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Fine Coal Filtration as Revealed by 3D Lattice-Boltzmann Simulations
Indexado
WoS WOS:000280775000011
Scopus SCOPUS_ID:77955563881
DOI 10.1080/19392699.2010.497114
Año 2010
Tipo artículo de investigación

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



The multiphase flow and dewatering that occurs during fine coal filtration can be described by the results from Lattice-Boltzmann (LB) simulations. As is known, important factors that influence the efficiency of filtration include: particle size distribution (pore network structure is determined by x-ray microcomputed tomography), pressure drop (flow rate), and wetting characteristics of the coal (water contact angle). Results from permeability experiments are used to validate preliminary simulations. Research is in progress to simulate coal filtration and to identify conditions that will lead to improved water removal and minimum cake moisture content.

Métricas Externas



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Disciplinas de Investigación



WOS
Energy & Fuels
Mining & Mineral Processing
Scopus
Energy Engineering And Power Technology
Chemical Engineering (All)
Fuel Technology
Mechanical Engineering
Geotechnical Engineering And Engineering Geology
SciELO
Sin Disciplinas

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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Lin, C. L. - Univ Utah - Estados Unidos
University of Utah, College of Mines and Earth Sciences - Estados Unidos
2 Miiler, J. D. - Univ Utah - Estados Unidos
University of Utah, College of Mines and Earth Sciences - Estados Unidos
3 Videla, A. R. Hombre Moly Cop - Chile
Scaw Metals Group - Chile
4 Mejia, J. A. - Univ Utah - Estados Unidos
University of Utah, College of Mines and Earth Sciences - Estados Unidos

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Financiamiento



Fuente
National Natural Science Foundation of China
Natural Science Foundation of Beijing Municipality
Beijing Nova Program
Provincial Natural Science Foundation of China
Medical Big Data and Artificial Intelligence Development Fund of the Chinese PLA General Hospital
Capital's Funds for Health Improvement and Research

Muestra la fuente de financiamiento declarada en la publicación.

Agradecimientos



Agradecimiento
Financial disclosure: This work was partially supported by grants from the Beijing Nova Program (2011114), the National Natural Science Foundation of China (81270610, 81770203, and 30800482), the Beijing Natural Science Foundation of China (7172200 and 7132217), the Capital's Funds for Health Improvement and Research (2016-1-4082), the Fund Sponsorship of the Capital Public Health Project for DH Liu, National Key Clinical Specialized Military Construction Project for DH Liu (Clinical Medicine), the Provincial Natural Science Foundation of China (818MS157), the Military Translational Medicine Fund of Chinese PLA General Hospital (ZH19003), and the Medical Big Data and Artificial Intelligence Development Fund of the Chinese PLA General Hospital (2019MBD-016).

Muestra la fuente de financiamiento declarada en la publicación.