Multi-objective optimization of thermophysical properties GO powders-DW/EG Nf by RSM, NSGA-II, ANN, MLP and ML

Abstract

In this study, prediction, modeling, and optimization have been performed for four TPH properties of graphene oxide nano powder-deionized water/ethylene glycol nf, which is unique compared to other studies. Response surface methodology, artificial neural networks based on multiple layers of perceptron, and algorithms based on machine learning have been developed for prediction and modeling. RSM modeling resulted in coefficients of determination of 0.9984, 0.9986, 0.9995, and 0.9996 for TC (k), density (ρ), SHC (cp), and viscosity (μ), respectively. The highest prediction errors for RSM models were 0.3644%, 0.0374%, 2.049%, and 0.2296% for k, ρ, μ, and cp. A higher temperature and a higher WF of NPs increased the TC of the nf. The maximum MLP model error was 0.43%, 6.59%, 12.64%, and 1.04% for ρ, cp, μ, and k, respectively. TC and SHC were optimized using the NSGA-II algorithm. The NSGA-II procedure indicated the maximum k and cp occurred at the highest temperatures. The temperature must be kept at its maximum to reach the optimal stage. Also, it was proven that temperature is a much more significant parameter than the nanoparticle WF.