground thermal energy storage tank is proposed based on the reference system to calculate the insulation thickness effect, the collector area, and an underground storage tank volume on the system performance according to real weather conditions at Jeju Island, South Korea. For this purpose, a mathematical model was established to calculate its operating performance. This mathematical model used the thermal response factor method to calculate the heat load and heat loss of the seasonal underground thermal energy storage tank. The results revealed that on days with different weather conditions, namely, clear weather, intermittent clouds sky, and overcast sky, the obtained solar fraction was 45.8%, 17.26%, and 0%, respectively. Using this method, we can save energy, space, and cost. This can then be applied to the solar-assisted house heating system in South Korea using the seasonal underground thermal energy storage tank.

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

Energies ◽

10.3390/en14113294 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3294

Author(s):

Carla Delmarre ◽

Marie-Anne Resmond ◽

Frédéric Kuznik ◽

Christian Obrecht ◽

Bao Chen ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Storage ◽

Thermal Energy ◽

Temperature Difference ◽

Thermal Energy Storage ◽

Heat Storage ◽

Experimental Results ◽

Physical Models ◽

Artificial Neural

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 3K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90K and 40K.

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