Journal
INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION
Volume 31, Issue 1, Pages 32-59Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/19392699.2010.534683
Keywords
ANFIS; ANN; Coal; Gross calorific value; Multiple regression; Soft computing
Categories
Ask authors/readers for more resources
Gross calorific value (GCV) is an important characteristic of coal and organic shale; the determination of GCV, however, is difficult, time-consuming, and expensive and is also a destructive analysis. In this article, the use of some soft computing techniques such as ANNs (artificial neural networks) and ANFIS (adaptive neuro-fuzzy inference system) for predicting GCV (gross calorific value) of coals is described and compared with the traditional statistical model of MR (multiple regression). This article shows that the constructed ANFIS models exhibit high performance for predicting GCV. The use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in investigations about the fuel.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available