Specific heat capacity of molten salt-based nanofluids in solar thermal applications: A paradigm of two modern ensemble machine learning methods

Abstract

The quantitative determination of specific heat capacity (SHC) of molten (nitrate) salt-based nanofluids helps to control the start-up heat and prevent overheating when deployed as a working heat transfer fluid in a wide range of solar thermal applications. Thus, accurate measurement of the SHC and capturing the melting point is of paramount importance in the molten salt-based nanofluids’ characterization analyses applied in solar collectors. In this research, two modern ensemble machine learning models, Extra Tree Regression (ETR) and AdaBoost Regression (ABR), were developed based on 2,384 datasets, including solid mass fraction (w), temperature (T), SHC of base fluid (CPBase), mean diameter (Dp), and density (ρp) of nanoparticle as all independent input variables and the SHC of molten salt-based nanofluids (CPMS-nf) as the target. Herein, the stepwise forward method and mutual information were addressed to determine the best input combination and sensitivity analysis. The provided models were validated using Random Forest (RF) and Boosted Regression Tree (BRT) as two powerful other ensemble models. The results demonstrated that ETR model in terms of (R = 0.9964, RMSE = 0.1566, and U95%=3.6062) outperformed the ABR (R = 0.9949, RMSE = 0.1855, and U95%=3.6009), RF (R = 0.9922, RMSE = 0.2326, and U95%=3.5904), and BRT (R = 0.9907, RMSE = 0.2508, and U95%=3.5857). The SHC of molten salt base fluid was identified as the most significant factor in estimating the SHC of molten salt-based nanofluids.