Prediction of coal grindability based on petrography, proximate and ultimate analysis using neural networks and particle swarm optimization technique
- Author
- HAMID REZA MODARRES1 ; KOR, Mohammad2 ; ABKHOSHK, Emad3 ; ALFI, Alireza1 ; HOWER, James C4
[1] Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran, Islamic Republic of
[2] Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran, Islamic Republic of
[3] Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak Tehran, Iran, Islamic Republic of
[4] Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, KY 40511, United States - Source
Energy exploration & exploitation. 2009, Vol 27, Num 3, pp 201-212, 12 p ; ref : 1 p.1/4
- Publisher
- Multi-Science, Brentwood
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D06 Energy / 001D06B Fuels / 001D06B01 Coal and derived products / 001D06B01B Structure, chemical and physical properties
- Discipline
- Energy
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 21946104
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1260/014459809789618821 
https://dx.doi.org/10.1260/014459809789618821
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