Elsevier

Solar Energy

Volume 227, October 2021, Pages 447-456
Solar Energy

Data-driven analysis of molten-salt nanofluids for specific heat enhancement using unsupervised machine learning methodologies

https://doi.org/10.1016/j.solener.2021.09.022Get rights and content

Highlights

  • The addition of nanoparticles, in small amounts, to molten salt is widely explored for enhancing the specific heat capacity.

  • Essential system parameters of molten salt nanofluids are temperature, density ratio, concentration, and nanoparticle size.

  • Hierarchical cluster analysis (HCA) and principal component analysis (PCA) are statistical tools for pattern identification and dimensionality reduction to analyze relationships between experimental data and samples.

  • We have applied HCA and PCA to molten salt nanofluid samples to gain insights into system parameters' influence on specific heat enhancement.

Abstract

High specific heat molten-salt is essential for sensible heat thermal energy storage. Current scientific researches focus on Molten-salt nanofluid as a potential solution. However, the causality between system parameters introduced in nanofluid preparation and specific heat enhancement is not clearly understood. Since difficulties are associated with identifying the explicit relations due to complex molecular interactions between molten-salt and nanoparticles, we inquired whether there is a common pattern/clusters in the nanofluid samples reported in earlier studies. The data-driven correlations among samples are explored by employing unsupervised machine learning methods: Hierarchical cluster analysis (HCA) and Principal component analysis (PCA). Three principal components, capturing 81.3% variation of the entire dataset, revealed that the descending order of contribution of the system parameters in the specific heat enhancement percent is concentration, temperature, density ratio, and nanoparticle size. The multivariate clusters emerging from HCA showed the interdependency of density ratio on the temperature, which significantly affects nanofluid's stability at higher concentration, causing a decrease in specific heat enhanced percent. Furthermore, the variation in nanoparticle size was found to have a negligible effect on specific heat enhancement.

Section snippets

Introduction and motivation

Intermittency in renewable energy sources is a challenge for energy-harvesting technologies. In this context, solar thermal power plants' electricity production gets disrupted by day-night, seasonal, and weather fluctuations. To mitigate this disruption, thermal energy storages (TES) are commonly employed in Concentrated Solar Power Plants (CSP). TES preserves energy in the form of latent/sensible heat which is collected during the pick available period for later use. Typical sensible heat

Dataset and methodologies

The data set of nanofluid samples for the present study is selected from previously published researches (Andreu-Cabedo et al., 2014, Chen et al., 2019, Chen et al., 2018, Chieruzzi et al., 2017, Chieruzzi et al., 2013, Dudda and Shin, 2013, Ho and Pan, 2014, Hu et al., 2017, Jo and Banerjee, 2015; 2014; Lasfargues et al., 2015, Li et al., 2019, Qiao et al., 2017, Schuller et al., 2015, Seo and Shin, 2016, Shin and Banerjee, 2015, Shin and Banerjee, 2014, 2013, 2011a, 2011b; Song et al., 2018,

Results and discussion

Fig. 4 displays the results of HCA in the form of a dendrogram/hierarchical tree. Here, 141 samples are hierarchically connected depending on the Euclidean distance measure and complete linkage/farthest neighbor clustering in a 5-dimensional space. The clusters are identified by cutting the hierarchical tree horizontally near four Euclidean measure to ensure that univariate outliers (samples having an extreme value in one variable only) concerning the system variables are accurately detected.

Conclusion

In this study, two unsupervised machine learning algorithms (HCA and PCA) are introduced to identify unseen clustering structures in 141 molten salt nanofluid samples, published in earlier experimental works. HCA effectively extracted four major clusters, two minor clusters, and two singletons by considering correlations between system parameters of nanofluid samples. The minor clusters and the singletons were the outling samples owing to univariate extreme values (larger nanoparticle size

CRediT authorship contribution statement

Dipti Ranjan Parida: Conceptualization, Data curation, Analysis, Writing – original draft. Nikhil Dani: Data curation, Writing – original draft. Saptarshi Basu: Research, Supervision, Writing – review & editing, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported by Ministry of Human Resource Development, and Ministry of New and Renewable Energy, Government of India, under the IMPRINT initiative (Project No. 4424)

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