Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm

Abstract

Nanofluids are now considered the most essential constituent of solar thermal collector due to their superior thermal performance over conventional fluids. An accurate determination of the thermal efficiency of the solar collector depends on the value of the specific heat capacity of the nanofluid. So far, limited attention has been devoted towards accurate modelling of specific heat capacity of nanofluids CPnf despite their relevance in many solar energy-related applications. Surprisingly, there are only two main analytic models for estimating the CPnf in the literature. In most of the reports, these models have shown considerable inconsistencies for predicting the values of CPnf. Moreover, the modelling performance of these models necessitates the need to develop accurate models for the prediction of CPnf. Herein, a Bayesian support vector regression (BSVR) model is proposed to estimate the specific heat capacity of Al2O3/ethylene glycol nanofluid. The model proposed was trained on eighty-four (84) experimental datasets and its predictive accuracy was validated on seventeen (17) new test set. The BSVR model exhibits high accuracy as measured by the values of Pearson's correlation coefficient and the absolute average relative deviation (AARD) of 99.95% and 0.1888, respectively. Remarkably, the accuracy obtained from the proposed BSVR model is an order of magnitude better than existing theoretical models. The proposed technique and model will be useful towards a more reliable and accurate computation of the efficiency of solar collectors.