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

Energies ◽  
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.

scholarly journals Transient Behavior Analysis for Solar Energy Storage in PCM-CFM Material Using Equivalent Heat Capacity Method as Storage Model

2019 ◽  
Vol 14 (2) ◽  
pp. 40-57
Author(s):  
Talib K. Murtadha ◽  
Hussien M M. S Salih ◽  
Ali D. Salman
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Experimental Results ◽  
Paraffin Wax ◽  
Storage Material ◽  
Copper Foam ◽  
Thermal Energy Storage Material ◽  
Flat Plate Collector

A paraffin wax and copper foam matrix were used as a thermal energy storage material in the double passes air solar chimney (SC) collector to get ventilation effect through daytime and after sunset. Air SC collector was installed in the south wall of an insulated test room and tested with different working angles (30o, 45o and 60o). Different SC types were used; single pass, double passes flat plate collector and double pass thermal energy storage box collector (TESB). A computational model based on the finite volume method for transient tw dimensional domains was carried out to describe the heat transfer and storage in the thermal energy storage material of collector. Also, equivalent specific heat method was employed to describe the heat storage and release in the mushy zone. Experimental results referred to an increase in thermal conductivity of paraffin wax that supported by copper foam matrix more than ten times. While the ventilation effect was still active for hours after the sun set, depending on the heat storage amount. Maximum ventilation mass flow rate with TESB collector was recorded with value equals to 36.651 kg/hr., when the overall discharge coefficient that was calculated for the system equals to 0.371. Experimental results showed that the best working angle range was 45~60o, and the highest air to the collector approaching temperature appeared to the double passes flat plate collector. Results gave greater heat storage efficiency of (47)% when the maximum solar radiation was 780 W/m2 at 12.00pm, while the energy summation through duration charge time was 18460 kJ. Computational results, depending on the equivalent heat capacity method for heat storage or release from phase change material that supported by copper foam matrix, showed the behavior of paraffin wax melting and solidification situation through periodic for charge and released heat from the solar collector. Also, these results gave agreement approaching the experimental results for the heat storage in the combined heat storage material, with standard error of 16.8%.

scholarly journals Thermal Energy Storage Performance of Tetrabutylammonium Acrylate Hydrate as Phase Change Materials

2021 ◽  
Vol 11 (11) ◽  
pp. 4848
Author(s):  
Hitoshi Kiyokawa ◽  
Hiroki Tokutomi ◽  
Shinichi Ishida ◽  
Hiroaki Nishi ◽  
Ryo Ohmura
Keyword(s):  
Energy Storage ◽  
Heat Exchanger ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Phase Change Materials ◽  
Heat Storage ◽  
Solid Phase ◽  
Agitation Rate ◽  
Mechanical Agitation ◽  
External Forces

Kinetic characteristics of thermal energy storage (TES) using tetrabutylammonium acrylate (TBAAc) hydrate were experimentally evaluated for practical use as PCMs. Mechanical agitation or ultrasonic vibration was added to detach the hydrate adhesion on the heat exchanger, which could be a thermal resistance. The effect of the external forces also was evaluated by changing their rotation rate and frequency. When the agitation rate was 600 rpm, the system achieved TES density of 140 MJ/m3 in 2.9 hours. This value is comparable to the ideal performance of ice TES when its solid phase fraction is 45%. UA/V (U: thermal transfer coefficient, A: surface area of the heat exchange coil, V: volume of the TES medium) is known as an index of the ease of heat transfer in a heat exchanger. UA/V obtained in this study was comparable to that of other common heat exchangers, which means the equivalent performance would be available by setting the similar UA/V. In this study, we succeeded in obtaining practical data for heat storage by TBAAc hydrate. The data obtained in this study will be a great help for the practical application of hydrate heat storage in the future.

scholarly journals Efficient Operation Method of Aquifer Thermal Energy Storage System Using Demand Response

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3129
Author(s):  
Jewon Oh ◽  
Daisuke Sumiyoshi ◽  
Masatoshi Nishioka ◽  
Hyunbae Kim
Keyword(s):  
Energy Storage ◽  
Power Consumption ◽  
Thermal Energy ◽  
Demand Response ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Storage System ◽  
Total Power ◽  
Operation Method ◽  
Total Power Consumption

The mass introduction of renewable energy is essential to reduce carbon dioxide emissions. We examined an operation method that combines the surplus energy of photovoltaic power generation using demand response (DR), which recognizes the balance between power supply and demand, with an aquifer heat storage system. In the case that predicts the occurrence of DR and performs DR storage and heat dissipation operation, the result was an operation that can suppress daytime power consumption without increasing total power consumption. Case 1-2, which performs nighttime heat storage operation for about 6 h, has become an operation that suppresses daytime power consumption by more than 60%. Furthermore, the increase in total power consumption was suppressed by combining DR heat storage operation. The long night heat storage operation did not use up the heat storage amount. Therefore, it is recommended to the heat storage operation at night as much as possible before DR occurs. In the target area of this study, the underground temperature was 19.1 °C, the room temperature during cooling was about 25 °C and groundwater could be used as the heat source. The aquifer thermal energy storage (ATES) system in this study uses three wells, and consists of a well that pumps groundwater, a heat storage well that stores heat and a well that used heat and then returns it. Care must be taken using such an operation method depending on the layer configuration.

scholarly journals Constant-volume vapor-liquid equilibrium for thermal energy storage: investigation of a new storage condition for solar thermal systems

2021 ◽  
Vol 238 ◽  
pp. 03004
Author(s):  
Abdullah Bamoshmoosh ◽  
Gianluca Valenti
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Exergy Analysis ◽  
Heat Storage ◽  
Storage Condition ◽  
Liquid Equilibrium ◽  
Liquid Systems ◽  
Transfer Properties ◽  
Vapor Liquid

The sector of thermal energy storage shows a number of alternatives that could have a relevant impact on the future of energy saving as well as renewable energy technologies. Among these, latent heat thermal energy storage technologies show promising results. Technologies that exploit solid-liquid phase change have already been widely proposed, but those technologies show common drawbacks limiting their application, such as high cost, low energy storage density and particularly low heat transfer properties. This work proposes to exploit the liquid-vapor phase transition in closed and constant volumes because it shows higher heat transfer properties. Consequently, the objective is to assess its energy storage performances in target temperature ranges. With respect to previous activity by the authors, this work proposes an exergy analysis of these systems, gives a methodology their deployment, and proposes a comparison between a new storage condition for solar thermal domestic hot water systems exploiting vapor-liquid equilibrium and conventional technologies. The exergy analysis is performed in reduced terms in order to have a generalized approach. Three hypothetical fluids with increasing degree of molecular complexity are considered in order to have a complete overview of the thermodynamic behavior of potential heat storage fluids. The analysis shows that the increased pressure of liquid systems has a major impact on exergy, resulting in vapor-liquid systems having less than 50% of the exergy variation of pressurized liquid systems. This is proven to have no impact on thermal energy storage. For the case study, the proposed methodology indicates that water itself is a strong candidate as a heat storage fluid in the new condition. Comparison shows that the new condition has a higher energy storage capacity at same volume. The useful temperature range is increased by 108% by setting a 10.5% volume vapor fraction at ambient temperature. The resulting improvement gives a 94% higher energy storage, with a maximum operating pressure of the system of less than 5 bar.

Thermal Energy Storage Thermal Response Model With Application to Thermal Management of High Power-Density Hand-Held Electronics

2012 ◽  
Vol 134 (1) ◽  
Author(s):  
R. A. Wirtz ◽  
K. Swanson ◽  
M. Yaquinto
Keyword(s):  
Energy Storage ◽  
Phase Change ◽  
Thermal Energy ◽  
High Power ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Thermal Response ◽  
High Power Density ◽  
Storage Volume ◽  
Change Material

An important design objective that is unique to hand-held units is the need to constrain two temperatures: the maximum temperature of the electronic components and the maximum skin temperature of the hand-held unit. The present work identifies and evaluates, through parametric modeling and experiments, the passive thermal energy storage volume characteristics and phase change material composite properties that are most suitable for thermal control of small form-factor, high power-density, hand-held electronics. A one-dimensional transient analytical model, based on an integral heat balance, is formulated and benchmarked. The model accurately simulates the heat storage/recovery process in a semi-infinite, “dry” phase change material slab. Dimensional analysis identifies the time and temperature metrics and nondimensional parameters that describe the heat storage/release process. Parametric analysis illustrates how changes in these nondimensional parameters affect thermal energy storage volume thermal response.

Analysis of Thermal Energy Storage to a Combined Heat and Power Plant

2021 ◽  
Vol 9 (9) ◽  
pp. 1313-1320
Author(s):  
Shahim Nisar
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Energy Demand ◽  
Power Plants ◽  
Heat Storage ◽  
Packed Bed ◽  
Water Tank ◽  
Latent Heat Storage ◽  
Storage Methods

Abstract: Thermal energy storage (TES) is a technology that stocks thermal energy by heating or cooling a storage medium so that the stored energy can be used at a later time for heating and cooling applications and power generation. TES systems are used particularly in buildings and in industrial processes. This paper is focused on TES technologies that provide a way of valorizing solar heat and reducing the energy demand of buildings. The principles of several energy storage methods and calculation of storage capacities are described. Sensible heat storage technologies, including water tank, underground and packed-bed storage methods, are briefly reviewed. Additionally, latent-heat storage systems associated with phase-change materials for use in solar heating/cooling of buildings, solar water heating, heat-pump systems, and concentrating solar power plants as well as thermo-chemical storage are discussed. Finally, cool thermal energy storage is also briefly reviewed and outstanding information on the performance and costs of TES systems are included.

Artificial Neural Network Model for Diagnosing the Performance and the Conditions of Air-Operated Valves

2020 ◽  
Author(s):  
Taeyun Kim ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Ilyoung Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Physical Models ◽  
Artificial Neural

Abstract Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

scholarly journals Artificial Neural Network (ANN) Modeling for Prediction of Pesticide Wastewater Degradation by FeGAC/H2O2 Process

2018 ◽  
Vol 65 ◽  
pp. 05004
Author(s):  
Augustine Chioma Affam ◽  
Malay Chaudhuri ◽  
Chee Chung Wong ◽  
Chee Swee Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Close Agreement ◽  
Transfer Functions ◽  
Cod Removal ◽  
Experimental Results ◽  
Ann Model ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The study examined artificial neural network (ANN) modeling for the prediction of chlorpyrifos, cypermethrin and chlorothalonil pesticides degradation by the FeGAC/H2O2 process. The operating condition was the optimum condition from a series of experiments. Under these conditions; FeGAC 5 g/L, H2O2 concentration 100 mg/L, pH 3 and 60 min reaction time, the COD removal obtained was 96.19%. The ANN model was developed using a three-layer multilayer perceptron (MLP) neural network to predict pesticide degradation in terms of COD removal. The configuration of the model with the smallest mean square error (MSE) of 0.000046 contained 5 inputs, 9 hidden and, 1 output neuron. The Levenberg–Marquardt backpropagation training algorithm was used for training the network, while tangent sigmoid and linear transfer functions were used at the hidden and output neurons, respectively. The predicted results were in close agreement with the experimental results with correlation coefficient (R2) of 0.9994 i.e. 99.94% showing a close agreement to the actual experimental results. The sensitivity analysis showed that FeGAC dose had the highest influence with relative importance of 25.33%. The results show how robust the ANN model could be in the prediction of the behavior of the FeGAC/H2O2 process.

scholarly journals Thermal properties of thermochemical heat storage materials

2020 ◽  
Vol 22 (8) ◽  
pp. 4617-4625 ◽  
Author(s):  
Julianne E. Bird ◽  
Terry D. Humphries ◽  
Mark Paskevicius ◽  
Lucas Poupin ◽  
Craig E. Buckley
Keyword(s):  
Thermal Properties ◽  
Energy Storage ◽  
Transport Properties ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Energy Storage Materials ◽  
Storage Materials ◽  
Thermochemical Heat Storage ◽  
Heat Storage Materials

The thermal transport properties of potential thermal energy storage materials have been measured using identical conditions enabling direct comparison.

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