Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications
The present study investigates the enhancement of latent heat capacity and thermal stability in hybrid nano-enhanced solid–solid phase change materials (SS-PCMs) using Neopentyl Glycol (NPG) as the base material. The key contribution of this work lies in incorporating copper oxide (CuO) and titanium...
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Energy |
| Online Access: | https://www.tandfonline.com/doi/10.1080/14786451.2025.2472162 |
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| author | Pradnya Sameer Deshpande R. Jyothilakshmi Lalitha Chinmayee H. M. B. S. Sridhar |
| author_facet | Pradnya Sameer Deshpande R. Jyothilakshmi Lalitha Chinmayee H. M. B. S. Sridhar |
| author_sort | Pradnya Sameer Deshpande |
| collection | DOAJ |
| description | The present study investigates the enhancement of latent heat capacity and thermal stability in hybrid nano-enhanced solid–solid phase change materials (SS-PCMs) using Neopentyl Glycol (NPG) as the base material. The key contribution of this work lies in incorporating copper oxide (CuO) and titanium dioxide (TiO₂) nanoparticles to optimize thermal performance and ensure long-term stability. CuO (1 wt.%) and TiO₂ (0.1, 0.3, 0.5,0.7 wt%) were introduced into the matrix, and the thermal properties were systematically evaluated using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) before and after 500 thermal cycles. The optimal composition, consisting of 1 wt% CuO and 0.3 wt% TiO₂, demonstrated an initial latent heat capacity of 117 J/g, which increased to 123 J/g post-cycling, indicating exceptional thermal stability and phase retention. To further enhance predictive capabilities and reduce experimental costs, an artificial neural network (ANN) model was developed using the Keras API in Python to estimate thermal behaviour. The model achieved a high coefficient of determination (R2 = 0.9479) and a low root-mean-square error (RMSE = 2.0307), underscoring its accuracy and reliability. These findings establish the efficacy of hybrid nanoparticle incorporation in improving SS-PCMs’ thermal properties and emphasise the viability of machine learning as a robust predictive tool, mitigating the time and economic constraints associated with extensive experimental investigations. |
| format | Article |
| id | doaj-art-1d62fd219f65439b848488cf5d9f253a |
| institution | DOAJ |
| issn | 1478-6451 1478-646X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Energy |
| spelling | doaj-art-1d62fd219f65439b848488cf5d9f253a2025-08-20T03:12:19ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2025-12-0144110.1080/14786451.2025.2472162Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applicationsPradnya Sameer Deshpande0R. Jyothilakshmi1Lalitha Chinmayee H. M.2B. S. Sridhar3Department of Mechanical Engineering, Ramaiah Institute of Technology, Bengaluru, IndiaDepartment of Mechanical Engineering, Ramaiah Institute of Technology, Bengaluru, IndiaDepartment of Computer Engineering, Ramaiah Institute of Technology, Bengaluru, IndiaDepartment of Industrial Engineering and Management, Ramaiah Institute of Technology, Bengaluru, IndiaThe present study investigates the enhancement of latent heat capacity and thermal stability in hybrid nano-enhanced solid–solid phase change materials (SS-PCMs) using Neopentyl Glycol (NPG) as the base material. The key contribution of this work lies in incorporating copper oxide (CuO) and titanium dioxide (TiO₂) nanoparticles to optimize thermal performance and ensure long-term stability. CuO (1 wt.%) and TiO₂ (0.1, 0.3, 0.5,0.7 wt%) were introduced into the matrix, and the thermal properties were systematically evaluated using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) before and after 500 thermal cycles. The optimal composition, consisting of 1 wt% CuO and 0.3 wt% TiO₂, demonstrated an initial latent heat capacity of 117 J/g, which increased to 123 J/g post-cycling, indicating exceptional thermal stability and phase retention. To further enhance predictive capabilities and reduce experimental costs, an artificial neural network (ANN) model was developed using the Keras API in Python to estimate thermal behaviour. The model achieved a high coefficient of determination (R2 = 0.9479) and a low root-mean-square error (RMSE = 2.0307), underscoring its accuracy and reliability. These findings establish the efficacy of hybrid nanoparticle incorporation in improving SS-PCMs’ thermal properties and emphasise the viability of machine learning as a robust predictive tool, mitigating the time and economic constraints associated with extensive experimental investigations.https://www.tandfonline.com/doi/10.1080/14786451.2025.2472162 |
| spellingShingle | Pradnya Sameer Deshpande R. Jyothilakshmi Lalitha Chinmayee H. M. B. S. Sridhar Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications International Journal of Sustainable Energy |
| title | Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications |
| title_full | Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications |
| title_fullStr | Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications |
| title_full_unstemmed | Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications |
| title_short | Experimental studies on latent heat capacity of hybrid nano-enhanced phase change materials using artificial neural network for energy storage applications |
| title_sort | experimental studies on latent heat capacity of hybrid nano enhanced phase change materials using artificial neural network for energy storage applications |
| url | https://www.tandfonline.com/doi/10.1080/14786451.2025.2472162 |
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