On the Use of Conventional and Soft Computing Models for Prediction of Gross Calorific Value (GCV) of Coal

YALÇIN ERİK N., YILMAZ I.

INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, cilt.31, sa.1, ss.32-59, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31 Sayı: 1
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1080/19392699.2010.534683
  • Dergi Adı: INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.32-59
  • Anahtar Kelimeler: ANFIS, ANN, Coal, Gross calorific value, Multiple regression, Soft computing, PROXIMATE ANALYSIS, CONTROL-SYSTEMS, HEATING VALUES, NEURAL-NETWORK, FUZZY-LOGIC, LIQUID, HHV
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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.