Towards estimating the thermal properties of carbon allotropes and their derivatives: Hybridization between the artificial neural network method and the experimental design approach

Abstract

Carbon allotropes and their derivatives have attracted the interest of many researchers owing to their ability to conduct heat. In this study, a multi-layer perception network (MLP) was developed to predict its thermal transport properties. Usually, trial and error are the appropriate solutions to find the number of hidden layers, the number of neurons, the activation, and the transfer function. To overcome this problem, we used the Surface Response Methodology (RSM), which identifies the influence of each parameter on the response and ends up finding a robust model of Artificial Neural Networks (ANN). The study demonstrated that hybridization between (ANN) and (RSM) methods is an efficient and powerful method for modeling the thermal conductivity of carbon allotropes; it allows to shorten the time to test different combinations. Notably, the study achieved exceptional results with a correlation coefficient surpassing 99.89%, a coefficient of determination exceeding 99.78%, and minimal RMSE (Root Mean Square Error).