Prediction of coal grindability based on petrography, proximate and ultimate analysis using neural networks and particle swarm optimization technique

Hamid Reza Modarres, Mohammad Kor, Emad Abkhoshk, Alireza Alfi, James C. Hower

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks - particle swarm optimization method gave the most accurate HGI prediction.

Original languageEnglish
Pages (from-to)201-212
Number of pages12
JournalEnergy Exploration and Exploitation
Volume27
Issue number3
DOIs
StatePublished - Jun 2009

Keywords

  • Coal petrography
  • Hardgrove grindability index
  • Neural networks
  • Particle swarm optimization

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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