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). Issue 16 (18th August 2019) - Yan, Hao; Mou, Yulan; Xu, Xuefeng; Du, Jinfeng; Wang, Rui; Liu, Pengjun
ABSTRACT: 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 R 2, 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 R 2 and AARD% values of 0.921% and 0.657%, respectively.