Abstract:
This work investigated the capability of multilayer perceptron artificial neural network
(MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network
(RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat
capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature
(Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids
(ILs). To this end, a comprehensive database of literature reviews was searched. The results of the
SGB model were more satisfactory than the other models. Furthermore, an analysis was done to
determine the outlying bad data points. It showed that most of the experimental data points were
located in a reliable zone for the development of the model. The mean squared error and R
2 were
0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN,
ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp
of ionanofluids was shown to be greater than other models. By eliminating the need for conducting
costly and time-consuming experiments, the SGB strategy showed its superiority compared with
experimental measurements. Furthermore, the SGB displayed great generalizability because of
the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore,
it can help chemical engineers and chemists by providing a model with low parameters that yields
satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed
that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had
the highest impact and ω had the lowest impact on Cp.
