Estimation of some coal parameters depending on petrographic and inorganic analyses by using Genetic algorithm and adaptive neuro-fuzzy inference systems

S. Chehreh Chelgani, F. Dehghan, James C. Hower

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Adaptive neuro-fuzzy inference systems (ANFIS) in combination with genetic algorithm (GA); provide valuable modeling approaches of complex systems for a wide range of coal samples. Evaluation of this combination (GA-ANFIS) showed that the GA-ANFIS approach can be utilized as an efficient tool for describing and estimating some of coal variables such as Hardgrove grindability index, gross calorific value, free swelling index, and maximum vitrinite reflectance with various coal analyses (proximate, ultimate, elemental, and petrographic analysis). Statistical factors (correlation coefficient, mean square error, and variance accounted for) and differences between actual and predicted values demonstrated that the GA-ANFIS can be applied successfully, and provide high accuracy for prediction of those coal variables.

Original languageEnglish
Pages (from-to)479-494
Number of pages16
JournalEnergy Exploration and Exploitation
Volume29
Issue number4
DOIs
StatePublished - Sep 1 2011

Keywords

  • ANFIS
  • Calorific value
  • Free swelling index
  • Genetic algorithm
  • Grindability index
  • Maximum vitrinite reflectance

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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