An adaptive neuro–fuzzy inference model for prediction of coal char conversion in CO2 gasification. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Tayyebi, Shokoufe; Hajjar, Zeinab
• Is Part Of: Energy sources. Volume 41:Issue 1(2019)
• Page Start: 21
• Page End: 25
• Publication Date: 2019-01-02
• Subjects: ANFIS algorithm -- coal char -- conversion -- gasification -- modeling; 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: 10.1080/15567036.2018.1486903
• Languages: English
• ISSNs: 1556-7036

Abstract

ABSTRACT: Today, in industry, the gasification of coal as an important energy source is an interesting perspective. In this study, the application of adaptive neuro–fuzzy inference system (ANFIS) technique for estimation of char conversion in CO2 gasification is investigated. The main variables affecting char conversion are particle size, reaction time, and reaction temperature, which are chosen as input variables of the proposed model. Experimental data which are gathered from the literature are applied for training, testing, and validation of developed ANFIS model. The results reveal the exact estimation of char conversions with the corresponding experimental values with the regression coefficients ( R 2 ) greater than 0.99.