Vote for your favorite mineral in #MinCup25! - Kosmochlor vs. Azurite
It's a battle of green vs blue as rare but vibrant chromium-bearing kosmochlor up against the deep blue copper alteration mineral azurite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Jorjani, E., Chehreh Chelgani, S., Mesroghli, Sh. (2007) Prediction of microbial desulfurization of coal using artificial neural networks. Minerals Engineering, 20 (14). 1285-1292 doi:10.1016/j.mineng.2007.07.003

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitlePrediction of microbial desulfurization of coal using artificial neural networks
JournalMinerals Engineering
AuthorsJorjani, E.Author
Chehreh Chelgani, S.Author
Mesroghli, Sh.Author
Year2007 (November)Volume20
Issue14
PublisherElsevier BV
DOIdoi:10.1016/j.mineng.2007.07.003Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID7716618Long-form Identifiermindat:1:5:7716618:0
GUID0
Full ReferenceJorjani, E., Chehreh Chelgani, S., Mesroghli, Sh. (2007) Prediction of microbial desulfurization of coal using artificial neural networks. Minerals Engineering, 20 (14). 1285-1292 doi:10.1016/j.mineng.2007.07.003
Plain TextJorjani, E., Chehreh Chelgani, S., Mesroghli, Sh. (2007) Prediction of microbial desulfurization of coal using artificial neural networks. Minerals Engineering, 20 (14). 1285-1292 doi:10.1016/j.mineng.2007.07.003
In(2007, November) Minerals Engineering Vol. 20 (14) Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Jorjani, E., Chehreh Chelgani, S., Mesroghli, Sh. (2008) Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel, 87 (12) 2727-2734 doi:10.1016/j.fuel.2008.01.029
Jorjani, E., Asadollahi Poorali, H., Sam, A., Chehreh Chelgani, S., Mesroghli, Sh., Shayestehfar, M.R. (2009) Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network. Minerals Engineering, 22 (11). 970-976 doi:10.1016/j.mineng.2009.03.003
Mesroghli, Sh., Jorjani, E., Chehreh Chelgani, S. (2009) Estimation of gross calorific value based on coal analysis using regression and artificial neural networks. International Journal of Coal Geology, 79 (1) 49-54 doi:10.1016/j.coal.2009.04.002
Jorjani, E., Mesroghli, Sh., Chelgani, S. Chehreh (2008) Prediction of operational parameters effect on coal flotation using artificial neural network. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material, 15 (5). 528-533 doi:10.1016/s1005-8850(08)60099-7
Jorjani, E., Bagherieh, A.H., Mesroghli, Sh., Chelgani, S. Chehreh (2008) Prediction of yttrium, lanthanum, cerium, and neodymium leaching recovery from apatite concentrate using artificial neural networks. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material, 15 (4). 367-374 doi:10.1016/s1005-8850(08)60070-5
Chelgani, S. Chehreh, Mesroghli, Sh., Hower, James C. (2010) Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network. International Journal of Coal Geology, 83 (1) 31-34 doi:10.1016/j.coal.2010.03.004
Chehreh Chelgani, S., Jorjani, E. (2009) Artificial neural network prediction of Al2O3 leaching recovery in the Bayer process—Jajarm alumina plant (Iran). Hydrometallurgy, 97 (1). 105-110 doi:10.1016/j.hydromet.2009.01.008
Jorjani, E., Hower, James C., Chehreh Chelgani, S., Shirazi, Mohsen A., Mesroghli, Sh. (2008) Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals. Fuel, 87 (6) 707-713 doi:10.1016/j.fuel.2007.05.044
Chehreh Chelgani, S., Jorjani, E. (2011) Microwave irradiation pretreatment and peroxyacetic acid desulfurization of coal and application of GRNN simultaneous predictor. Fuel, 90 (11) 3156-3163 doi:10.1016/j.fuel.2011.06.045
Golshani, T., Jorjani, E., Chelgani, S. Chehreh, Shafaei, S.Z., Nafechi, Y. Heidari (2013) Modeling and process optimization for microbial desulfurization of coal by using a two-level full factorial design. International Journal of Mining Science and Technology, 23 (2). 261-265 doi:10.1016/j.ijmst.2013.04.009
DEHGHAN, S, SATTARI, Gh, CHEHREH CHELGANI, S, ALIABADI, MA (2010) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Science and Technology (China), 20 (1). 41-46 doi:10.1016/s1674-5264(09)60158-7
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 9, 2025 15:39:23
Go to top of page