Adsorption of Cyclohexane onto Activated Nanoporous Graphene: Modeling Using Artificial Neural Network

Industries pollute the environment by emitting organic substances known as Volatile Organic Compounds (VOC). One of the outstanding materials utilized to eliminate VOCs is nanoporous graphene. However, graphene's physical and chemical characteristics are influenced by a range of factors, including activation temperature, mass ratio, activation duration, adsorption capacity, N2 adsorption-desorption, and morphology, Among other factors, the porosity of graphene is one of the crucial which has a direct influence on the adsorption capacity. In the current study, the adsorption capacity of graphene was investigated using cyclohexane and n-hexane adsorbents. In addition, the neural network has been employed to predict the adsorption capacity of graphene, and the Levenberg–Marquardt backpropagation (LM-BP) mechanism was utilized to determine model accuracy. The results show that at an activation temperature of 700°C, and mass ratio of  6, cyclohexane displayed a better performance with an adsorption capacity of 500 mg/g, as a comparison to n-hexane. The model demonstrated a suitable prediction with a  correlation coefficient of 0.99966 (R2) within the range of cyclohexane parameters such as impregnation ratio, activation time, and activation temperature between 3 to 9, 120 to 180 min, and 500 to 700°C  respectively.