Experimental analysis of Shape-Stabilized PCM applied to a Direct-Absorption evacuated tube solar collector exploiting sodium acetate trihydrate and graphite

Sadeghi, Gholamabbas; Mehrali, Mohammad; Shahi, Mina; Brem, Gerrit; Mahmoudi, Amir Houshang

doi:10.1016/j.enconman.2022.116176

Cited by 21 publications

(8 citation statements)

References 33 publications

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“…In contrast, SHAP values give a general account of how each feature contributes to the dataset as a whole, which can give a better idea of how the model works. 206 Decision trees and rule-based models are also easy to comprehend, and can be used with more complicated models to clarify what occurred after the fact. By comparing the results from different simple and complicated models, scholars can be sure that the forecasts are correct and reliable.…”

Section: Interpretability and Explainability Of ML Modelsmentioning

confidence: 99%

“…LIME creates simpler, easier‐to‐understand substitute models for specific cases, which can allow a researcher to understand how the model makes a prediction for each sample. In contrast, SHAP values give a general account of how each feature contributes to the dataset as a whole, which can give a better idea of how the model works 206 . Decision trees and rule‐based models are also easy to comprehend, and can be used with more complicated models to clarify what occurred after the fact.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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Section: Interpretability and Explainability Of ML Modelsmentioning

confidence: 99%

Section: Challenges and Limitationsmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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“…Certain organic PCMs, such as paraffin wax, polyethylene glycol, and sugar alcohols, have limited practical applications because of their flammability, low energy storage density, and high cost. In contrast, inorganic PCMs, particularly hydrated salts, have attracted much attention for low- and medium-temperature thermal energy storage owing to their advantages such as high heat-storage density, stable phase-change temperature, and nonflammability. − Among these, sodium acetate trihydrate (SAT, CH 3 COONa·3H 2 O) has the advantages of high latent heat of phase change, high energy storage density, and suitable phase-change temperature, making it a promising PCM. , However, supercooling, phase separation phenomena, low thermal conductivity, poor flexibility, and irrecoverable fractures of SAT have severely restricted its application in practical thermal energy management. − …”

Section: Introductionmentioning

confidence: 99%

Composite Material with Excellent Thermal Management and Self-Healing Performance for Battery Temperature Control and Human Thermal Therapy

Li,

Wu,

Tang

et al. 2024

ACS Appl. Polym. Mater.

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Inorganic phase-change materials are promising materials for realizing personal thermal therapy and thermal management of electronic devices owing to their advantages of high energy storage density and stable phase-change temperature. However, the poor flexibility and irrecoverable fracturing of inorganic phase-change materials are long-standing bottlenecks in achieving efficient thermal management. This study proposed a method for preparing self-healing and flexible sodium acetate trihydrate (SAT)/expanded graphite (EG)/borax/sodium carboxymethyl cellulose (CMC) (labeled as SEBC)-based polyacrylamide (PAM)/chitosan (CS)/SEBC hydrogel composites with a self-healing ability and a certain degree of flexibility by constructing PAM/CS flexible dual-networked hydrogels for thermal management. The cross-linked dual network was formed by PAM and CS-wrapped SEBC to prepare self-healing PAM/CS/SEBC hydrogel composites. A 50 wt % content of PAM/CS enabled the composite to exhibit excellent flexibility and 87.9% self-healing efficiency. The composites that were damaged and self-healed showed thermal/conductive properties and thermal stabilities close to those of the original composites, owing to their excellent self-healing ability. Furthermore, energy devices based on flexible PAM/CS/SEBC hydrogel composites were developed to realize personal thermal therapy and efficient battery thermal management. Overall, this study provides a promising route for the fabrication of flexible and self-healing inorganic phase-change materials for thermal management.

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“…Flow rate changes had minimal impact. Cost analysis showed a 6-year payback period and 5.4-ton annual CO 2 emissions reduction 18 . A study investigated using a back pipe in vacuum tube solar collectors to remove stagnant regions and improve thermal performance.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation on a solar parabolic collector using water-based multi-walled carbon-nanotube with low volume concentrations

Talugeri

Pattana

Nasi

et al. 2023

Sci Rep

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A limited experimental work was on multi-walled carbon nanotube (MWCNT)—water nanofluid with surfactant in the solar parabolic collector at low volume concentrations. At high-volume concentrated nanofluid, the pressure drop was more due to an increase in the viscosity of the working fluid and an increase in the nanoparticle cost; hence it is not economical. This report attempted to use Sodium Dodecyl Benzene Sulfonate (SDBS) surfactant in the low-volume concentrated MWCNT-water nanofluid to establish effective heat transfer in solar parabolic collector applications. The stable MWCNT-water nanofluid was prepared at 0.0158, 0.0238, and 0.0317 volume concentrations. The experiments were conducted from 10:00 to 16:00 at 6, 6.5 and 7 L/min flow rates concerning ASHRAE Standards. At the 7 L/min flow rate of the working fluid, having a minimum temperature difference between the working fluid and absorber tube leads to better heat transfer. The increased volume concentration of MWCNT in the water enhances the surface area interaction between water and MWCNT nanoparticles. This results in maximum solar parabolic collector efficiency at 0.0317 vol% with a 7 L/min flow rate and 10–11% higher than the distilled water.

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Experimental analysis of Shape-Stabilized PCM applied to a Direct-Absorption evacuated tube solar collector exploiting sodium acetate trihydrate and graphite

Cited by 21 publications

References 33 publications

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Composite Material with Excellent Thermal Management and Self-Healing Performance for Battery Temperature Control and Human Thermal Therapy

Experimental investigation on a solar parabolic collector using water-based multi-walled carbon-nanotube with low volume concentrations

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