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 R2 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.
Highlights
The efficiency of conventional heat-transfer fluids is augmented by using nanofluids as a novel technique [1,2,3,4,5]
Randomization of the elements guards against overfitting in this method. This current study aimed to investigate these debates through an examination and prediction of the Cp of ionanofluids as a function of the nanoparticle concentration (x) and the operational temperature (T), molecular weight (Mw ), acentric factor (ω), and critical temperature (Tc ) of pure ionic liquids (ILs) using a group method of data handling techniques
We evaluated our forecasting model by comparing it with experimental data using three accuracy measurements: R2, mean relative error (MRE%), and the mean squared error (MSE)
Summary
The efficiency of conventional heat-transfer fluids is augmented by using nanofluids as a novel technique [1,2,3,4,5]. A similar idea has been observed in the chemical-enhanced oil recovery methods, which increase the amount of recovered oil by adding a very small amount of nanoparticles to the injected water [19] This group of nanofluids with ILs as the base fluids that show higher thermophysical properties is called ionanofluids or nanoparticle-enhanced ILs (NEILs) [16]. The accuracy of the estimation is maximized and overfitting is minimized via the utilization of a small number of training data points This algorithm reduces the demands regarding the transformation of the input or the selection of features, which is an advantage when performing in a high dimensional space. We evaluated our forecasting model by comparing it with experimental data using three accuracy measurements: R2 , mean relative error (MRE%), and the mean squared error (MSE)
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