Stability analysis of the thermocline thermal energy storage system during high flow rates for solar process heating applications

Reddy, K. S.; Pradeep, N.

doi:10.1016/j.solener.2021.08.026

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…Figure 3 shows that during charging, the temperature distribution moves from the bottom to the top direction, and the width of the thermocline thickness increases with the passage of time. This behavior has been verified in many previous studies, such as [ 6 , 38 , 39 , 40 ]. Evolution of temperature distribution and thermocline thickness during charging is shown in Figure 3 .…”

Section: Methodssupporting

confidence: 84%

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Soomro

Mokhtar

Salilew

et al. 2022

Sensors

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In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.

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Section: Methodssupporting

confidence: 84%

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Soomro

Mokhtar

Salilew

et al. 2022

Sensors

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“…S7 shows the numerical results of the energy efficiency and remained thermocline thickness at cutoff time with HTF flow rate ranging from 0.12 to 2.5 L min -1 (𝑅𝑒=0. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The inlet fluid temperature has negligible effect on the energy efficiency and the remained thermocline thickness.…”

Section: S5 Optimal Flow Velocitymentioning

confidence: 99%

“…However, depending on different filler configurations and heat transfer mechanisms, the impact of injecting flow on the thermal performance is different. For example, Reddy and Pradeep [24] numerically observed that the radial non-uniform temperature distribution in their SHTPB tank (H/D tank =4) is negligible in laminar flow owing to the porous packed bed working as flow distributor, but not in turbulent condition. Wang et al…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical study on the thermocline behavior of packed-bed storage tank with sensible fillers

et al. 2023

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“…Constructing a heterogeneous structure for these interconnected channels (vessels) and nanopores (pits) to exploit each component’s advantages remains challenging. In order to transform bamboo into a solar-driven device, surface treatment and high-temperature approaches have been used for absorption capacity enhancement, as the inner orientational channels are usually perpendicular to the water surface. − W. Wang and co-workers proposed a porous carbonized bamboo through high-temperature carbonization (1000 °C). The inner orientational channels were parallel to the water surface in this bamboo evaporator via an arch bridge structure.…”

Section: Introductionmentioning

confidence: 99%

Orientational Bamboo Veneer Evaporator for Enhanced Interfacial Solar Steam Generation

Feng,

Tan,

et al. 2024

ACS Sustainable Chem. Eng.

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Given the green developments in interfacial solar steam generation (ISSG), a biobased evaporator with excellent photothermal conversion efficiency is urgently needed to address the freshwater shortage. However, it is still challenging that biobased evaporators like wood and bamboo are underutilized due to the capillary actions in different channel directions. Herein, we investigated the channel structural properties of bamboo veneer evaporators for contrast analysis between bamboo transverse sections (BTR) and bamboo tangential sections (BTA). The one-step torrefaction method was used to improve the optical absorption of bamboo lignin in the 200–2500 nm band. Owing to the perpendicular channel design, the BTR evaporator conducting straight energy loss to the bulk water restricts the performance. The BTA evaporator’s parallel channel design with a micronano unit presents better photothermal management (surface temperature could reach 57.1 °C under 1 kW/m2 illumination), leading to efficient evaporation performance (1.425 kg/m2 h). These findings contribute to our cognition of solar-driven water transport and suggest a parallel channel ISSG design to develop efficient biobased ISSG in freshwater produce from seawater or sewage.

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Stability analysis of the thermocline thermal energy storage system during high flow rates for solar process heating applications

Cited by 17 publications

References 37 publications

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

Experimental and numerical study on the thermocline behavior of packed-bed storage tank with sensible fillers

Orientational Bamboo Veneer Evaporator for Enhanced Interfacial Solar Steam Generation

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