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)

• Record Type: Journal Article
• Authors: Yan, Hao; Mou, Yulan; Xu, Xuefeng; Du, Jinfeng; Wang, Rui; Liu, Pengjun
• Is Part Of: Energy sources. Volume 41:Issue 16(2019)
• Page Start: 1983
• Page End: 1992
• Publication Date: 2019-08-18
• Subjects: Methane adsorption -- activated carbon -- neural network -- ANFIS -- GRNN; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; Power (Mechanics) -- Periodicals; 333.7905
• DOI: 10.1080/15567036.2018.1548527
• Languages: English
• ISSNs: 1556-7036

Abstract

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.