Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm - Ibrahim, Olanrewaju Alade; Abd Rahman, Mohd Amiruddin; A. Saleh, Tawfik (Elsevier, 2019)
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.