A comparative assessment of predicting CH4 adsorption on different activated carbons using generalized regression neural network (GRNN), and adaptive network-based fuzzy inference system (ANFIS).

The aim of this work is to predict the adsorption of methane on various activated carbon using to intelligent models including Generalized Regression Neural Network (GRNN), and adaptive network-based fuzzy inference system (ANFIS). Methane is the major component of natural gas, coal bed gas, and some exhaust gases of petrochemical or chemical units. Therefore, a fundamental study on the adsorption was encouraged by engineering concerns. In this regards, the precise prediction of CH₄ adsorption is of great interest and importance. The model is developed using a comprehensive database obtained from the literature. The outcomes of the model were compared with the experimental data. The values of the statistical parameters R², RMSE, and AARD% reveal that the ANFIS model is more accurate. Results showed that the developed model accurately predicts CH₄ adsorption on activated carbons with an overall R² and AARD% values of 0.921% and 0.657%, respectively. [ABSTRACT FROM AUTHOR]